rafmacalaba/datause-encoder-data huggingface dataset:
**_Overall poverty and inequality decline._** Tables 12 and 13 report the Foster-GreerThorbecke (FGT) poverty headcount ratio and general entropy indices with particular focus on the Gini coefficient. About 1.6 percent of households move out of poverty with the implementation of the wage subsidy, with the head count ratio declining from 49.1 to 47.6 percent.<sup>19</sup> The employment effect also offsets the addition of a social security tax with a 1.5 percent reduction in the poverty rate.

**<u>Table 12: Poverty Indicators, by Region, Population Deciles, and Education  Level</u>**

||<br>**Pover**|<br>**ty Indicato**|<br>**rs (%)**|<br>**Variation f**|<br>**rom Base**|<br>**Difference**|
|---|---|---|---|---|---|---|
||**Base**|**Sim1**|**Sim2**|**Sim1**|**Sim2**|**Sim2-Sim1**|
|**_1. National Headcount Rati_**|**_o_**||||||
|Proportion of Poor|0.491|0.476|0.477|-1.6|-1.46|0.09|
|**_2. Regional Decomposition_**<br>|**_(Headco_**<br>|**_unt ratio)_**<br>|||||
|Urban|0.335|0.319|0.320|-1.6|-1.51|0.08|
|Rural|0.721|0.706|0.707|-1.5|-1.39|0.09|
|**_3. Population Deciles_**<br>|||||||
|Poorest|0.952|0.929|0.929|-2.3|-2.24|0.03|
|2|0.934|0.909|0.909|-2.5|-2.53|0.01|
|3|0.884|0.859|0.860|-2.5|-2.39|0.07|
|4|0.796|0.768|0.769|-2.8|-2.69|0.11|
|5|0.561|0.538|0.539|-2.2|-2.15|0.07|
|6|0.351|0.343|0.343|-0.8|-0.80|0.00|
|7|0.186|0.179|0.183|-0.7|-0.29|0.42|
|8|0.114|0.107|0.108|-0.7|-0.54|0.12|
|9|0.078|0.074|0.074|-0.5|-0.45|0.02|
|Richest|0.085|0.050|0.050|-3.6|-3.55|0.03|
|**_4. Level of Education of He_**<br>|**_ad of the_**<br>|**_Househol_**<br>|**_d (Headc_**<br>|**_ount Ratio)_**<br>|||
|No Schooling|0.783|0.774|0.774|-0.9|-0.86|0.01|
|Grade 0-6|0.620|0.605|0.606|-1.5|-1.36|0.17|
|Grade 7-9|0.479|0.447|0.448|-3.2|-3.09|0.13|
|Grade 10-12|0.257|0.241|0.241|-1.6|-1.57|0.05|
|NTC Level|0.146|0.126|0.126|-2.0|-1.99|0.00|
|Degree andPost Graduate|0.078|0.066|0.067|-1.2|-1.05|0.10|

Notes: Poverty line is taken as 1 dollar per day. Exchange rate is rand 6.95 /1 dollar in 2000 prices. Per capita income is used as a welfare measure of household.

Simulation 1: 10 percent wage subsidy given to employers for low- and semi-skill in formal sector. Simulation 2: A social security tax is introduced relative to simulation 1.

> 19 These results are based on using $1 as a poverty line per day per person.

- 23 -
factor loadings for the informality indicators. The questions used to build this indicator are shown in Appendix C).<sup>2</sup> Building on Grover and Pereira-López (forthcoming), we dig deeper into the differences between the extensive and the intensive margins of informality which, as explained by Ulyssea (2018, 2020) have very different implications in terms of policies to encourage formality. To this end, we construct two sub-indices. PCA 1 restricts to operational indicators and, therefore, accounts for the extensive margin of informality (no business license, no VAT registration number, no registration under company law). PCA 2 focuses on the intensive margin of informality and more direct measures (no contributions to social insurance schemes and no bank account). Thus, a firm could be formal on the extensive margin, pay taxes and have a registration but still not comply with the regulations regarding social security for employees, for example.<sup>3</sup> In our analysis, we also account for heterogeneity among informal firms according to their motivation for informality. Following the methodology in Grover and Pereira-López (forthcoming), we classify informal firms into three categories: necessity, De Soto, and parasite.<sup>4</sup> This breakdown of informal firms allows us to test how different are some of the constraints in the context of crises within informal firms as well as if these motivations can also condition the responses of firms in terms of investment decisions and margins of adjustment.

