rafmacalaba/datause-encoder-data huggingface dataset:
_Connecting Lagging and Leading Regions: The role of labor mobility_ model his destination location decision, we need to know what he would earn in every other location. Properly recovering these counterfactual wages can be quite difficult. In this part of the paper, we adopt the relatively simple approach of using the average wage earned by conditionally similar individuals in those other locations. Practically, this means that we run a separate log wage regression for each AMC: where<sup>_Z_</sup> _i_ is a vector of variables describing individual _i_ , including age, sex, education level and occupation dummy variables and  _k_ is a set of wage parameters for location _k_ . Table 1 reports summaries of regression procedures to predict counterfactual wages.

We model moving costs as a function of migration distance, which is calculated from the longitude and latitude of the center of the individual’s birth state and destination AMC. Except for a log-linear function of migration distance, we may also specify moving costs using a set of distance dummies.

Our primary interest is in the role played by regional differences in local public services on migration decisions. We focus on variables describing (1) local infrastructure (i.e., % households with access to piped water, sewerage, and electric lights), (2) access to healthcare (i.e., number of hospitals), and (3) network infrastructure (i.e., transportation costs to the state capital and São Paulo). Any list of local attributes would, however, necessarily be incomplete. As described above, we use census data from two years to control non-parametrically for all local attributes that do not vary over time. Table 2 summarizes regional differences in access to public services. Differences in water and sanitation services are quite stark. Access to piped water is 52 percent in the Northeast, compared to 72 percent in the Southeast. In terms of access to sewage facilities, connection rates in the Northeast were 13 percent in 2000 – compared to 58 percent in the Southeast.

# **3. Main Findings on Migration Choice**

13
# **I. INTRODUCTION**

Evening hour and nighttime lights (henceforth nightlights) serve as a good proxy for economic activity, and consequently economic growth, because consumption and production during the evening require some form of lighting. The relationship between nightlight intensity, calculated as the sum of lights divided by the area, and economic activity is typically captured through a constant elasticity between the two variables, known as the inverse Henderson elasticity (Henderson et al. 2012). In addition to the fact that nightlight data are easy and inexpensive to collect, measures of economic activity based on nightlights have some crucial advantages. Nightlight observations include informal activity, are available in near real-time, are independent of official sources, and can be aggregated to arbitrary spatial groupings. In this paper, we first extend the analysis of Henderson et al. (2012) to South Asia<sup>1</sup> and show that the correlation observed between economic activity and nightlights holds in this region. We then present a measure of monthly economic activity at the district level based on cleaned Visible Infrared Imaging Radiometer Suite (VIIRS) nightlight data and rural population estimates derived from surveys. In addition, we demonstrate how this new measure can deepen our understanding of recent economic episodes. First, we assess the economic costs of the 2015 earthquake in Nepal and argue that the official estimates of the damage may be too high. Second, we assess the effect of the disruption caused by demonetization in India in November 2016 and show that it was stronger in poorer and more rural districts. Third, we show that an increase in the number of killed and injured in conflict in Afghanistan is negatively correlated with monthly and quarterly, but not annual, economic performance at the district level.

> 1 For our analysis, South Asia includes the lowing eight countries: Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka.

2
**Figure 9: Magnitude of Changes in Education Spending over Five-Year Periods** _a. Distribution of five-year growth episodes for total b. Distribution of five-year growth episodes for spending education spending, 1999–2015 (constant 2015 PPP as a % of GDP, 1999–2015 –2015 2015_

_b. Distribution of five-year growth episodes for spending as a % of GDP, 1999–2015 –2015 2015_ _$)_ _Source:_ World Bank calculations based on UIS and IMF data.

