rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.15:** Public Goods Provision, Heterogeneity by Nepotistic Networks

|||**Public Good**|**s Index**||
|---|---|---|---|---|
|_Villages where..._|Village head has<br>no relative ingovt|Village head has<br>at least 1 relative ingovt|No bureaucrat<br>w/parent ingovt|At least 1 bureaucrat<br>w/parent ingovt|
||(1)|(2)|(3)|(4)|
|New village head|0.773<sup>_∗∗_</sup>|-0.069|0.603<sup>_∗∗_</sup>|0.484|
||(0.359)|(0.388)|(0.282)|(0.526)|
|Observations|191|133|295|81|
|Control mean|0.17|0.36|0.17|0.40|
|Robust p-value|0.020|0.683|0.022|0.309|
|MSE-opt. bandwidth|20.6|16.1|19.2|23.8|
|Effective obs.|86|47|129|38|
|Not nepotistic vs Nepotistic_p_||0.022||0.287|

_Notes_ : This table reports RD estimates of _γ_ in equation (1) obtained via the non-parametric method from Calonico et al. (2014). The dependent variable is a standardized index of local public service provision constructed using the 2021 Podes data, as in column 1 of Table 7. The sample includes: in column 1, all villages in which the village head reports having no relative in the village government; in column 2, all villages in which the village head reports having at least one relative in the village government; in column 3, all villages in which no bureaucrat who was appointed before the most recent election reports a parent previously served as a village official; and in column 4, all villages in which at least one bureaucrat who was appointed before the most recent election reports a parent previously served as a village official. See Section 5 for details.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.

**Table A.16:** Effects on Old-Serving Nepotistic Appointees

||Proportion|Binary(=1 if any)|
|---|---|---|
||(1)|(2)|
|New village head|-0.036<br>(0.068)|-0.017<br>(0.130)|
|Observations|510|510|
|Control mean|0.15|0.25|
|Robust p-value|0.825|0.933|
|MSE-opt. bandwidth|22.9|21.3|
|Effective obs.|263|252|

_Notes_ : This table reports RD estimates of _γ_ in equation (1). In column 1, the dependent variable is the share of bureaucrats who were appointed before the most recent election and report that a family member previously served as a village official. In column 2, the dependent variable is a dummy equal to one if at least one such bureaucrat is present in a village. See Section 5 for details.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.

54
electricity. Lastly, Sub-Saharan Africa encounters significant difficulties with nutrition, cooking fuel, sanitation, drinking water, electricity, and housing.

Table 6: MPI and GDP by region

|VARIABLES|(1)<br>_MPI_|(2)<br>_MPI_|(3)<br>_MPI_|(4)<br>_MPI_|(5)<br>_MPI_|(6)<br>_MPI_|
|---|---|---|---|---|---|---|
|Region|_Arab_<br>_states_|_East Asia_<br>_and the_<br>_Pacific_|_Europe_<br>_and_<br>_Central_<br>_Asia_|_Latin_<br>_America_<br>_and the_<br>_Caribbean_|_South_<br>_Asia_|_Sub-_<br>_Saharan_<br>_Africa_|
|Change in GDP|||||||
|(current + lagged)|-1.221***|-0.843***|-0.989***|-1.290***|-1.138***|-1.245***|
||(0.257)|(0.212)|(0.254)|(0.237)|(0.238)|(0.319)|
|Region dummy|-0.018|-0.005|0.062|-0.037**|0.047**|0.012|
||(0.011)|(0.028)|(0.155)|(0.018)|(0.022)|(0.017)|
|Region dummy x<br>change in GDP|-0.778|-0.674|-1.987|1.044*|-1.043*|1.011***|
||(0.473)|(0.564)|(3.474)|(0.596)|(0.542)|(0.374)|
|Constant|-0.033***|-0.042***|-0.040***|-0.032***|-0.038***|-0.049***|
||(0.010)|(0.008)|(0.006)|(0.009)|(0.008)|(0.016)|
|Observations|127|127|127|127|127|127|
|R-squared|0.153|0.164|0.157|0.152|0.136|0.230|

**Note:** Robust standard errors are presented in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

To summarize the first part of the analyses, our results confirmed that economic growth consistently reduces the MPI, consistent with the findings from Balasubramanian et al. (2023) and Santos et al. (2019). The effect varies significantly across different income groups, regions, and levels of resource dependency. Specifically, the impact is less pronounced in low-income countries, Sub-Saharan Africa, Latin America, and the Caribbean, and in resource-dependent countries. Additionally, while economic growth generally leads to a reduction in MPI components, these effects often appear with a temporal lag.

