Small Bus Econ (2010) 34:187–201 DOI 10.1007/s11187-008-9113-2
Corporate taxes and the demand for labor and capital in developing countries Rodrigo A. Cerda Æ Felipe Larrain
Accepted: 7 March 2008 / Published online: 23 April 2008 Springer Science+Business Media, LLC. 2008
Abstract This article provides evidence about the impact of corporate taxation on both labor and capital demand by private companies in a developing economy, using firm level data from Chile. Our results show that higher corporate tax rates reduce not only the demand for capital, but also the demand for labor due to complementarities between both inputs. An interesting element of the results presented in this article is the asymmetry between the effects of taxation according to company size. The impact on labor demand is significantly higher in large corporations than in small enterprises, while the demand for capital is more responsive to corporate tax changes in small firms. We can explain these results based on differences in credit constraints according to firm size. Keywords Corporate taxes Labor Capital Small firms Developing economies JEL Classifications
H25 H32 L26
R. A. Cerda (&) F. Larrain Economics, Pontificia Universidad Catolica de Chile, Vicuna Mackenna 4860, Macul, Santiago, Chile e-mail:
[email protected]
1 Introduction The impact of taxation on firm behavior has been the focus of much research in economics. In particular, the effect of corporate tax policy on the equilibrium level of the capital stock and the timing of investment is a topic of major interest for economists. Since the early contributions of Jorgenson (1963), Hall and Jorgenson (1967), and Tobin (1969) there exists a relatively wide consensus among economists on the fact that higher corporate taxes tend to decrease investment and the long run capital stock. While capital is an important input for small business firms, labor is by far the most fundamental production input and bears the larger share of production costs in developing economies. In that scenario, and given the complementarities between labor and capital, the impact of corporate taxes on firms may be quite large through potential distortions on capital and labor inputs. As far as we know, there is no evidence about this channel. This article attempts to fill this gap by providing evidence on the impact of corporate taxation on the capital stock and labor demand, using data for the Chilean economy from 1981 to 1996. Why the focus on Chile? While the behavior of Chilean firms across sizes is similar to the behavior of companies in other countries (see Benavente et al. 2005) a major difference for Chilean firms is the volatility of the corporate tax rate in our period of analysis. In fact, the corporate tax rate varied widely in this time
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period, from almost 50% initially to 0% in 1990 (distributed profits were subject to a 10% rate while retained earnings paid a 0% rate), finally stabilizing at 15% by the end of our sample. It is important to stress the high variability of Chile´s corporate tax over the period 1981–1996. The corporate tax rate started at over 45% in 1981, dropped to 10% between 1986 and 1988, and to 0 in 1989, then rose again to 15% in 1990 and stayed there until the end of the sample period. The 1984 tax reform aimed to provide investment and saving incentives; as part of it the corporate tax rate was reduced from 46% to 10% in just 2 years. It is important to remember that the Chilean economy experienced a sharp recession during 1982–1983, so this reform was made to recover high growth rates. In 1989 the incentives got to the highest point when the corporate tax rate reached 0%. In 1990, however, the first democratic government after military rule raised this rate to 15%.1 This high variability is important because it provides a unique opportunity to identify more accurately the effect that the corporate tax has on labor and capital demand. This is—on our view— the main contribution of this article, which might allow to obtain lessons of interest to other countries that decide to implement corporate tax reforms. There are other reasons to focus on Chile as well. Studying Chile is also interesting because (a) there is mixed evidence about the impact of corporate taxation on investment and the capital stock (see Cerda and Larraı´n 2005; Hsieh and Parker 2006; Bustos et al. 2004; Vergara 2007) and therefore, it is important to provide new evidence on this topic and (b) there is no evidence on the potential impact of corporate taxation on labor demand at the firm level. We use micro data from a series of Chilean surveys on manufacturing firms of very different sizes. The richness of this dataset is exploited in this article. On the one hand, the large variation in the corporate tax rate will allow us to disentangle the impact of corporate taxation on firms’ decisions. On the other hand, we will be able to study how the effects differ by firm size. This is an important distinction stressed by the literature in the presence of financial constraints. We are interested in exploring
R. A. Cerda, F. Larrain
this distinction because there is a large heterogeneity among firms in the Chilean economy, as discussed in Sect. 3 below. The article proceeds as follows. Section 2 reviews the empirical literature. Then, Sect. 3 presents a discussion of heterogeneity in firms in the case of Chile while Sect. 4 presents a simple reduced form model. Section 5 discusses the data and the methodology. Section 6 presents and discusses the results. Finally, Sect. 7 concludes.
2 Literature review The international evidence on corporate taxation, investment, and labor demand is mixed. Cressy (1996) uses a database of about 2,000 startups in England. Controlling for human capital,2 he concludes that the correlation between firm survival and the availability of financial capital is spurious, and, in fact, the relevant binding restriction is human capital; thus, he says, firms appear to ‘‘self-select’’ their financing: those with greater human capital are more willing to take up bank credit. Reinforcing his point, Cressy (2006) argues that firms starting with lower human capital tend to choose wrong combinations of risk and growth that make them more likely to fail early. Carrol et al. (2000) analyze the effect on labor demand of the 1986 tax reform in the U.S. Concentrating on sole proprietors, they conclude that a reduction of 10% in the personal income tax rate increases the probability that a firm (or the proprietor) hires someone by 12.1%. They also show empirically that increases in the marginal tax rate decrease the rate of growth of wage bills. Studies of investment and corporate taxation in Chile are recent. Medina and Valde´s (1998) use Chilean microeconomic data of publicly traded firms to analyze the importance of cash flow on firms’ investment decisions. Their results support the idea that the availability of internal funds matter for investment decisions beyond project profitability. Although they do not directly test the link between
2
1
The sample period is mainly determined by data availability. Nonetheless, the corporate tax rate has had little variability over the last 10 years; it is currently 17%.
