rafmacalaba/datause-encoder-data huggingface dataset:
currently widowed). For dispossessed Muslim (Christian) women, the bulk of the property is inherited by the husband’s family in 40 (56) and by the children in 36 (18) percent of cases. The literature supports the view that a widow’s children inheriting is typically a favorable outcome for widows. This outcome is clearly more common for Muslim widows. Within each religious group, remarried widows are more likely to have been dispossessed than current widows, suggesting that inheritance outcomes matter to a women’s agency and choices. These shares are of a similar magnitude in urban areas.

A few factors will cloud inter-group comparisons. Monogamous couples, more common among Nigerian Christians, and urban couples are more likely to own a house that can be inherited by a surviving spouse. Clearly wives of polygamous men need to share inheritance with co-wives and possibly brothers-in-law and their families. The descriptive statistics shown in Table 9 probably reflect differences in characteristics such as location, ethnicity, and education levels between the two religion groups. We estimated the probability of being dispossessed using a linear probability model in which the covariates are the ones included in the model presented in Table 3 with the exception of log wealth (as it encompasses data on asset ownership). We use the sample of ever-widowed women and estimate a pooled model with a dummy for being Muslim. The results confirm that there are large differences even after controlling for covariates: relative to their Christian counterparts, Muslim ever-widows are 19 (11) percentage points less likely to be dispossessed in urban and rural areas respectively (significant at the 1 and 10 percent levels).

# **6. Conclusions**

The paper has examined the pronounced and significant gap in nutritional status favoring Christian over Muslim women in Nigeria. Using pooled DHS data for 2008 and 2013, we find that taking account of women’s individual and household level characteristics eliminates the gap on average. Indeed, a decomposition analysis shows that the bulk of the disparity—90 percent in rural and 87 percent in urban areas—is attributable to differences in women’s underlying attributes, rather than to different returns to those attributes associated with religion. Women’s education, household wealth and demographics are among the most important contributory factors, along with ethnicity in rural, and state of residence, in urban Nigeria. Overall, Muslim women simply live in households and locations with worse endowments. Religion per se plays 28
**Figure A7: Shock decomposition during key episodes of sharp U.S. interest rates movements** A. Bond market turmoil of 1994

B. Build-up to global financial crisis <!-- Start of picture text --> Percentage points Yield, change<br>Reaction shocks Real shocks<br>9 Inflation shocks 2-year bond (RHS)2<br>7 10-year bond (RHS) 1.5<br>5<br>1<br>3<br>0.5<br>1<br>0<br>-1<br>-3 -0.5<br>-5 -1<br>D. Hiking cycle in second half of 2010s<br>Percentage points Yield, change<br>Reaction shocks Real shocks<br>Inflation shocks 2-year bond (RHS)<br>4 10-year bond (RHS) 1<br>3<br>2 0.5<br>1<br>0 0<br>-1<br>-2 -0.5<br>Jan-93 Mar-93 May-93 Jul-93 Sep-93 Nov-93 Jan-94 Mar-94 May-94 Jul-94 Sep-94 Nov-94 Jan-95<br>Jun-15 Oct-15 Feb-16 Jun-16 Oct-16 Feb-17 Jun-17 Oct-17 Feb-18 Jun-18 Oct-18<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points Yield, change<br>Reaction shocks Real shocks<br>Inflation shocks 2-year bond (RHS)<br>10-year bond (RHS)<br>2<br>3<br>1<br>1<br>-1 0<br>-3 -1<br>Jun-03 Oct-03 Feb-04 Jun-04 Oct-04 Feb-05 Jun-05 Oct-05 Feb-06 Jun-06 Oct-06<br><!-- End of picture text --> _Source:_ World Bank _Notes:_ Based on a sign-restricted Bayesian VAR model with stochastic volatility. “Shocks” reflect 3-month moving sum of identified monthly shocks. Yields are 3-month-on-3-month changes.

