rafmacalaba/datause-encoder-data huggingface dataset:
# Table A.13: Women’s employment outcomes

|Full Sample<br>(1)|Rural<br>(2)|Urban<br>(3)|
|---|---|---|
|**Panel A: Worked last week**|||
|Fraction of female leaders<br>-0.023|-0.073|0.120|
|(0.105)|(0.121)|(0.122)|
|Observations<br>62,122|43,981|18,141|
|Outcome mean<br>0.250|0.267|0.209|
|**Panel B: Worked last 12 months**|||
|Fraction of female leaders<br>-0.032|-0.080|0.038|
|(0.096)|(0.106)|(0.150)|
|Observations<br>62,122|43,981|18,141|
|Outcome mean<br>0.334|0.365|0.259|
|**Panel C: Worked in agriculture last week**|||
|Fraction of female leaders<br>0.014|0.008|0.024|
|(0.085)|(0.107)|(0.051)|
|Observations<br>61,573|43,650|17,923|
|Outcome mean<br>0.126|0.166|0.031|
|**Panel D: Worked in non-agriculture last week**|||
|Fraction of female leaders<br>-0.046|-0.083|0.074|
|(0.051)|(0.054)|(0.100)|
|Observations<br>61,573|43,650|17,923|
|Outcome mean<br>0.117|0.096|0.168|
|State fxed efects<br>x|x|x|
|District characteristics<br>x|x|x|
|Individual characteristics<br>x|x|x|

_Notes_ : Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are indicator variables for whether the woman worked last week (Panel A), whether she worked last 12 months (Panel B), whether she worked in agriculture last week (Panel C), and whether she worked in non-agricultural sector last week (Panel D). The column (1) presents estimates for the full sample, while the columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC/ST population in the district, and the female and male literacy rates. Robust standard errors * are clustered at the district level. ***, **, and denote significance at the 1, 5, and 10 percent levels.

40
Estimating the Economic and Distributional Impacts of the RCEP Figure 4 Average tariff applied in RCEP countries by sector, to RCEP and to the rest of the world Trade weighted average <!-- Start of picture text --> Total Agriculture<br>Total Manufacturing<br>Total Natural Resources<br>Total<br>Meat products<br>Motor vehicles and parts<br>Crops<br>Food and beverages<br>Wearing apparel and leather<br>Non metallic minerals<br>Textiles<br>Other transport equipment<br>Chemical, rubber, plastics<br>Animal products<br>Other manufactures<br>Electrical equipment and machinery<br>Basic pharmaceuticals<br>Metals<br>Wood and paper products<br>Petroleum, coal products<br>Utilities<br>Other extraction<br>Fossil fuels<br>0.0% 5.0% 10.0% 15.0% 20.0% 25.0%<br>ROW RCEP<br><!-- End of picture text --> Source: own elaboration with data from MacMap and Comtrade Barriers to imports may also take the form of NTMs, which range from restrictions on foreign providers of services, to sanitary, phytosanitary, technical, or price control measures, among others. In order to assess the level of protection from NTMs, estimations of ad valorem equivalents (AVE) are found in the literature (Tables AI.1 and AI.2 in Appendix I compare the different estimates available). Recently, two studies estimate AVEs of NTMs for a wide range of countries: Cadot et al. (2018) and Kee and Nicita (2017) (see Appendix I). For services, there are recent estimations by Hoekman and Shepherd (2019). The estimated AVEs of NTMs on services are much higher than on goods (Figure 5). This might be related to the techniques used to estimate the AVE, but also to the fact that as tariffs are not imposed on services, protection through NTMs is higher. The most protected services sectors in RCEP are Public administration and defense, Construction, Trade, Tourist services, and Communications.