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As measures of human capital, he uses—among others—the number and average age of proprietors, and their work and business experience in the area of work. After controlling for this, he concludes than financial rationing is not supported by the evidence.
Corporate taxes and the demand for labor and capital in developing countries
corporate taxation and investment, their evidence suggests that increasing the tax on retained earnings reduces investment. Hsieh and Parker (2006) argue that corporate tax reform was a significant and direct cause of Chile´s investment boom. Specifically, they use aggregate, industry, and plant-level data to show that the reduction in taxation of retained earnings in 1984– 1986 increased the amount of available funds to liquidity constrained firms, hence increasing investment in these companies. A corollary of this result is that in countries with poorly developed financial markets, such as Chile was in 1984, increasing taxes on retained profits removes internal funds for financially constrained firms, thus decreasing investment. Bustos , Engel and Galetovic (BEG 2004) provide a different type of econometric analysis. They study the investment process using an annual panel of 83 firms, with data ranging from 1985 to 1995, and report no significant impact of the corporate tax rate on the long run capital stock. Nonetheless, their dataset includes only firms classified as corporations (‘‘Sociedades Anonimas Abiertas’’), whose size is considerably larger than the average Chilean firm size; thus, they are probably not financially constrained. Vergara (2007) investigates empirically the link between the income tax reform in the 1980s and Chile’s investment performance since the reform. Using macro data for the period 1975–2003, his evidence indicates that the tax reform explains an increase in private investment of three percentage points of GDP. On the other hand, micro data of 87 publicly traded companies for the period 1980–2002 confirms the evidence that investment was positively affected by the tax reform. Cerda and Larraı´n (2005) contribute in having a larger sample that includes small, medium, and large firms. Using a different econometric methodology (fixed-effect and Arellano-Bond dynamic panel data estimations), their results show that the impact of corporate taxation on investment is larger in small firms (Table 1). There is also some evidence about the impact of corporate taxes on labor demand for Chile through potential complementarities. Martinez et al. (2001) conclude that the labor to cost of capital elasticity is near 0.2. Thus, a higher corporate tax rate associated with a larger cost of capital should depress labor demand.
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3 Firm behavior and firm size in Chile3 In order to motivate our discussion about the different behavior of firms according to size we start with some basic statistics. Table 2 presents a description of Chilean companies by firm size according to the Chilean Tax Revenue Service (Servicio de Impuestos Internos, SII) classification, and shows that there is large heterogeneity. Big firms that have a very large fraction of total sales, employ only 10% of total labor and represent only 1% of the total numbers of firms. On the other hand, small and medium sized firms (PYMEs) account for 20% of total sales and 50% of total employment. Our micro data source is the annual national industrial survey, ENIA (Encuesta Nacional Industrial Anual), compiled annually on manufacturing firms by Chile´s national statistics office, INE (Instituto Nacional de Estadı´sticas). Our dataset contains information of approximately 970 firms on sales, investment, value added, location, and manufacturing sub-sector (at the four ISIC level), among other variables. The firms in our dataset are very heterogeneous, as can be seen in Tables 3 and 4. Table 3 describes firms by size in 1996; as can be seen in the table, our dataset focuses on PYMEs (small and medium size) and large firms. In fact, ENIA includes only firms which employ at least ten workers in the year of the survey; this excludes micro firms that account for nearly 3.7% of total sales and 40% of total labor in Chile. Further, the information contained in ENIA is solely of manufacturing firms, a sector which accounts for about 16% of total employment in the Chilean economy. Nevertheless, the manufacturing sector is of special interest given the heterogeneity of firm sizes and sub-sectors in the sample. Table 4 describes firms by number of workers. As seen in the table, over 95% of firms hire less than 500 workers; our dataset also considers, however, some 25 firms per year which hire more than 500 workers, and only an average of 6 to 7 firms per year with one thousand or more workers. Table 5 provides the summary statistics of the main variables in our dataset, classified by annual sales. Our time period is between 1985 and 1996 which are years after the 1982 economic crisis and
3
This section is partially based on Cerda and Larraı´n (2005).
