rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8229

# **Abstract**

In 2013, Mexico’s Social Pension Program for the Elderly was expanded by changing its eligibility threshold from age 70 to age 65. Using pooled cross-sectional data from Mexico’s National Household Income and Expenditure Survey, the exogenous variation around eligibility age was exploited to uncover the causal effects of this expansion on extreme poverty and labor supply of the newly eligible population, and to explore potential transmission mechanisms. Applying quasi-experimental methods, results show that the expansion of Mexico’s Social Pension Program for the Elderly not only reduced the probability of the elderly being extreme poor, but it also reduced the extreme poverty gap, and the extreme poverty severity indexes of the elderly population. These effects on extreme poverty are generalizable to all individuals of the treated household. The results suggest that the expansion of the Social Pension Program for the Elderly did not have short-term effects on the labor force participation of the elderly. Accordingly, the analysis does not find that the program reduced labor income. In contrast with other impact evaluations of similar programs, the analysis does not find that the expansion of Mexico’s program had a crowding out effect on domestic or international private transfers to the elderly.

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

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
However, since our dataset consists exclusively of women already engaging in transactional sex, the most that our analysis can show is evidence of transfers providing an insurance function for women already in the market. To provide some evidence on the decision to enter transactional sex, we conducted a short survey in April and May 2010. As it had been several years since we had conducted the diaries (and many women had moved, left sex work, or died), we surveyed a separate sample of 99 women. These women are currently active in the peer groups, and are similar in age and in experience in transactional sex to the women who participated in the diaries. It is important to note that these women were not randomly selected and all of them participate in the peer groups.<sup>37</sup> In the survey, we asked women questions about why they entered sex work originally, why they continue in sex work, and why they see regular clients. We also asked questions about whether they expect help from regular clients, and whom they most rely upon in times of need. To avoid influencing responses, we asked each question in two ways: (1) by asking the questions in an open-ended way and coding all responses that were given, and (2) by reading the woman a list of all possible responses and checking those to which she responded yes. The unprompted questions were always read first.

The results are presented in Table 7. The top of the Table reports responses to questions about motivations for entering sex work. As expected, the income premium to sex work is cited by the vast majority of women: 79 percent of women report this as a reason even when not prompted, and 100 percent do when prompted. Also of interest, however, is that between 78 and 99 percent of women report that they entered because they encountered an adverse shock and needed the income to cope with it. Smaller but significant percentages of women report love or looking for a husband as reasons for entry.

> 37. Apart from age and experience, we did not collect detailed demographic information from these women.

31
is little we can offer in terms of systematic evidence, in Appendix B we document similar, although less stark, evidence on the relative performance of the different prediction algorithms in the well-known Hegre and Sambanis (2006) dataset.

# **5. Policy Implications and Conclusions**

A robust predictive framework for conflict events can provide policymakers with the opportunity to respond more proactively to different risks of conflict, including with policy interventions to address the structural causes or triggers of conflict. This papers aims to strengthen predictions of conflict by evaluating the predictive performance of alternative algorithms that might form the basis for such a framework. We have considered the in-sample and out-of-sample predictive power of four classification rules: two conventional algorithms based on probit regressions and random forests, and two unconventional algorithms designed to directly minimize the prediction loss function – the linear and threshold classifiers. In this particular setting, we find that the threshold classifier dominates the other classification algorithms in terms of in-sample and out-of-sample predictive power, particularly in models with more explanatory variables. Moreover, we have argued that the simplicity of the threshold classifier makes it an attractive tool when conflict predictions are intended to inform policy discussion around aid and conflict-prevention strategies.

While these differences across prediction algorithms are non-trivial, it is important not to lose sight of the fact that the out-of-sample predictive power of these methods is modest. While the bestperforming threshold classifier can correctly predict over 90 percent of conflict events, it also incorrectly classifies around 30 percent of non-conflict events as conflict (in Model 3 in Table 4). Whether this rather high rate of “false alarms” is acceptable or not depends in large part on the policy purposes for which these classification algorithms are intended. For example, if resources for conflict prevention programs are scarce, allocating them using a classification rule with a high rate of “false alarms” may result in a highly inefficient targeting of these resources to places where they are most needed. This suggests that before using any of these classification algorithms for policy purposes, careful thought should be given to the weight on Type 1 and Type 2 errors in the prediction loss function.