The overall distribution of these 852 five-year growth episodes shows that spending has grown in most cases (Figure 9). Consistent with our findings from Section 2, real public education spending increased in 83 percent of these five-year growth episodes. Over all five-year growth episodes, public education spending grew by a median of 18 percent, but there was wide variation between countries and time periods. The changes ranged from a 71 percent decline in spending in Malta between 2003 and 2008 to a 166 percent increase in spending in Afghanistan from 2006 to 2011. However, public education spending as a proportion of GDP changed only slightly, with the median five-year episode being associated with a 0.1 percentage point increase in public education spending as a percentage of GDP.

There was considerable variation in the characteristics of five-year changes in public education spending across and within income groups. The average increase in public education spending growth was much higher for the low-income group (39 percent) than the high-income group (13 percent) (Table 5).<sup>10</sup> Real public education spending for countries in the top growth quartile<sup>11</sup> grew by an average of 36 percent for high-income countries and 92 percent for low-income countries. Patterns among countries in the lowest growth quartile were less clear and ranged from an average decline of 4 percent for low-income countries to an average decline of 15 percent for the upper-middle-income group (Table 5).

> 10 Annex Table A5 includes details on the second and third quartiles.

> 11 Those country growth episodes in the 75th percentile in terms of the growth rate in real public education spending.

23
- Danon, A., J. Das, A. De Barros, and D. Filmer (2023): “Cognitive and Socioemotional Skills in Low-Income Countries: Measurement and Associations with Schooling and Earnings,” World Bank Policy Research Working Paper 10309.

- Deming, D. (2017): “The Growing Importance of Social Skills in the Labor Market,” _Quarterly Journal of Economics_ , 132, 1593–1640.

- Deming, D. J. (2022): “Four facts about human capital,” _Journal of Economic Perspectives_ , 36, 75– 102.

- Dimitriadis, S. and R. Koning (2022): “Social skills improve business performance: evidence from a randomized control trial with entrepreneurs in Togo,” _Management Science_ , 68, 8635– 8657.

- Groh, M., N. Krishnan, D. McKenzie, and T. Vishwanath (2016): “The impact of soft skills training on female youth employment: evidence from a randomized experiment in Jordan,” _IZA Journal of Labor & Development_ , 5.

- Heckman, J. J. and T. Kautz (2012): “Hard evidence on soft skills,” _Labour economics_ , 19, 451–464.

- Heinrich, C. and S. Housemann (2021): “Worker Hard and Soft Skills and Labor Market Outcomes: A Lens through the Temporary Help Industry,” Working Paper. Vanderbilt University.

- International Monetary Fund (2021): “Policy Responses to COVID 19,” Tech. rep., International Monetary Fund.

- Laajaj, R. and K. Macours (2021): “Measuring Skills in Developing Countries,” _Journal of Human Resources_ , 56, 1254–1295.

- Le Barbanchon, T., D. Ubfal, and F. Araya (2023): “The Effects of Working While in School: Evidence from Employment Lotteries,” _American Economic Journal: Applied Economics_ , 15, 383– 410.

- Osman, A. and J. Speer (2022): “Are Soft Skills Enough? Experimental Evidence on Skill Complementary for College Graduates,” Working PAper. Available at SSRN: https://ssrn.com/abstract=417856.

- Rwandan National Institute of Statistics (2020-2022): “Quarterly Rwanda Labour Force Survey Reports,” Tech. rep., National Institute of Statistics of Rwanda.

- ——— (2021): “Rwanda demographic and health survey 2019-20.” Tech. rep., National Institute of Statistics of Rwanda - NISR, Ministry of Health - MOH, and ICF.

- ——— (2022): “Rwanda Labour Force Survey,” Tech. rep., National Institute of Statistics of Rwanda.

- Sandvik, J. J., R. E. Saouma, N. T. Seegert, and C. T. Stanton (2020): “Workplace Knowledge Flows,” _The Quarterly Journal of Economics_ , 135, 1635–1680.

- Ubfal, D., I. Arraiz, D. Beuermann, M. Frese, A. Maffioli, and D. Verch (2022): “The Impact of Soft-Skills Training for Entrepreneurs in Jamaica,” _World Developmet_ , 152, 105787.

