rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Osorio, de Barros, and Filmer (2018) l</mark> everages an experimental design in the same Cambodian sample to demonstrate zero causal impact of additional years of schooling on SEM skills. Interestingly, even though schools appear not central to the production of SEM skills, the mean level of SEM skills is similar to those in rich-country populations. This suggests either that population-level measures do not indicate substantial deficits or that they cannot be used for cross-country comparisons due to population-dependent reference points.

One reason why schools may not be very good at producing these skills is because they focus on _other_ unmeasured skills, which are then rewarded in the labor market. Therefore, in order to evaluate this possibility, the third set of results turns to the association of our skills measures with labor force participation and earnings. Labor force participation (LFP) among men who are not enrolled in school or college at the time of the survey in 2018 is 85%, compared to 5% for women, consistent with low female labor force participation rates in South Asia (Field and Vyborny, 2016). Labor force participation increases with years of schooling for women but declines for men. This pattern could reflect different search patterns as men with more years of schooling may be “waiting it out” as they search for a better job, while women in our sample appear to have a limited window during which they can participate in the labor force before marriage. However, conditioning on years of schooling, cognitive skills are not correlated with LFP for either sex and SEM skills are correlated with LFP only for men. Schooling is important for LFP, but specific cognitive and SEM skills less so.

The lack of correlation between cognitive skills and LFP no longer holds once we turn to the correlation between labor earnings. In our data, every year of education is associated with 3.4%-4.1% higher monthly median labor earnings for men and 1.7%-1.9% higher monthly mean earnings for women (the median earning for women is zero at all years of schooling). Restricted to the sample of working adults, the estimates are 5.2%-5.3% for men and 4.8%-5.9% for women.<sup>3</sup> Importantly, and in contrast to labor force participation, labor earnings are correlated with both cognitive and SEM skills, conditional on years of schooling. In our preferred median regressions, a standard deviation

> 3 One explanation why the association with labor earnings is smaller for men than the 10%-12% usually found in the literature is that Pakistan, like many other countries, has seen a sharp increase in the return to college education and a decline in the return to primary or secondary education (Montenegro and Patrinos, 2021). In our sample, 9% of the men are still enrolled in education (college), and of these, 80% are not yet in the labor force. If all the returns to education come from those currently enrolled in college, it is too early to pick this up in our sample.

4
Table 1: Average and heterogenous marginal impacts

||||Combined impacts of|
|---|---|---|---|
||One more km closer,<br>without internet|Internet access without<br>road access improvement|one more km closer<br>and internet access|
|**DHS**||||
|Employed last year (in p.p.)<br>|0.8<br>|6.7<br>|9.1<br>|
|Currently working (in p.p.)|0.8|4.5|6.5|
|Low skilled worker (in p.p.)<br>|0.5|-11.2|-12.6|
|Services (in p.p.)|0.1|13.9|16.9|
|Skilled worker (in p.p.)|0.1|10.7|13.0|
|High skilled worker (in p.p.)|0.1|8.1|9.7|
|**LSMS**||||
|Hours worked last week|0.5|-4.4|0.1|
|**WBES**||||
|Hours of operation per week|3.8|0.5|4.4|
|Number of full time employees|0.7|2.02|2.9|
|Full time skilled employee<br>|1.0|1.0|2.07|
|Full time unskilled employee|0.4|1.1|1.6|
|Telecommunications as an obstacle (in p.p|.)<br>3.6|-6.4|-3.2|
|Percentage of losses (in p.p.)<br>|-0.7|-2.1|-3.0|
|Government ownership share (in p.p.)|-0.3|1.2|1|
|Contract with government (in p.p.)|0.2|16.1|17.1|
|**DHS**||||
|**Employed last year (in p.p.)**<br>||||
|Average|0.8|6.7|9.1|
|Urban|0.6|12.0|21.6|
|Rural|0.8|3.5|4.5|
|Male|0.4|4.7|6.3|
|Female|0.9|7.7|10.5|
|**Currently working (in p.p.)**||||
|Average|0.8|4.5|6.5|
|Urban|0.7|11.0|19.7|
|Rural|0.8|1.7|2.4|
|Male|0.5|3.9|4.4|
|Female|0.9|6.7|9.2|
|**LSMS**||||
|**Hours worked last week (in p.p.)**||||
|Average|0.5|-4.4|0.1|
|Urban|4.8|-2.7|6.8|
|Rural|0.3|-3.6|-0.1|
|Male<br>Female|0.0<br>0.0|-5.0<br>-4.0|-0.5<br>-0.3|

_Note: Summary of average and heterogeneous impacts based on tables of results in the Appendix. All results are calculated for a distance to main road equal to 1km. In that case, the road variable takes the value -1 when calculating the interaction effect using the regression tables in the Appendix._ 24
conservative estimate of the risk associated with a release of each chemical.