In the comparison of AVEs of NTMs on goods estimated by Cadot et al. (2018) and Kee and Nicita (2017), we find significant differences. In most sectors, the estimations by Cadot et al. are higher, except for two specific sectors, i.e., Other manufactures and Metals, where estimates by Kee and Nicita are higher. Both studies find a higher protection level on agriculture and food sectors, especially in Meat products and Food and beverages. In this study, we use the estimates by Cadot et al. (2018), which we found to have a more comprehensive coverage of the countries of interest and more intuitive estimates at the sectoral level, but we run a sensitivity analysis with the estimates by Kee and Nicita, presented in Appendix II. Using the database by Kee and Nicita results in slightly higher impacts on real income for RCEP as a whole, with heterogeneity of country and sectoral results. However, the results are broadly consistent across the two NTM data sets.
<!-- Start of picture text --> Dominican Republic Ecuador<br>14 10 4.8<br>12 1.6 4.7<br>8<br>10 4.6<br>1.5<br>8 6 4.5<br>6 1.5 4.4<br>4 4.3<br>4<br>1.5 4.2<br>2 2<br>4.1<br>0 1.5 0 4<br>Remittances/GDP REER Remittances/GDP REER<br>El Salvador Guatemala<br>10<br>17 4.9<br>4.8<br>15 4.8 8<br>4.7<br>4.7<br>13 6<br>4.6 4.6<br>11 4.5 4 4.5<br>9 4.4 2 4.4<br>7 4.3<br>0 4.3<br>Remittances/GDP REER Remittances/GDP REER<br>Haiti Honduras<br>50 5.2 14 5.1<br>40 5 12 5<br>10<br>4.9<br>30 4.8 8<br>4.8<br>20 4.6 6<br>4.7<br>4<br>10 4.4<br>2 4.6<br>0 4.2 0 4.5<br>Remittances/GDP REER Remittances/GDP REER<br>Jamaica Nicaragua<br>20 5<br>12 4.8<br>4.9<br>15 10 4.8<br>4.8<br>8<br>4.7<br>10 4.7<br>6<br>4.6 4 4.7<br>5<br>4.5 2 4.6<br>0 4.4 0 4.6<br>Remittances/GDP REER Remittances/GDP REER<br>Log of REER Log of REER<br>Percent of GDP<br>Percent of GDP<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Log of REER Log of REER<br>Percent of GDP<br>Percent of GDP<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Log of REER Percent of GDP Log of REER<br>Percent of GDP<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Percent of GDP Log of REER Percent of GDP Log of REER<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Note: increases in the REER index indicate a real appreciation. Source: Author’s calculations.

10
households in participating communities, a relatively low saturation level compared to cash transfer programs elsewhere.<sup>37</sup>

# _Assets_

Cash transfer impacts on assets are more limited. There is no effect on the total value of assets in absence of shocks (Table 9). Shocks have a negative effect on the overall value of assets, and this effect is mitigated by the cash transfers, suggesting that households smooth assets (as in Zimmerman and Carter, 2003; and Carter and Lybbert, 2012).<sup>38</sup> We do not find impacts on housing variables either (type of roof, etc., available upon request), though these are not expected to change over the duration of a short program.

Table 10 documents impacts on livestock. There is no impact on an overall livestock index.<sup>39</sup> The only significant impacts are an increase in the likelihood to own goats or sheep, though it is compensated by a decrease in the likelihood of holding poultry. Shocks do not affect livestock holding, which is consistent with several studies showing that livestock is not widely used as a buffer to shocks in the Sahel (Fafchamps et al., 1998; Kazianga and Udry, 2006; Carter and Lybbert, 2012). Not surprisingly given that shocks do not affect livestock, the interaction between cash transfers and shocks is not significant, except for a small positive effect on having poultry.

# **5 Discussion and conclusion**

Do cash transfers enhance households’ resilience by protecting them from the welfare effects of shocks? In this paper, we answer this question by analyzing data from the randomized evaluation of a government-led, multi-year cash transfer program in rural Niger combined with satellite data on exogenous weather shocks. The setting is one of the poorest in the world and a particularly shock-prone environment. We document average program impacts on a wide range of outcomes. We then analyze heterogeneity in impacts by exposure to drought shocks. By doing so, we

> 37 Premand and Barry (2020) do not find that the Niger cash transfer program affects product prices.

> 38 The earlier evaluation of a Niger cash transfer pilot did not find significant impacts on durables 18 months after project termination either (Stoeffler et al., 2019).

> 39 This contrasts which a significant increase 18 months observed after the end of the transfers in the impact evaluation of an earlier cash transfer pilot in Niger (Stoeffler et al., 2019).