17
of micronutrient interventions such as fortification of staples and vitamin distributions during periods of high food prices.

Second, household survey consumption modules often include questions on the quantity of food items consumed or the expenditure on food items, but not on the frequencies with which the food items are consumed. Given the low cost of adding questions on the frequencies of food intake, it may be beneficial to consider augmenting household surveys, particularly for populations that are vulnerable to food insecurity. Measures of dietary diversity are useful tools when detailed food journals or anthropometric data are not available and so micronutrient and macronutrient intake cannot be measured directly. Ruel (2003) discusses some of the benefits and costs of indicators of dietary diversity. In the nutrition literature, Alexander and Thomson (1992) discuss the importance of collecting frequency data in addition to quantity intake data. They demonstrate that both the quantity of food ingested and the intake frequency are important determinants of dietinduced diseases; they argue that looking solely at quantity data could be misleading. Our findings are consistent with this view.

Finally and more broadly, examining household responses using OLS estimation may be misleading since this estimator imposes the restriction that the marginal effects are constant across the distribution. Our analysis indicates that behavioral responses of those at the bottom of several key distributions are substantially different from those at the top of the distribution. This paper demonstrates the benefits of using the unconditional quantile regression estimator to examine marginal effects at various quantiles of the unconditional distribution.

# References

Alexander, C. J., & Thomson, F. J. (1992). The threshold effect: Consequences of change in the frequency of food intake in the presence of a functional threshold. _Medical Hypotheses, 39_ (3), 302-308. doi: Doi: 10.1016/0306-9877(92)90128-y Block, S. A., Kiess, L., Webb, P., Kosen, S., Moench-Pfanner, R., Bloem, M. W., & Peter Timmer, C. (2004). Macro shocks and micro outcomes: child nutrition during Indonesia's crisis. _Economics & Human Biology, 2_ (1), 21-44. doi: DOI: 10.1016/j.ehb.2003.12.007 Buchinsky, M. (1994). Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression. _Econometrica, 62_ (2), 405-458.

22
Table 4.2 presents estimated distributions of expected enrollments associated with three education policy options. The first option entails a reduction in multigrade teaching through the provision of additional teachers. Glick and Sahn estimated that a 50 percent reduction in multigrade teaching could be achieved by adding on average one teacher to each school currently using this method. The results associated with this policy option indicate that it would be progressive to the extent that the increase in primary enrollment rate is larger for the bottom 3 quintiles than for the top two. Specifically, relative marginal shares indicate that both the second and third quintiles’ share of the increase in the enrollment induced by the policy is proportionately 13 percent higher than their shares of the rural primary school age population.

Table 4.2. Distributional Impact of Education Policy Options in Madagascar

|Quintile|Baseline Average Benefits|Poli|cy 1|Poli|cy 2|Poli|cy 3|
|---|---|---|---|---|---|---|---|
|||Aq|Mq|Aq|Mq|Aq|Mq|
|1|0.77|0.78|0.97|0.76|0.38|0.78|0.87|
|2|1.01|1.02|1.13|1.00|0.46|1.01|1.09|
|3|0.98|0.99|1.13|1.00|1.69|0.99|1.13|
|4|1.18|1.16|0.88|1.19|1.62|1.17|1.04|
|5|1.38|1.34|0.69|1.38|1.23|1.35|0.78|
|All|1.00|1.00|1.00|1.00|1.00|1.00|1.00|

Source: Glick and Sahn (2006) For policy option 2 that combines option one with an increase in school fees, the results show that lower quintiles would lose while the top ones would gain in terms of changes in enrollments. The bottom two quintiles would get respectively from new enrollments 0.38 and .46 of their school age population shares. Adding cost recovery to option 1 risks to reverse the progressive nature of multigrade reduction<sup>46</sup> . The authors of the study suggest that this outcome might be avoided by structuring the cost recovery

> 46 Glick and Sahn also found that the demand for primary schooling by the poor is substantially more priceelastic than that of the non-poor.  Thus increasing public school fees will increase inequality in the distribution of primary school benefits as measured by the distribution of enrollments.  This is the major behavioral factor explaining the distributional outcome of this simulation.