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32

|Table 8:<br>Household pr<br>(dF/dX calcul|obability of ado<br>ated from Probi|pting impro<br>t estimates)|ved agricultu<br>.|ral practices<br>l|
|---|---|---|---|---|
||Used Agro-<br>chemicals|Used<br>fertilizer|Used<br>Improved<br>Seeds|Used credit<br>for<br>agricultural<br>improvements|
|Village social capital|.057<br>(2.35)|.075<br>(2.45)|_.015_<br>(.737)|.027<br>(1.66)|
|Household size|.012<br>(3.25)|-.006<br>(1.43)|.004<br>(1.03)|-.0019<br>(.742)|
|Average household adult<br>education|.019<br>(5.00)|.0078<br>(1.56)|.010<br>(2.30)|.0044<br>(1.21)|
|Female Head|-.102<br>(2.89)|-.112<br>(3.46)|-.114<br>(3.51)|.0035<br>(.143)|
|Assets|.049<br>(2.45)|.110<br>(6.28)|.058<br>(2.63)|.0069<br>(.606)|
|Self employed in<br>agriculture|.046<br>(1.49)|-.035<br>(.958)|-.037<br>(1.06)|.027<br>(1.03)|
|Median distance to market|-.013<br>(2.34)|_.005_<br>(.855)|_-.005_<br>(1.16)|_-.0052_<br>(1.51)|
|Observed probability<br>Pred. Probability at means<br>N|.217<br>_.155_<br>772|.197<br>.129<br>734|.169<br>.125<br>765|.093<br>.078<br>842|
|Pseudo R-Squared<sup>3</sup>|.204|.254|.147|.071|

Notes: 1) The t-statistics reported are the Huber corrected for the probit regression coefficients, not the t-statistics of the reported marginal effects.

2) Included in the regression but not reported were dummy variables for agroclimatic zones, and for missing values of the assets, schooling, and distance to market variables.

These results on the adoption of improved practices are consistent with at least three of the stories about the effect of social capital: innovation diffusion, overcoming market failures
relies on the plausible assumption that within-country variation in rainfall over time is orthogonal to other determinants of import tari¤s.

# 4 Data

Our empirical analysis makes use of the following sets of data:

1. Import tari¤s We use annual data on bilateral applied tari¤s by country and product (SITC 2-dig., Rev. 4) from the TRAINS database of the United Nations Conference on Trade and Development. These data are generally available from 1988 onwards, but the extent of time coverage di¤ers considerably across countries. We construct both simple and import-weighted averages of bilateral applied tari¤s by countryproduct-year. For a given country-product pair, the former measures are de...ned as the simple average of applied tari¤s on imports from the various source countries in year t. The latter measures are de...ned as the weighted average of such bilateral import tari¤s, where the weights are the share of each source country in total countryproduct imports in a base period.<sup>13</sup> Data on bilateral imports used to construct these weights come from the United Nations Commodity Trade Statistics Database (COMTRADE). To account for tari¤ binding, we will further make use of analogous information on bound tari¤ rates.<sup>14</sup> These data also come from TRAINS, but are only available from 1995.

2. Rainfall and irrigation We use the data set on population-weighted rainfall levels by country-year constructed and made available online by Dell et al. (2012). The original source of the historical rainfall data is the Terrestrial Air Temperature and Precipitation: 1900-2006 Gridded Monthly Time Series (Version 1.01), compiled by Kenji Matsuura and Cort Willmott (2007) in conjunction with NASA. This database provides worldwide (terrestrial) monthly precipitation information at 0:5�0:5 degree resolution. Dell et al. (2012) use geospatial software to aggregate these rainfall data to the country-year level, weighting by the population distribution within the country.<sup>15</sup> We further use country-level data on the proportion of agricultural land that is irrigated. These data come from the World Development Indicators of the World Bank, and are generally available for the period 2001-2009.

We combine these sets of data to construct an unbalanced panel at the product-countryyear level, covering 70 nations over the period 1988-2006. Table A.1 in the appendix

> 13 The base period is the ...rst year for which tari¤ data are available for the tari¤-setting country.

> 14 The most-favored-nation tari¤ rate resulting from negotiations associated with GATT/WTO membership.

> 15 These data are also used by Jones and Olken (2010).