# _Advanced growth measures and the MPI_

In the second part of our analysis, we focus on how economic growth influences the MPI. In this section, we measure economic growth in various ways: 1) as labor productivity, 2) as factors of production growth, 3) as expenditure approach components, and 4) from a sustainability perspective (Table 7).

17
40

# **Table 11. Marginal Budget Shares for Non-Remittance and Remittance-Receiving Household, Indonesia, 2007**

||**No remittances**<br>**in 2000, no**<br>**remittances in**<br>**2007**|**Remittances**<br>**in 2000, no**<br>**remittances**<br>**in 2007**|**No remitt**<br>**remitta**|**ances in 2000,**<br>**nces in 2007**|**Percent**<br>**Difference**|**Remitta**<br>**remitta**|**nces in 2000,**<br>**nces in 2007**|**Percent**<br>**Difference**|**ATT**<br>**Overall**<br>**% difference**|
|---|---|---|---|---|---|---|---|---|---|
||Predicted<br>(1)|Predicted<br>(2)|Predicted<br>(3)|Counterfactual<br>(4)|(3) vs (4)|Predicted<br>(5)|Counterfactual<br>(6)|(5) vs (6)|(7)|
|Food|0.432|0.460|0.479|0.440|8.9***<br>(7.0)|0.470|0.438|7.4**<br>(2.29)|8.46*<br>(1.72)|
|Education|0.084|0.041|0.075|0.085|‐11.0<br>(‐1.1)|0.051|0.028|78.9<br>(0.64)|9.76<br>(‐0.30)|
|Housing|0.121|0.178|0.088|0.118|‐25.8***<br>(‐2.75)|0.028|0.183|‐84.7***<br>(‐5.4)|‐39.11***<br>(‐3.3)|
|Health|0.034|0.064|0.033|0.035|‐5.6<br>(0.50)|0.094|0.064|46.1*<br>(1.75)|6.37<br>(1.45)|
|Others|0.328|0.257|0.325|0.322|0.8***<br>(4.45)|0.357|0.286|24.7*<br>(1.97)|6.31<br>(1.68)|
||1.000|1.000|1.000|1.000||1.000|1.000|1.000|1.000|

Notes: Column (7) shows the Average Treatment Effects (ATT) of remittances on indicator _i_ . It is calculated as the weighted average of two ATT that are calculated subtracting column (4) from (3) and column (6) from (5). T-statistics shown in parenthesis. T-tests conducted using clustered standard errors and weighting observations.

***  Significant at the 0.01 level.  ** Significant at the 0.05 level. * Significant at the 0.10 level.
- work in nonagricultural activities: includes work in nonagricultural activities outside the household and work in household enterprise.

Finally, in addition to participation variables, we consider whether a person is unemployed. A person is unemployed in the VLSS data if the person has not worked in the last 7 days (either for wages or within the household) but reports looking for paid work in the last 7 days.

The VLSS not only provides information on participation in various categories of work, but also hours of work spent in these activities. As a result, we create variables that capture hours worked in all of the above work categories. Unfortunately, the VLSS questionnaire changed how it collects information on hours worked in household agriculture between survey rounds (participation questions and hours in all other types of work are identical between rounds of the VLSS). The 1993 questionnaire asks how many hours on average the respondent works in self-employed agriculture in the last 7 days and (separately) as an agricultural worker on another household's farm. The 1998 questionnaire asks the identical question for work in agriculture outside of the respondent's own household. However, no such question is asked for selfemployed agricultural work. Instead, the 1998 questionnaire disaggregates the within the household agricultural work into 4 different tasks (planting and harvesting, livestock maintenance, processing, marketing) and 3 different categories of agricultural production (crop and fruit production, aquatic cultivation, and forestry), asking hours worked in peak and nonpeak seasons over the last twelve months in each of these categories.