49
**Table 4. Economic Outcomes OLS and Quantile (Median) Regression Results by Outcome and Round**

|||**OLS**|**In**|**come**|**Median**|||||
|---|---|---|---|---|---|---|---|---|---|
||**2008**|**2011**|**2013**|**2008**|**2011**|**2013**||||
|**INDH**|714|880|443|556|491|928||||
||(544)|(727)|(714)|(349)|(549)|(713)||||
|**Community poverty rate**|-268|-209|6|-198|-96|-121||||
||(179)|(287)|(231)|(127)|(216)|(228)||||
|**Constant**|<br>12,385**|<br>11,058|<br>4,900|<br>9,375***|<br>6,656|<br>7,405||||
||(5,081)|(8,249)|(6,501)|(3,611)|(6,292)|(6,455)||||
|**N (Observations)**|8,986|9,000|8,604|8,986|9,000|8,604||||
|||**OLS**|**Cons**|**umption**|**Median**|||||
||**2008**|**2011**|**2013**|**2008**|**2011**|**2013**||||
|**INDH**|584*|932**|75|424*|664**|68||||
||(332)|(453)|(352)|(239)|(310)|(316)||||
|**Community poverty rate**|-206*|-172|-44|-165**|-149|-45||||
||(105)|(145)|(113)|(83)|(120)|(97)||||
|**Constant**|9,758***|8,442**|4,484|7,975***|7,276**|4,046||||
||(2,979)|(4,125)|(3,204)|(2,367)|(3,433)|(2,743)||||
|**N (Observations)**|8,986|9,002|8,608|8,986|9,002|8,608||||
||||**W**|**ealth**||||||
|||**OLS**|||**Median**|||||
||**2008**|**2011**|**2013**|**2008**|**2011**|**2013**||||
|**INDH**|5,215|2,962|458|928|-2,502|928||||
||<br>(3,849)|<br>(5,846)|(5,689)|(731)|<br>(3,085)|(732)||||
|**Community poverty rate**|-2,442*|625|1,098|-187|1,152|-121||||
||(1,331)|(1,802)|(1,907)|(251)|(903)|(229)||||
|**Constant**|82,096**|1,252|-7,684|9,118|-2,2933|7,405||||
||(38,102)|(50,827)|(54,298)|(7,178)|(25,276)|(6,468)||||
|**N (Observations)**|8,986|8,966|8,600|8,986|8,966|8,604||||
|||**Log Inco**|**me**|**Lo**|**g Consum**<br>**OLS**|**ption**<br>||**Log Wealt**|**h**|
||**2008**|**2011**|**2013**|**2008**|**2011**|**2013**|**2008**|**2011**|**2013**|
|**INDH**|0.125|0.165|0.184|0.125*|0.207**|0.029|0.324|0.273|0.040|
||(0.094)|(0.128)|(0.134)|(0.069)|(0.103)|(0.098)|(0.219)|(0.283)|(0.146)|
|**Community poverty rate**|<br>˗0.042|<br>˗0.020|<br>-0.033|<br>-0.043*|<br>-0.029|<br>-0.019|<br>-0.103|<br>-0.073|<br>0.026|
||(0.033)|(0.046)|(0.044)|(0.022)|(0.035)|(0.033)|(0.075)|(0.093)|(0.048)|
|**Constant**|<br>9.448***|<br>8.812***|<br>9.196***|<br>9.338***|<br>8.837***|<br>8.499***|<br>11.348***|<br>11.267***|<br>8.875***|
||(0.928)|(1.322)|(1.250)|(0.625)|(1.004)|(0.940)|(2.122)|(2.612)|(1.359)|
|**N (Observations)**|8,966|8,919|8,604|8,977|9,002|8,608|8,979|8,966|8,598|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ *p < 0.1; **p < 0.05; ***p < 0.01.