A difficulty arose in converting the ordinal scale ranking of toxicological risk associated with particular chemicals to a measure of the total risk posed by all releases from a facility. The approach adopted in this study was to multiply the quantity of each Tri chemical reported by a facility by its toxicological potency ranking, and then to sum the risk-weighted quantities for all chemicals released by the facility. Acknowledging the questionable validity of using an ordinal scale in an arithmetic procedure, two forms of weighting were used to test the sensitivity of the results. First, the EPA toxicological potency ratings were simply reversed, giving a linear weighting scale from 1 to 4. Four weights were used, although there are only three toxicological potency ratings, because those TRI chemicals yet to be assigned a toxicological rating (see section 3.2.2 above) were grouped together with the lowest weighting. Second, an exponential weighting was used for the four groups, rising by orders of magnitude from 1 to 1,000. This methodology generated four measures of risk-weighted releases and transfers for each facility:

- @ linear acute human health and terrestrial ecotoxicity; ® exponential acute human health and terrestrial ecotoxicity;

- @ linear acute aquatic ecotoxicity;

- ® exponential acute aquatic ecotoxicity.

In addition, two TRI totals unweighted for risk were calculated for each facility: 20
**Table 2: Costs of Security-Related Risks**

|**Materialization of**<br>**Security-Related Risks**|**Direct Costs**|**Costs of Coping**<br>**Strategies**|**Costs of Reduction /**<br>**Mitigation Strategies**|
|---|---|---|---|
|**Investment Climate**||||
|Violation of property rights,<br>destruction, criminal<br>activities|Loss of property,<br>destruction|Resources for<br>reconstruction; macro<br>instability; capital flight;<br>reliance on privately-<br>provided security|Fiscal cost of security<br>sector; risk-adverse<br>investment behavior;<br>protection costs; capital<br>flight, weak FDI; shift<br>toward informal sector|
|Disrupted trade|Lower trade|Inefficient trade,<br>smuggling|Reliance on informal<br>trade networks; adverse<br>to trade-related sectors|
|Weaker human and social<br>capital and institutions|[see below]|[see below]|[see below]|
|**Human / Social Capital**||||
|Injuries, death,<br>psychological damage|Loss of human and<br>social capital|Health-related costs;<br>possibly lower provision<br>of education|Brain flight, migration|
|Forced displacement, large<br>numbers of combatants|Loss of social capital<br>and skills||Brain flight, migration|
|Loss of jobs, assets, income|Difficulty to smooth<br>consumption|Sale of productive assets;<br>shift to illegal activities|Risk-adverse<br>consumption; possibly<br>excessive savings|
|Financing of security sector,<br>destruction of health /<br>education infrastructure|Reduced provision of<br>health and education<br>services|Resources required for<br>reconstruction (possibly<br>resulting in excessive<br>taxes or deficit financing<br>and inflation); poor<br>quality of alternative<br>mechanisms for|<br>(including in the form of<br>capital flight)|
|||providingsocial services||
|**Public Institutions**||||
|Weak revenue base|Poor services|Possibly excessive<br>enforcement|Shift to informal sector;<br>excessive reliance on<br>trade taxes|
|Weak political participation|Lack of inclusion, and<br>weaker decision-<br>making processes;<br>weak legitimacy|Short-term economic<br>policies|Secrecy [needs to be<br>elaborated]|
|Widespread corruption|Impact on<br>households’ revenues<br>and investment<br>climate|Encourage private deals<br>with corrupt officials|Possibly excessive<br>control or deregulation|

_Note: Direct costs include the direct impacts if the risk materializes. The costs of coping strategies include the indirect impact of coping with the direct impact once the risk has materialized. The costs of reduction and mitigation strategies include the indirect impact of adjusting behavior and incurring costs ex ante to reduce the likelihood of the risk or its potential cost._

