rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 1. Indexed wage bill by treatment group over time <!-- Start of picture text --> 110<br>105<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>No letter Soft letter Strong letter<br>Index (January=100)<br>January February March April May June July August September October November December<br><!-- End of picture text --> Source: Authors’ calculations. Figure plots the aggregate payroll declared by the firm given random assignment to the employer letter. Values are indexed relative to January wage levels (wage in January=100 for all treatment groups) _i. Cross sectional impacts of letters on declaration behavior_ Table 1 below reports the difference in difference estimators on the treatment effect between the baseline (January) and each subsequent period, ignoring the time trend in payroll declarations over the 12-month period (which is incredibly noisy, as shown above). The results highlight the differential impact of the soft letter on the payroll reported by the firm, and no impact of the strong letter (and in fact an immediate negative, albeit marginally significant impact) relative to the group assigned to receive no letter. The important findings from this estimation are the strong negative time effect in the first few months after the onset of the pandemic (reflecting a drop in economic activity, and thus wages paid out) and the isolated effect of the letters. Payroll declarations dropped between 4 and 30 percent between March and July (significant at the 1% level except for the July declaration month), returning to a statistically identical amount beginning in August and even increasing by 3% in December (albeit only significant at the 10% level). The first post-implementation filing period (February, where the deadline for reporting was March 20<sup>th</sup> , about one week after the onset of the pandemic) in
19 information on approximately 4300 of the original 4800 households interviewed in 1992/1993, but has a total sample size of 6002 households due to an expanded budget and sample design.

The second setting is China, where we draw on 2000-2004 panel data from 800 and 700 rural households from the two western provinces of Inner Mongolia and Gansu, respectively. Following introduction of the household responsibility system in 1978, provinces in China experienced dramatic change, first along the coast, but later also in its hinterland. The surveys examined here document this change as background to a World Bank-supported poor area development program in both provinces. Consumption data were collected using the diary method and the same questionnaire was administered following the same survey procedures throughout the panel. Poverty incidence (using the national poverty line) declined sharply from 19 percent in 2000 to 6.2 percent in Inner Mongolia and halved from 24.3 to 11.8 percent in Gansu.

# **_Performance of SAE poverty predictions using  expenditures and asset models_**

The VLSS data allow testing the performance of consumption models using both expenditure sub-components and household assets (Tables 2 and 3). The following subcategories of total expenditures were considered to estimate the consumption models: expenditures on food excluding rice (model 1); expenditures on all food (model 2); "30day" nonfood expenditures, which include those expenditures that are asked with a 30day recall period (model 3)<sup>15</sup> ; "one-year" nonfood expenditures with a one year recall

> 15 This is similar to the specification by Deaton (2003)  in his analysis of India's poverty numbers across NSS rounds.
allocation” as one in which the provider with the highest competence is matched to the posting with the largest outpatient caseload; the provider with the second-highest competence to the posting with the second-largest outpatient caseload; and so on. We restrict hypothetical reallocations to postings within the same health facility level and rural/urban setting (together, a “sector”) in each country, while holding the caseload constant at each posting.

To calculate the potential gains from provider reallocation, we first computed the share of vignette conditions that the average provider could correctly treat, weighted by the observed outpatient caseload at each posting. Then, we reallocated providers to the “optimal allocation” taking the constraints into account till all existing providers were assigned to all existing postings. This process produced the highest possible proportions of vignettes correctly treated when weighted by the observed outpatient caseloads. Following the reallocation simulations, we re-computed the predicted correct management shares for the average patient and calculated the realized gains from the reallocation. Regardless of the practicability of any such reallocation, the resulting estimate is a valid measure of the quality implication of provider misallocation. The **Supplemental Appendix** discusses the process in further detail.

# **3 Results: Provider Caseloads and Patient Crowding**

## **3.1 Caseload Patterns for PHC Providers**

The first part of our results establishes the overall patterns in daily outpatient caseloads across the countries in our sample. The top panel of **Table 1** presents summary statistics for each country as well as for all countries in our sample (as a country-level average of medians). The median facility in the average country sample saw on average 25.9 patients per day, ranging from 2.1 in Nigeria to 87.2 patients per day in Malawi (Column 4, Table 1). The median provider in the average country saw on average 10.9 patients per day, with country-level medians ranging from 1.2 (Nigeria) to 20.7 patients per day (
Besides, loans from PACS were perceived as less likely to be repaid even if a good harvest followed a drought than were either loans from commercials banks or MFIs.

Finally, respondents answered a hypothetical question that asked to provide a ranking of lenders they would pay first if they had an outstanding loan from different lenders but did not have enough money to meet all repayment obligations. As it turns out, PACS loans always appeared last in the repayment priority list, behind all other loan types. The first in the list was always a loan from an MFI. Thus, the frequent announcement of relief measures appears to undermine the culture of repayment.

