rafmacalaba/datause-encoder-data huggingface dataset:
are only available at a more aggregated level at the 2-digit KBLI level of aggregation. As such, mapping sectors 1:1 to the list of business activities in the regulation is not possible. Instead, we are able to map the sectors in the regulation to 5-digit KBLI and then calculate the share of 5-digit KBLI that are liberalized within a 2-digit sector.

There are a total of 68 sectors at 2-digit KBLI. Among these, 6 of the sectors belong to the primary sector, 22 to the secondary sectors, and 40 in the tertiary sector, which represents 9 percent, 32 percent and 59 percent respectively (see Table A1 which lists 62 sectors excluding the primary sector). Between 2019 and Q3 of 2022, the primary sector represented about 7 percent of FDI values, the secondary sector 49 percent, and the tertiary sector 43 percent. The FDI values in the secondary sector increased in this time period (35 percent share in 2019 to 58 percent share in Q3 of 2022) while the share of the tertiary sector decreased (58 percent in 2019 to 43 percent in Q3 of 2022) and primary sectors stayed about the same. Our analysis focuses on non-commodity sectors, that is, we exclude the primary sector, and trends in FDI before and after the period of the reform.

A total of 24 of the 2-digit KBLI non-commodity sectors (secondary and tertiary), representing 39 percent, had 100 percent of the 5-digit sub-sectors within them liberalized by Perpres 49/2021 (i.e fully liberalized). The share between primary, secondary, and tertiary sectors in terms of FDI values within these fully liberalized sectors is broadly similar to all of FDI.

This analysis will be based on Perpres 49/2021 which came into effect in May 2021 as an amendment to 10 of 2021 on Investment Business Sectors. The new regulation revokes the 2016 Negative List (promulgated under Perpres 44 of 2016 on the List of Business Activities Closed and Open to Investment) and provides a new, significantly liberalized, investment list. Therefore, in the remainder of this section, the before-policy period is defined as the period from the first quarter of 2019 to the first quarter of 2021 and the after-policy period is from the third quarter of 2021 to the third quarter of 2023 for realized FDI. For planned FDI, which is at a monthly level, the pre-policy is from January 2019 to May 2021, and the post-policy is from June 2021 to March 2022.

7
Bertrand, Marianne (2004): ”From the invisible handshake to the invisible hand? How import competition changes the employment relationship,” _Journal of Labor Economics_ , 22(4): 723-765.

Brambilla, Irene, Daniel Lederman and Guido Porto (2012): ”Exports, export destinations, and skills,” _American Economic Review_ , 102(7): 3406-3438.

Bustos, Paula (2011): ”Trade liberalization, exports, and technology upgrading: Evidence on the impact of MERCOSUR on Argentinian firms,” _American Economic Review_ , 101(1): 304-340.

Davis, Donald R. and James Harrigan (2011): ”Good jobs, bad jobs, and trade liberalization,” _Journal of International Economics_ , 84(1): 26-36.

Egger, Hartmut, Peter Egger and Udo Kreickemeier (2013): ”Trade, wages, and profits,” _European Economic Review_ , 64(3): 332-350.

Egger, Hartmut and Udo Kreickemeier (2009): ”Firm heterogeneity and the labor market effects of trade liberalization,” _International Economic Review_ , 50(1): 187-216.

Greenaway, David and Richard Kneller (2007): ”Firm heterogeneity, exporting and foreign direct investment,” _Economic Journal_ , 117(517): F134-F161.

Greenaway, David and Douglas Nelson (2000): ”The assessment: Globalization and labour-market adjustment,” _Oxford Review of Economic Policy_ , 16(3): 1-11.

Guadalupe, Maria (2007): ”Product market competition, returns to skill, and wage inequality,” _Journal of Labor Economics_ , 25: 439-474.

Fr ́ıas, Judith, David Kaplan and Eric Verhoogen (2012): ”Exports and within-plant wage distributions: Evidence from Mexico,” CEPR Discussion Papers 8835.

Falvey, Rod, David Greenaway and Joana Silva (2010): ”Trade liberalization and human capital adjustment,” _Journal of International Economics_ , 81(2): 230-239.