27
Table 6: Sibling and Intergenerational Correlations: Less Developed Region Sample

||All ch|ildren|Brother|s (Men)|Sisters (|Women)|
|---|---|---|---|---|---|---|
||1993|2006|1993|2006|1993|2006|
||||PAN<br>|EL A<br>|||
|Sibling Correlation (SC)|0.609***|0.627***|0.607***|0.637***|0.781***|0.741***|
||(47.152)|(56.770)|(34.284)|(38.772)|(34.933)|(36.863)|
|Intergenerational Correlation<br>(IGC)|0.589|0.589|0.553|0.560|0.690|0.628|
||(54.94)***|(67.48)***|(42.86)***|(50.64)***|(42.49)***|(50.67)***|
|IGC squared|0.347|0.347|0.306|0.314|0.476|0.394|
|Proportion of SC explained by<br>IGC|0.569|0.553|0.503|0.492|0.610|0.532|
|Controling for Caste and<br>religion|||PAN|EL B|||
|Sibling Correlation (SC)|0.594***|0.580***|0.594***|0.597***|0.767***|0.713***|
||(44.814)|(47.923)|(32.539)|(33.837)|(32.366)|(31.123)|
|Intergenerational Correlation<br>(IGC)|0.571|0.533|0.536|0.512|0.665|0.564|
||(50.10)***|(55.29)***|(38.89)***|(42.73)***|(38.29)***|(39.22)***|
|IGC squared|0.326|0.284|0.287|0.262|0.442|0.318|
|Proportion of SC explained by<br>IGC|0.548|0.490|0.484|0.439|0.577|0.446|
|Controling for Neighborhood<br>FE|||PAN|EL C|||
|Sibling Correlation (SC)|0.362***|0.407***|0.377***|0.433***|0.472***|0.496***|
||(19.365)|(25.833)|(14.565)|(19.480)|(8.581)|(12.446)|
|Intergenerational Correlation<br>(IGC)|0.487|0.473|0.479|0.468|0.557|0.503|
||(46.74)***|(54.21)***|(36.91)***|(42.26)***|(33.56)***|(37.56)***|
|IGC squared|0.237|0.224|0.229|0.219|0.310|0.253|
|Proportion of SC explained by<br>IGC|0.654|0.550|0.609|0.505|0.658|0.510|
|No. of observations|7,136|11,055|4,928|6,917|2,208|4,138|

Robust t statistics in parentheses. Standard errors corrected for clustering at family level * significant at 10%; ** significant at 5%; *** significant at 1% 41
- lii -

# PINANCING URBAN SERVICES IN LATIN AMERICA: Spatial Distribution Issues

# TABLE OF CONTENTS

||||Table References|
|---|---|---|---|
|ABST|RACT|||
|SUMM|ARY OF|THE REPORT|Box 0.1|
|I.|RISI|NG DEMAND FOR URBAN SERVICES||
||1.1|The Region's Urbanization Process||
||1.2|Population Distribution|Table 1.2|
||1.3|Population Movements||
||1.4|Urban Hierarchies|Table 1.4|
||1.5|Urban Services Defined||
||1.6|Types of Urban Services||
||1.7|Assignment of Public Service Functions<br>||
|||Among Levels of Goverranent|Table 1.7|
||1.8|Diversity of Urban Service Demaid||
||1.9|Increasing Service Standards|Table 1.9|
||1.10|Variety of Institutional Responses|Table 1.10|
||1.11|Reasons for Public Intervention||
||1.12|Political Overtones||
||1.13|Fiscal Impact of Urban Services Provision|Table 1.13|
||1.14|Urban Service Finance as Policy Concern|Table 1.14|
||1.15|Positive Features of Urbanization|Table 1.15|
||1.16|Resource Mobilization Potential||
||1.17|The New 'Growth Management' Approach||
||1.18|Summing Up||
|II.|CURR<br>|ENT CONSTRAINTS TO URBAN EXPENDITURES<br>|<br> <br> <br>|
||2.1|The Debt Crisis|Tables<br>2.1<br>(a,<br>b, c&d)|
||2.2|Domestic<br>Inflation|Table<br>2.2|
||2.3|External<br>and Domestic<br>Borrowing|Table<br>2.3<br>(note)|
||2.4|Expenditure<br>Effects<br>of the Recession||
||2.5|Progressive<br>Budgetary<br>Displacement|Tables<br>1.13,<br>2.5<br>(a,<br>&b)|
||||and 3.10b|
||2.6|Revenue Effects of the Recession||
||2.7|Centralized Economic Policy and Political<br>Democratization||
||2.8|Growing Fiscal Dependence of Subnational<br>Authorities||
||2.9|Planning by Negotiations||
||2.10|Political Use of Negotiated Transfers||
||<br>2.11<br>|<br>Erosion of Political Responsibility||
||2.12|SurmingUp||
|III.|ARE L|AC COVERNMENTS<br>TRULY DECENTRALIZING?||
||3.1|Structure and Role of the Public Sector|Table 3.1|
||3.2|Politics of Intergovernmental Relations||
||3.3|Devolutive rather than Aggregative Governance||
||3.4|Parastatal Sector||
||3.5|Management of Public Enterprises||
- IMF. 2018. “Manufacturing Jobs: Implications for Productivity and Inequality.” In _World Economic Outlook April 2018: Cyclical Upswing, Structural Change_ . Washington, DC: IMF. 5, 3.2, A.1