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Table 1 Main studies of investment in Chile
HP
BEG
V
CL
Sample source
ENIA
FECUS
FECUS
ENIA
Sectors
Manufactures
All
All
Manufactures
Size Period
All 1982–1990
Large 1985–1995
Large All 1980–2002 1981–1996
Estimation method
Panel
Weighted least squares
Fixedeffect panel
Fixed-effects and Arellano-Bond dynamic panel
Modeling strategy
Reduced form model
Structural model, intertemporal optimization
Reduced form model
Reduced form model
Main results
Significant effect in financially constrained firms
Small effect, and positive for some years
Significant effect
Significant effect in small and medium size firms
Table 2 PYMEs and corporations
Number of firms % of the total
(1) Micro firms
(2) Small firms
(3) Medium size firms
(4) PYMEs
535.338
93.923
13.164
107.087
82.5
14.5
2.0
16.5
3.7
10.1
10.1
20.2
51.096
12.562
% of sales Average sales in Ufs
456
7.161
(5) Large firms
(6) Total
6.066
648.491
1.0
100
76.1
100
836.395
10.274
Employment (%) Corfo
38.7
35.2
12.5
47.7
9.7
100
Employment (%) CASEN
40.4
36.6
13.0
49.6
10.1
100
Source: Cabrera et al. (2002). In the table, UF means Unidades de Fomento, a Chilean inflation-indexed monetary unit. The data on sales and number of firms is from the SII. Micro firms are those with annual sales up to 2.400 UFs (US$93,600, app. today) per year, while small firms annual sales range between 2,400–25,000 UFs (US$93,600–975,000) and medium sized firms sales are from 25,000 to 100,000 UFs (US$975,000–3,900,000). Large firms are those with annual sales larger than 100,000 UFs. PYME denotes small and medium size firms. The data on employment comes from the Chilean Development Corporation (Corfo) and a national survey of individuals carried out in October 2000 (CASEN)
before the effect of the Asian crisis impacted Chile. In the table we report profitability (Prof)4, the investment rate (investment as fraction of the lagged capital stock) and the number of workers. This last variable is tabulated for 7 ranges of workers, 2 being the smallest range (10 to 19 workers) and 8 the largest (more than 1,000 workers). The number of observations per year in each firm classification (small, medium, and large) is quite stable. Firms PY
it We define Profit as Prof it ¼ PKt aY dit , where the index i t Kit indicates firm and t indicates year, the variable Yit is the aggregate value added per firm, Kit is the capital stock per firm PY while PKt , the relative price of output vis-a`-vis capital is t measured by means of the national account price deflators. In order to construct this variable we assume a capital stock share in output a = 0.4.
4
123
seem to differ, however, within each classification over time. In fact, small firms showed an average profitability equal to 0.77 and an average investment rate equal to 1% in 1985. Those variables increased to 1.7 and 4% in 1996. A similar pattern occurs in the case of medium and large firms—medium firms increased their investment rate from 1% in 1985 to 4% in 1996 while large firms increased their investment from 2% in 1985 to 6% in 1996. The average number of workers does not vary considerably over time across small and medium sized firms. Small firms had similar number of employees in 1985 and 1996 (approximately 12 workers). Similarly, medium sized firms had around 16 employees in 1985 while they had approximately 18 employees in 1996. The number of employees did increase in large firms,
Corporate taxes and the demand for labor and capital in developing countries
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Table 3 Firms by size, 1996
Yearly sales, in UF thousands
Micro
Small
Medium
PYME
(Less than 2.4)
(Between 2.4 and 25 UF)
(Between 25 and 100)
Large
Total firms
(Above 100)
Number of firms
7
332
275
607
366
973
(% of total)
0.7%
34.1%
28.3%
62.4%
37.6%
100%
Source: Authors calculations using ENIA, 1996 Table 4 Firm description by number of workers, ENIA, various years Size (number of workers)
All years Freq.
1985 %
Cum.
1996
Freq.
%
Cum.
Freq.
%
Cum.
Firm size by number of workers 10–19
3,768
22.78
22.78
255
26.21
26.21
214
21.99
21.99
20–49
6,420
38.81
61.59
399
41.01
67.21
325
33.4
55.4
50–99
2,655
16.05
77.64
142
14.59
81.81
166
17.06
72.46
100–199
1,716
10.37
88.02
88
9.04
90.85
124
12.74
85.2
200–499
1,429
8.64
96.66
65
6.68
97.53
101
10.38
95.58
500–999
405
2.45
99.11
18
1.85
99.38
35
3.6
99.18
[1000
148
0.89
6
0.62
8
0.82
100
100
100
Source: Authors calculations using ENIA, various years
however, from an average of 56 workers in 1985 to almost 82 workers in 1996. Thus, the data suggests a change in the pattern of firm investment, even when we consider different firm sizes, while employment decisions seem stable over time in small and medium firms. Figures 1 and 2 provide an idea of the potential credit restrictions faced by firms. Figure 1 shows the number of banks providing credits to firms. As shown in the figure, smaller firms have access to a lower number of banks than larger firms. This observation might be explained by Fig. 2, which shows that smaller firms have a larger fraction of unpaid debt; thus, banks probably recognize the higher risk of lending to smaller companies, and appear to react by cutting credit to them. Summing up, this section shows a large heterogeneity of behavior across firm size, mainly in labor demand. Furthermore, smaller firms appear more credit constraint compared to large firms.
(K), labor (L), and an intermediate importable input (M). Let w, c, and x denote the wage, cost of capital, and price of the imported good, respectively. Following the user cost of capital approach, the e cost of capital is given by c ¼ ðr þ d ptþ1 pt Þð1 sÞ where r is the relevant interest rate, d is the depreciation rate, petþ1 p is the expected capital gain, and s corresponds to the corporate tax rate. Using this notation and following standard microeconomic theory, firms solve a cost minimization problem: C ðY; c; w; xÞ ¼ min cK þ wL þ xM s:t: K;L;M
Y fi ðK; L; M Þ:
ð1Þ
From Shepard’s lemma, we obtain labor and capital demands: Ld ¼
oCðY; c; w; xÞ ¼ LðY; c; w; xÞ; ow
ð2Þ
4 A reduced form model
Kd ¼
oCðY; c; w; xÞ ¼ K ðY; c; w; xÞ: oc
ð3Þ
We assume each firm is endowed with a production function that depends on three inputs: capital
Following Hamermesh‘s (1993) linear approximation of the demand functions above yields:
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Table 5 Firm description by size, ENIA 1985 and 1996 Year
Firms size
Variable
Observations
Mean
Std. Dev.
