rafmacalaba/datause-encoder-data huggingface dataset:
age, while overall, there is a decline of 27 percentage points in attendance rates between LS entry and USE exit ages.

For Cohort 4, in Argentina, it can be seen that the change in the proportion enrolled between LS entry and exit ages is of 9 points —5 point less than in Cohort 2— while it is of 14 points during USE –as compared to 13 points for Cohort 2 (Table A.4). The difference between enrollment at LS entry and USE exit age is 23 percentage points. Therefore, there is an overall reduction in dropout from 27 to 23 points, all of which is explained by the lower dropout at LS ages. Table 1 summarizes the changes across cohorts based on the data in Tables A.3 and A.4 for all countries. The first column presents the change in enrollment rates for LS for Cohort 2 while the second column includes the same change for Cohort 4. The following two columns present changes in enrollment at USE age and the final columns include the full LS entry to USE exit period.

In the case of LS age, with the exception of Bolivia, there is an important decline in school dropout across the region between Cohorts 2 and 4 (Table 1, columns 1 to 3). Costa Rica stands out with the greatest decline of 18 percent. On average, for the region as a whole, dropout at LS ages declines by 8 points during this period. However, in the case of school attendance during USE age the opposite pattern is observed, since 10 countries show increases in dropout when comparing Cohorts 2 and 4 (Table 1, columns 4 to 6). Chile stands out with the largest declines in school dropout in the region, with a 22 percent lower rate for Cohort 4 than for Cohort 2. On average the differences across countries cancel out, so at the regional level there is no change.

The fact that for a majority of countries in the region dropout at USE age increased and that in only 3 out of 18 – República Bolivariana de Venezuela, the Dominican Republic and Chile- is there a significant reduction, is the main stylized fact to be explored in the following sections. As mentioned, this result is of even greater interest in light of the positive macroeconomic environment observed during these years.

Finally, we summarize dropout rates by taking enrollment at LS entry and at USE exit ages as reference –that is, the full secondary cycle (Table 1, columns 7-10). The main result is that in all countries, except Guatemala, the decline in dropout at LS cancels out the increase during USE, resulting in lower dropout rates overall. At the regional level, the reduction by 8 points at LS age combined with no change for USE age results in an overall 8 percent decline.

# **2.2 Patterns of dropout across Latin America**

The information on school trajectories can be used to verify the point at which dropout during the LS-USE span takes place (Tables A.3 and A.4) by comparing school attendance rates for each cohort observed at different ages. This is of interest since the policy approach for increasing school attendance in countries where youth leave school more prematurely (say, at early LS age) is not necessarily the same as that necessary for addressing school exit at later stages when students are of working and USE age.

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flows have gone to Latin America, and five countries (Argentina, Brazil, Mexico, the Republic of Korea, and Turkey) account for two thirds of the portfolio flows to developing countries between 1989 and 1993.

Equity portfolio investment increased from $3.5 billion in 1989 to $13.2 billion in 1993. This increase, primarily to East Asia and Latin America, is in part a result of the opening up of equity markets to foreigners--for example, Korea and Mexico, which both relaxed restrictions on foreign ownership, and China, where **firimls** have started to list stocks on the Hong Kong exchange. More recently, stock market liberalization in India is resulting in a significant amount of private portfolio flows, especially through closed-end country funds, and East-European countries are experiencing large portfolio inflows, as well.

Portfolio debt instruments (bonds, certificates of deposit, and commercial paper) Continue to dominate portfolio investment, increasing over tenfold from $4 billion in 1989 to an estimated $42.6 billion in 1993. This increase has been associated with greater sophistication and diversification of borrowing instruments.