Standard errors (in parentheses) clustered on commune level. Quantile regression standard errors are bootstrapped with re-sampling at the commune and household levels.

18
# **4. Use of Finance: Formal vs. Informal Firms**

This section first presents some basic bivariate analysis to document the differences in use of finance, financing patterns and reasons for not applying for loans between formal and informal firms. The section then presents multivariate regression analysis that builds upon and strengthens the conclusions obtained from the bivariate analysis. This analysis verifies whether differences in the use of finance by firms based on their registration status hold once various firm level and country level characteristics are controlled for.<sup>15</sup> Data pertaining to formal firms is obtained from the Enterprise Surveys. To make the comparison between formal and informal firms more aligned and meaningful, only small-sized firms from the 13 countries in the original sample are used from the Enterprise Surveys. This results in a sample of approximately 1500 formal firms. Similarly the sample on informal firms is also restricted to small-sized firms only. Also for each country in the sample, it is made sure that the year in which surveys for both formal and informal firms were conducted is similar.

# **_4.1    Bivariate Analysis_**

Looking at the use of bank accounts by firms, Figure 9 shows that on average 53 percent of informal firms have bank accounts to run their business compared with 86 percent of firms in the formal sector. These percentages highlight a significant difference in the use of bank accounts between registered and non-registered firms. Figure 9 shows a similar picture for use of loans by firms in the formal and informal sector.<sup>16</sup> Only 12 percent of firms in the informal sector have loans compared to 47 percent formal sector firms.

> 15 The analysis in this section shows a positive correlation between formalization and financial inclusion and besides confirming our a priori knowledge, the results obtained quantify for the first the differences between the use of finance among formal and informal firms for a cross-sectional sample of countries. 16 For formal sector, loans include loan and/or an overdraft facility from a financial institution. Loans for informal firms refer to loan from any of the following source: Bank, moneylenders, MFIs and trade credit.

16
reflecting the overall good performance of the global economy in 1999. The standard deviation is 1.68, reflecting the significant variation in performance across firms. The country-average ROA is 0.08, with the highest average performance of 0.11 in India and the lowest performance of 0.01 in Brazil.

We add three country-level measures of legal efficacy. The first is Judicial Efficiency, which is an index constructed by the International Country Risk Guide (2000). The second is Shareholder Rights, which is the sum of dummies identifying one-share/onevote, proxy by mail, unblocked shares, cumulative vote/proportional representation, preemptive rights, oppressed minority, and % of shares needed to call a shareholders meeting (La Porta et al., 1998.) The third is Legality, which is an index of legal system and institutional environment constructed as a weighted average of Judicial Efficiency (same as our first index), Rule of Law, Corruption, Risk of Expropriation, and Risk of Contract Repudiation (this index is constructed using principal components analysis by Berkowitz, Pistor and Richard, 2002). We use three different measures in order to cover separately the existence of laws (Shareholder Rights) and the effectiveness of their implementation (Judicial Efficiency), as well as the overall legal environment (Legality.) Summary statistics and sample distributions for the legal indicators are given in Table 1, Panel C. The average Judicial Efficiency is 13.88 (out of 20), the average Shareholder Rights is 3.57 (out of 5), and the average Legality is 6.30 (out of 10.) There is large variation across countries, emphasizing regional and global difference in the quality and effectiveness of laws around the world.

9
that persists into the middle run.