Min
Max
Firm size by yearly sales 1985
Small
1985
Medium
1985
Large
1996
Small
1996
Medium
1996
Large
Prof
152
0.77
0.85
0.02
4.25
Inv/k(-1) Workers
152 152
0.01 2.27
0.03 0.45
-0.14 2.00
0.29 3.00
Prof
289
0.74
0.78
0.01
4.15
Inv/k(-1)
289
0.01
0.09
-0.22
1.24
Workers
289
2.65
0.55
2.00
4.00
Prof
511
0.60
0.67
0.01
4.63 1.15
Inv/k(-1)
511
0.02
0.09
-0.36
Workers
511
4.13
1.30
2.00
8.00
Prof
136
1.70
1.24
-0.48
4.92 1.04
Inv/k(-1)
136
0.04
0.14
-0.24
Workers
136
2.24
0.43
2.00
3.00
Prof
259
1.77
1.18
-0.37
4.83 3.03
Inv/k(-1)
259
0.04
0.24
-0.76
Workers
259
2.86
0.60
2.00
5.00
Prof
485
1.30
0.98
-0.33
4.98
Inv/k(-1)
485
0.06
0.15
-0.79
0.97
Workers
485
4.66
1.34
2.00
8.00
Source: ENIA 1985, 1996. Small and medium firms are those with annual sales between 2.400 and 25.000 UFs (72.500 to 754.000 US$) and 25.000–100.000 UFs (754.000–3.000.000 US$). Large firms are those with annual sales larger than 100.000 UFs (US$ 3.000.000). The variable Inv/k(-1) is the investment rate as a fraction of lagged capital stock while the variable Workers indicates the number of workers employed per firm. This variable has value ranging from 2 to 8, where 2 indicates firms with 10 to 19 workers; 3 indicates 20 to 49; 4 indicates 50 to 99; 5 indicates 100 to 199; 6 indicates 200 to 499; 7 indicates 500 to 999 and 8 indicates more than 1,000 workers per firm 4 3,7
3,5 3
3
2,8 2,5
2,5
2
2,3 1,9
2
132 - 264.5
264.5 - 661.5
2,1
1,8
1,5
1
0,5
0 63.5 - 132
661.5 - 1323
1323 - 2646
2646 - 5292
5292 - 15877
15877 - 26462
26462 and +
Fig. 1 Number of banks by firm size, Chile 2003. Source: Benavente et al. (2005). The firm categories are defined by sales per year, in thousands of US dollars, in 2003
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Corporate taxes and the demand for labor and capital in developing countries
193
6
4,9
5
3,9
4
3,7
3 2,6 2,2
2
1
0,7
0,6
0,5
2646 - 5292
5292 - 15877
15877 - 26462
0,01
0 63.5 - 132
132 - 264.5
264.5 - 661.5
661.5 - 1323
1323 - 2646
26462 and +
Fig. 2 Unpaid financial debt by firm size. Source: Benavente et al. (2005). The firm categories are defined by sales per year, in thousands of US dollars, in 2003
log Ld ¼ a0 þ a1 log w þ a2 log c þ a3 log x þ a4 log Y;
ð4Þ
log K d ¼ b0 þ b1 log w þ b2 log c þ b3 log x þ b4 log Y:
ð5Þ
Finally, replacing c and using the approximation log(1 - s) & -s, we can write the conditional labor and capital demand functions as: log Ld ¼ a0 þ a1 log w þ a2 log ðr þ dÞ þ a3 log x þ a4 log Y a5 s; ð6Þ log K ¼ b0 þ b1 log w þ b2 log ðr þ dÞ þ b3 log x þ b4 log Y b5 s: d
ð7Þ We assume no capital gains in Eqs. 6 and 7. This assumption is necessary because we do not have such data for each firm in our microeconomic dataset. Therefore, to use the same definition of variables in both the aggregate and microeconomic data analysis, we use only the real interest rate plus the depreciation rate.