- Ivanic, M., and W. Martin. 2018. “Sectoral Productivity Growth and Poverty Reduction: National and Global Impacts.” _World Development_ 109:429–439. 3.2

- Kinfemichael, B., and A. Morshed. 2019. “Unconditional convergence of labor productivity in the service sector.” _Journal of Macroeconomics_ 59:217–229. 3.2, 3.3.2

- Kongsamut, P., S. Rebelo, and D. Xie. 2001. “Beyond Balanced Growth.” _Review of Economic Studies_ 68:869– 882. 1

- Lagakos, D. 2016. “Explaining Cross-Country Productivity Differences in Retail Trade.” _Journal of Political Economy_ 124:579–620. 3.2

- Ligon, E., and E. Sadoulet. 2018. “Estimating the Relative Benefits of Agricultural Growth on the Distribution of Expenditures.” _World Development_ 109:417–428. 3.2

- Lowder, S.K., J. Skoet, and T. Raney. 2016. “The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide.” _World Development_ 87:16–29. 1

- Martins, P.M.G. 2019. “Structural Change: Pace, Patterns and Determinants.” _Review of Development Economics_ 23:1–32. 5

- McMillan, M., D. Rodrik, and Í. Verduzco-Gallo. 2014. “Globalization, Structural Change, and Productivity Growth, with an Update on Africa.” _World Development_ 63:11–32. 2.2, 5, 7, A.1

- Mensah, E.B., and A. Szirmai. 2018. “Africa Sector Database (ASD): Expansion and Update.” MERIT Working Papers 2018-020, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology, Maastricht, Netherland. 2.1

- Ngai, R., and C. Pissarides. 2007. “Structural Change in a Multisector Model of Growth.” _American Economic Review_ 97:429–443. 1

- Restuccia, D., D.T. Yang, and X. Zhu. 2008. “Agriculture and aggregate productivity: A quantitative crosscountry analysis.” _Journal of Monetary Economics_ 55:234–250. 2

14
Figure 5 presents the monthly variation in program-level disbursement and loans recovered, adjusted by the number of borrowers, for all five major credit categories of MFIs for the data periods of 2004-2011. A higher level of disbursement per borrower indicates a credit deepening, while a higher level of loan recovery implies success of microfinance institutions. We find a seasonal pattern in loan disbursement and recovery – low in February, high in the middle of the year and low again during September to November. The gap between the amount disbursed and recovered is, however, the highest in February and lowest in June and December—the months when MFIs fix up accounts and publish reports. Both indicators reach their global minima during September-November, which is the _Monga_ season.

The seasonal pattern in lending and loan recovery also shows there is a seasonality of financial intermediation even with MFIs active in rural areas. Figure 6 presents the seasonal distribution of recovered loan as a percentage of loans outstanding. A large amount in recovered loans as a percentage of outstanding loans means a high loan repayment rate for the borrowers. The loan recovery rate as a percentage of loans outstanding varies from 10 to 20 percent. There is a cyclical pattern in the indicator and it drops during the lean months of February-March and September-November. Table 7 presents the loan recovery rate by month, which shows similar seasonal patterns over time.

Figure 8 presents average monthly savings mobilized by MFIs, mostly from members. Savings per member is an indicator of the financial strength of a financial institution. High savings enable an MFI to lend and invest more from its own stock without needing to borrow. Interestingly, savings accrued to MFIs from members also drop during February-March and September-November, a pattern similar to the seasonality of household income and consumption.

We also examine the overall performance of MFIs measured in terms of loan defaults to PKSF as a result of seasonality of agriculture. In every month, there is some ‘recoverable’ loan amount that the POs owe to PKSF. If an organization fails to repay the recoverable amount in a month, that amount is 10
- 63 -

building services and furniture. The key empirical question here concerns the income elasticity of demand for rurally produced non-faim goods and services. In terms of assessing the overall growth of the rural economy, the question to be answered is what is the total magnitude of these linkages?