# **5. Conclusion**

If an expanded role for government has to be the solution, care should be taken in ensuring the effects of government interventions have the intended consequences. There is strong evidence pointing to the crucial importance of government institutions regarding the functioning of the financial sector in general and access to credit in particular. However, instead of replacing private agents in the market, governments can make a more valuable contribution in securing an adequate environment for private credit institutions to flourish and to improve institutional mechanisms in ways that further the availability of credit to poor individuals. The range of policies that would contribute to this goal goes beyond the extent of this article: building these institutions involves decision-making with long time-horizons, and the guidance provided by existing research is only partial and incomplete. We have, however, some good indications on where the right path might lie. Providing good institutions, such as efficient, speedy and fair courts is an area where only the government can play an active role. Also, governments can provide much needed registries of credit information, liens and property ownership; and make sure legislation is reliable and property rights and responsibilities of companies and financial institutions are properly defined. Reforms in these areas may be difficult to accomplish in the short term, but there are many other dimensions where progress comes at lower costs: Djankov, McLiesh and Shleifer (2007
Table 4: Roads and Battles (ACLED)

||(1)<br>events|(2)<br>events|(3)<br>events|(4)<br>fatalities|(5)<br>fatalities|(6)<br>fatalities|
|---|---|---|---|---|---|---|
|project ongoing|-0.032|||-0.024|||
||(0.020)|||(0.028)|||
|project completed||-0.081***|||-0.071||
|||(0.029)|||(0.043)||
|road project stock|||-0.034*|||-0.031|
||||(0.020)|||(0.029)|
|Observations|18722|18722|18722|18722|18722|18722|
|R2|.527|.527|.527|.409|.41|.41|

Note: * p _<_ 0.05, ** p _<_ 0.01, *** p _<_ 0.001. Standard errors are clustered at the cell level. Cell and year-fixed effects are always included. Events and fatalities are in logs (+1).

Overall, the association between roads and violence reduction is strongest for violence that targets civilians and there is some evidence on reductions of battles in the ACLED data. Road projects are not associated with a reduction in state and non-state violence.

In additional results in Appendix table A4 we show that ruggedness, measured by strong slopes in a cell, do not seem to interact strongly with roads. However, for one-sided violence we find that the effect of road project is
We combine several nationwide micro data sets. First, we rely on the 2002 and 2018 national censuses to quantify and analyze changes in FLFP over this period. The censuses capture information on FLFP together with individual and household socioeconomic characteristics and local labor markets including age, education, ethnicity, marital status, household composition, labor market status of household members, and household infrastructure characteristics. Second, we compute municipality and department level variables from the 2017-2018 Census of Human Resources of the Central Government (INE), 2017 Crime statistics (INE), and 2017 Latinobarómetro survey to create measures of social norms and attitudes towards women. These variables capture the extent to which women participate in household decision making, the share of females among high-wage public sector employees (as a proxy of public visibility), rates of intrafamily violence against women, and the share of individuals endorsing gender parity in parliament or in the judicial system. Finally, we rely on subnational information on social public policy produced by INE, the Foundation for the Development of Guatemala (FUNDESA in its Spanish acronym), and the Central American Institute of Fiscal Studies (ICEFI in its Spanish acronym) to assess its impact on the women’s incentives to participate in the labor market. These policy variables include the number of preprimary centers in each municipality, public spending in education and health at the department level, and a measure of municipal road accessibility (all measured in 2017).

Our main sample is composed of women in the active age of 25 to 49 years-old living in households where they or their spouses are household heads (see Berlinski and Galiani, 2007). We start by assessing changes in FLFP between 2002 and 2018. We assume the probability of FLFP in each year can be represented by a linear probability model. We explore the Oaxaca-Blinder decomposition (Blinder, 1973; Oaxaca, 1973) using individual and household characteristics and local labor market variables as possible drivers of this change. Our decomposition results point to significant unexplained differences in FLFP over time.

We
household surveys as methods that used either replacement or reweighting. Replacement involves replacing some part of the top of the income distribution in the household surveys parametrically- e.g. with values from the Pareto distribution with the parameter estimated using the rest of the survey distribution. Reweighting involves increasing the weights of those at the top of the distribution to account for unit non-response. We now discuss reweighting for unit non-response as well as imputation and parametric replacement, both of which can be classified as methods using replacement. Lustig (2020) highlighted that the replacement and reweighting distinction can also be applied to solutions that use administrative data, and we discuss solutions using administrative data in the following section.

# _Using survey data only_

In this section we first discuss reweighting as a solution to non-random unit non-response and imputation to solve non-random item non-response. These are methods with long histories in the statistics literature and solve very specific problems with specific solutions. We then discuss using consumption data and imputing top incomes using the survey data.