5 Microeconomic evidence: data and methodology In this section, we analyze the effect of the corporate tax on Chilean companies using the microeconomic
data provided by ENIA (the Chilean manufacturing industries survey, compiled by the National Institute of Statistics, INE) for the period 1986–1996. As already argued, the variability of the corporate tax during the period under study, makes it possible to extrapolate the effect of tax rate changes over labor demand and capital demand for different tax rates. The dataset includes approximately 970 companies which present a lot of heterogeneity among them, particularly in terms of size, as discussed in Sect. 3. In order to construct the depreciation rate, which also varies across firm and time, we follow BEG (2004) by assuming that depreciation rates vary across types of capital. Specifically, we assume that buildings depreciate at 2% per year, while machinery and vehicles depreciate at 8% and 15% annually, respectively. As our dataset allows us to distinguish each type of capital in every company, we compute average annual depreciation rates as weighted averages (see the Appendix). One problem of the ENIA dataset available is that we only have interval-coded employment data, that is, we only know the range within which the number of workers hired by the firm is. This makes it necessary to use an index model in the estimation of the parameters. The range within which the quantity of labor demanded by the firm is located coincides with the ranges of the data presented in Table 4. Let
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a1 \ a2 \ … \ a7 be the known lower and upper limits of each interval in which the employment variable could be located. It follows that a1 = 10, a2 = 19, a3 = 49, a4 = 99, a5 = 199, a6 = 499, a7 = 999. Further, suppose the labor demand of the firm is given by the following equation, which is the panel data counterpart of Eq. 6: log Ldit ¼ Xit b þ li þ eit ; log Ldit Xi NðXit b; r2 Þ; ð8Þ where X denotes a set of explanatory variables, which in our case includes the cost of capital, the wage rate, the real exchange rate, and value added while li is a firm’s fixed effect and eit is a well-behaved random shock. The value of the observed variable L is determined in the following way: 8 1 if a1 Ldit a2 > > > < 2 if a2 \Ld a3 it ð9Þ Lit ¼ . .. . > > . . > : 7 if Ldit [ a7
ð10Þ which is the panel data counterpart of Eq. 7 and as above li represents a firm’s fixed effect and eit is a well-behaved random shock. In this case, the dependent variable is not coded in intervals and, therefore, we might follow the traditional panel methodology.
GDP gap (lagged)
15.0 10.0 5.0 0.0 -5.0 -10.0 -15.0 -20.0
1996
1995
1994
1993
1992
1991
1990
Fig. 3 Aggregate variables—gap
1989
1988
1987
1986
1985
1984
1983
1982
1981
123
log Kit ¼ b0 þ li þ b1 log wit þ b2 log ðr þ oÞit þ b3 log xit þ b4 log Yit b5 st þ eit :
1980
In order to deal with this particularity of the data we estimate Eqs. 8 and 9 through the maximum likelihood method of panel data ordered probit model on intervals. The estimation results may be interpreted as if we had observed the quantity of labor demanded for each firm and estimated EðLdit jXit Þ ¼ Xit b by the random-effect panel data method. In this case, our ability to estimate Eq. 8 is due to the assumption that Ld given X satisfies the classical lineal regression model assumptions, e.g., EðLdit jXit Þ ¼ Xit b and r2i ¼ VarðLdit jXit Þ do not depend on the value of X. These assumptions imply that our estimators are consistent (Wooldridge 2002). Our microeconomic estimations use as set of explanatory variables: the logarithms of the real wage, the cost of capital, and of value added by the firm; the corporate tax rate; some business cycle variables such as the aggregate credit–GDP ratio; and the lagged output gap. All the firms included in the ENIA survey are in manufacturing; thereby, as a proxy of the real wage we use the manufacturing mean nominal wage deflated by the CPI. As a measure of the cost of capital we use the real interest rate from 90 days to 1 year plus the depreciation rate, calculated as shown in the Appendix. The output gap corresponds to the difference between the actual GDP growth rate and its trend.
This variable attempts to capture the cyclical effect of economic growth over employment. This variable tries to control for the cyclical element of growth and investment, and is used in lagged form to capture costly investment decisions and allocation choices. The expected sign of this coefficient is positive due to the pro-cyclical behavior of investment rates in the economy. We also include the bank reserve requirement ratio (measured as a fraction of GDP) as an attempt to measure global liquidity constraints faced by firms, which ultimately determine how much investment the firms can perform on a year to year basis. Higher reserve requirement rates mean reduced liquidity and reduced financing sources for firms wishing to invest. Note that this variable shows a negative trend from the mid 1980s to the beginning of the 1990s, when it stabilized at a lower level. The negative slope in the trend of this variable in the mid 1980s might be explained by the large GDP growth rates observed in the Chilean recovery after the 1982 economic crisis. In fact, since 1985–1986 and until 1997 Chile experienced a golden period in which the GDP growth rates averaged over 7% per annum (Figs. 3 and 4) We may directly estimate the capital stock demand function as:
%
Corporate taxes and the demand for labor and capital in developing countries
Private Banks Reserve Requirements
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
16 14 12 10 8 6 4 2 0
Fig. 4 Aggregate variables—reserve requirement
In this case, we will provide estimations obtained using the fixed effect panel data method.