15
<!-- Start of picture text --> oO<br>N<br>LS<br>“g \ LS<br>\<br>— Retail and Recreation<br>S —— Grocery and Pharmacy<br>‘ — Parks<br>—— Transit stations<br>VA —— Workplaces<br>fo) — Residential<br>a<br>2020m1 2020m4 2020m7 2020m10 2021m1<br>Date<br><!-- End of picture text -->
of the electoral census).<sup>14</sup> Table B.4 describes the five presidential elections within the study period, the elected candidate, and the type of election (ordinary or due to an extraordinary event). Appendix B provides detailed information on the outcome construction and data sources. Figures C.2 and C.3 present results for the geographic distribution of election outcomes before and after 2013, showing a sharp nationwide decrease in electoral turnout and opposition support.

One relevant concern is the possibility of data manipulation. To address this, we search for abnormal data patterns consistent with manipulation, following the analysis first proposed by Klimek et al. (2012). The authors propose a test that consists of making two-way scatterplots of election results, illustrating the relationship between electoral turnout and support for the incumbent by municipality, which in our case means support for Ch ́avez and Maduro. Manipulation is evident graphically when there is a clear linear positive trend with concentration along the diagonal, indicating that for each vote added to the electoral turnout, support for the incumbent systematically increases by one vote as well. Elections with no manipulation typically show no obvious correlation between the variables.

The results of this exercise, illustrated in Figure B.2, suggest there was no systematic manipulation before 2018. In fact, the results are consistent with international electoral reports from the Carter Center for 1998–2021 (Carter Center, 2024). However, there seems to be evidence of potential electoral manipulation in 2018.<sup>15</sup> Consequently, we include the 2024 presidential elections in our main specifications in order to validate our results. The opposition directly collected results of the 2024 elections and thus addressed concerns about manipulation by the incumbents.<sup>16</sup> This exercise supports our main findings and we com14We fix the electoral census at the year 2000 to keep the denominator constant in our estimates and to facilitate interpretation of the coefficients. Nevertheless, we also verify that our main results are not mechanical and remain consistent when using the contemporaneous electoral census that accounts for the population decline.

> 15The opposition formally boycotted the 2018 presidential election, which contaminated turnout and opposition support measures for that year. We address this by including results for the 2024 presidential election, which saw full opposition participation.

> 16The data are publicly available at Resultados con VZLA (2024).

19
<!-- Start of picture text --> Figure 2: Elasticities of TFR with respect to Female<br>Secondary Education, 1975 and FP Services, 1982<br>Elasticities of TFR<br>0.6<br>-|- Fem ed.(tot.effect) -<br>FP service (tot.eff)<br>0.4 -<br>0.2<br>-0.2<br>-0.4 -<br>-0.6 0 10 , 20 . 30 . 40 . 50 . 60 70 80 90 100<br>Levels of Female Secondary Enroll. 1975 & FP Services, 1982<br>Note: Elasticities computed from eq.4 Table 1. holding all other variables constant at their mean value.<br>(Means; Female Sec. Enroll. (1975). 19% Family Planning Service Score (1982), 25% TFR 5.3)<br><!-- End of picture text -->
large amounts relative to household income (assigned to the high indebtedness script), financial institutions significantly reduced the amount granted if the application was finally approved.

Thus, firms seem to follow their own self-interest, which may explain why recent policy initiatives around product transparency and basic savings accounts have been met with little success. The limited impact of policies to promote transparency and mandated products is not new, and in this sense the paper contributes to the recent literature that uses audit studies to assess financial advice for financial investment (Mullainathan et al, 2012) and life insurance (Anagol et al, 2012).

The remainder of the paper is organized as follows. Section 2 describes the regulatory environment and financial market for low-income households in Mexico, Peru and Ghana. Section 3 reviews the predictions of Gabaix and Laibson (2006). Section 4 describes the experimental design while Section 5 describes the data and empirical strategy. Section 6 reports the results and Section 7 concludes.