# **2.1. Security and the Investment Climate**

Lack of security weakens the investment climate (World Bank, 2004). This is obvious in the case of conflict, which not only reduces the incentives for investment but also weakens physical capital (through direct damage, or by diverting funds from operation and 5
**Table 3: An Empirical Equilibrium Model of the Global Cement Industry.** Continuous market characteristics are in natural logs unless otherwise specified.

||Courn|ot|Conduct Pa|rameter|P-value|
|---|---|---|---|---|---|
|Variable<br>|Coefcient|S.e.|Coefcient|S.e.|_H_0: Equality|
|Demand:||||||
|Constant|4.919|2.552|4.919|2.538|1|
|Year (=2017)|-0.117|0.099|-0.117|0.099|1|
|Price of cement USD|-1.268|0.329|-1.272|0.327|0.99|
|Exchange rate USD/LCU|0.016|0.017|0.015|0.017|0.97|
|Construction share of GDP|0.398|0.095|0.397|0.095|0.99|
|GDP per capita|0.35|0.047|0.353|0.047|0.96|
|Population|0.88|0.044|0.879|0.044|0.98|
|Supply with domestic production capacity:||||||
|Constant|4.208|0.484|4.698|0.158|0.34|
|Year (=2017)|0.211|0.283|-0.053|0.09|0.37|
|Cement exports of neighbor|-0.007|0.027|-0.031|0.011|0.41|
|Exchange rate USD/LCU|0.009|0.02|0.007|0.013|0.94|
|Gypsum deposits (=1)|0.43|0.376|-0.009|0.094|0.26|
|Price of diesel|0.318|0.092|0.303|0.055|0.89|
|Road density|-0.016|0.051|-0.049|0.024|0.56|
|Rule of law (Z-score)|-0.097|0.091|-0.015|0.042|0.41|
|Supply without domestic production capacity|:|||||
|Constant|5.46|0.379|5.464|0.378|0.99|
|Year (=2017)|-0.364|0.166|-0.351|0.166|0.95|
|Exchange rate USD/LCU|-0.013|0.066|-0.007|0.066|0.95|
|Fixed cost:||||||
|Constant|17.556|0.14|17.268|0.18|0.21|
|Year (=2017)|-0.287|0.264|-0.402|0.288|0.77|
|Exchange rate USD/LCU|0.059|0.061|0.046|0.089|0.9|
|Procedures to start business (Z-score)|0.309|0.151|0.338|0.168|0.9|
|Rule of law|0.221|0.163|0.249|0.233|0.92|
|Standard deviation term|1.394|0.119|1.608|0.141|0.25|
|Conduct:||||||
|Constant|||0.688|0.257||
|Concentration of urban centers (Z-score)|||-0.165|0.061||
|Multimarket contact (Z-score)|||0.166|0.098||
|Sargan-Hansen J-Statistic|||2.032|||
|Sargan-Hansen test p-value|||0.362|||

38
# **Table 17 Estimates of the tariff equivalents of ad valorem and specific tariffs and tariff rate quotas on Tunisian imports, 2001 (percent)**

|_Sector_|_Imports from EU_|_Imports from_<br>_GAFTA_|_Imports from Rest_<br>_of World_|
|---|---|---|---|
|Live animals, products|87.9|18.8|75.4|
|Dairy products|60.7|9.2|108.3|
|Coffee, etc, sugar, cut flowers|78.6|20.9|21.2|
|Fruit and veg|74.0|67.0|85.1|
|Cereals|87.7|15.4|56.6|
|Oil seeds and fats|14.7|114.1|21.8|
|Beverages and tobacco|32.5|17.2|34.8|
|Other agricultural products|28.4|12.4|45.9|
|Fish and products|41.4|20.4|27.0|
|Mineral products|7.2|6.0|16.4|
|Metals, products|9.3|16.7|27.0|
|Chemical etc|6.0|7.9|25.9|
|Leather products|14.5|21.0|38.1|
|Wood products, pulp, paper|16.8|14.6|25.6|
|Textiles and apparel|18.0|9.0|32.6|
|Transport equipment|7.5|13.4|19.5|
|Other machinery and equipment|7.9|7.4|18.2|
|Electrical machinery|4.8|12.4|20.3|
|Other manufacturing|21.7|19.3|34.0|

_Source_ : GTAP version 6 database.