6 Results In this section we present and discuss the principal results obtained using the methodology described above. Table 6 shows the results concerning Eqs. 8 to 10. In the table, we initially show the results when the explanatory variables included are value added, ownprice and the corporate tax rate. Next, we include cross-price effects and the business cycle control variables. In general, the estimates show that the value added coefficient is positive and significant while the ownprice effect is negative. The cross-price effects between labor and capital are negative in both demand functions, indicating potential complementarities between these inputs. The real exchange rate is positive and significant in the labor demand equation while non-significant in the capital demand equation, indicating potential substitution between imported inputs and labor while no substitution between imported inputs and capital. The non significant coefficient of the exchange rate in the capital demand equation may be explained if the effect is captured by the own-price elasticity. The business cycle variables indicate that less stringent financial conditions in the economy are associated with larger labor and capital demands. In this case, it might be easier to obtain financial credit to purchase capital goods and/or pay salaries. The ‘‘gap’’ variable is non significant in the labor demand equation but has a negative coefficient in the capital demand equation. This last effect might be capturing the fact that in the expansive part of the business
195
cycle, firms anticipate future slackness in demand, and therefore, become reluctant to accumulate capital. Our main result, however, shows that increases in the corporate tax rate have a negative and significant effect over both labor and capital demand by firms. The impact is almost twice as large on labor demand as compared to the impact on the capital stock; an increase of 1% in corporate taxation reduces the capital stock by almost 0.12% and labor demand by 0.2%. These results are highly consistent across the different estimations. 6.1 Discriminating by firm size Now we turn to analyze how the different parameters change when we choose different sub-samples according to firm size, measured according to total sales from each company during a specific year. In fact, we define firm size by the average sales volume during the period of analysis, excluding years in which the firms were absent of our dataset. It could be argued that average sales might not be a good measure of size if firms switch across categories over the sample. This would be the case if firms growth over time. In order to obtain an idea of this potential problem, Table 7 reproduces the transition matrix of firms across firm size between 1990 and 1999. Note that the rows of the table not necessarily add up to one, as a large fraction of firms die in that period of time. From the table we can see that, in general, firms face two main options: either they die or they mainly remain of similar size. This evidence indicates that the measure we are using to deal with firm size in our unbalanced panel would not have significant measurement error. Table 8a and b present estimations by quintile of firm size. The first column in both tables represents the quintile with lowest firm sales while the fifth column is the quintile with largest size. As the table shows, the impact of the tax rate on capital demand is negative in all the quintiles. Furthermore, the impact in the first column is significant, while it is not on quintiles 2 to 4. It becomes significant in the 5th quintile. The results also show—in general—a negative impact of the corporate tax rate on labor demand, but the unique quintile with a significant impact is the fifth. In the latter case, the impact of the corporate tax is twice as large as the estimate obtained in the
123
123
973
Groups
973
10,690 973
10,690
0.5161
973
10,690
0.5209
973
973
10,690
(P = 0.00) 10,690
2115.20
1875.51 (P = 0.00)
973
10,690
(P = 0.00)
5236.52
(3.70)
.0036**
(-1.97)
-.186*
.695** (4.14)
(-6.53)
-.282**
(-2.10)
-.3851**
(68.3)
.328**
Ln(L)
973
10,690
(P = 0.00)
5247.54
(-1.34)
(3.71)
.0037**
(-2.11)
-.198**
.732** (4.43)
(-6.74)
-.289**
(-1.93)
-.35*
(68.4)
.328**
Ln(L)
-.0011
(–1.63)
-.14*
.292** (3.98)
(-3.93)
-.163**
(-9.36)
-.57**
(39.06)
.25**
Ln(L)
(5.67)
(7.98)
(8.02)
(-1.85)
-.166*
(-17.1)
-.734**
(38.3)
.254**
Ln(L)
-.0033**
.0052**
(-2.11)
-.128**
.0364 (0.33)
(-1.16)
.0052**
(-2.66)
-.1614**
.14808 (1.35)
(-2.00)
-.036
(-7.16)
-.862**
(19.7)
.0944**
Ln(K)
Ln(.) indicates natural logs. The symbols * and ** indicate the variables are significant at the 5% and 1% levels, respectively. The first column shows the baseline estimation, which includes the logarithm of the real wage, the logarithm of the cost of capital, the logarithm of the firm’s value added, the logarithm of the real exchange rate, the corporate tax rate, the ratio Credit over GDP (measured as the bank requirement ratio) and the output gap (which corresponds to the difference between the actual GDP growth rate and its trend). The estimates of the capital demand equation are obtained by using the fixed-effect panel data method while the labor demand estimates are obtained from the (random effect) panel data ordered probit method on intervals
10,690
Observations
R
2
Gap 0.48
(-2.83)
(-21.1)
0.45
-.129**
-0.11**
Corporate tax
Credit/PIB
-.634** (-10.6)
(0.45)
(-1.58)
-.061**
(-6.38)
.0171
-.0483
(-35.5)
(19.37)
.0922** -.761**
(15.1)
(-18.8)
Ln(K)
-1.65**
.0865**
-.0693**
Ln(K)
Ln(RER)
Ln(CC)
Ln(wage)
Ln(value added)
Ln(K)
Table 6 Capital and labor demand estimates, micro data (ENIA 1981–1996)
196 R. A. Cerda, F. Larrain
Corporate taxes and the demand for labor and capital in developing countries
197
Table 7 Transition matrix in a 10-year horizon (1990–1999) Micro firms
Small firms
Medium firms
Large firms
Micro firms
0.14
0.08
0.03
0.02
Small firms
0.02
0.39
0.03
0.00
Medium firms Large firms
0.01 0.01
0.19 0.01
0.31 0.09
0.07 0.51
Source: Benavente et al. (2005)
regression that used the complete sample: in large firms the impact of an increase of 10% in the corporate tax rate is a 4.5% decline in labor demand. In order to check the robustness of our results, we estimate rolling regressions based on firm sales volume. We sort firms in ascending order according to the average mean sales volume of each firm.5 Each regression includes approximately 600 firms. Figure 5 presents the results associated with the tax coefficient in the case of capital demand while Fig. 6 shows the evolution of the corporate tax coefficient in the labor demand function. The figures allow us to analyze how the magnitude of the effect differs across firm size. In the figures, we present 10% confidence intervals. The impact of corporate taxation on the capital stock is negative and significant across different firm sizes. The effect is not huge, but is larger in small firms. Furthermore, the impact is significant only in small firms and we cannot reject the coefficient being zero in the case of medium and large firms. These results are consistent with those obtained by Cerda and Larraı´n (2005) concerning investment behavior. In the case of labor demand, we also find a negative impact of the corporate tax rate in all the rolling regressions. Perhaps surprisingly, however, we find that the bigger the size of the firm, the larger is the negative effect of the corporate tax over employment. Moreover, this effect is significant in the case of medium and large sized firms. The principal change in the results occurs at the regression number 150, which corresponds to the 430th smaller firm in the sample. This firm sells, on average, about US$250,000 (7,250 UFs) monthly. Thus, firms in the 60% percentile or bigger show a significantly larger effect. Our estimates suggest that the impact of the corporate tax rate on firm investment decisions is 5
To rank firms, we compute the average firm’s sales over our time period.