# **Table 5 Base Case: Selected Indicators**

||**1991**|**1992**|**1993**|**1994**|<br>**1995**|<br>**1996**|
|---|---|---|---|---|---|---|
|**(Percentage Growth)**|||||||
|GDP at Market Prices<br>||5.0|**3.0**|3.0|3.0|**3.0**|
|Total Consumption||5.3|3.1|3.1|3.1|3.1|
|Total Investment||5.7|3.0|3.0|_3.0_|_3.0_|
|Exports of G&NFS<br>||3.2|3.8|3.6|3.6|3.6|
|Imports of G&NFS||4.7|4.2|3.9|3.9|3.9|
|Inflation||80.0|51.1|34.2|24.3|2(0.0|
|Devaluation Rate||77.0|48.1|31.2|21.3|17.0|
|Real Exchange Rate||1.3|1.0|0.7|_0.5_|0.4|
|**(As % Share of GDP)**|||||||
|Budget Deficit (- Surplus)|-0.4|0.4|-3.1|-3.0|-2.7|-2.7|
|Operational Deficit|-0.4|-10.3|-9.6|-6.4|-4.7|-3.9|
|Primary Deficit<br>|-2.2|-0.6|-4.1|-3.9|-3.6|-3.6|
|Inflationary Tax (Base)|3.1|2.9|1.8|1.2|0.9|0.7|
|QFD of Central Bank|-2.3|-2.4|-3.1|-2.3|-1.8|-1.5|
|Current Account Deficit|0.9|-0.8|-0.7|-0.6|_-0.5_|-0.5|
|Foreign Debt/Exports GNFS|343.7|326.8|308.8|292.1|276.0|260.6|
|**Performance Indicators (%)**|||||||
|Average Spread|3.9|0.3|0.1|0.0|-(. 1|-0.1|
|Net Interest Margin: II-IE / TA|3.3|0.3|0.1|0.0|(.()|-0.1|
|NIM (US$)|1.4|0.8|0.8|0.8|0.7|0.7|
|NIM (LCU)|1.9|-0.5|-0.7|-0.8|-0.7|-0.7|
|Efficiency: NOM/Employees|1.6|0.2|0.1|0.0|-(.1|-0.1|
|Return on Assets (ROA): NIBT/TAA|-3.0|2.0|0.1|0.1|3.0|0.7|
|Leverage: 1/Capital Adequacy (times)|9.76|6.45|6.80|7.16|4.8|4.1|
|Return on Equity: ROA*Leverage|-29.7|13.0|0.9|0.6|13.1|2.8|
|Equity / Total Assets|10.2|15.5|14.7|14.0|22.7|23.7|
|Cash & Banks / j'otal Assets|46.4|40.0|38.6|37.7|43.7|44.5|
|Net Loans / Deposits|73.7|60.0|60.9|61.2|61.6|61.5|
|Reserve Req. / Total Deposits|5.7|6.6|6.6|6.6|6.6|6.6|
|Non-Int. Exp./Total Op. Expense|0.0|1.0|0.9|0.8|0.7|0.6|

36
- 52 -

exposure on LDC loans and need for further provisioning. Measures are being drafted accordingly by the Fed and will be implemented in stages, by 1992.54j The question of coverage of contingent off-balance sheet risks in commercial banks is more complex. Banks have taken on underwriting risks for the refinancing of securitized loan facilities, such as RUFs and NIFs,55/ and have guaranteed performance risks in a large volume of interest-rate and currency swaps. In several instances risk premiums to cover the banks' underlying commitments are insufficient to compensate them for potential contingent losses.561 Thus there is now a substantial financial risk that could erode the banks' future solvency. To remedy this situation will require that central banks attempt to evaluate the risks involved, determine appropriate reserves and capital ratios for these risks, and, in the extreme case, limit the banks' freedom to undertake such contingent risks beyond appropriate limits.

Finally, there is the broader question of systemic risk. Again this goes back to the October 1987 stock market crash. As pointed out in the Brady-Commission report: "the massive volume, violent price volatility, and staggering demands on clearing and credit raised the possibility of a full scale financial system breakdown".57- While the unification of regulatory 54/ The New York Times, 2/1/88, pp. Al and D8.

55/ The volume of NIFs has reached staggering proportions. During the four years from 1884 to 1987, their total was of $104.7 billion. OECD, op. cit., Table A, p.9.

56/ OECD, op. cit., p.8 and BIS, April 1986, p.<sup>190</sup> .

57/ Brady commission, op.cit., January 1988, p.<sup>69</sup> .