**Table 1: Geographical Distribution of Portfolio Investment in Developing Countries**

||||**1989-199**|**3**||
|---|---|---|---|---|---|
||**1989**|**1990**|**1991**|**1992**|**1993e**|
||||**_(US$_**_B_|_illions)_||
|**All Developing Countries**|**7.5**|**9.3**|**20.3**|**36.8**|**55.7**|
|**East Asia &****_Pacific_**|**2.9**|**3.1**|**4.0**|**10.2**|**15.9**|
|**South Asia**|**0.7**|**0.4**|**0.2**|**0.3**|**0.6**|
|**Europe & C. Asia**|**2.3**|**1.9**|**0.8**|**4.8**|**9.2**|
|**Latn America & Caribbean**|**1****_A_**|**3.8**|**15.0**|**20.5**|**27.2**|
|**Middle East & N. Afica**|**0.2**|**0.1**|**0.0**|**0.0**|**0.0**|
|**Sub-Saharan Africa**|**0.0**|**0.0**|**0.0**|**0.9**|**0.2**|
|**Global Funds**|**0.1**|**0.0**|**0.3**|**0.1**|**2.6**|
|**_Source:_**<br>**_World Bank,_**_World Deb_|_t Tables,_**_199_**|**_3-94._**_e= e_|_stimated_|||

A closer look at the gross portfolio flows data indicate that the share of equity portfolio investment in Latin America in total portfolio flows to that region has declined

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rules on subsidies and on movements of capital. In some cases, discrimination is simply not possible: if a country limits subsidies to domestic producers or establishes a competition authority in fulfillment of its PTA commitments, these reforms benefit both members and nonmembers of the PTA. In other cases, discrimination is feasible but unlikely for economic or legal reasons: in services, market access is generally granted through reforms of domestic regulation, such as rules on foreign participation or access to essential facilities, which are hard to undertake in a way that grants privileged access.

The sample covers 96 countries, including all major economies, for the period 20022014. The choice of the initial year is due to the poor quality and availability of tariff data before 2002. In addition to the database on the content of deep trade agreements, we use trade and trade policy data from standard sources. Export data at the HS product level are from the _United Nations Commodity Trade Statistics Database_ (UN-COMTRADE). Additional data on bilateral time-invariant covariates, used in a series of robustness checks, come from the CEPII _geodist_ and _gravity_ databases. Tariff data, from the United Nations Conference on Trade and Development TRAINS, and import demand elasticities at the at the 6-digit level, from Kee et al. (2008), are used to construct the Tariff Trade Restrictiveness Index (TTRI) and the Relative Preferential Margin index (RPM). Finally, data on PTAs no longer in force come from Egger and Larch (2008) and Bilateral Investment Treaties (BITs) from the United Nations Conference on Trade and Development’s Investment Policy Hub.<sup>17</sup> Before moving to the econometric analysis, we take a first look at the data. Over the 2002-2014 period, the share of country pairs with PTAs increased from 9 to about 29 percent (Table 2). During the same period, average tariffs (TTRI) were cut by half while depth, irrespective of legal feasibility, increased by a factor of three. As countries reduced bilateral tariffs, the average relative preference margins (RPM) and its standard deviation decreased as well. The two trends together indicate widespread tariff reductions which are less likely to have increased trade diversion. In terms of the content of PTAs, the summary statistics show that there were minor differences (before 2014) between depth constructed using legally enforceable provisions subject to dispute settlement (“Depth LE” or “Depth Legally Enforceable”), and depth constructed on the basis of legally enforceable language (“Depth WLE” or “Depth Weakly Legally Enforceable”). There is also some evidence that the newest

> 17 The data are freely accessible at <u>http://www.ewf.uni-bayreuth.de/en/research/RTA-data/index.html</u> and <u>http://investmentpolicyhub.unctad.org/IIA</u> respectively.

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were interviewed during both the IHPS 2013 and the FIAS 2015, 299 households were also interviewed during the IHPS 2016.

# **3.1. Household Aid Receipt and Livelihood Outcomes**

Both the IHPS and FIAS collected information on whether the household received food or cash aid from a variety of sources. We use a dichotomous variable equal to 1 if the household received aid from any government or NGO source. Both surveys also elicited comprehensive information on household food and non-food consumption, which allow us to construct our dependent variables, namely total non-food expenditures per capita in natural logarithms, the total value of food consumption per capita in natural logarithms; total value of food purchases per capita; total calories consumed per capita in natural logarithms; and food consumption score. We hypothesize that shocks will reduce these outcomes. With respect to food aid, we hypothesize that the total value of food consumption, calories per capita, food consumption score and school participation rates will increase with aid receipt. Food purchases may either increase or decrease, in part because some aid is distributed in cash.