This change in the questionnaire might lead to substantive differences in reporting of hours worked within the household agriculture between the two survey years. First, there is no reason to expect labor supply in the last 7 days to be representative of usual hours worked in any given season. Second, the context of a question affects both respondent’s interpretation of the 14
effects on social capital will be greater in more educated villages. Further, while care has been taken in selecting the control group, it is possible that, within the treatment municipalities, some villages are too poor to find a comparable village in the set of control municipalities.

We attempt to deal with those potential sources of bias by using propensity score matching. The key assumption with this method is that the treatment and the control groups are similar along unobserved characteristics. Given the transparent and rule-based criteria used to select municipalities eligible for the project, this hypothesis appears warranted. However, we do not attempt to provide propensity score matching estimates of the impacts of _funding_ as the likelihood that a village proposal is selected for funding is likely to be driven by unobservables.

Practically, we start by generating a propensity function that assesses a village’s likelihood of participation in the project ( _i.e._ , of going through one cycle of _preparation_ ). We then match treatment villages with villages with similar propensity of participation in the project but in which the project was not implemented. Results from the probit regression are available in Table 7 while the pre- and post-match density if participation propensity are available in Figures 1 and 2, respectively. For each measure of social capital, our outcome of interest is the village-level changes in the variable considered between 2003 and 2006. We use both nearest neighbor and the kernel (Epanechnikov) matching procedures. To deal with concerns over common support, we drop the treatment observations whose propensity is higher than the maximum propensity of the controls and drop the 5% of control observations with the lowest propensity. Results, discussed in the next Section, are displayed in Table 5 and 6.

Given the likely importance of village-level unobserved characteristics in determining success in receiving funds from the project, we do not attempt to provide propensity score matching estimates of the impact of having a proposal funded.

Finally, as discussed in Section 2, it is possible that the project social capital impacts will 18
<!-- Start of picture text --> Figure 6: Comparison of Select DS-ACS and DS-SRS Parameters over 10,000 Simulations<br>2000<br>SD of Population Estimates' Distributions by Method<br>1500<br>DS-ACS, C=10 1000<br>DS-ACS, C=5<br>DS-ACS, C=2 8:1 DS-SRS 500<br>DS-ACS, C=1<br>0<br>4% 5% 9% 11% 16% 21% 27% 33% 51%<br>Total Squares Enumerated (Fieldwork Effort)<br><!-- End of picture text --> _Note: Authors’ calculations based on 10,000 simulation runs. Fieldwork effort (x-axis) represents the mean share of block areas enumerated over all runs; the standard deviation (SD, y-axis) is the standard deviation of all 10,000 of the population estimates. DS: disproportionate, stratified indicates a comparatively high initial starting share in high-density nightlight areas (Figure 4). For Simple Random Sampling (SRS) this ratio is 8:1. For ACS, this ratio is 5:3 for initial starting squares, as the expansions in the high-density areas are expected to make the ratio more disproportionate._ Figure 6 replicates Figure 4 but with all simulated samples using the two-category stratification. As without stratification, the ACS methods (now denoted with the prefix DS for disproportionate, stratified) require far less fieldwork effort for a given standard deviation (that is, holding the y-axis value constant). Very importantly, for a given level of sufficiently high fieldwork effort, ACS methods all provide less variable population estimates. This is partially due to the fact that, with well-defined strata, the underestimations that resulted before are minimized; compare Appendix A.5 to A.6. Note that since expansions will necessarily require that more BAs are enumerated, fieldwork effort (especially at lower expansion thresholds) will trend upward. Lastly, readers should note that even ACS without stratification generally reduces the standard deviation of population estimates and reduces fieldwork burden even over SRS with stratification. We provide an illustration of this in Appendix A.7; Appendix A.8 also replicates these exercises 20
# <mark>WPS3512</mark>