130. A recent study draws the conclusion that rural non-farm production in Africa has been growing more rapidly than employment in agriculture and, therefore, that the share of non-farm activities in total rural employment has been growing. Other data seem to point to the fact that service, commerce and restaurants are the fastest growing non-farm sectors, and that these are of greater significance than manufacturing and construction. Recent farm management studies confirm that African agricultural households allocate a significant part of their household labor to remunerative non-farm activities.

131. Backward linkages from agriculture to rural input suppliers do exist in SSA. Blacksmithing is one activity that reaches its peak during the height of the agricultural season because of the demand for repair services. But in general, compared to Asia, the backward linkages in SSA are weak. This is related to the current stage of the envolvement of farming systems. Currently the use of inputs is low and the use of animal traction and farm implements is far less prevelant in SSA than in Asia. Even though these backward linkages are weak today, if the other measures discussed earlier to facilitate accelerated agricultural development are undertaken, it may be expected that these linkage multipliers will grow.
inequality stagnation are also evident when using other inequality measures, such as the Mean log deviation, the ratio between the 90th percentile and the 10th percentile, and the Atkinson index.

Figure 3. Inequality trends in Latin America represented by different inequality measures, 2003-2012 Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank). Note: The regional Gini coefficient is computed based on pooled country-specific data for 17 countries. The methodology to pool these datasets is explained in footnote 1 and the years used for each country in the weighted average are detailed in Table A2 in the annex. Inequality measures include households with zero incomes, whose incomes are censored to 0.01 daily USD and who. Households with zero incomes represent 0.3 percent of the population. Results are robust to different censoring thresholds and to the exclusion of households with zero incomes. See Table A1for detailed inequality indicators by country.

# **3. Unpacking income inequality trends between and within countries in Latin America**

Regional income inequality patterns reflect, not only the evolution of inequality within countries, but also between countries, capturing differences between the countries in the levels of income across the region. According to Lakner and Milanovic (2013), the most important source of global inequality over the 1988-2008 period has been income differences between countries, explaining about 75 percent of global inequality. One potential explanation for the regional inequality stagnation documented above might be diverging income trends or different rates of income growth across countries in the region – as countries like Chile and Uruguay continue its steady income grow while others like Nicaragua and Honduras continue to lag behind. While decomposing regional inequality into the two components (within and between countries) shows a growing divergence in incomes across countries (particularly since 2008), the major determinant of income inequality in Latin America is within-country income distribution.<sup>12</sup> In the last decade, growing income polarization across countries has led the between component of Theil index to increase from 0.98 to 2.04, causing its

> 12 Similar to what has happened to the global distribution of income, where the upward movement of China changed the overall shape of the global distribution from a twin-peaked to an almost single-peaked distribution over the last twenty years (Lakner and Milanovic, 2013), in Latin America, the rightward shift experienced by Brazil, the Andean countries, and to a lesser extent the Southern Cone countries changed the overall shape of the regional income distribution from a twinpeaked into a single-peaked distribution over the last decade. In contrast, the distribution of income in Mexico and the Central America region have remained relatively constant (see Figure A1 in the Annex).

6
15 future. It follows that this type of problem should be analyzed in the framework of incomplete information, which is the course we shall pursue.

The formal analysis of the forgoing problem involves the following issues. What is the possible motivation of a policy maker to cheat? How is it possible for hini to conceal his intentions? How does the degree of credibility affect the policies of the cheating policy maker? What does lack of credibility imply for the bonest policy maker? Can the latter pursue policies which may remove the uncertainty about his intentions?

# **3.** IMPLICATIONS OF **BARRO'S** MODEL OF INCOMPLETE INFORMATION

We may use the important paper by Barro (1986) on incomplete information as a starting point for dealing with the foregoing questions. Here the credibility issue is modelled through uncertainty about the type of the policy - maker. There are two types one who is always true to his commitment (denoted S for "strong") and one who is incapable of honorit.g his commitments but may have an incentive to mimic S temporarily if this is justified ex-post (we denote the second type by W for 'weak"). The public is uncertain about the identity of the type in office and assigns an initial probability (a 1) that the policy maker is S. The duration of the game is fixed at T periods.<sup>5</sup> The utility function of both policy makers for each period is of the form given by (1) in Part I.

In this model S, who can overcome the problem of dynamic inconsistency, announces a rule 1=0 to which he adheres ex-post. If the policy maker happels to be S and is believed to be such, then the rule will give him a better outcome than the discretionary strategy of setting r=b/a for all periods (t).

The policy maker W will certainly adopt the discretionary strategy for the last period (T) since he is not bound by any commitment. However, in the