# **3.2. Crop Production and Extreme Rainfall Events**

While our goal in this paper is to evaluate the impacts of extreme rainfall events on welfare outcomes, it is instructive to look at impacts on plot-level maize yields that are computed from the survey data.<sup>6,7</sup> McCarthy et al. (2021) systematically evaluated the explanatory performance of a wide range of drought and flood shock measures from different data sources using IHPS and FIAS sample data. That work finds that the most robust measure for flood shocks is a dichotomous measure based on an index of

> 6 Maize yield estimates provided by the agricultural extension services at the extension planning area level are also used in the MVAC model for targeting purposes, as detailed in the subsequent section.

> 7 Maize harvest is computed in dried grain- and kilogram-equivalent terms. In doing that, harvests that are reported in non-standard measurement units are converted into kilogram-equivalent terms using the IHPS conversion factors. Subsequently, maize harvest is divided by GPS-based plot area in hectares to obtain plot-level maize yields.

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among rural women might be due to health extension workers in rural areas whose major activities include delivering family planning services.

<!-- Start of picture text --> Figure 1d. Trends in maternal health services by wealth ranking, residence and education<br>(2000-2014)<br>Contraception ANC4+ SBA<br>100 Rich/Poor 100 Wealth Quintile 100 Rich/Poor 100 Wealth Quintile 100 Rich/Poor 100 Wealth Quintile<br>80 rich (top 60%)poor (bottom 40%) 80 richestrichermiddle 80 poor (bottom 40%)rich (top 60%) 80 richestrichermiddle 80 rich (top 60%)poor (bottom 40%) 80 richestrichermiddle<br>60 60 poorpoorest 60 60 poorpoorest 60 60 poorpoorest<br>40 40 40 40 40 40<br>20 20 20 20 20 20<br>0 0 0 0 0 0<br>2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014<br>100 Urban/Rural 100 Education 100 Urban/Rural 100 Education 100 Urban/Rural 100 Education<br>80 urbanrural 80 secondary+primaryno education 80 ruralurban 80 no edusecondary+primary ca tion 80 urbanrural 80 secondaryprimaryno education +<br>60 60 60 60 60 60<br>40 40 40 40 40 40<br>20 20 20 20 20 20<br>0 0 0 0 0 0<br>2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014 2000 2005 20112014<br>Note: Computed from DHS data. Income is based on the household's wealth index. The vertical lines in the graphs are 95% confidence intervals.<br><!-- End of picture text -->

# _3.2.2 Absolute inequalities: Rate differences_

Further to the graphical illustrations presented in the previous section, we examined whether the changes in absolute gaps between different groups were significant. We tested if these differences between groups at the earlier survey were significantly different to that of the differences at the latest survey available to the indicator. Tables 3a & b present the results. The values in the tables are percentage points except for NMR, IMR and U5MR where the number of births per 1,000 live births is reported. For all indicators, the earlier survey report year is 2000 (the first DHS). The latest survey report year is 2014 (the mini DHS) for the nutrition and mortality indicators and for the 17
data come from the Ministry of Education’s annual Census of Schools (Censo Escolar).<sup>21</sup> The 15% payment to the state fund _did_ count toward the 25% minimum expenditure on education, but 10% more was required. All fund receipts had to go to education. If receipts did not reach a federal minimum per primary school child, the federal government would ‘top off’ the fund, bringing it to this level.<sup>22</sup> This ensured universal primary education of the required quality before municipalities spent the remaining 2/5 of mandated education spending. In 2007, the policy continued under “Fund for the Development and Maintenance of Basic Education” (FUNDEB),<sup>23</sup> which gradually increased the fraction of revenue paid to the fund and added other levels of education to the redistribution algorithm. See Appendix B for more detail.