As noted above, a policy goal of an exercise such as this is to inform frameworks for anticipating the outbreak of conflict. To illustrate how the classification algorithms discussed here might do so in practice, we generate a list of countries that would be considered to be at risk of conflict, based on the most recently-available information in our dataset on the various predictors of conflict, and the four 27
goods and materials - this is a sample we exploit in more detail in the coming Sections. We also show that for an overwhelming proportion of commitments values are non-zero and can be matched to some verification and payment – allowing us to track the entire cycle of budget execution. We also highlight that our dataset currently encompasses over 3.5 trillion BRL in payments (in 2021 prices) or the equivalent of 38% of GDP in 2021, made to over 9 million different unique agents (identified by national tax IDs).

We provide an illustrative example of the nature of our data in Figure 2, highlighting a case in which we can track the entire process of public procurement and budget execution.<sup>18</sup> In this figure we zoom in on one case involving the town hall of Abatiá – a municipality with less than 10,000 inhabitants but relatively high income in the state or Paraná – which initiated a tender to procure uniforms for basic health employees, identified by ID 1336360. The tender was officially published in November 2018, with an initial budget of 17,116 BRL. The auction took place on December 13 of the same year, during which bids were received for 23 items, comprising different types of t-shirts and bags. The tender resulted in two winners, one for supplying t-shirts and another for supplying bags. Subsequently, the tender was officially homologated on December 18.

One day after the tender’s homologation, four separate commitments were made – meaning that the local government set apart funds to pay for the purchases, once goods were delivered. Verification of delivery happened in mid-May 2019, five months after the end of the tender. Less than 30 days later, three separate payments were made to different agents that won the contracts, with total values of 11,377 BRL and 3,890 BRL. For the purchase of this relatively simple good, a total of 235 days elapsed between the tender publication and the last payment.

The example above shows the potential of this newly constructed dataset in providing researchers, policy makers, and the civil society with a granular view of how local governments in Brazil acquire goods and services and execute their budgets. It also illustrates the potential for similar datasets to be developed in other countries where scattered data might exist but require upfront investment to be collected, cleaned, and harmonized.

# **3 Validation**

The flagship dataset for information on municipal public finances in Brazil is the _Sistema de Informações Contábeis e Fiscais do Setor Público Brasileiro_ (SICONFI).<sup>19</sup> SICONFI contains selfreported information on municipalities’ revenues, expenditures, and balance sheets starting in 1989, with details on amounts per category and budget execution phase (i.e. commitment, ver-

> 18We note that this connection between public procurement and budget execution is currently only possible for the state of Parana (PR), which provides a table connecting tender IDs to commitment IDs. Approximately 78% of the procurement IDs and 38% of total commitment IDs can be found within this correspondence table.

> 19The dataset was formerly called _Finanças Brasileiras_ (Finbra).

9
**Table A2 Non-Tax Parameters** (in percent)

||**Canada**|**Mexico**|**U. S.**|
|---|---|---|---|
|**Expected inflation rate**|1.7|21.7|2.3|
|**Expected real interest**ratea|6.1|6.1|6.1|
|**Debt to assets ratio**||||
|Debt raised abroad to home capital|40.0|40.0|40.0|
|Debt to assets ratio in home country|40.0|40.0|40.0|
|**Economic depreciation rate**||||
|Manufacturing||||
|Buildings|3.8|3.8|3.8|
|Machinery|16.4|16.4|16.4|
|Services||||
|Buildings|3.5|3.5|3.5|
|Machinery|24.4|24.4|24.4|
|**Capital structure by asset type**||||
|Manufacturing||||
|Buildings|24.0|24.0|24.0|
|Machinery|38.1|38.1|38.1|
|Inventory|35.9|35.9|_35.9_|
|Land|2.0|2.0|2.0|
|Services||||
|Buildings|60.6|60.6|60.6|
|Machinery|11.7|11.7|11.7|
|Inventory|9.5|9.5|_9.5_|
|Land|18.2|18.2|18.2|

a The expected real interest rate of 6.1 percent is derived from the U.S. inflation rate and bank lending rate based on the IMF, _International Financial Statistics,_ March 2000.

24
# **4. The Stark Dichotomy between Rich and Poor Countries**

What explains the contrast between the mortality burdens of rich and poor countries? Whereas the previous discussion focused on the inequality between individual countries, this section will highlight how these inequalities manifest themselves at a more aggregated level of the country groupings of the World Bank income classification. This section argues that the inequality we currently observe between rich and poor countries is excessive. We arrive at this conclusion through a demographic lens and discuss whether other factors are likely to sway the conclusion in a different direction.

