rafmacalaba/datause-encoder-data huggingface dataset:
**Table** **<u>6: Changes in Average Firm-Level Employment across Districts</u>**

|||<br>lo|<br>g<br>i<br>Emp|||
|---|---|---|---|---|---|
||Light<br>manufacturing|Heavy<br>manufacturing|Other<br>industry|WRT<br>services|Other<br>services|
||(1)|(2)|(3)|(4)|(5)|
|×<br>MDist<br>South|–0.007|0.209**|0.128|0.060|0.114***|
||(0.059)|(0.086)|(0.198)|(0.042)|(0.042)|
|MDist|0.019|0.033|0.130|0.040|–0.027|
||(0.041)|(0.066)|(0.129)|(0.026)|(0.031)|
|South|0.333***|0.308***|0.112|0.453***|0.349***|
||(0.070)|(0.115)|(0.271)|(0.052)|(0.055)|
|Constant|4.053***|3.498***|3.117***|2.598***|2.689***|
||(0.197)|(0.330)|(0.249)|(0.226)|(0.053)|
|_N_|3,851|1,560|814|5,618|10,903|
|_R_<sup>_2_</sup>-statistic|0.356|0.484|0.264|0.345|0.290|

_Source:_ Authors’ estimates based on data from the 2014 Integrated Business Establishment Survey.

_Note:_ This table presents estimates for equation 3. Dependent variable = log employment in manufacturing. Other industry = those industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. Other services = those services other than wholesale and retail trade; they include transport, communications, finance, commerce, government services, and private services. All regressions control for the informality status of a firm, the type of ownership, the type of legal organization, and region. Standard errors are reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

27
mously across countries, and is always far below the potential number implied by exporting all products to all destinations. The median number of nonzero product-destination entries per exporter is 3,055, going from a minimum of 10 to a maximum of 195,417. The median number of nonzero entries is less than half of one percent of the potential number. Baldwin and Harrigan (2007) have previously made the observation that many potential productdestination ‡ows are absent and relate the incidence of zeros to distance and importer size. Here we show that this is another important dimension of variation in the degree of success of exports. In the next section we systematically relate this to concentration and the prevalence of big hits.

# 2.2 Correlates of concentration

Our main focus is on the distribution of value across product-destination export ‡ows. However, we want to ...rst place the statistics above in context. To do so, we provide a brief descriptive analysis of export patterns and concentration ratios. We start by illustrating the very strong (log-linear) association between the number of nonzero product-destination export ‡ows and the value of total manufacturing exports, as can be seen in Figure 3. One way to succeed at exporting is to export more products to more places. This is a result of absolute advantage, which allows penetrating more markets with more products.

Larger economies export more products to more destinations by virtue of sheer size and diversity, and richer countries might have a better chance to penetrate more markets due to better technology. This relationship between the number of product-destination export ‡ows, size and income is well captured by the following regression, which we ...t to data on 135 countries log (number of nonzero export ‡ows) = �12:73<sup>percapita);</sup> (0:084)<sup>+ 0</sup> (0:<sup>:</sup> 043)<sup>64�log (GDP)+ 0</sup> (0:<sup>:</sup> 084)<sup>65�log (GDP</sup> where robust standard errors are reported in parentheses and R<sup>2</sup> = 0:8.<sup>12</sup> Poorer and smaller economies indeed penetrate less markets with less products.

However, in terms of explaining export success, this is not the entire story, as Figure 4 shows. Even after controlling for size (GDP) and income (GDP per capita) the association between export success and the number of nonzero product-destination export ‡ows remains

> 12 We could obtain GDP data for only 135 countries in our sample.

7
competitive market wage would likely increase the overall level of employment. Results in this paper indicate that a large wage gap between male and female employees exists, mainly in the private sector, due to the fact that low-skilled males earn wages that are higher that those of low-skilled women; despite little labor occupational segregation and despite the fact that women display higher human capital indicators. The introduction of a minimum wage between 40 and 50 Denars per hour would contribute to a decrease in the gender wage gap of between 15 and 23%. This occurs because introducing a minimum wage is likely to be more binding for low-skilled women than for low-skilled men and thereby likely to improve the wage level of low-skilled women by more than it would among low-skilled men. However, in order to get a significant improvement in the wage level of low-skilled women, a rather high minimum wage (close to the median wage) would need to be introduced. As such, while the introduction of a minimum wage may likely contribute to increase the wages of low-educated women and their levels of participation in the labor market; it may also contribute adversely to employment. The employment effects of minimum wage policy should be treated and assessed with particular care in Macedonia given its very low employment – and high unemployment – rates.