Municipalities benefitting from the reform looked poor compared to others in their state (even if they were rich in the Brazil-wide sense). In 1998, the average municipality got back 1.1 times what it paid in. At the 25th percentile of net receipts, municipalities got back 1/3 of the amount paid in. At the 75th percentile, they got back 1.6 times the amount paid in. For a municipality paying 15% of revenue, this implied a loss equivalent to 10% of revenue at the 25th percentile, and a gain equivalent to 9% of revenue at the 75th percentile. These are large effects that likely led them to re-optimize spending. Table 2 summarizes the frequency of high, moderate, and low net FUNDEF receipts per child in 1998 by high, moderate, and low average income per capita. Many rich municipalities gained a lot, and many poor ones lost a lot.

A few factors limited FUNDEF crowd-out of local money. First, all fund receipts had to go to education (by law). Beneficiaries could potentially use fund receipts for other purposes by reducing _voluntary_ (above 25% of revenue) education spending. However, 37% of municipalities had _zero_ voluntary education spending in 1997 (the minimum was binding), and it was close to binding for many more. If these municipalities cut taxes in response to new money, they would have to reduce non-education spending, which might not be possible. Second, many

> 21The National Institute of Educational Studies and Research (Instituto Nacional de Estudos e Pesquisas Educacionais An ́ısio Teixeira) (INEP), an agency under the Ministry of Education, conducts the survey. State and municipal departments of education collect the data, with public and private school cooperation. The transfer of public funds depends on schools’ cooperation with the survey, making the public school data especially reliable.

> 22In 1998, six states got federal top-off: Bahia, Cear ́a, Maranh ̃ao, Par ́a, Pernambuco, and Piau ́ı. The per-child minimum changed annually. It began to vary with child age (in 2000) and school type (rural/ urban) (in 2005).

> 23In Portuguese, Fundo de Manuten ̧c ̃ao e Desenvolvimento da Educa ̧c ̃ao B ́asica.

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# **VI. Concluding Remarks**

This paper introduced the expanded and updated version of the Financial Structure Database and documented recent trends in the development of financial institutions and markets. There is an increasing awareness that any sound financial sector analysis and policy advice have to be based on appropriate data capturing the different dimensions of financial sector development. This database is one of many efforts at the World Bank to provide such data.

This paper has also shown how financial sector indicators can be used for cross-country comparisons over time. We found a general deepening of financial markets and institutions over time, which is more pronounced in the high-income countries and more pronounced for markets than for banks. Other income groups and regions of the world have made progress as well, although not to the same extent. This is reason for concern, as cross-country comparisons have shown that financial sector development has a stronger impact on growth in low- and middle- than in high-income countries (Aghion et al., 2005). We also found an increasing trend towards globalization in financial service provision; it remains to be seen whether this trend will continue during and after the current global financial crisis.

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# **V. Conclusion**

Analysis of household panel surveys has suggested that for many households poverty is a temporary condition. This is hopeful news for poverty alleviation because it implies that the total resources needed are less than what would be the case without such mobility. The challenge though is to target funds to the long-term poor and to design appropriate programs which complement the resources of the temporary poor. To do so, one needs to know their characteristics. This paper provided evidence that endowments of human and physical capital and changes in them help explain why a household can successfully escape poverty, in addition to its demographic and other socio-economic characteristics.

In the case of Cote d'Ivoire, we found that in urban areas human capital is the most important endowment to explain welfare changes over time. Households with well educated members experienced welfare increases or below average decreases in a two-year period when the average household recorded 15-20% welfare losses. What seems to have mattered though is the skills learned through education, and not any diplomas obtained. These formal expressions of achievement may have worked against some households in having oriented them too much towards a formal labor market in a time when employment growth came almost entirely from small enterprises.

In rural areas, physical capital, especially the amount of land and farm equipment mattered most. Smallholders were more prone to suffer welfare losses. Related to this were changes in income composition. Households with diversified sources of income managed better, especially if they had an important source of non-farm income.