# **2.3 Estimation Methodology**

To analyze the relationship between informality, firm-level constraints, and decisions during the pandemic, we estimate the specification in equation 2: 2 On a related note, Medvedev and Oviedo (2016) build a formality index for Ecuador using seven different aspects of formality (public notary, official registry, written contracts, municipal license, social security, receipts for purchases, and taxpayer number).

3A key difference of this indicator against the definitions from Ulyssea (2018, 2020), is that we are not conditioning the intensive margin on being formal on the extensive margin. To test the robustness of our results, we implement the non-linear approach and construct an intensive informality indicator conditional on being formal on the extensive margin.

4For this classification, first, we estimate the following equation for labor productivity: Where _γs_ are sector fixed effects; _φc_ are country fixed effects; and _νj_ are size effects, where _j_ =small, medium, and large. Then, we calculate the 25<sup>_th_</sup> and the 75<sup>_th_</sup> percentiles of the distribution of this conditional productivity by country and sector for fully formal firms. Those informal firms that exhibit productivity below the 25<sup>_th_</sup> percentile are classified as necessity firms, those between the 25<sup>_th_</sup> and the 75<sup>_th_</sup> percentiles as De Soto firms, and those above the 75<sup>_th_</sup> are parasites (see Appendix Figure A1 for the distributions).

6
selecting appropriate areas and developing plans for leaving some areas untouched and for making major investments in the flora and fauna as well as the technological infrastructure of other areas (Michel, 2009). This requires substantial investment in scientific modeling (Nelson et al., 2009). Fortunately, recent breakthroughs in using geographic information systems and indepth knowledge of the biophysical settings to map ecological systems over time are beginning to provide the tools needed for more careful planning for improvements in ecological systems (Daily et al., 2009). The models, however, need to be developed at multiple scales and decisionmaking units so that they can then focus at diverse scales to determine which policies can be adopted to improve carbon sequestration in line with the ecology at that particular scale.

Given the complexity and changing nature of the problems involved in coping with climate change, there are no “optimal” solutions that can be used to make substantial reductions in the level of greenhouse gases emitted into the atmosphere. A major reduction in emissions is, however, needed. The advantage of a polycentric approach is that it encourages experimental efforts at multiple levels, as well as the development of methods for assessing the benefits and costs of particular strategies adopted in one type of ecosystem and comparing these with results obtained in other ecosystems. A strong commitment to finding ways of reducing individual emissions is an important element for coping with climate change. Building such a commitment, and the trust that others are also taking responsibility, can be more effectively undertaken in small- to medium-scale governance units that are linked through information networks and monitoring at all levels.