Besides minimum wage policy, other policies aimed at strengthening market competition and at improving women’s job conditions and wages should be explored. Promoting higher wages for low-skilled women workers is likely to boost their participation, reduce the gender wage gap, and eventually contribute to poverty reduction (Angel-Urdinola and Wodon, 2006). Finally, the question of whether or not Macedonia has regional markets with monopsony power needs further testing, and more research aimed to understand the demand-side of the labor market in the country should be conducted.

# **References**

Aaronson, D.; French, E.; MacDonald, J. (2008). The Minimum Wage, Restaurant Prices, and Labor Market Structure. _Journal of Human Resources_ , vol. 43(3) 688-720

- Angel-Urdinola, D. (2008). Can a Minimum Wage Increase have an Adverse Inpact on Inequality? Evidence from two Latin American Economies. _Journal of Economics Inequality_ , vol. 6 (1): 57-71

- Angel-Urdinola, D. and Macias-Essedin, V (2008). _Macedonia Employment Profile, 2004-2006_ . Mimeo. The World Bank, 2008

- Angel-Urdinola, D; and Wodon, Q. (2006). The Gender Wage Gap and Poverty in ColombiaPreview. _Labour_ , vol. 20 (4): 721-39

- Becker, G. 1971. “The Economics of Discrimination”. Chicago University Press. Chicago, IL.

9
Equations 13a and 13b are used to calculate the ad valorem equivalence for the TRI index in the different transport services sectors, and these are the values we report. In (13a and 13b), we use the own country shipping margins in the denominator. The impact of the barrier is part of the denominator, which reduces the calculated AVE. If we use (13), the upper bound is unity or 100 percent TE or AVE. Consistent with the view that barriers are lowest in Singapore, if we calculate the TRIj<TRIS, we assume that AVE = 0.

Finally, note that it is straightforward to convert the TE in (14) to one with unrestricted shipping margins in the denominator. In particular, if TE* is the ad valorem equivalent with unrestricted country shipping margins in the denominator, then TE* may be derived from (13) as TE* = TE/[1-TE] (14) To illustrate an example, consider the detailed calculation of AVE for air and rail transport services sector in Albania and the USA where the following raw data are obtained from Kang (2000), except for TRI which comes from our estimation in present study.

62
A practical problem with this approach is that cross‐country data on adult height are relatively scarce. Moreover, the interpretation of the cross‐country variation is clouded by genetic differences in populations of different countries. To address this problem, Weil (2007) suggests using a more widely‐ available summary indicator of health, adult survival rates (ASR). The basic insight is that ASR also improves within countries over time with improvements in latent health, in the same way that adult height does. As a result, the within‐country over‐time relationship between improvements in adult height and ASR can be used to transform the “return” to height into a “return” to ASR, and furthermore, ASR can be more meaningfully compared across countries .<sup>17</sup> Specifically, this means that γ�z in the expression for human capital can be replaced with γ��� �ASR , where γ��� �γ������ �β������,��� and β������,��� is the slope coefficient from a regression of adult height on ASR. Intuitively, this slope coefficient captures how both adult height and adult survival rates improve when latent health improves, and this relationship can be used to convert the “return” to height into a “return” to ASR.<sup>18</sup> Again, however, the correct interpretation is not that there is a labour market “return” to adult survival rates. Rather, the correct interpretation is that when latent health improves to the extent that ASR increases by one percentage point, then worker productivity increases by γ��� percentage points.

A complementary strategy to solve the problem of data scarcity for adult height is to instead use data on stunting in childhood, which has become increasingly available, particularly in low‐income countries where stunting is common and recognized as an important marker of poor early childhood development outcomes. Although country coverage of stunting data is less complete than for ASR, a benefit of stunting as a proxy for health is that there is direct evidence on the links between height in childhood and adult height. Evidence from cohort studies that track individuals over time provide evidence that height deficits in childhood persist into adulthood. This relationship can be used to create a link between stunting rates and likely future adult height, which analogously is referred to as β������,�������� . This can then be used to derive an alternative measure of the contribution of health to future adult productivity, γ�������� �STUNTING , where γ�������� �γ������ � β������,�������� . The same caveats of interpretation apply to this measure as do to γ��� .