In urban as well as rural areas, the size and composition of the household were key factors in affecting welfare changes. There were different effects from the size and composition at the onset of the period and from changes occurring during the period, but both effects reinforced one another. Larger households suffered larger welfare declines and those experiencing increases in size were not able to compensate sufficiently to maintain welfare levels.
_Figure 2. State Government Resources –2002 distribution_ <!-- Start of picture text --> 10,000<br>9,000<br>8,000<br>7,000<br>6,000<br>PAFEF<br>Aportaciones<br>5,000<br>Participaciones<br>Own Revenues<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>MxP per capita<br>Chiapas Guerrero Oaxaca Veracruz-llave Hidalgo San Luis Potosí Puebla Campeche Tabasco Michoacán de Ocampo Yucatán Zacatecas Guanajuato Nayarit Sinaloa Querétaro de Arteaga Durango Tlaxcala Morelos Quintana Roo México Colima Tamaulipas Sonora Jalisco Chihuahua Baja California Sur Aguascalientes Coahuila de Zaragoza Baja California Nuevo León Distrito Federal Weighted Average<br><!-- End of picture text --> _Source_ : SHCP and Bank staff calculations.

The _aportaciones_ —transfers earmarked for education, health, social investment, and public security—amount to about half of all federal transfers and are the most unequally distributed. This is shown in figure 3 (the per capita distribution of each component of the _aportaciones_ ) and in the calculation of the coefficient of variation of per capita resources, which is 28 for _aportaciones_ , and 32 for _participaciones_ .<sup>4</sup> The aportaciones are not correlated with poverty; they do not give more help to poorer states on average, nor less.

> 4 The Ramo 25 resources for DF are considered FAEB.

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and above the policy change on women’s (individual or joint) access to registered land can be computed as 3 1 2 ) and its statistical significance be tested using standard tests. A key project assumption was that, without program-induced policy reform, high cost (and poor quality) of γγ −(γγ + γγ registration pre-reform might have limited participation in land or financial markets. To assess impacts in this dimension, we use the above framework but replace flows ( i ) with (lagged) stocks ( i −1). Potential endogeneity of sporadic registration arising from the fact that individuals may register a plot to immediately rr RR transact it or take out a mortgage on it are addressed by instrumenting i −1with the second lag i −2. pp pp Denoting instrumented variables by i −1 and distinguishing stocks of plots registered/titled sporadically RR RR pp before and after the reform as well as systematically,<sup>15</sup> we estimate RR<sup>�</sup> i 1 i −1 2 i −1 3 i −1 i (3) pp,p pp pp,p ppii vv,p ppii ii ii where i is the number of formally registered plots in block YY = αα + θθ RR<sup>�</sup> + θθ RR� + θθ RR _i_ that had either a transfer or a mortgage + λλ + εε registered in _t_ and 1 2 and 3 are coefficients to be estimated. Specifically, 1 is the likelihood of being YY transferred or mortgaged for plots that had been registered sporadically before 2010; 2 denotes the θθ , θθ θθ θθ combined impact of reform-induced reduction of barriers to first-time (sporadic) registration and the θθ reduction in transaction costs of subsequent registration brought about by policy reform on the likelihood of registering a transfer or mortgage on a property; and 3 denotes the combined effects of policy reform and elimination of first-time registration cost on the likelihood of registering a transfer or mortgage on a θθ property. The net impacts of reducing barriers to first time sporadic registration or of systematic registration can then be expressed as ( 2 1) and ( 3 2), respectively. **4. Econometric results** θθ −θθ θθ −θθ Estimates of titling impacts using the DID design show insignificant impacts on market participation or investment. By contrast, analysis of administrative data indicate significant positive effects, overall and for systematic and sporadic titling separately, on the gender of registered right holders as well as policy reform effects on the share of titled plots being subject to a registered land or mortgage. Restricting the sample to the areas chosen as treatment and control for the DID specification affects estimated coefficients’ magnitude but not significance and comparing registry to survey data for the same household points towards measurement error as a key factor that biases estimated program effects towards zero. We use this to assess distributional effects and discuss why, despite large impacts, urban land policy reform or titling more generally is rarely observed in practice.

> 15 Unit prices for registered transactions of US$15/m2 and US$12/m2 in the pre-and post-implementation period and US$17/m2 during implementation are difficult to interpret without additional information on property characteristics.

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