39
Table 6 -- Treatment Effects on Normalized Test Score: Heterogeneity by Pre-Program Test Score

|Panel A: 2012||Normalized Score||
|---|---|---|---|
||All Students<br>(1)|Boys<br>(2)|Girls<br>(3)|
|Treatment = Pre-program testD/E|0.242"|O.279%**|0.205**|
||(0.099)|(0.104)|(0.099)|
|Treatment ~ Pre-program testC|0.352***|0.334***|0.364***|
||(0.071)|(0.075)|(0.072)|
|Treatment ~ Pre-program testB|0.466***|0.462***|0.466***|
||(0.084)|(0.083)|(0.092)|
|Treatment = Pre-program testA|0.056|0.065|0.031|
||(0.046)|(0.044)|(0.064)|
|Observations|17,460|8,509|8.951|
|R-squared|0.614|0.628|0.601|
|Meanofdepvarincontrol|0.000|0.040|-0.039|
|<br>P-value: Effect forD/E = EffectforC|0.154|0.534|0.043|
|P-value: Effect forC=Effect forB|0.082|0.067|0.153|
|P-value: Effect forB =Effect forA|0.000|0.000|0.000|
|Panel B: 2013||Normalized Score||
||All Students|Boys|Girls|
||(1)|(2)|(3)|
|Treatment » Pre-program testD/E|0.152|0.146|0.164|
||(0.133)|(0.136)|(0.139)|
|Treatment = Pre-program testC|0.205**|0.225***|0.189**|
||(0.085)|(0.085)|(0.093)|
|Treatment = Pre-program testB|0.081|0.071|0.091|
||(0.060)|(0.065)|(0.062)|
|Treatment * Pre-program testA|0.004|0.009|-0.005|
||(0.022)|(0.023)|(0.033)|
|Observations|15,984|8.193|7.791|
|R-squared|0.549|0.531|0.348|
|Mean ofdep var in control|0.000|0.004|-0.004|
|P-value: Effect forD/E = EffectforC|0.571|0.429|0.808|
|P-value: Effect forC =Effect forB|0.035|0.011|0.159|
|P-value:EffectforB=EffectforA|0.199|0342|0.128|

‘Notes: Robust standard errors in parentheses, clustered by village. *** p<0.01, ** p<0.05,* p<0.1.Allanalysesare restricted to test-takers, defined as students who were present for pre- and post-program tests in a given year (2012 or 2013). Observations are at the student-subject-level. All specifications include controls for 2011 (pre-program) enrollment by gender, indicators of baseline pre-program test score, cohort fixed effects, and subject (Hindi, English, Math) fixed effects.
# **Table A3: Robustness Checks – Interacting Water and Electricity Outages, and Including Additional Control Variables**

|**Y = Log of Sales**|**Water x Electricity**<br>**(1)**|**Additional variables:**<br>**Finance, Corruption**<br>**(2)**|
|---|---|---|
|Log of labor (lnL)|0.769***|0.759***|
||(0.022)|(0.023)|
|Log of capital (replacement value) (lnK)|0.020**|0.017*|
||(0.010)|(0.010)|
|Log of raw materials (costs) (lnM)|0.238***|0.238***|
||(0.011)|(0.011)|
|No. of water shortages per day in a typical month|-0.143*|-0.096**|
||(0.073)|(0.045)|
|Water incidents x electricity incidents|0.013||
||(0.010)||
|Water duration x electricity duration|-0.000||
||(0.000)||
|Expected to give gifts to public officials (to get things done) Y/N||0.018|
|||(0.048)|
|Ownership - government/state (%)||0.003|
|||(0.002)|
|Purchased fixed asset Y/N||0.032|
|||(0.030)|
|Establishment has a line of credit or loan at this time||0.069**|
|||(0.034)|
|Average duration of water shortage|0.003|0.002|
||(0.004)|(0.005)|
|No. of electrical outages per day in a typical month|0.012|-0.002|
||(0.023)|(0.024)|
|Average duration of electrical outages (hours)|0.001|0.001|
||(0.001)|(0.001)|
|lnLlnK|-0.008***|-0.007***|
||(0.001)|(0.001)|
|lnLlnM|-0.035***|-0.035***|
||(0.001)|(0.001)|
|lnKlnM|0.002**|0.002**|
||(0.001)|(0.001)|
|lnL<sup>2</sup>|0.042***|0.042***|
||(0.002)|(0.002)|

34
Grants for entrepreneurship are also targeted in ways that could account for their greater impact relative to microcredit. Similar to results for grants to the ultra-poor, entrepreneurship grants have been shown to have larger impacts on those who start with fewer assets. Whereas many microfinance programs target female borrowers, entrepreneurship grants have shown substantially larger gains in profitability for males. Moreover, while microcredit provides an influx of cash to borrowers, there is some evidence that in-kind entrepreneurship grants (in the form of equipment or inventories) may deliver better results than cash grants (Fafchamps et al., 2013). Finally, better educated entrepreneurs have benefited more from these grants. Since men tend to have more formal education than women in many developing country contexts, this could in part account for the gender disparity, and help explain the smaller impacts of microcredit targeted to women.

