rafmacalaba/data-use-mentions-extended huggingface dataset:
used to predict future poverty levels. Such an analysis would require an extremely large and detailed dataset. For example, predicting future poverty values in Guatemala would require CDR data from 2006 and 2011, as well as corresponding poverty survey data for the same time periods. However, this analysis was based on CDR data from 2013 and rural poverty levels for 2006 and 2011, and no national or urban poverty data were provided. As a result, the predictive model was trained with the 2013 CDR data and the 2006 rural poverty data, and the 2013 CDR data were used to predict rural poverty levels in 2011. While this is the best methodological approach given the data constraints, the preliminary results showed low R<sup>2</sup> values at around 0.29 for regression and maximum F1 scores of 0.6.

**15**
treatment. Clearly, the number of observations without choice of treatment critically affects identification – at the limit, if there is only one such observation in the data, the model with both exogenous variables X and treatment c is not identified – and yet the authors proceed as if this were just another two-stage least squares problem. They seem to recognize there might be a problem, but then fail to properly address it: _Because of the unusual design of the PK estimator, no theory is available to measure instrument weakness in terms of its potential for distorting estimates. We find some evidence, however, that credit eligibility is a weak instrument for credit uptake. Differentiating credit by gender exacerbates the weakness_ . (p. 27) If there is no statistical theory, then on what basis is there evidence?

Finally, one can easily simulate PK-like data sets and (i) estimate the model as PK do, by maximum likelihood, and (ii) estimate the model as RM do, using the wrongly augmented set of instruments and 2SLS.<sup>16</sup> The results of generating 200 such datasets are provided in Table 1.

Using the RM approach, the simulated data generating process has a positive bias for males and a negative bias for females<sup>17</sup> . Using PK’s maximum likelihood method there is absolutely no evidence of bias arising from “weak instruments.” The mean value for the F- statistic for the first-stage regressions given by equations (4) and (5) are 3.47 and 3.41, respectively. Using RM’s 2SLS method, however, there is strong evidence of bias in the direction of the least squares bias. The male parameters are almost twice as large as the true values, and the female parameters are one-third too small, so that RM’s method finds that male and female credit effects are about the same while the true female effect is three times as large as the true male credit effect. The
# **<mark>Introduction: Measurement of Public Administration and the Global</mark>**

# **<mark>Survey of Public Servants</mark>**

<mark>How governments are administered is a critical determinant of socio-economic outcomes (Evans and Rauch 1999; Finan, Olken and Pande 2015; Pepinsky, Pierskalla and Sacks 2017; Besley, Burgess, Khan and Xu 2022). Understanding how public administrations function and differ around the world is crucial for identifying the drivers of successful administrations. Recent research in economics, political science and public administration has capitalized on the benefits of direct surveys of public servants to investigate the functioning of the state (for example, Rasul and Rogger 2018; Rasul, Rogger and Williams 2020; Bertrand et al. 2020; Raffler 2022; Harris et al., Forthcoming). This paper presents an</mark> initiative – the Global Survey of Public Servants - to collect and harmonize large-scale, comparable survey data on public servants.

<mark>Questions around how to strengthen the functioning of state administrations pose an immediate measurement challenge: how can differences between public administrations be effectively measured? A valuable response to this has been to generate quantitative indicators based on surveying experts, households and firms, and constructing country-level governance indicators based on their responses (Transparency International 2021; World Bank 2022). These indicators have made important contributions to our understanding of comparative public administration by quantifying perceptions of experts and of those interacting with government (such as citizens and firms) around the world.</mark> <mark>However, limitations of these indicators exist (Arndt and Oman 2006). For example, country-level governance indicators suffer from the fact that differences within countries across organizations are often larger than cross-country differences (Meyer-Sahling, Schuster, and Mikkelsen 2018), and country-level scores are often not informative to</mark> 2
s consent reduces the overall impact of the data and limits the role they can play in contributing to transparency in an economy. In only a handful of countries do PCRs share their data with private credit bureaus; this also restricts the development of the credit information industry. Finally, only 15 of the 57 PCRs in the survey are required by law to allow consumers access to their own credit reports and 29 PCRs explicitly deny borrowers any access – a violation of internationally recognized data protection rules. Not surprisingly therefore, countries with PCRs tend also to

> 14 Argentina’s PCR does include data from credit card companies given on a voluntary basis under an agreement of reciprocity.

> 15 According to survey results, non-financial firms may access PCR data in only 2 countries, insurance firms may only access PCR data in 3 and in general small businesses cannot request reports from the PCR, even with the authorization of the borrower - these remarks exclude the case of Argentina where information was freely available to all on the internet for “punctual inquiries” - inquiries regarding a single individual.