> 17 Considering the within‐country over‐time variation controls for time‐invariant factors contributing to cross‐ country differences in height.

> 18 An underlying assumption here is that latent health is a single scalar index and that the observed indicators all respond to this single underlying measure.  See Weil (2007), Section III.C for a discussion of the consequences of relaxing this assumption.

38
<!-- Start of picture text --> wo<br>irs)<br>wm<br>N<br>2<br>2<br>oh<br>&<br>2<br>or<br>ot<br>ro]<br>Sw<br>om<br>=<br>owt<br>=<br>=<br>5 6 7 8 9 10 11 12 13 14 15 16 17 #18 #19 20<br>Recall period<br>———— Deceased within 5 years ——— Deceased in 1 month<br><!-- End of picture text --> <!-- Start of picture text --> wo<br>oe)<br>wm<br>N<br>wo<br>2<br>oO<br>Ew<br>2<br>oD<br>Tw<br>BS<br>£t<br>o<br>E=are)<br>a<br>‘o<br>swN<br>=<br>5 6 7 8 9 10 11 12 13 14 15 16 17 #18 #19 20<br>Recall period<br>———— Deceased within 5 years ——— Deceased in 1 month<br><!-- End of picture text -->
- 13 -

The augmented Dickey-Fuller test with 13 lags is performed to test for stationary. The number of lags is selected following the above argument. As shown in Table 3, all variables in the three cases, but one, have been found stationary in both level and first-difference terms. At least the unit root hypothesis is strongly rejected at the conventional significance level. The hydrology data associated with the Thac Mo Reservoir is exceptional. It may potentially exhibit a unit root; but with the seasonally adjusted series the null hypothesis can be easily rejected (ADF test statistics is estimated at -10.819). Additional attention will be paid to this unit root case in the following analysis.

Table 3. Unit Root Test

||Vishnugad<br>Pipalkoti<br>(India)|Upper<br>Kotmale<br>(Sri Lanka)|Thac Mo<br>(Vietnam)|
|---|---|---|---|
|ln_HYDRO_|-4.324 <sup>***</sup>|-4.166 <sup>***</sup>|-1.827|
|ln_TEMP_|-5.432 <sup>***</sup>|-3.656 <sup>***</sup>|-3.286 <sup>**</sup>|
|ln_PREC_|-6.469 <sup>***</sup>|-4.619 <sup>***</sup>|-3.645 <sup>***</sup>|
|Δln_HYDRO_|-6.128 <sup>***</sup>|-9.805 <sup>***</sup>|-6.400 <sup>***</sup>|
|Δln_TEMP_|-15.860 <sup>***</sup>|-7.667 <sup>***</sup>|-6.780 <sup>***</sup>|
|Δln_PREC_|-16.656 <sup>***</sup>|-8.573 <sup>***</sup>|-8.592 <sup>***</sup>|

Note: *** 1 % significance level, ** 5 % significance level, and * 10% significance level.

The above discussion allows us to apply the VAR model with deterministic periodicity and trend components. Consider the following system of equations with three variable, i.e., ln _HYDRO_ , ln _TEMP_ , and ln _PREC_ : where<sup>_z_</sup> _t_<sup>is a 3×1 random vector,</sup> _Ap_ is a 3 × 3 matrix of parameters to be estimated, and _cmonth_ is a set of monthly dummy variables. In addition, ρ is a linear detrending parameter. An alternative way of estimating the hydrological model may be to first remove seasonality and trends and then analyze detrended series (e.g., Salas, 1993). However, the current model with deterministic seasonality included in the system is expected to yield a more efficient estimate.
# Table 10. Relationship Between Inequality and Human Capital (Alternative Instrument)