In short, targeting and repayment obligations can potentially account for a large share of the difference in the impact of microcredit versus grants. Buera, Kaboski, and Shin (2016) argue that comparisons with a third type of intervention, village fund programs, are instructive in assessing the relative importance of repayment versus targeting since they represent a middle ground between grants and microcredit. Village funds are constituted from a grant to the village as a whole and the loans that are extended to individual borrowers carry more favorable terms than typical microcredit contracts (7-8% interest rates and a single repayment at the end of the loan).<sup>15</sup> Comparisons with microcredit borrowers therefore provide a rough indication of the potential impact of making the terms of microcredit contracts less rigid. And indeed, take-up 15 The village fund studies are also instructive because they were undertaken in China and Thailand, countries where the gender gap in education is relatively small. Similarly positive results for male and female borrowers from village funds suggest that the gender disparities found for microcredit might be eliminated if loans were extended to equally capable men and women.

28
- 17 -

# Table 5: EilOR CCFCTION FCO LATION: COO DITY PRICES AND INIEiEST RATES

33 Conodity. Index (sample 1952-88) A LnlCP33 * - 0.015 - 0.459 A LnICP33t-2<sup>+0.193A LnlCP33t</sup> 3 (0 112) (0.103) (0.125) 1 ,.989A TRBt 2 - 0o174 Zt(1.075) (0.iX3) R2 0.604 s.e a 0.073 Serial Correlation iN : F 13,291 - 0.22. Halmality Xz2) '0.085 Forecast Stability Test Chow (10,29) a 1.39 Agricultural Commoditiles (sample 1952-88) A LnICPAG * - 0.020 - 0.427A LnlCPAGt_2<sup>+0.181A LnICPAGt-</sup> 3 (0.019) (0.115) (0.107) - 1e903 A TBt-2 - 0.186 Zt(1.043) (0.093) R2 - 0.604 s.e. **u** 0.074 Serial Corr*latlon LM:F 13.291 a 0.47 Normality X(2) *5,346 Forecast Stability Test : Chow (10,22) a 1.45

# Minerals and Metals (sample 1954-88)

A LnICPMMi a -0.003 -0.474 A LnlCPh94t_2 + 0.432A LnlCPW4t_5 (0.017) (0.176) (0.172) -4.839 A TR1t_5 - 0.210 Zt(1.426) (0.104) R2 **a** 0.552 s.e. * 0.096 Serial Correlation LM:F 13,271 a 0.67 Normality : X 2 ) '088 Forecast Stability Test : Chow (10,20) a 0.70
**Policy, Research, and External Affairs**

a *

# **Debt and International Finance**

# WPS 622

This paper -- a product of the Debt and Intemational Finance D:vision, International Economics Department -is part of a larger effort in PRE to understand the secondary market lor developing country debtaspairtol'itsanatlysisofvoluntarymarket-baseddebtanddebt.serviccreduction. Copiesare available free from the World Bank, 1818 **H** Street NW, Washington DC 20433. Please contact Sheilah King- **Watson, room S8-040, extension 33730 (40 pages).** the largest U.S. banks increases discounts by nearly 25 cents on the dollar.

Bank **loans** to many developing countries trade the largest U.S. banks increases discounts by at a discount on the secondary market. These nearly 25 cents on the dollar. discounts are typically assumed to reflect only the repayment prospects of the borrower country. They explain their results with a simulation model of a representative bank with minimum But Ozler and Huizinga demonstrate that capital requirements, flat-rate deposit insurance, factors in the creditor countries have a major and limited liability. The bank's portfolio impact on secondary market prices. Th ., adjustment decision involves trading risky empirical investigation suggests a systematic foreign loans in **tihe** secondary market or making relationship between secondary market prices shon-term domestic loans. T'he model yields a and the size distribution of banks' portfolios. negative relationship between the banks' exposure to developing countries and discounts in the There is a strong negative correlation secondary market.