# **2. Context**

Financial markets in Mexico, Peru and Ghana have recently been transformed by the appearance of new providers like the consumer goods chains Elektra, Wal-Mart and Coppel in Mexico and new intermediation channels such as agent and mobile banking. This increase in the supply of financial services has coincided with a rapid expansion of household debt to GDP from 8.7 percent in 2000 to 14.3 percent in 2011 in Mexico and from 3 percent in 2005 to 6.5 percent in 2014 in Peru (SBS, 2014). Microfinance institutions in Ghana have expanded the customer base from 1.1 million in 2001 to 8 million as of 2013 (GHAMFIN, 2014). This expansion of credit in Ghana has been fueled by the emergence of Microfinance Companies, new smaller providers that mobilize deposits and provide credit. By 2014, many of these Microfinance Companies had become insolvent. As of November 2015 there were still a total of 468 microfinance institutions registered with Bank of Ghana.<sup>8</sup> Among the components of household debt, consumer debt has been a key driver of this increase, rising in Mexico from 7.7 percent in 1994 as a share of banking sector debt to 23 percent in 2011. Consumer credit in Mexico in 2011 represented 40 percent of all bank earnings in interest and fees and charges.

Much of this growth in consumer credit comes from bringing low-income individuals that relied on informal sources into the formal financial system for the first time. These individuals tend to have lower levels of education and may thus be worse equipped when deciding among financial products (Lusardi and Mitchell, 2011).

The irregular (and sometimes unpredictable) income flows of many low-income individuals suggest

> 8 Bank of Ghana website (accessed April 2016):

> https://www.bog.gov.gh/privatecontent/Banking_Supervision/LIST%20OF%20LICENCED%20MICROFINANCE%20INSTIT UTIONS%20AS%20AT%20NOV%202015.pdf

5
and the local DB line (hereafter the “DB gap”), so that a minimum income is guaranteed. However, very little is known about the performance of the program in reaching the poor, even though it is evidently one of the largest cash transfer programs in the developing world. On paper, the program eliminates poverty (at least by its own definition of who is poor). But how close does it come to this ideal in practice?

This paper offers the first systematic assessment of _Di Bao_ ’s performance, based on independently-collected household survey data. We use the program as a case study for addressing a number of long-standing concerns about how effective transfer programs are in reducing poverty in developing countries. We focus on two issues that have clouded inferences from past work. Firstly, the performance of a program such as _Di Bao_ will depend in part on <u>behavioral responses. Yet in assessing targeting performance and poverty impacts it is common</u> practice to simply deduct transfers received from post-transfer income to estimate pre-transfer income. Here there are concerns that recipients’ labor supply or private transfer receipts will fall in response to DB, such that the net income gains are lower than the actual money received. On paper, the design of DB implies high marginal tax rates, which suggests that there may be strong incentive effects, which could undermine the program’s effectiveness against poverty. The literature on the design of such programs suggests that the benefit withdrawal rate (BWR) — the amount by which the transfer payment falls for each extra unit of pre-transfer income — should be positive, but less than one. For programs aiming to reduce poverty a BWR around one half is consistent with evidence on the relevant income elasticity of labor supply (Kanbur et al., 1995). Taken literally, DB’s aim of exactly filling the poverty gaps implies a BWR that is too high. However, it should not be assumed that any program operates exactly the way it is designed. There are many ways that the local administrators can dampen the marginal tax rates to avoid 3
develops a methodology that helps analyze these data and identify deficiencies. The examples of Pakistan and Cambodia are used illustratively to showcase how such an analysis could be done and inform the reform process. The foundation of expenditure data comes from the transaction profile. Harvesting and analyzing this provides critical insight into the governing political economy and an opportunity for a more targeted engagement.

# **2.Materials and Methods**

All expenditure transactions that are routed through the FMIS are reflected in the general ledger without exception. Each transaction originates from a spending unit and must be executed against the full chart of accounts, and specify the amount, payee including recipient account number, and the time of transactions. Depending on the detail of the chart of accounts, the transaction may capture source of funds, the organizational code, the purpose of the expenditure (economic classification / line item), in which jurisdiction the transaction happened and for what program or sub/program it was intended. Each line of a typical budget execution report that is produced by the FMIS would be an aggregate of multiple transactions against a particular charge code. The format that the data structure of financial transactions in an FMIS typically takes is given in table 1 below.