|Dependent Variable is:|S|hare of Population Tertiary Educat|ion|
|---|---|---|---|
||(1)|(2)<br>Excluding Top and Bottom<br>5<sup>th</sup>Percentile of Inequality|(3)<br>Excluding Top and Bottom<br>5<sup>th</sup>Percentile of Inequality &<br>Transition Countries|
|Inequalityit|1.48**|2.65**|2.57**|
||(0.74)|(1.29)|(1.29)|
|Inequalityit|-0.16*|-0.27*|-0.26*|
|*ln(yit-1)|(0.09)|(0.15)|(0.16)|
|ln(yit-1)|0.01<br>(0.01)|-0.00<br>(0.02)|-0.00<br>(0.02)|
|||First Stage: Inequalityit||
|SWratioi*Oil pricet-2|0.44***<br>(0.14)|0.32***<br>(0.14)|0.28**<br>(0.12)|
|SWratioi*Oil pricet-2|-0.05***|-0.03**|-0.03**|
|*ln(yit-1)|(0.01)|(0.01)|(0.01)|
|||First Stage: Inequalityit*ln(yit|-1)|
|SWratioi*Oil pricet-2|4.10***<br>(1.11)|3.09***<br>(1.02)|2.77***<br>(1.04)|
|SWratioi*Oil pricet-2|-0.37***|-0.19*|-0.18*|
|<br>*ln(yit-1)|(0.10)|(0.10)|(0.10)|
|Country FE|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|
|Observations|487|436|428|

Note: The method of estimation is two-stage least squares. The inequality variable is the market Gini from Solt (2015). Robust standard errors are shown in parentheses. *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

35
<mark>Gneezy, Uri, and Aldo Rustichini. "Gender and Competition at a Young Age."</mark> _<mark>American Economic</mark> Review_ 94, no. 2 (2004): 377‐81.

<mark>Goldin, Claudia. "A Grand Gender Convergence: Its Last Chapter."</mark> _<mark>American Economic Review</mark>_ <mark>104,</mark> no. 4 (2014): 1091‐119.

<mark>Grant, Monica J., and Jere R. Behrman. "Gender Gaps in Educational Attainment in Less Developed</mark> Countries." _Population and Development Review_ 36, no. 1 (2010): 71‐89.

<mark>Groshen, Erica L. "The Structure of the Female/Male Wage Differential : Is It Who You Are, What You</mark> Do, or Where You Work?" _Journal of Human Resources_ (1991): 457‐72.

<mark>Hausmann, Ricardo, Laura D. Tyson, Yasmina Bekhouche, and Saadia Zahidi. "The Global Gender Gap</mark> 2012." In _World Economic Forum_ . 2013.

<mark>Migheli, Matteo. "Gender at Work: Incentives and Self‐sorting."</mark> _<mark>Journal of Behavioral and</mark> Experimental Economics_ 55 (2015): 10‐18.

Mincer, Jacob: _Schooling, Experience, and Earnings_ , Columbia University Press: New York, (2004).

Montt, Guillermo. "The Causes and Consequences of Field‐of‐study Mismatch." (2015).

<mark>Nordin, Martin, Inga Persson, and Dan‐Olof Rooth. "Education–occupation Mismatch: Is There an</mark> Income Penalty?" _Economics of Education Review_ 29, no. 6 (2010): 1047‐059.

<mark>Petersen, Trond, and Laurie A. Morgan. "Separate and Unequal: Occupation‐Establishment Sex</mark> Segregation and the Gender Wage Gap." _American Journal of Sociology_ 101, no. 2 (1995): 329‐65.

<mark>Robst, John. "Education and Job Match: The Relatedness of College Major and Work."</mark> _<mark>Economics of</mark> Education Review_ 26, no. 4 (2007a): 397‐407.

<mark>Robst, John. "Education, College Major, and Job Match: Gender Differences in Reasons for</mark> Mismatch." _Education Economics_ 15, no. 2 (2007b): 159‐75.

<mark>World Bank. 2008.</mark> _<mark>Gender in Agriculture Sourcebook (English)</mark>_ <mark>. Agriculture and Rural Development.</mark> Washington, DC: World Bank.

<mark>World Bank. 2012</mark> _<mark>. World Development Report 2012 : Gender Equality and Development.</mark>_ Washington, DC: World Bank.

<mark>World Bank. 2012.</mark> _<mark>Toward Gender Equality in East Asia and the Pacific : A Companion to the World Development Report.</mark>_ <mark>World Bank East Asia and Pacific Regional Report. Washington, DC: World</mark> Bank.

<mark>Zafar, Basit. "College Major Choice and the Gender Gap."</mark> _<mark>Journal of Human Resources</mark>_ <mark>43, no. 3</mark> (2013): 545‐95.