There is a strong negative correlation between discounts in the secondary market and U.S. banks' heavy exposure to developing country debt. They estimate that every US$4 billion increase in a large bank's exposure to a country reduces the discount 10 to 15 cents on the dollar.

This is because flat-rate deposit insuraince, combined with limited liability, encourages banks to gamble with depositors' money **and** to choose a more heavily concentrated developing country loan ponfolio. Similarly, poorly capitalized banks with deposit insurance benefit more than well-capitalized banks do fromii a risky developing country loan ponfblio.

'Fhey also find that discounts and total bank capital are strongly positively correlated over time: a US$8 billion increase in the capital of Thc PRE Working Paper Scrics disscminates thc findings of work under way in **tiC** Bank's Policy, Reseairch. and External Affairs Complex. An objectivc ofthescries is to get thcsc findings out quickly, cven ifpresentations ar less Ithan fully 1 )1 ishld The findings. interpretahions, andl conclusionc in these paperc do not necessarily reprent official Rank **polio-**

**Pioduced** hs the l'RE Disweniinaion C'enicr
# **Table 1: Baseline** **<u>Characteristics of Households and Children</u>**

||All|No<br>Subsidy<br>(Control)|6-Month<br>Subsidy<br>(Treatment)|Difference<br>(C - T)|<br>P-value<br>Difference|
|---|---|---|---|---|---|
|Panel A: Household Characteristics|(1)|(2)|(3)|(4)|(5)|
|Size of household|4.81|4.76|4.85|-0.09|0.258|
|Number of children 11 and under|1.34|1.35|1.34|0.01|0.717|
|Household income|5399|5058|5712|-653.41|0.052|
|Panel B: Parent Characteristics||||||
|Years of education|9.30|9.25|9.36|-0.11|0.624|
|Hours Worked Per Day|10.08|9.86|10.27|-0.41|0.001|
|Commute to work (miles)|3.60|3.48|3.71|-0.23|0.524|
|Number of times sick|2.44|2.58|2.31|0.26|0.095|
|Forgone treatment|0.25|0.25|0.24|0.02|0.446|
|Ever visit health provider|0.76|0.77|0.75|0.02|0.260|
|Total number of visits, all providers|4.28|4.46|4.11|0.35|0.260|
|Total healthexpenditures|827.76|903.58|758.59|144.99|0.225|
|Households/Parents (N)|1614|770|844|--|--|
|Panel C: Child Characteristics||||||
|Age|8.00|7.94|8.06|-0.12|0.459|
|Female|0.48|0.48|0.49|-0.01|0.473|
|Ever sick|0.76|0.76|0.77|-0.01|0.843|
|Number of times sick|2.16|2.19|2.13|0.06|0.602|
|Forgone treatment|0.18|0.20|0.17|0.03|0.153|
|Ever visit health provider|0.75|0.75|0.76|0.00|0.983|
|EMP visits|0.12|0.13|0.11|0.02|0.704|
|Public health facility visits|0.88|0.89|0.87|0.03|0.679|
|Private health facility visits|1.04|1.12|0.96|0.16|0.302|
|Pharmacy visits|1.80|1.80|1.80|0.00|0.916|
|Total number of visits, all providers|3.82|3.90|3.74|0.16|0.435|
|Total healthexpenditures|569.21|533.23|601.76|-68.53|0.302|
|Children(N)|2996|1423|1573|--|--|

_Notes:_ Above are averages for the full sample (Column 1), Control group (Column 2), and the Treatment group who were awarded a 6 month subsidy (Column 3). Income is defined as reported monthly household income. Valid income data are not available for 174 families, and commuting data are not available for 432 families. Health providers consist of EMPs, public clinics, pharmacies, private hospitals, private doctors, public hospitals, and laboratories. All health and visit variables are reported during the past year. All income and expenditure data are in 2008 Cordobas. Children who were not sick in the past year are included as zeros for number of times sick and all visit/spending variables. Forgone treatment in past year due to lack of money was calculated to be zero for children who were not sick in the past year. Column (5) represents the p-value from a t-test of means between treatment groups including market and round fixed effects. Standard errors of t-tests are clustered at the family level. *** p<0.01,** p<0.05,* p<0.1 _Source:_ Authors’ analysis based on data described in paper.