47
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _30_ Table A4—Summary of estimation results.

|Country|Avg. infation|Max. draw-down|Avg. volatility|CV-score|
|---|---|---|---|---|
|Afghanistan|5.76%|-44.02%|8.69%|0.87|
|Burkina Faso|4.08%|-37.74%|15.25%|0.77|
|Burundi|3.90%|-27.74%|13.26%|0.77|
|Cameroon|1.25%|-20.40%|7.01%|0.95|
|Central Afr. Rep.|1.07%|-29.92%|19.36%|0.75|
|Chad|4.17%|-45.92%|19.02%|0.69|
|Congo, Rep.|1.56%|-24.17%|11.76%|0.87|
|Congo, Dem. Rep.|7.63%|-16.67%|8.64%|0.87|
|Gambia, The|4.92%|-15.31%|10.52%|0.71|
|Guinea-Bissau|1.62%|-18.56%|14.51%|0.81|
|Haiti|7.60%|-34.82%|11.98%|0.82|
|Iraq|-1.07%|-25.58%|5.12%|0.90|
|Lao PDR|0.83%|-3.44%|1.56%|0.93|
|Lebanon|20.35%|-21.96%|14.07%|0.92|
|Liberia|11.67%|-17.95%|10.57%|0.93|
|Mali|2.46%|-23.97%|8.63%|0.84|
|Mozambique|9.10%|-29.33%|9.40%|0.84|
|Myanmar|1.82%|-38.76%|11.83%|0.74|
|Niger|4.03%|-23.71%|9.84%|0.87|
|Nigeria|7.82%|-20.89%|7.44%|0.92|
|Somalia|5.54%|-48.67%|12.95%|0.83|
|South Sudan|43.03%|-29.55%|25.11%|0.87|
|Sudan|47.16%|-30.24%|18.93%|0.87|
|Syrian Arab Rep.|35.25%|-23.49%|17.01%|0.88|
|Yemen, Rep.|9.55%|-23.84%|14.13%|0.78|

_Note:_ The first three columns respectively report average annualized inflation, maximum draw-down, and average annual realized volatility in percentages. The final column reports the cross-validated confidence score that ranges from 0 to 1 for the final food price index using the calculations from the paper. Additional cross-validation statistics can be found on the World Bank Data Catalog page where a live version of the data base is maintained. _Source:_ The statistics have been prepared by the author for this paper based on end-of-August (2021) food price data from World Food Program.
# **Table 1A: Growth and Uphill Flows of Sophisticated Exports** **<u>(Cross-Sectional Regressions; Scale-free measure of Uphill flows)</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Variable||Depende|nt variable is|Annual aver|age growth 1|994-2003||
|Initial per capita GDP (log)|-0.013*<br>(0.007)|-0.007<br>(0.005)|-0.007<br>(0.005)|-0.003<br>(0.005)|-0.008<br>(0.005)|-0.004<br>(0.007)|-0.009<br>(0.006)|
|Uphill flows for 1990 (median sophistication)|0.017***<br>(0.005)|0.011***<br>(0.003)|||||0.014*<br>(0.008)|
|Years of primary schooling|-0.004<br>(0.013)|-0.015<br>(0.009)|-0.015<br>(0.009)|-0.003<br>(0.011)|-0.015<br>(0.009)|-0.011<br>(0.010)|-0.015*<br>(0.009)|
|Capital stock|-0.000<br>(0.006)|0.000<br>(0.005)|-0.000<br>(0.005)|-0.001<br>(0.005)|-0.000<br>(0.005)|-0.000<br>(0.005)|0.000<br>(0.005)|
|Institutional quality (Rule of law)|0.009***<br>(0.003)|0.008***<br>(0.002)|0.008***<br>(0.003)|0.008***<br>(0.003)|0.009***<br>(0.003)|0.008***<br>(0.003)|0.008***<br>(0.002)|
|Uphill flows for 1990 (75th percentile<br>sophistication)|||0.011***<br>(0.003)|||||
|Uphill flows for 1990 of sophisticated relative to<br>unsophisticated products||||||0.007*<br>(0.004)||
|Sophistication of exports|||||0.013**<br>(0.006)|||
|Average income level of destination of<br>sophisticated (median) exports|||||0.011***<br>(0.004)|||
|Uphill flows for 1995 (median sophistication)||||0.007**<br>(0.003)||||
|Observations|58|56|56|60|56|56|55|
|Adjusted R-squared|0.260|0.321|0.303|0.224|0.308|0.205|0.328|
|F-test|5.60|7.19|8.33|7.01|6.01|5.80|5.00|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Columns 2 onward exclude China and Ireland. Column 7 is an IV estimation with population and area (logs) serving as instruments for uphill flows.