17
dollars. “Average loan size” is Average Loan Balance per borrower. “Avg nominal interest” is Yield on Gross Loan Portfolio (and includes fees and interest; not adjusted for inflation). “PAR” is portfolio at risk.

Cull, et al. (2017) use the MIX Market database to analyze MFIs between 2005 and 2009, reflecting 3,845 institution-years and 291 million borrower-years.<sup>12</sup> Again, the focus is on variation among for-profit microfinance banks, credit unions and cooperatives, NGOs, non-bank financial institutions (NBFIs), and public-sector rural banks.<sup>13</sup> Cull et al. (2017) highlight the challenge created by the high fixed costs in lending described above. They estimate a median unit cost of $14 in operating expenses for each $100 of loans outstanding (in 2005-09). The distribution of unit costs is skewed, though, as seen in Figure 2. Institutions making small loans face particularly high unit costs. The horizontal axis measures average loan sizes normalized by the income per capita at the 20<sup>th</sup> percentile of the income distribution in an institution’s country. High fixed costs imply cost advantages when making larger loans (holding all else the same). The median commercial microfinance bank makes loans that are, on average, three times larger than the median NGO. The median 12 The 2009 data include 930 institutions with a combined 80.1 million borrowers. The Microfinance Information eXchange, Inc. (MIX) provided data through an agreement with the World Bank Research Department. Confidentiality of institution-level data has been maintained. The MIX Market now collects data on social outcomes, although it did not do so initially. 13 As noted by Cull, et al. (2017): “Participation in the MIX database is voluntary, and the microfinance institutions in the sample tend to feature institutions that stress financial objectives and profitability (though the database has become more broadly representative as it has expanded over time). The skew is shown by Bauchet and Morduch (2010) who calculate that the average operational self-sufficiency ratio (a measure of organizational efficiency) of institutions reporting to the larger, socially-focused Microcredit Summit Campaign database is 95 percent, compared to 115 percent for institutions reporting to the MIX Market. Scores above 100 percent reflect ‘operational self-sufficiency.’” 17
The patterns in Tables 4-7 are robust to many specification variants in addition to the variations described above when outlining our data and empirical strategy. One important test is that we find similar results when restricting to district-industries that have more than 50,000 employees, suggesting these outcomes are not sensitive to small sample sizes of female and male establishments.As another robustness check on our metric design, we find similar results when winsorizing our metrics at their 5% and 95% levels to weaken the influence of extreme values.

# **Instrumental Variable Analysis Using 1994 Incumbent Structures**

Our final analysis turns to the question of identification. Our analysis thus far takes the incumbent industrial structures as exogenous to predict the gender ratio of new entrants. There are several potential issues with this approach. First, reverse causality may be present, where the anticipation of many female entrants encourages females in the prior period to own businesses to link to the new firms. Second, omitted variable biases may be present. Our use of conditional estimations with district-year and industry-year fixed effects would require that such omitted factors exist at the district-industry level. An example would be a very inspirational female business leader in the district-industry that encourages both past and future women in the district to engage in firm ownership. Finally, measurement error likely exists that biases downward our estimates of the importance of incumbent industrial structures. This measurement error could be due to the sampled micro-data (vs. a census of local businesses) or incomplete metrics for how industries interact.

To analyze these challenges, we instrument in Tables 8 and 9 for incumbent industrial conditions in 2000 and 2005 using the industrial conditions that existed in 1994 in the manufacturing sector. The earliest year for which the female ownership question was asked is 1994. These data are only collected for the manufacturing sector.

Returning to the challenges facing the least squares estimates, the 1994 incumbent industrial conditions and the role of women are more exogenous to the entry conditions in 2000 and 2005 than the contemporaneous incumbent conditions. To the extent that reverse causality persisted, the anticipation of future entry would need to span about a decade in duration. Fernandes and Sharma (2011) discuss how the spatial locations of manufacturing firms in India’s formal sector have adjusted substantially since the large-scale deregulations of the 1980s and 1990s. Prior to these deregulations, spatial location decisions for firms were set to a large degree by the government with the goal to promote general equality across regions. By reaching as far back into this regulated period as possible, we hopefully capture incumbent conditions that are not being determined by anticipation of female entry conditions after 2000.