## **Policy Mix, Public Debt Management and Fiscal Rules: Lessons from the 2002 Brazilian Crisis**<sup>**1**</sup>

### Santiago Herrera

World Bank

#### _Abstract_

_Despite significant progress in economic reform throughout the 1990s and an exemplary development of the policymaking framework in the second part of the decade, Brazil suffered a major public debt and currency crisis in 2002. Though the political origin of the uncertainty cannot be ignored, the paper identifies other sources of uncertainty emanating from the policymaking framework: fiscal policy was not responsive to the shocks, public debt instruments were used with several objectives to stabilize the currency and lengthen maturity, and there was inadequate supervision of agents holding public debt. Most of the flaws have been fixed following the crisis: the primary fiscal balance has been increased, sending the signal that it is a flexible instrument that will be used to ensure commitment of the sovereign to honor its obligations; the central bank formally transferred to the Treasury the remaining debt-issuance functions, facilitating a more adequate balancing of different risks involved in debt management; and mutual funds’ public debt holdings are better regulated, ensuring that end-investors have the proper information to assess the risk of the institutions in which they invest._

#### World Bank Policy Research Working Paper 3512, February 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 This is a revised version of the report “Brazil: Lessons for a More Robust Policymaking Framework” (Report 2834, March 2004)  Eliana Cardoso, Edgardo Favaro, and Luis Serven were peer reviewers and provided valuable comments. Fernando Blanco, Mauricio Carrizosa, Augusto de la Torre, Fred Jensen, Guillermo Perry, Mark Thomas, Antonio Velandia,  and  Joachim von Amsberg made useful comments to earlier drafts.  The author acknowledges and thanks the staff from the central bank of Brazil and the Ministry of Finance, in particular Anderson Silva and Otavio Medeiros, for their patience and collaboration in explaining the intricacies of the Brazilian public debt.   Finally, the author thanks Olivier Blanchard, Carlo Favero, Francesco Giavazzi, Alessandro Missale, and Charles Wyplosz for their comments and contributions to the original Bank  project.

1
Figure $4.1: Uganda cereal production per capita, 1961-2014 <!-- Start of picture text --> oa 125 "><br>i<br>apf 4<br>eeS V0"-2 tttVane ot<br>= 50<br>25<br>0<br>—OMO - O—- OM ~~ O- OMY Oo - MDH Y- O- OM DO—-<br>owe oC Onrnrrne-r-woanowmwWoooaooanmnqac$croocooo--<br>AADAanananraanranaanraaosnanaonaonanaocccdecdcrecccosao<br>ssss<br><!-- End of picture text --> Figure $4.2: Uganda Food Production per capita, 1961-2014 <!-- Start of picture text --> ==i 80 er.y” >» ahelns n. — et”a.<br>% 60<br>o<br>Ke)<br>& 40<br>20<br>0<br>—-AONM-DO—- YDMNOM-DO—-— DMO DO DNAS DO YVMNOH8 HY D—<br>CHKHOKODOOMDADAeee DADADAADAADAAAADADDAADEOCcCScCSoCCSoCCSoCHS NM MY ME WOWDDADAHDDAODADGDGCCSCSOoC-—te et INN NOUN U -<br><!-- End of picture text -->
12% for the squared term). The threshold value of the volumetric price for the maximum probability is 0.072 or 0.092.

After we limit our sample to the WUGs with remaining members (33 observations), we have a clearer picture of their decision-making mechanism. The simplicity of water flow direction is highly significant, indicating its correlation with the effectiveness of a manager’s initiative for successful management. This result seems to be consistent with the findings of Ray and Williams (2002), who found that the spatial asymmetry of water distribution makes solutions difficult. Khwaja (2009) found that good project design (e.g., simple design) improves management success, even when social capital endowment is low. Our result, which highlights the importance of simplicity, also supports his argument.

A puzzling result is a positive coefficient for the number of ponds for the manager appointment choice. This variable presumably captures some aspects of topographical simplicity that the flow direction variable does not capture.