## **A World with Two Pandemics?**

It is useful to first summarize the results of the previous section through the lens of the income classification of the World Bank (Figure 7). The figure clearly illustrates the unequal distribution of the mortality burden across income categories. The current epicenters identified in the figure are mainly HICs, even though several UMICs are emerging with Ecuador, Peru and Brazil taking the lead. Across income categories, the pattern is clear: the severity of the pandemic’s impact appears to drop for countries at lower per capita income levels.<sup>6</sup> Figure 7. The Distribution of Relative Severity across Countries by Income Classification <!-- Start of picture text --> Belgium<br>40<br>30 Spain<br>United Kingdom<br>Ireland<br>Sweden<br>Italy<br>20 France<br>Ecuador<br>Netherlands<br>United States<br>Luxembourg<br>Switzerland Peru<br>10 Canada Brazil<br>Portugal Iran Moldova<br>Turkey Honduras<br>Dominican Rep Bolivia Liberia<br>0<br>High Upper−middle Lower−middle Low<br>income income income income<br>countries countries countries countries<br>Cumulative severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization.

> 6 The variation in Figure 7 is not explained by developing countries facing high all-cause mortality to begin with. Crude mortality rates are in fact lower in developing countries than in HICs. As of 2016, 874 per 100,000 people died in HICs, compared to 730, 734 and 812 for UMICs, LMICs and LICs (WHO, 2018b). This contrasts with the monotonous decline one would see in the corresponding age-adjusted mortality rates  using a standard population (Ahmad and others, 2001).

14
<!-- Start of picture text --> Per Capita Consumption Food Share in Total Expenditure<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile<br>95% Confidence Interval OLS<br>95% Confidence Interval OLS<br>RIF-OLS<br>RIF-OLS<br>.4 .2<br>.2<br>0<br>0<br>-.2<br>-.2<br>-.4 -.4<br><!-- End of picture text --> 101
Annex III - Figure 1: Distribution of poor and non poor across economic regions <!-- Start of picture text --> 40<br>30 7<br>25 _<br>15<br>0- Npoor-98<br>Nploor-94<br>| Ouest Sud Centm Centm-I' ~.Poor-98<br>| Sud N d Nord O | . Poor-94<br>Bobo ilic Sud-Est<br>V ilu s|<br><!-- End of picture text -->

Annex III - Figure 2: Distribution of poor and non poor across socioeconomic groups <!-- Start of picture text --> 80 . ...<br>70<br>60<br>301<br>20<br>o .2 1 _ _, _ / Poor-98<br>n E 9 9 E ' P<br>24 LL<br><!-- End of picture text --> 24
As before, we recover the country fixed effects from the first-stage regression and regress them on the same national-level variables (keeping only one institutional variable at a time). Table 5 presents the findings. Model (1) is the full specification where the institutional variable is the Quality of Land Administration. Models (2) and (3) respectively substitute Rural land tenure insecurity and Enabling the business of agriculture to the Quality of Land Administration. Model (3) is our preferred specification because it has the greatest fit, although it also has the lowest number of observations due to data limitations for Enabling the business of environment. The signs of the coefficients are consistent across all specifications, with cropland per capita consistently associated with greater movements into agriculture—possibly capturing countries that have greater specialization in agriculture—and with GDP per capita and the Gini index associated with lower movements into agriculture. This mirrors our results for land degradation, where national wealth and inequality were associated with faster cropland loss. As regards institutional variables, only Enabling the business of agriculture is loosely significant (at the 15 percent level) in Model (3): A one increase in the standard deviation of this indicator increases the pace of movement into agriculture by 2.1 percent.

**Table 5 – National-level drivers of cropland gains**

|VARIABLES|(1)<br>Model 1|(2)<br>Model 2|(3)<br>Model 3|
|---|---|---|---|
|Cropland per capita (2003)|0.00527†|0.00257|0.00500†|
||(0.00362)|(0.00267)|(0.00328)|
|Quality of land administration|0.000160<br>(0.00163)|||
|Rural land tenure insecurity||0.00629<br>(0.00696)||
|Enabling the business of agriculture|||0.000927†<br>(0.000566)|
|GDP per capita|-5.60e-07|-1.67e-07|-2.19e-06**|
||(1.02e-06)|(7.55e-07)|(1.00e-06)|
|Gini index|-0.00187†|-0.00245***|-0.00305**|
||(0.00118)|(0.000927)|(0.00119)|
|Constant|0.103*|0.136***|0.139***|
||(0.0537)|(0.0408)|(0.0465)|
|Observations|100|82|63|
|R-squared|0.051|0.112|0.149|

_Note: The regressions presented in this table are applied to the sample of pixels that were not cropland in 2003 but located within 3 kilometers of existing 2003 cropland pixels. Cropland per capita is from MODIS 2018; the Quality of Land Administration is from Doing Business 2020; GDP per capita is in current US$, the Gini Index uses the latest value reported in the World Development Indicators. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1,_ † _p<0.15_ .