18
# **Appendix**

_Table 1: Frequency of plots recording a shock, by country-year_

|Country|Year|Frequency of plots<br>recording a disaster<br>shock|Frequency of p<br>a drough|lots recording<br>t shock|Frequenc<br>recording a|y of plots<br>flood shock|Frequency of<br>irregula|plots recording an<br>r rain shock|Frequ<br>recordin|ency of plots<br>g a pest shock|
|---|---|---|---|---|---|---|---|---|---|---|
|||||Disaster||Disaster|||||
||||All plots|<br>plots|All plots|<br>plots|All plots|Disaster plots|All plots|Disaster plots|
||2011 - 2012|31.19 %|16.96 %|55.16 %|missing|missing|11.39 %|37.41 %|4.89 %|16.06 %|
|Ethii|2013 - 2014|28.32 %|11.14 %|40.14 %|missing|missing|9.94 %|36.60 %|7.35 %|30.52 %|
|opa|2015 - 2016|53.83 %|41.89 %|78.29 %|missing|missing|17.55 %|34.88 %|3.48 %|7.97 %|
||2018 - 2019|39.76 %|19.81 %|50.63 %|missing|missing|14.15 %|37.17 %|6.95 %|21.12 %|
||2009 - 2010|40.40 %|24.34 %|60.26 %|missing|missing|missing|missing|1.05 %|2.61 %|
|Mli|2012 - 2013|36.82 %|8.18 %|22.22 %|8.64 %|23.47 %|17.80 %|48.33 %|2.85 %|7.75 %|
|aaw|2015 - 2016|64.54 %|30.77 %|47.68 %|1.06 %|1.56 %|40.61 %|62.92 %|2.21 %|3.42 %|
||2018 - 2019|47.18 %|5.60 %|11.86 %|21.65 %|45.81 %|19.77 %|41.91 %|7.97 %|16.76 %|
|Mli|2014|18.59 %|14.44 %|77.71 %|missing|missing|1.75 %|9.41 %|1.05 %|5.63 %|
|a|2017|24.07 %|22.78 %|94.64 %|missing|missing|0.45 %|1.89 %|3.08 %|1.97 %|
|Ni|2011|89.88 %|65.81 %|73.22 %|0.64 %|0.71 %|missing|missing|27.69 %|30.81 %|
|ger|2014|40.11 %|23.43 %|58.41 %|2.75 %|6.86 %|missing|missing|11.08 %|27.63 %|
|Nigeria|2019|11.39 %|2.59 %|26.06 %|4.88 %|50.72 %|missing|missing|5.49 %|48.85 %|
||2008|32.21 %|14.52 %|47.11 %|missing|missing|missing|missing|13.99 %|45.45 %|
||2010|36.70 %|21.52 %|60.74 %|missing|missing|missing|missing|10.00 %|28.29 %|
|Tanzania|2012|32.78 %|19.91 %|62.79 %|missing|missing|missing|missing|9.86 %|31.05 %|
||2014|23.96 %|11.38 %|49.72 %|missing|missing|missing|missing|8.05 %|35.00 %|
||2019|40.89 %|25.80 %|65.03 %|missing|missing|missing|missing|12.39 %|31.21 %|
|All<br>countries|2008 – 2019|36.34 %|22.17 %|61.66 %|6.80 %|14.44 %|10.49 %|31.31 %|6.30 %|16.83 %|

Note: some questionnaires (the IHPS, for example) allow respondents to report multiple shock types on a single plot.

22
choice of the base category. Albeit, independently of the choice of base category, the approximation residual terms should be small. Thus, both the residual difference and the wage structure associated to each covariate depend on the choice of base group. The contribution of each covariate can be interpreted as the “policy effect” of changing the distribution of �from its value in the base group at time ��1. This is usually referred to as the change in the dispersion of the distribution over time, or simply, as the dispersion enhancing effect under ���. , . � and ���. , . �.