negative, as suggested by economic theory. Moreover, there also appears to be a negative impact on labor demand probably due to potential complementarities in production between labor and capital. Firm size is an important element when considering the impact of taxes; what seems initially somewhat paradoxical is that the impact of taxes is larger in small firms if we consider capital demand but is larger in medium and large firms if we consider labor demand. Our interpretation concerning capital demand is that small and medium sized firms are generally credit constrained and need to use their internally generated funds to finance investment. Larger tax rates decrease their availability of internal funds, but this might not be so important in large firms with easy access to financial markets. This may explain and provide consistency to the different results reported by BEG (2004) and Cerda and Larraı´n (2005) for Chile. By contrast, in the case of labor demand, the argument is different. Corporate taxes have larger impact on labor demand in large firms which are not credit constrained and, thus, have more flexibility to hire or fire labor when there are—as our results suggest—complementarities between capital and labor. Note that firing workers in Chile has a particularly significant financial dimension due to high severance payments, as documented, among other, by Heckman and Pages (2000). Small firms, on the other hand, may face less flexibility in firing labor as they might incur in financial costs which are considerable in the context of credit constraints.
7 Conclusions This article provides empirical evidence about the impact of the corporate tax rate on firms’ decisions in Chile. Chile is quite an interesting case to focus on, because firms faced considerable changes in the
123
198
R. A. Cerda, F. Larrain
Table 8 (a) Capital demand and (b) labor demand estimates, micro data (ENIA 1981–1996), by quintiles Ln(K) Q1
Ln(K) Q2
Ln(K) Q3
Ln(K) Q4
Ln(K) Q5
-.005
.002
.066**
0.074**
0.071**
(-0.67)
(0.33)
(5.06)
(7.48)
(6.21)
Ln(wage)
-1.86**
-1.49**
-.499**
-0.111
0.22
(-7.86)
(-6.33)
(-2.51)
(-0.63)
(1.17)
Ln(CC)
.386**
0.183
-.107**
-.284**
-.160**
(3.52)
(1.7)
(-1.77)
(-4.08)
(-2.00)
Ln(RER)
-.76
-.502**
.236
.479**
.410*
(-3.05)
(-2.12)
(1.24)
(2.54)
(1.93)
Corporate tax
-.174*
-.025
-.109
-.033
-.16*
(-1.74)
(-0.27)
(-1.28)
(-0.40)
(-1.86) 0.24
(a) Capital demand estimates Ln(value added)
R
2
0.02
0.02
0.046
0.05
Observations
2,136
2,143
2,132
2,145
2,134
Groups
195
195
194
195
194
Ln(L) Q1
Ln(L) Q2
Ln(L) Q3
Ln(L) Q4
Ln(L) Q5
.067**
.18**
.216**
.20**
.235**
(6.32)
(12.7)
(12.9)
(12.5)
(14.97)
-0.44
-.91**
-.638
.250
.328
(-1.52)
(-2.40)
(-1.60)
(0.61)
(0.75)
-.069
.010
.0335
-.120
-.239**
(-1.01)
(0.12)
(0.34)
(-1.19)
(2.15)
.074
-.083
.170
.437
.926**
(0.28)
(-0.24)
(0.46)
(1.15)
(2.30)
.093
-.251
-.039
-.0008
-.454**
(0.62)
(-1.27)
(-0.84)
(-0.01)
(-2.07)
56.58
221.47
278.59
488.35
460.08 (P = 0.00)
(b) Labor demand estimates Ln(value added) Ln(wage) Ln(CC) Ln(RER) Corporate tax v2
(P = 0.00)
(P = 0.00)
(P = 0.00)
(P = 0.00)
Observations
2,136
2,143
2,132
2,145
2,134
Groups
195
195
194
195
194
Ln(.) indicates natural logs. The symbols * and ** indicate the variables are significant at the 5% and 1% levels, respectively. The first column shows the baseline estimation, which includes the logarithm of the real wage, the logarithm of the cost of capital, the logarithm of the firm’s value added, the logarithm of the real exchange rate and the corporate tax rate. The next columns include the aggregate credit-GDP ratio and the lagged output gap. The estimates of the capital demand equation are obtained by using the fixedeffect panel data method while the labor demand estimates are obtained from the (random effect) panel data ordered probit method on intervals
corporate tax rate during the period of analysis. Furthermore, our data allows us to distinguish between ‘‘low’’ and ‘‘high’’ net worth firms. In our estimates we find significant differences in the impact of the corporate tax rate according to firm size, which suggests the existence of borrowing constraints on ‘‘low net worth’’ firms.