15
# **4. Results**

Standardized scores based on the results of the PD survey are used for the following analyses: (1) overall partner country performance in 2010; (2) characteristics of FCS performance in 2010; (3) FCS partner country progress between 2005 and 2010; (4) overall development partner performance in 2010; and (5) development partner performance in FCS in 2010. The first three analyses focus on partner countries, while the remaining two analyses focus on development partners. The results of the analyses identify key aid effectiveness challenges in FCS and also provide good performance examples drawing from both FCS and development partners.

# **(1) Partner Country Performance in 2010**

The sum of standardized scores across indicators provides a comprehensive picture of a partner country‘s performance relative to that of other countries. By definition, if the sum is zero, the country‘s aid effectiveness performance is an average. If it is positive (negative), performance of the country is above (below) average. It is important to note that all indicators are treated equally. In other words, there are no different weights among indicators and correlations among indicators have not taken into account. Table 4 and figure 2 show the results of the sum of the standardized scores in all countries<sup>10</sup> .

**Table** **<u>4: Results by Regions and Country Groups</u>**

||**Total **|**IDA**|**IBRD**|**FCS**|**Non-FCS**|
|---|---|---|---|---|---|
|Africa|0.4|0.3|0.1|-1.0|1.4|
|EastAsia andPacific|1.0|0.1|0.9|-0
functions until numerical convergence is achieved for the states on every wage and time allocation that a household might select.

**This environment increases the model’s outputs and the possibility of discussing gender gaps in time use, and also allows us to develop a framework that explains in more detail the observed labor market gender gaps.** Conditional that each spouse belongs to a particular home productivity type, our mechanism works as follows: husbands and wives of the high home productivity type will allocate more time to home production, relative to their low home productivity counterparts. This characteristic will be active at any labor market state that the couple might be in: for high productivity types, the labor market options are less appealing since eventually they will need to give up home production time. Gender search costs will be shared by all husbands and wives and will directly affect the nonparticipation or unemployment decision. Therefore, high home productivity husbands and wives have a higher probability of staying out of the labor market because the value of their nonmarket option is high. Conversely, low home productive types will participate more in the labor market and given that production at home is not so valuable, they are more prone to work; consequently, they are more willing to accept jobs at lower wages.

**Thus, the heterogeneity in our model has both impacts on the allocation between leisure and home production, and a direct effect on participation rates and accepted wages.** The framework in this paper, including spouses as the unit of study,<sup>11</sup> helps to disentangle whether observed gender gaps are due to differences in the labor market or differences in gender roles regarding leisure and home production.

# **3. Stylized Facts for the Regional Roma Survey Sample**

**This paper relies on data from the 2017 Regional Roma Surveys (RRS), the most comprehensive surveys to date on income, living conditions, and human development outcomes among**

> 11 Modeling other adults’ decisions is out
increase is notably progressive if dismissed workers move to unemployment or inactivity, and the pool of still employed workers is higher paid, on average.

**Further studies should evaluate the fiscal implications of raising the minimum wage.** On the expenditure side, this can prompt several fiscal considerations for the public sector. These include a potential uptick in the wage bill, considering the current indexation system in the public sector pay system. Additionally, budget reallocations may be necessary to accommodate increased wages for public sector employees. Furthermore, there may be concerns regarding inflationary pressures stemming from higher costs of goods and services provided. Conversely, raising minimum wages could reduce the need for certain social welfare programs, particularly as low-wage workers, who tend to be overrepresented among poor families, earn more income. This, in turn, could translate into government savings concerning social assistance and welfare disbursements. On the revenue side, raising the minimum wage might also yield higher tax revenues for the government, especially given the flat Personal Income Tax (PIT) structure. Nonetheless, this could be fully or partially offset by the potential job losses or reduced working hours, resulting in lower overall tax revenue.

> 28 This is estimated based on gross labor income in tax administrative data.

41
Unlike the preceding measures, _TD_ directly reflects the program’s coverage of the target group. In fact it can be readily shown that _TD_ = ( _CR_ − _P_ ) /(1 − _H_ ) where _P_ ≡ _N_ ( _D_ = /)1 _N_ is the * overall participation rate. (The corresponding formula for _TD_<sup>*</sup> is _TD_ = ( _CGH_ − )1 _P_ /(1 − _H_ ) .) On implementing these measures on the UHSS data, we find that 7.7% of the population had a net income (observed income _minus_ DB receipts) below the relevant DB line (Table 1). So the program’s total participation is equivalent to about half of the eligible population, defined as those with income below the DB line for the relevant municipality. However, there is some leakage to ineligible households, as can be seen in Table 1.<sup>19</sup> About 40% of DB recipients are ineligible according to these data (0.43=1.69/3.91), giving _TR_ =0.57. Almost three-quarters of those who are eligible are not being covered by the program (0.71=5.48/7.71), i.e., _CR_ =0.29.