# _VII. The Determinants of Water Use_

We run five regression models that explain the reservoir water use measured in the same manner as in table 3. Note that our full sample includes those who completely exited for individual irrigation; their reservoir water use is zero. The first model uses a full sample and employs the same reduced-form structure as the previous individual-level analysis. In the second model, we relax the assumption of a quadratic function and check more carefully whether water use increases after the threshold value. For this, we estimate a spline regression model, setting the threshold value indicated by the first model as a knot. This model approximates a nonlinear impact of price in a more flexible manner by allowing different slopes for two connected lines at the knot. The third model uses a sub-sample (n = 84) of reservoir water users, in order to examine the possibility of overuse at high price levels by the remaining farmers. The fourth model is a spline regression version of the third model. We run the fifth model by replacing the price variables with institutional dummies because the reduced form cannot directly indicate the impact of institution on reservoir water use. In this model, we set the no change case under areabased pricing as the base category, so that the estimated coefficients show the magnitude of water savings. We run this model for the subsample of reservoir water users because reservoir water use by individual irrigation users is zero by definition. Needless to say, the institutional 18
Boys’ enrollment in public schools <!-- Start of picture text --> Unadjusted difference between treatment and comparison districts<br>co<br>on pe na<br>o ae ea<br>2) Siem, we Teen,<br>i) Teme elt Ween,<br>ore<br>=) ee ae A<br>2 a oe ae ~—e OE eer,<br>_ N eal<br>c sonsnnnnene 999 a TTS ae<br>:o aee ee ee<br>= o en TN<br>SOo _eeee"_peceeeneten<br>c<br>Lu<br>ce)<br>2003 2004 2005 2006 2007 2008<br>Year<br>--------- Lower bound see Comparison District<br>oe Treaiment District <-------- Upper Bound<br><!-- End of picture text --> Source: Author's calculations using EMIS School Cansus Data
with its closest equivalent in the ISIC classification. We then aggregate the HS data to build measures of tariffs at the 3-digit level. To perform the aggregate, we start from the next-to-lowest subheading, calculate the median of the item belonging to it, and iterate on this procedure.<sup>3</sup> We end up with a panel data set of import tariffs for the manufacturing sector across time.

Figure 1 provides some insights into the nature of trade policy and trade reform in different years. It depicts key percentiles of the distribution of import tariffs.

Figure 1 Distribution of Median Tariffs per 3 digit sector <!-- Start of picture text --> 200<br>150<br>100<br>50<br>0<br>1974 1978 1980 1982 1989 1990 1994 2001<br>excludes outside values<br>Median Tariff, 3digit ISIC<br><!-- End of picture text --> Note: Distribution of median tariff within each 3-digit ISIC manufacturing sector for selected years. The ends of the boxes are the 1st and 3rd quartiles, while the line within the boxes corresponds to the median. The ends of the bars show the points which are furthest away from the ends of the box, but at a distance not larger than 3/2 the interquartile range. We do not show outside values (points which are even further away). The quantiles are calculated weighting each sector by its employment level.

The recent historical Argentine trade policy is characterized by at least three different periods. Our starting point in 1974 was one of high protection, with average tariffs in excess of 100 percent, and sectors with median tariffs in excess of 200 percent. Starting in 1976, tariffs were abruptly reduced. The average tariff was cut by two thirds in three years, dropping from slightly above 100 percent in 1976, to 47 percent in 1978 and to 32 percent 3See the Data Appendix for further details on sources of information, the matching of Harmonized System and ISIC classification, and the aggregation procedure.

5
Policy ReseaRch WoRking PaPeR 4570

# **Abstract**

This paper develops a seven-region comparative static computable general equilibrium model of Russia to assess the impact of accession to the World Trade Organization on these seven regions (the federal okrugs) of Russia. In order to assess poverty and distributional impacts, the model includes ten households in each of the seven federal okrugs, where household data are taken from the Household Budget Survey of Rosstat. The model allows for foreign direct investment in business services and endogenous productivity effects from additional varieties of business services and goods, which the analysis shows are crucial to the results. National welfare gains are about 4.5 percent of gross domestic product in the model, but in a constant returns to scale model they are only 0.1 percent. All deciles of the population in all seven federal okrugs can be expected to significantly gain from Russian World Trade Organization accession, but due to the capacity of their regions to attract foreign direct investment, households in the Northwest region gain the most, followed by households in the Far East and Volga regions. Households in Siberia and the Urals gain the least. Distribution impacts within regions are rather flat for the first nine deciles; but the richest decile of the population in the three regions that attract a lot of foreign investment gains significantly more than the other nine representative households in those regions.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to assess the impact of trade and foreign direct investment liberalization in business services on income growth and poverty reduction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dtarr@worldbank.org.

_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