123
The microeconomic evidence provided here supports a significant negative impact of increases in the corporate tax rate on both capital and labor demands. Firm size plays an important role, though one that is not homogeneous. Capital demand is more responsive to a corporate tax change among small firms, while labor demand responds more to this change among large firms.
Corporate taxes and the demand for labor and capital in developing countries
141
151
161
171
181
191
201
211
221
231
241
251
261
271
151
161
171
181
191
201
211
221
231
241
251
261
271
131
141
121
111
91
101
81
71
61
51
41
31
21
0
1
0,05
11
Fig. 5 Impact of corporate tax on the capital stock
199
-0,05
-0,1
-0,15
-0,2
-0,25
Fig. 6 Impact of corporate tax on employment
0,2
0,1
131
121
111
101
91
81
71
61
51
41
31
21
1
11
0
-0,1
-0,2
-0,3
-0,4
-0,5
These results provide support to the view that the corporate tax rate is an important determinant of private investment, especially in developing countries—where credit constraints are more significant than in developed economies—and especially for small and medium sized firms, which face larger borrowing constraints than big firms and, thus, depend more heavily on retained earnings to finance their investment. We interpret the larger effect of corporate tax changes on labor demand among large firms as a result of their flexibility to hire or fire labor because they are not credit constrained; thus, these firms can afford the financial implications of hiring and firing, especially in the face of large severance payments, as in Chile.
Acknowledgements We thank useful comments from two anonymous referees and the editor Rui Baptista. We also acknowledge the extremely able research assistance of Francisco Parro and Felipe Varas. All remaining errors remain own responsibility.
Appendix A. Measuring the capital stock and depreciation rate The construction of the capital stock in the micro dataset follows a reversal version of the perpetual inventory method. Let us define:
123
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R. A. Cerda, F. Larrain
S = buildings (e), machinery (m) or vehicles (v); Kits = capital stock type s of firm i at year t; Iits = investment type s of firm i at year t; ds = type s depreciation rate. Our dataset has available data on annual investment plus capital stock at the end of the sample. Capital stock data in other periods might be missing. In order to construct the series of capital stock in each firm, we assume the following annual depreciation rates: de: buildings, 2%; dm: machinery, 8%; dv: vehicles, 15%.
min cK þ wL þ xM
ð11Þ
Also, let T be the final period of our sample; as we s s have available KiT , we may obtain KiT1 as follows: s s s ¼ ðKiT IiT1 Þ=ð1 ds Þ: KiT1
ð12Þ
By recursion, we may obtain the sequence of capital stock as in: Tt Tj t X 1 1 S S Kit ¼ Kit IijS : ð13Þ 1 ds 1 ds j¼T1 Once we obtain the series of each type of capital stock per firm, we obtain the total capital stock per firm by adding up the all the types of capital as in: Kit ¼ Kite þ Kitm þ Kitv :
ð14Þ
The average depreciation rate is calculated as: dit ¼ ½ðEit de Þ þ ðMit dm Þ þ ðVit dv Þ= ðEit þ Mit þ Vit Þ;
where Y, K, L and M are output, capital, labor, and an intermediate imported good. Let w, c, and x denote the wage, the cost of capital and the price of the imported good. Following the user cost of capital approach we assume the cost of capital is given by c ¼ r þ d petþ1 pt ð1 sÞ; where r is the relevant interest rate, d is the depreciation rate, pet¼1 p is the expected capital gain and s correspond to the corporate tax rate. Then, we can write the following cost minimization problem of the firm: K;L;M
We assume that the capital stock type s in a firm follows the traditional law of motion: s Kiðtþ1Þ ¼ Kits (1 ds Þ þ Iits .
Y ¼ AK aK LaL M aM ;
ð15Þ
Subject to Y ¼ AK aK LaL M aM ;
with the associated first-order conditions: w aL K ¼ ; c aK L w aL M ¼ : x aM L Rearranging the first-order conditions and replacing them into the production function leads to the following labor demand equation: log Ld ¼ b0 b1 log w þ b2 log c þ b3 log x þ b4 log Y; where aK aM b0 ¼ log A þ aK log þ aM log aL aL ðaL þ aK þ aM Þ; b1 ¼ ðaK þ aM Þ=ðaL þ aK þ aM Þ; b2 ¼ aK =ðaL þ aK þ aM Þ; b3 ¼ aM =ðaL þ aK þ aM Þ; b4 ¼ 1=ðaL þ aK þ aM Þ:
where Eit, Mit, Vit are capital stock in buildings, machinery and vehicles in year t, respectively.
Finally, replacing c and using the approximation log(1 - s) & -s, we obtain the conditional labor demand function, Eq. 6 in the text, as in:
B. The reduced form equations
log Ld ¼ b0 b1 log w þ b2 log ðr þ dÞ þ b3 log x þ b4 log Y b5 s:
This section presents the cost-minimization problem faced by the firm. We assume the firms use labor, capital, and an imported intermediate good in the production of the final good. The firm has a CobbDouglas production function of the form
123
ð6Þ In Eq. 6 we assume that there are no capital gains. This assumption is necessary because we do not have such data for each firm in our microeconomic dataset.
Corporate taxes and the demand for labor and capital in developing countries
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