We find that _SHARE_ =64%, _CI_ =0.78 and _CGH_ =8.3. This is excellent targeting performance by international standards. For example, Coady et al., (2004a,b) provide estimates of _CGH_ for 122 programs across 48 developing countries. Argentina’s _Trabajar_ program has a _CGH_ = 4.0, making it the best performer by this measure amongst all programs surveyed by Coady et al.<sup>20</sup> The median _CGH_ across the 122 programs is 1.25. By this measure, DB is a clear outlier in targeting performance internationally.

Turning to our fourth measure of targeting performance we
Only some of the most recent studies have looked at whether aid raised the export capacity of _recipient_ countries. Cali and te Velde (2009) regressed trade costs and the value of exports on control variables and lagged AFT disbursements, using data from the OECD’s Credit Reporting System. Their sample was a panel of countries covering 1995-2007, and they dealt with endogeneity and measurement errors in AFT flows by instrumenting them with the World Bank’s index of civil liberties. The message that seems to emerge across their various specifications is that aid to trade facilitation and infrastructure seems (by and large) to have a significant effect (the former on trade costs, the latter on export values), while aid to productive capacity is insignificant. When looking at sectorally-targeted aid, controlling for country _×_ sector fixed effects (comparative advantage) they found, again, that aid to infrastructure had a significant impact but aid to productive capacity didn’t. Brenton and von Uexkull (2009) combined mirrored product-level (HS4) export data with export-development (ED) aid data from the GTZ (covering 1975-2005) and from the OECD/WTO Trade Capacity Building Database (covering 2001-2005) for 48 countries. They useda differencein-differences (DID)<sup>2</sup> approach where the performance variable was exports and regressors included lagged exports, country and year _×_ product fixed effects, and contemporaneous and lagged aid coded in binary form (ED program in force = 1). By and large, once outliers were dropped, matching yielded insignificant coefficients in spite of the relatively large sample size, suggesting that once selection effects were taken into account, export-development programs provided little significant boost to exports. All in all, it is fair to say that, as the literature stands, the effect of AFT on the export performance of beneficiary countries has not been clearly established on the basis of aggregate numbers.

Among TRA programs, technical-assistance ones have scarcely been evaluated.<sup>3</sup> A brochure
decision-making within firms. The measure of the decentralization for investment was in terms of “The largest expenditure (in rupees) a plant manager (or other managers) could typically make without a Director’s signature”, which had an average of 12,608 rupees (about $250). Finally, the number of days the owners spend each week at the factory is a revealed preference measure of decentralization. The owners are usually located either at their head-offices in Mumbai (which they prefer as it dramatically reduces their commute) or at the factory (if it needs direct management from them).

To combine all eight decentralization measures into one index we took the first principal component, which we called the decentralization index. We found changes in this index were strongly and significantly correlated with changes in management across firms, as better management led to more decentralization. Table 3 looks at this in a regression format: 21
. Some list current prices only, others post recent but not current prices, and a handful lists both current and historical prices. Among the most detailed and timely is the online price database in Chile— mandated by a resolution issued in January 2012—which is also available on iPhone, BlackBerry, and Android. The database gives viewers the choice of displaying data in order of increasing or decreasing price and the address of each filling station, prices, and the date and time of the last price change. The government of Guatemala highlights on its Web site the filling stations with the lowest prices in the Guatemala City Metropolitan Area with their addresses and street maps every week. Instituto Nicaragüense de Energía (Nicaraguan Institute of Energy) surveys about 75 filling stations in Managua every week and provides details on the three highest-priced and three lowest-priced filling stations. The Authority for Consumer Protection and Competition in Panama, proclaiming “an informed consumer has power,” collects and posts gasoline and diesel prices at filling stations every four weeks, highlighting those with low prices. The Ministry of Economy in El Salvador until 2012 was posting lowest prices in three parts of the country as saving tips. Argentina, Pakistan, Peru, and the Philippines also post detailed price information by company.

For price competition to benefit consumers, however, it is important that there be effective monitoring and enforcement of technical standards, or else a competitive fuel market can lead to partial or total product degradation, with a low-quality product (adulterated, mislabeled, or short-weighted) driving out a high-quality product.
marginal costs that have been estimated at US$0.10 per kilowatt-hour (figure 6). The associated hidden costs of 1.4 percent of GDP remain a significant macro-economic savings amounting to more than US$300 million annually. Hence the importance of continuing tariff reforms to close this remaining financial shortfall in the sector.