27
Males experience some similar changes in participation rates as females between 1993 and 1998. Boys ages 6-15 experience a decline in overall participation between 1993 and 1998 due to a decline in participation in all activities other than household production. This decline is predominately driven by a decline in participation in household agriculture. Young men ages 16-24 also experience a decline in overall participation stemming from declines in participation in all activities other than household production (the participation increases by 5 percentage points) and wage work (mostly due to increased participation in nonagricultural activities). However, men ages 25-64 do not change their overall participation rates. Unlike women ages 25-64, men do not appear to increase their participation in household agriculture significantly. However, men 25-64 do increase their participation in wage work in nonagricultural activities. Overall, participation in market work increases slightly for men ages 25-64 and declines drastically for boys and men ages 16-24. These declines in market work for boys and men ages 16-24 are mostly driven by declines in participation in household agriculture. Moreover, total hours worked conditional on working declines for males ages 6-15 and 16-24 (mostly due to declines in hours worked in household agriculture and household production). However, total hours worked conditional on working increases for men ages 25-64. For women ages 25-64 the increase in hours worked occurs due to more time spent in household agriculture (there is also a large increase for women in hours in nonagricultural wage work, but these increases are concentrated among only 5.6 percent of the women). For men ages 25-64, most of this increase occurs due to an increase in hours spent in wage work in nonagricultural activities and household enterprise. As with women, there is suggestive evidence in the VLSS that higher wages may lurk behind these changes in labor supply. Men that work for wages earn 70 percent higher real wages in 1998 than they earned in 1993. It seems likely that the value of time in nonagricultural 20
**Table 2 (Cont.). Structure of the Banking Sector**

||1990|1991|1992|1993|1994|1995|1996|1997|1998|
|---|---|---|---|---|---|---|---|---|---|
|Latvia||||||||||
|Number of banks||14|50|62|56|42|35|32|27|
|o/w domestic||||||31|21|17|12|
|Foreign||||||11|14|15|15|
|Asset share of state banks, %|||||7.2|9.9|6.9|6.8|8.5|
|Banks (per mln people)||5.3|19.0|24.0|22.0|16.7|14.1|13.0|11.1|
|Lithuania||||||||||
|Number of banks||||26|22|12|12|11|10|
|o/w domestic||||26|22|12|9|7|_5_|
|foreign||||0|0|0|3|4|5|
|Asset share of state banks. %||||53.6|48.0|62.5|54.9|48.8|45.3|
|Banks (per min people)||||7.0|5.9|3.2|3.2|3.0|2.7|
|_Cis_||||||||||
|**Georgia**||||||||||
|Number of banks|||75|179|226|101|61|53|43|
|o/w domestic|||||225|98|_55_<br>|45|34<br>|
|foreign|||||1|3|6|8|9|
|<br>Asset share of state banks. %|98.4|92.5|77.6|72.7|67.9|45.8|0.0|0.0|0.0|
|Banks (permln people)|||13.7|32.9|41.7|18.6|11.3|9.8|7.9|
|Kazakhstan||||||||||
|Number of banks|30|72|155|204|184|130|101|82|71|
|o/w domestic||71|154|199|176|122|92|60|51|
|foreign||1|1|_5_|8|8|9|22|20|
|Asset share of state banks. %||19.3|4.6|n.a.|n.a.|24.3|28.4|45.4|23.0|
|Banks (per mln people)|1.8|4.4|9.4|12.4|11.3|8.1|6.3|5.2|4.5|
|Kyrgyz Rep.<br>||||||||||
|Number of banks|6|10|15|20|18|18|18|20|23|
|o/w domestic|6|10|14|19|15|15|15|17|17|
|foreign|0|0|1|1|3|3|3|3|6|
|<br>Asset share of state banks. %|100.0|98.8|n.a.|n.a.|77.3|69.7|5.0|9.8|0.0|
|Banks (per mln people)|1.4|2.2|3.3|4.5|4.0|4.0|3.9|4.3|4.9|
|Ukraine||||||||||
|Number of banks|||133|211|228|230|229|227|n.a.|
|o/w domestic|||||227|229|223|215|n.a.|
|foreign|||||I|1|6|12|n.a.|
|Asset share of state banks. %||||||||||
|Banks (per mln people)||||2.5|4.1|4.4|4.5|4.5|4.5|