25
21 Halpezzi and Mayo (1987), and ii) Those using hedonic price indices as the housing price variables, following Straszheim (1973); for a recent example see Ravallion (1988a).

9. For excellent expositions see Deaton and Muellbauer (1980a, Chapter 7) and Diewert (1980).

10. On the broader welfare issues involved in making "cost-of-living' or "real income" comparisons, see Sen (1979).

11. In theory, household characteristics can be introduced into the AIDS model by allowing any of its underlying parameters to be household specific. The following method is the simplest way of introducing such effects. We experimented with more complicated multiplicative effects in the empirical work but were unable to obtain satisfactory results.

12. It would be theoretically preferable to model the demand for individual housing attributes rather than the composite commodity. However there are so many of the former (13 in our hedonic index) that this was not deemed to be computationally feasible.

13. Although the model is not linear in parameters, all pi.rameters can be uniquely identified from the OLS coefficients (Ravallio-s, 1989). This ceases to hold with two or more equations.

14. Again see the surveys by Mayo (1981) and Malpezzi and Mayo (1987).

15. Deaton and Muellbauer (1980b) advocate a price index approximation to

- (13) which makes the model linear in parameters without cross-equation restrictions and so estimable by OLS. However, as Deaton and Muellbauer point out, for satisfactory results this requires that individual prices are reasonably collinear. This is unlikely to hold in a household level cross-section, though it is more plausible in a time series application.
- A technological change in financial markets and in the processing of information, which has allowed institutional investors in developed countries to spread their country risks through greater portfolio diversification.

- Greater access of developing-country borrowers to industrial-country financial markets after the easing of regulations in some major industrial capital markets. Regulation changes include the U.S. Security and Exchange Commission (SEC)'s Rule 144a and the new credit guidelines for the Samurai bond market introduced in August 1992.4

# External factors: a review of the literature

Several authors have attempted to quantify the importance of external and domestic factors in attracting capital inflows to developing countries. The pioneer work in this area, Calvo and others (1993), uses monthly data on the change in international reserves (a proxy for monthly capital inflows) as the dependent variable,-during 1988-91 for ten Latin American countries. The authors conclude that the drop in interest rates and the recession in industrial countries during those years accounted for about 50 percent of the variability in these flows, but the authors do not include any domestic variables to explain the inflows. Similarly, Fernandez-Arias (1994) uses quarterly data on portfolio capital flows (bonds plus equity) for thirteen countries during 1989-93 and concludes that about 86 percent of the increase in flows to these countries since 1989 is due to a fall in interest rates in international markets. Nevertheless, like Calvo and others (1993), Femandez-Arias falls short of including and trying to identify relevant domestic variables and, therefore, does not provide straightforward economic policy recommendations.5 Recently, there have been clear signs that international interest rates will recover from the record low observed during 1992-93-a clear threat to developing countries.<sup>6</sup> Yet the increase in both real and

> 4 The introduction of Rule 144A in the U.S. stock exchanges has considerably simplified trading in foreign equities by eliminating costly settlements delays, registration difficulties, and dividend payment problems. Under Rule 144A, qualified institutional buyers in the United States no longer need to hold securities for a two-year period before they can be sold. Foreign issuers can now gain access to a relatively large number of U.S. institutional investors. Similarly, the credit rating standards for public placements of bonds in Japan were relaxed according to the new credit guidelines for the Samurai bond market. This may also imply the existence of an specification error and biases in their results.

> **6** The six-month dollar denominated LIBO rate reached 3.9 and 3.4 percent in 1992 and 1993, respectively. IMF (1994: 127).

5