Tanzania’s power supply sector remains vulnerable to hydrological conditions, and the pressing need to expand and diversify generation capacity. A major drought in the mid-2000s caused Tanzania to face a major supply crisis. The crisis was precipitated by drought that drastically reduced the country’s capacity to generate hydro-power – it’s main source of electric energy. This forced businesses to rely on own generation, increasing the costs of production, complicating the survival of businesses (especially small ones), and generally making the economy less competitive. The crisis led to high levels of power outages resulting in economic losses that have been valued as high as 4 percent of GDP, one of the highest reported in Africa (figure 4). Power outages increased the demand for expensive emergency diesel power generation, which cost the country almost 1 percent of GDP per year. With the end of the drought, reliability of power supply improved. According to occasional enterprise surveys, the number of annual days of power outages reported by firms fell from 63 days in 2005 to 47 days in 2008. Per capita power consumption also increased substantially over this period from 48 to 81 kilowatt-hours per capita per year (table 2). Unfortunately, the drought situation of 2006 was repeated in 2011, with similar consequences.

Furthermore, power supply and electricity access in Tanzania remain extremely low in absolute terms. Both installed generation capacity and power consumption in Tanzania are comparable if not slightly worse than the benchmark for low income countries (table 2). National power consumption at less 8
Consistency of fixed effects estimates, which subtract level differences across groups, depends on a properly linear specification. Column 4 demonstrates that a quadratic term for village-level open defecation is not statistically significant, and indeed changes signs with and without district fixed effects. Potential non-linear relationships between village sanitation coverage and child height will be considered in more detail in 4.1.2.

# **4 Evidence from pooled Indian and African surveys**

Do differences in village-level sanitation coverage explain the difference in height between rural children in India and in sub-Saharan Africa? If so, is this just a spurious reflection of other correlated variables? This section addresses these questions using pooled child-level data from the rural parts of nine DHS surveys: India’s 2005-6 NFHS-3 and eight surveys from Africa in the 2000s.<sup>19</sup> In particular, the DHS surveys nearest 2005 (and balanced before and after) were selected from the five largest African countries available;<sup>20</sup> the included countries account for 46 percent of the 2012 population of sub-Sahara Africa.

The argument of this section proceeds in several stages, building to a statistical decomposition of the India-Africa height difference, in the sense of Oaxaca-Blinder. First, section 4.1 verifies an association between village-level sanitation and height within the two regions. In particular, this section assesses the linearity of the relationship (assumed by some decomposition techniques), and notes that a village-level effect implies the presence of negative externalities. Then, section 4.2 considers a paradox implied by Deaton’s (2007) finding that height is not strongly associated with GDP: the within-region association between open defecation and well-being has a different slope from the across-region association. Next, section

> 19Here I again follow the WHO recommendation of dropping observations with height-for-age _z_ -scores more than 6 standard deviations from the mean.

> 20This excludes South Africa, where height has not been measured in a DHS survey. Beyond this data availability constraint, this exclusion may be appropriate
be larger than the immediate effect on human lives.

# **B. Pregnancies and births data**

Violence data by municipality and month of occurrence are matched to household survey data from the Colombian DHS on the month of pregnancy (then aggregated and reported by trimester) and the municipality of residence of the mother. DHS are coordinated nationally representative crosssection surveys conducted in developing countries, which collect detailed information from women in reproductive age (13-49 years old in Colombia) and their households. These surveys include the history of live births in the five years before each survey. I use the V, VI and VII survey rounds (corresponding to 2005, 2010 and 2015),<sup>26</sup> which together account for live births occurring between November 1999 and March 2016 (pregnancies starting in February 1999).

DHS also include the characteristics of children, including their weight at birth, the main measure of newborn health used here. This is the most widely used measure in the literature to capture the fetal environment and has also been used to show the effects of early conditions later in life. Other measures of newborn health are underdeveloped and have not become widespread (Conti 2013). The main general limitation of DHS data is the potential missing birthweight information.<sup>27</sup> All results in the chapter need to be interpreted bearing this data restriction in mind. I also use neonatal mortality (death before the 28th day of life) as a complementary measure of newborn health, as

> 25 This region is also mostly dominated by the Amazon rainforest. DHS are representative of 99% of the country and the uncovered areas are disperse rural areas in this part of the country.

> 26 DHS surveys have been conducted in Colombia since 1986 but earlier rounds lack some crucial information for this study, particularly about pregnancy or publicly released identifiers to match municipalities with external data sets.

> 27 The child’s length at birth or the mother’s assessment about the relative size of