# **5. Results**

In this section, we discuss the results of applying the FFL decomposition methodology to the RLMS data. As the interest lies in studying the evolution of inequality over the last decade, we focus on the period 2002 – 2012.<sup>20</sup> By 2002, the Russian economy had almost fully recovered from the effects of the 1998 financial crisis, and real wages and inequality had returned to precrisis levels. The main reforms of the economic transition had been fully adopted. Thus, restricting the analysis to the 2002-2012 period allows us to explore the changes in the wage distribution that took place over the last decade, isolating them, to a good extent, from the main effects of the financial crisis and the economic transition. Since the 2008 crisis was of short duration in Russia, we also divide the analysis in two equally long sub-periods: 2002-2007 and 2007-2012. The main drawback of the RLMS data for our objective is that industry information is only available from 2005 onwards. Thus the analysis of demand side factors is only carried on for the second period 2007-2012.<sup>21</sup> The variables included in the decomposition directly relate to the potential explanations for the reduction in wage inequality. These include minimum wage, type of firm, and firm size for _institutional factors_<sup>22</sup> ; four different levels of education dummies (high school dropouts, complete

> 20 While the RLMS survey was conducted in 2013 as well, this year is not included in the estimations as data is not yet available in English for this year at the time of the writing of this paper.

> 21 Results for the sub-period 2007-2012 can be found in Annex A3.

> 22 Ferreira, Firpo and Messina (2014) also include informality measures in their decomposition analysis. Although informality is such a concern in the Russian Federation as in Latin America, it has been on the rise and thus would have been interested to evaluate. However, the potential variables that could be used to measure it are not comparable between 2002 and 2012.

24
Policy Research Working Paper 7550

# **Abstract**

Evidence from developed country data suggests that cognitive and non-cognitive skills contribute to improved labor market outcomes. This paper tests this hypothesis in a developing country by using an individual-level data set from Peru that incorporates modules to measure cognitive and non-cognitive skills. The paper estimates a structural latent model with unobserved heterogeneity to capture full ability rather than just measured skill. It also applies standard ordinary least squares techniques for comparison. The analysis confirms that cognitive and non-cognitive skills are positively correlated with a range of labor market outcomes in Peru. In particular, cognitive skills positively correlate with wages and the probability of being a wage worker, white-collar, and formal worker, with verbal fluency and numeric ability playing particularly strong roles. The results are robust to methodology. The patterns are less uniform for non-cognitive skills. For instance, perseverance of effort (grit) emerges strongly for most outcomes regardless of methodology. However, plasticity—an aggregation of openness to experience and emotional stability—is only correlated with employment, and only when using the structural latent model. The ordinary least squares method also finds that the disaggregated non-cognitive skills of kindness, cooperation, emotional stability, and openness to experience emerge significantly, mostly for the wage estimates. The different results derived from the ordinary least squares and the structural model with latent skills suggest strong measurement bias in most non-cognitive skills measurement. These findings, although only correlational because of the use of a single cross-section, suggest that recent efforts by the Peruvian government to incorporate non-cognitive skill development into the school curriculum are justified.

This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at wcunningham@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
seems to stabilize for establishments with 25 or more workers (see Table 4 for details). Plausibly, high concentration of informality in small firms has to do with the fact that they are less monitored; on the other hand, as Perry et al. (2007) argue _,_ formality can be seen as an input in the production process for which small firms have little need.

The following five economic activities feature highest dependent informality rates: hotels and restaurants (20%), personal and household services (18%), construction (14%), agriculture (13%), and trade<sup>19</sup> (11%). The first four activities in this list are also found among the top five in each of the four European regions (see Table 4 for details).

# **2.5 The dynamics of informal employment**

We conclude this section with a brief overview of the dynamics of informal employment. Table 5 presents changes (in % points) in estimated prevalence of informal employment and unemployment in the extended labor force of European countries between ESS rounds: round 4 (2008-2009), round 3 (2006-2007), and round 2 (2004-2005). In most cases the changes in both dependent and own-account informal employment are statistically insignificant and small. Between rounds 2 and 3, there have been significant increases in the share of employees without contracts in Portugal (3.6 points), Denmark (2.7 points), Estonia (2.0 points), and Spain (1.0 points). In Portugal and Estonia this has been accompanied by a comparable decrease in the share of informal self-employment, whilst the latter went up as well in Denmark and Spain. A significant decrease in total informal employment between rounds 2 and 3 is found only in the UK (3.3 points) and Slovenia (2.6 points).