15
# **5. Basic Robustness Checks**

In this section of the paper we present a number of straighforward variations on our benchmark specification in order to verify the robustness of these findings. A first potential concern with the benchmark results in the previous section is that they could be driven by a small number of influential observations. This concern is particularly important given the large swings in aid and the real exchange rate documented in Table 1. To verify that our results are not sensitive to a few country-year observations, we use an approach suggested in Hadi (1992) to identify influential observations in the reduced-form and the first-stage regressions (the ratio of the corresponding two slope coefficients at each horizon being the 2SLS estimates of the impulse response). This first basic robustness check is reported in the first panel of Table 3. This procedure identifes 75 (38) (41) influential observations in the full (high aid) (IDA) samples, or only about 3 percent of the total number of observations. Moreover, the estimated impulse responses change only minimally from the benchmark specification when these observations are excluded. The main notable difference is that the strength of the first-stage relationship increases, particularly in the full sample where the first-stage F-statistic increases from 71 to 116.

As a second robustness check, we vary the number of lags of the real effective exchange rate included in the local projections regressions. In the benchmark specification, we included two lags of the real exchange rate, as the first and second lags are consistently significant across all the specifications we considered. In the second panel of Table 3, we report the estimated impulse obtained when a third lag of the real exchange rate is included. Once again, the estimated impulse responses are practically indistinguishable from the benchmark estimates. We note also that the coefficient on the third lag of the real exchange rate is not significantly different from zero, providing some justification for the inclusion of only two lags in our benchmark specification.

We next consider a more conservative version of our identification strategy. Recall that the key identifying assumption underlying the predicted disbursements instrument is that loan approvals in a given year do not anticipate macroeconomic shocks in subsequent years. Conditional on this assumption, predicted disbursements on loans approved in previous years are uncorrelated with contemporaneous macroeconomic shocks. In the third panel of Table 3, we make the weaker assumption that loan approvals may react to macroeconomic shocks in same year and the following year, but not two or more years into the future. We then construct an alternative version of the instrument which excludes predicted disbursements on loans approved in the same and previous year.

12
with SMC members from the male and female head of the household. A student questionnaire was administered to sampled children regardless of school-going status. Standardized tests were administered at this time to primary school-age children (ages 7-13), for Math, English and Sindhi.<sup>29</sup>

# **3.2 Outcome variables**

As described in section 2.2, the pilot interventions could potentially impact a number of outcomes: school functioning; student enrollment; availability of teachers; teacher attendance; school infrastructure; and learning outcomes. In order to ensure accuracy and adherence to the study timeline, we measure all variables through directly observed survey data.

For school functioning, we measure whether schools were open at the unannounced visit at baseline and endline.<sup>30</sup> Since a number of schools switched status between the visits, it is important to capture the direction of change from open to closed and vice versa, in relation to the treatments. This generates three specific outcome indicators: number of schools open; number of schools open at baseline which were closed at endline; and number of schools closed at baseline which had reopened at endline.

> 29 Standardized tests for English, Math and Sindhi were administered to children ages 7-13 at the household level both for the baseline and endline. The instruments were designed, pre-tested and piloted based on students’ textbooks and curriculum standards for Grades 1-5 of primary school. We fitted a two-parameter item response theory (2pl-IRT) model for English and Math scores to account for what students of similar knowledge and skills ought to know, by distinguishing between difficulty level (see Appendix B for details). Sindhi tests include open-ended question items and hence an IRT score cannot be fitted.

> 30 If a school was found closed, verification was sought from community members. If a school was closed for one or two days only, a revisit was made when the school was reopened and the school was not recorded as having been closed. If a school was confirmed by community members to have been closed for some time, and/or was found closed on both of two revisits, it was recorded as closed.

29
country.<sup>6</sup> Furthermore, Türkeş (1996) has observed that for the period 1930-1993, the areaaveraged annual rainfall series has been on the decline slightly throughout Turkey, and particularly over the Black Sea and the Mediterranean regions.

<!-- Start of picture text --> 1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>Years<br>Figure 4.2: Annual total precipitation (mm), Adana<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Years<br>197519761977197819791980198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007<br>197519761977197819791980198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007<br>Annual total precipitation (mm)<br>Annual total precipitation (mm)<br><!-- End of picture text -->

**Figure 4.3: Annual total precipitation (mm), Diyarbakır**

- 6 According to data from Turkish State Meteorological Service Website, http: meteor.gov.tr

52