<mark>Zveglich, Joseph E., and Yana Van Der Meulen Rodgers. "Occupational Segregation and the Gender Wage Gap in a Dynamic East Asian Economy."</mark> _<mark>Southern Economic Journal</mark>_ <mark>70, no. 4 (2004): 850‐75.</mark> 32
_PoLh_ **_Fbcal Crisb_** _21_ Table 8: Enterprise Taxation and the Government Budget: Foreca,sts behind the Budget Laws vs. Actual Outcomes

||1990 Bu|dget|1991 Bud|get|
|---|---|---|---|---|
||Forecast|Actual|Forecast|Actual|
|Profitability as a % of<br>sales at producer prices|18.6|23.7|23.5|7.1|
|Profit tax as a % of<br>sales at producer prices|6.1|8.5|9.2|4.5|
|Unit labor costs|16.9|18.1|16.8|22.6|
|Amortisation deductions<br>as a % of sales<br>at producer prices|1.9|3.4|6.8|6.8|
|Profitability before<br>amortisation deductions<br>as_d_% of sales<br>at producer prices|20.5|27.1|30.3|13.9|
|Payroll taxes as a % of<br>sales at producer prices|6.2|6.6|6.3|8.6|
|Profit + payroll taxes<br>as a % of sales<br>at producer prices|12.3|15.1|_15.5_|13.1|

Notes: "Profit" = Historical cost profit ("financial result"). Coverage of the enterprise sector differs slightly from previous tables.

This is already close to European levels, but an average<sup>rate of 20% or more seemns not</sup> unreasonable. Again allowing for exports, increasing the<sup>average VAT rdte by</sup><sup>_5%_would raise revenues</sup> amounting to an additional 1-2 percent of GDP - a significant<sup>but not a huge amount. To compensate</sup> for the entire drop in the taxation of the inflation bias,<sup>by contrast, VAT would have to be introduced</sup> at an average rate of something like 30%, which is not practical.

A useful interim measure for the period until the introduction<sup>of VAT would be an increase in</sup> the existing turnover tax. This tax arnounted to about 7% of sales in 1988-89<sup>but fell to 5% in 1990 and</sup> increased only slightly in 1991 to _5.5%._ The reasons<sup>for the fall in revenues are not clear; the tax take</sup>
Table 9 suggest, in line with descriptive evidence, that NREGS participation increases the propensity to make land-related investment. The size of the estimated effect, 22.2 percent for all households versus 22.6 percent and 22.0 percent for scheduled castes and tribes and other caste households, respectively, does not vary much across subgroups, suggesting that scheduled castes and tribes do not suffer disadvantages. Lack of significance for the program participation dummy in the regressions with pre-program investment supports the notion that these are program effects rather than pre-program differences.

Appendix Table A3 reports regression results on NREGS-supported land investment. We use the same specification as in equation (2), where the dependent variable becomes the dummy variable indicating NREGS-supported land investment from 2006 to 2008. The results point to a 22.9 percent increase in propensity of NREGS-supported land investment for all NREGS-participating households, 22.6 percent increase for scheduled castes and tribes, and 22.8 percent for other castes. Again, we note that the magnitude of the estimated effect on NREGS-supported investment is close to the magnitude of the estimated effect on all land investment, suggesting that NREGS did not crowd out private investment or investment supported by other projects. We also note that scheduled caste and scheduled tribe participants were not discriminated against in land-related investment activities supported by NREGS.

# **5. Conclusion and Implications**

Our study complements a large literature on general NREGS impacts by exploring effects on participants in one Indian state with a good implementation record. Methodologically, two rounds of pre-program data allow the use of an identification strategy that combines triple difference estimates with propensity score matching. Substantively, we contribute to the literature in a number of ways. First, we show that shortterm direct impacts on energy and protein intake differ from the more general investment impacts observed in the medium term. Second, we find that direct impacts are almost exclusively concentrated within scheduled casts and tribes and those with members relying primarily on the casual labor market. Third, the hypothesis of NREGS crowding out other employment types is rejected; program expenditures translate almost directly into additional employment. Finally, investment in land improvement, partly on participants’ fields, emerges as a potential pathway for NREGS effects to materialize.

While all this suggests that NREGS is well targeted and has significant impacts, Andhra Pradesh is generally considered to be one of the better-performing states in terms of NREGS implementation. Using similar pre- and post-program data at the household level to extend the analysis to other states where implementation is much weaker could allow researchers not only to measure the aggregate impact of the program but also to better understand the impact of specific implementation arrangements (for example, social audits or electronic funds transfers), an area that would be of great interest to policymakers.

15