The changes between rounds 3 and 4 are of course of special interest because in all countries most of the round 4 field work was during the early stage of crisis (2008/q4 or 2009). From a theoretical perspective, the effect of the recession on informal dependent employment is ambiguous. On the supply side, the workers are likely to be more willing to accept informal employment. On the demand side, there is likely to be much less work left out for outsiders, as private sector employees across Europe have seen substantial working time reductions, and both the firms and the households do not have money for irregular (not urgent) tasks. While firms do have strong incentives to reduce costs via tax avoidance, they might prefer paying envelope wages to workers already on the payroll to using unregistered workers. Yet there is an incentive to conduct as much repair and construction as possible while informal labor is cheap, and this is likely to have a positive effect on informal self-

> 19 Including repair of motor vehicles.

20
We then introduce other control variables one at a time, and then simultaneously. We divide the controls into three di¤erent sets: (i) variables indicating agro-climatic endowments; (ii) variables indicating access to infrastructure and services; and (iii) variables depicting locational sorting of households. The mixed e¤ects model speci...ed in equation (4) is estimated using the Restricted Maximum Likelihood (REML) technique which is implemented by the xtmixed command in Stata. The spatial disparity coe¢ cient (�) and its con...dence intervals are estimated by applying the NLCOM command after the mixed e¤ect estimation. All estimations are done for the full sample and for rural and urban samples separately. We also perform several robustness checks.

# 4.1 Spatial Disparity in Welfare

We start with the result from our simplest speci...cation of equation (4) which included no controls other than age and gender dummies for the household head. This base line estimate of spatial disparity in the pooled sample is 0.363 in 2000 and 0.266 in 2010. The estimate for 2000 implies that more than a third of the variations in log of per capita consumption can be explained by location alone. The spatial di¤erences in welfare narrowed considerably in 2010 as just above a quarter of variations in LRPCE can be attributed to location alone. The spatial disparity in urban areas is much higher in both years compared with that in rural areas. The estimates for urban areas indicate considerable decline in disparity between 2000 and 2010 -from 0.385 to 0.298. The estimates of spatial disparity indices for rural areas –0.218 in 2000 and 0.199 in 2010 –are smaller in magnitudes compared with urban areas and they declined only slightly between these two years. Table 2 reveals a trend of strong spatial convergence in welfare within urban areas and in the country as a whole during the decade of 2000.

The lower panels of Table 2 report estimates of the spatial disparity indices when agro-climatic controls are added separately and simultaneously (last panel). In the results reported in panel B, Zij vector included average annual rainfall over 30 years (1980 to 2009) as a control. Inclusion of 15
38 This paper uses the health sector reforms implemented by ECA countries over the 1990s and early 2000s as an “experiment” to investigate empirically the system-wide impacts of introducing fee-for-service (FFS, encompassing both per procedure and per diem mechanisms) and patient-based payment PBP (PBP, mainly per case and DRGbased systems) as the main hospital reimbursement method—relative to the previous communist system of line-item, historical budgets—on a set of outcomes including hospital activity rates and capacity utilization, national health spending, and mortality amenable to health care.

In our empirical work, we use panel data from 28 ECA countries for the period 1990-2004 and employ, in addition to a simpler regression-based difference-indifferences approach, two generalizations of the latter model in order to control for the potential endogeneity of provider payment reforms: a random trend model (allowing for linearly time-changing unobservables which may vary at different rates between countries, and may be correlated with both the choice of hospital payment method and the outcome of interest) and a differential trend model (allowing for nonlinearly time-changing unobservables which may vary at different rates between _groups_ of countries—i.e. budget, FFS and PBP countries—and may be correlated with the choice of hospital payment method and health outcomes). In the latter two models, we assess the validity of the parallel trend assumption which is inherent to the simpler difference-in-differences approach and use the results of the tests to identify the most suitable model for our data among the three alternatives, for the case of each health outcome. We also formally test for the presence of reverse causality in payment reforms (that is, reforms being driven by changes in our health sector outcomes); in the event, we find that, for all but one of the selected health outcomes, our econometric models perform well in controlling for the potential endogeneity of FFS