_Source: EBRD Transition Report, 1999_ 6
_Figure 13: Legal and business context for women and promotion of gender equality in schools_ <!-- Start of picture text --> 4.8<br>Philippines<br>Lao PDR<br>4.7<br>4.6 Peru<br>Nigeria Congo, Dem. Rep.<br>4.5<br>Vietnam<br>Indonesia<br>4.4<br>Ghana<br>4.3<br>Bangladesh<br>4.2 Mongolia<br>4.1 Pakistan<br>Uganda<br>4<br>40 50 60 70 80 90 100<br>Women, Business and Law Index 2023<br>Disagrees")<br>(5="Strongly agrees", 1= "Strongly<br>School should promote gender equality<br><!-- End of picture text --> Source: CGD–World Bank 2022 survey and Women, Business and Law, 2023 (World Bank).

# 5 Other Priorities in Education Outcompete Investments in Inclusion

Although at least 80 percent of all respondents in the 2022 survey claim they would support accommodations to facilitate the inclusion of children with disabilities in regular classrooms, investing in a program for students with disabilities is the least often selected option in the discrete choice experiment (Figure 14). Less than 50 percent of all 2022 respondents<sup>15</sup> selected investing in a program for students with disabilities, preferring in-service teacher training (60 percent) or early-grade reading (62 percent). The second-lowest frequency option was investing in laptops (38 percent). Officials from the EAP region were as likely as other respondents to select the project supporting inclusion (47 percent of respondents) than outside of the region (48 percent of respondents). In the EAP region, the project supporting inclusion was selected more often (47 percent) than the provision of free meals in primary schools (38 percent).

> 15 These figures come from the raw data of the experiment, not the probabilities computed a posteriori which are displayed in Figure 14.

17
_Infrastructures,2015_ as the share of fiscal spending of a state in non-infrastructure projects (out of total fiscal spending) in 2015.<sup>40</sup> We then introduce this variable in equation 1, both in levels and in interactions with the _Postt_ and _DebtStates,16Q1_ variables, to examine differences in state-level outcomes of economic activity.

Results displayed in the first two columns of Table 8 indicate that the ex-ante composition of fiscal spending indeed affects the subsequent behavior of macro outcomes of states. More concretely, in a state with average ex ante public indebtedness after the passage of the FD Law (~0.86), a one-standard-deviation increase in the share of public spending in non-infrastructure projects is associated with an increase of around 0.062*0.028*0.86 = 0.15 p.p. of GDP growth and an increase in employment growth of 0.09 p.p.. These findings suggest that the crowding-out effect was greater in states where governments were spending a larger fraction of their budget on projects unrelated with public investment. Thus, the marginal return of capital reallocation from public towards private firms is higher in states channeling relatively more resources on expenditures different from public infrastructure projects.

Similarly, we analyze the impact of the FD Law on poverty rates and income inequality of states of varying ex ante indebtedness and composition of public spending. Our results are summarized in Columns 3 to 6 of Table 8. The estimated coefficients of the triple interaction in Columns 4 and 5 indicate that at similar ex ante indebtedness levels, states that ex ante channeled more public spending in non-infrastructure projects experience higher reductions (increases) in their moderate (extreme) poverty rates. For a state of average ex ante public indebtedness, a onestandard-deviation increase in the ex-ante share of public spending in non-infrastructure is associated with a reduction in moderate poverty of 2.8 percent and an increase in extreme poverty of 5.8 percent.

We further investigate the differential impact of the FD Law on lending outcomes to private firms by states’ type of public spending. The results, displayed in Column 7 of Table 8, suggest that the unwinding of crowding out experienced after the Law was stronger in states spending more on non-infrastructure projects. More concretely, in a state with average ex ante public

> 40 Results are qualitatively similar if we average a state’s investment rate over the previous three or five years.

32