_Table 1 Example of Data Capture by Transactions in FMIS_

|**Transaction ID**|**Time**<br>**Stamp**<br>**(date)**|**Source**<br>**of**<br>**Funds**|**Full ch**<br>**Org**<br>**Code**|**art of acco**<br>**Purpose**<br>**Code**<br>**(Line**<br>**Item)**|**unts segment**<br>**Location**<br>**Code**<br>**Prog/Sub‐**<br>**Prog**<br>**Code**|**Amount**|**Payee (and**<br>**account**<br>**number)**|
|---|---|---|---|---|---|---|---|
|Transaction 1||||||||
|Transaction 2||||||||
|...||||||||
|Transaction n||||||||

_Source:_ Authors.

These data can naturally be broken down and analyzed in many ways. For example, the analyst may be interested in which spending units are making the largest transactions and how can one ensure that these are carefully monitored. It allows an assessment for price differentials for the same goods across governments across different suppliers. Should a government wish to digitize payments it would have to work with service providers who can be identified through this database. The timing of transactions is an 7
using these self-reported variables on the right-hand side could create an endogeneity problem. One way around this problem is to aggregate school costs up to the cluster level (see Mason and Khandker, 1997). 1 include two cluster school costs variables-the average costs of schooling in the cluster (tuition, uniform, books and other fees) and the average distance to school (in minutes) to school in the cluster.

The indirect costs of schooling comprise the opportunity cost of children's time. These should typically include the value of forgone time in labor and housework. From an estimation standpoin **t** his is a tricky issue. The problem here is to correctly estimate the value of lost time in foregone activities. Another problem is to specify a structural simultaneous equations system **26** that allows the inclusion of indirect costs (an endogenous variable) on the right-hand side of a schooling equation. This would require coming up with appropriate exclusion restrictions to identify the equations in the system, something which is hard to think of. In my specification I specify reduced form equation with only the direct costs of schooling included on the right hand side.

Recent literature on the determinants of schooling has found quality of schooling to be an important determinant also. A problem that arises from this is that in the absence of controls for quality of schooling, the estimated "price" effects of the cost of schooling on demand may be clouded by quality effects (Mason and Khandker, 1997). In the absence of good quality of school information in the Peru LSMS, there is little that can be done to control for school quality. The estimation results with respect to school costs should therefore be interpreted cautiously.

> **26** See for example Tzannatos (1995).

15
Figure 2: Partial scatter plots <!-- Start of picture text --> (a) Education SP aid (b) Education TC<br>−.2 0 .2 .4 .6 −2 −1 0 1 2 3<br>Education SP aid Education TC<br>(c) Health SP aid (d) Health TC<br>−.4 −.2 0 .2 .4 −.5 0 .5 1<br>Health SP aid Health TC<br>4 4<br>2 2<br>0 0<br>Public education expenditure −2 Public education expenditure −2<br>−4 −4<br>3 3<br>2 2<br>1 1<br>Public health expenditure 0 Public health expenditure 0<br>−1 −1<br><!-- End of picture text --> Note: Partial scatter plots in the full (solid line) and reduced (dotted line) samples correspond to the FE results in Tables 4 and 6, respectively. + denotes observations that are excluded from the reduced sample. Source: Author’s analysis based on data described in the text.

FE estimates inconsistent.

Therefore, I also examine the results that are obtained when the strict exogeneity assumption is relaxed by employing a system GMM estimator (Arellano and Bover, 1995; Blundell and Bond, 1998). This estimator further enables the consistent estimation of a more general model that includes a lagged dependent variable (which removes the serial correlation in _εit_ ):<sup>31</sup> Equation (12) is estimated using a two-step system GMM estimator applying Windmeijer’s (2005) correction for the downward bias in the two-step standard errors. All education (health) aid prefix code variables, support to NGOs, and trade are treated as endogenous, whereas all other variables are treated as predetermined. Time dummies are treated as strictly exogenous and are thus added to the instrument matrix without transformation. I reduce the risk of overfitting by restricting the maximum number of lags of the level variables that 22