# Reweighting for unit non-response

Unit non-response is unlikely to be MCAR. Ravallion (2022) discusses solutions for non-random unit non-response. One is to find replacement observations, but this is unlikely to solve non-random nonresponse, since presumably the same biases affecting original non-response will affect which households respond as replacements. One benefit of replacement is that it does raise the sample size, which may be important for studying the top of the income distribution since it can reduce sparseness problems.

A commonly used method to solve unit non-response is reweighting, where the respondents in different groups (“weighting classes”) are upweighted to represent themselves and those in the same group that did not respond. The reweighting groups are often regions (Korinek et al. (2006)), which implies that for the method to be successful non-response must be random within regions, or that non-response is “ignorable” within regions (Ravallion, 2022). The size of the regions used is likely to affect the proportion of bias the reweighting can solve,
raise IPV by reducing the opportunity cost of men’s time (Blattman and Miguel 2010)<sup>12</sup> , and increasing exposure of the woman to the man if he spends more time at home (Chin, 2002).<sup>13</sup> Similarly to this paper, but using Spanish data, Alonso-Borrego and Carrasco (2017) and TurPrats (2017) find that improved female employment opportunities increase violence against women for some (although not all) women.<sup>14</sup> Alonso-Borrego and Carrasco (2017) find that female employment reduces violence only if the female’s partner is employed as well. Tur-Prats (2017) finds that in Spanish territories with a historical nuclear family tradition a decrease in female unemployment relative to male unemployment increases IPV due to cultural norms pertaining to gender.<sup>15</sup> This study shows the critical role of women’s access to divorce in explaining this relationship across a number of developing countries covering a wide geographical area. It may be that access to divorce is itself influenced by historical cultural norms pertaining to gender. However given the wide geographical scope of our study it is not possible to use consistent controls for such historical cultural norms, so the study cannot tease out the separate influence of gendered norms vs access to divorce in this study.

Section 2 describes the data and Section 3 the methodology. Section 4 provides the estimation results, Section 5 the robustness checks, and Section 6 provides a discussion and conclusions.

# **2 Data**

> 12 The literature on civil conflict emphasizes the opportunity cost of time as a causal factor. It argues that economic shocks that leave men without jobs leave them vulnerable to being drawn into soldiering (Blattman and Miguel 2010).

> 13 The idea that exposure or inadvertent increases in time that the partners spend together increases IPV is analogous to the idea in the wider crime literature that incarceration reduces crime.

> 14 There is no similar evidence
survey for at least two reasons. By pure chance we might end up with an imbalance in the distribution of user‐charges such that, for example, poor households were consistently asked about high consumption charges in comparison to their rich counterparts. Moreover, user‐ charges (often the key design element in a WTP survey) may not be the only factor that impacts demand. Other ‘demand shifters’ such as connection costs, income, education, etc. are likely to matter.<sup>2</sup> Some of these ‘policy levers’ such as connection costs and service quality can be evaluated through a more general class of SP surveys – conjoint experiments (see for example, Sills et al., [2004], Yang et al. [2005], and Mansfield et al. [forthcoming]). That still leaves a large residual heterogeneity. Our paper shows how regression modeling can be an important first step in accounting for, incorporating, and using this additional information on the population of interest.

In our analysis, the regression modeling shows that a complex combination of factors drives expressed demand for service improvements. While poverty and costs are found to be key determinants of demand, we also find that location, self‐provision, and perceptions matter as well, and that sub‐sets of these factors matter differently for sub‐samples of the population. For example, these models suggest that poor households are more influenced by the proposed monthly consumption charge. We also find that perceptions, water quality, and safety nets affect poor households’ WTP differently than non‐poor. The regression models also show that all else equal, fewer households in the overall sample will connect to the network if this service improvement is provided by the private sector, in contrast to a reformed public sector.

The estimated coefficients from the regression modeling can also be used to further evaluate the uptake of piped water connections. In this paper, we use the model to evaluate the uptake of private piped
|445.44|476.36|488.30|471.56|476.22||446.54|448.92|484.81|
|<br>Sold rapeseed (y/n)|0.99|0.98|1.00|1.00|0.95|1.00||1.00|0.97|0.99|
|if yes, share of harvest sold|0.98|0.99||0.98|0.97|0.97||1.00|0.99|0.96|
|Input cost|285.24|227.42|589.57|342.48|254.00|333.55||301.72|285.99|272.35|
|Netrevenue|946.34|890.36|650.72|1,054.44|804.10|1,065.59||924.06|890.02|1,005.38|

_Source:_ Own computation from MAPF/WB/EU small producer survey 33
market failures that could be corrected through an SOE can help differentiate among economic sectors. By distinguishing between economic sectors according to the presence (or absence) of specific market failures connected to the process of producing its goods or services, their intensity, and the appropriateness of an SOE as a (potential) corrective measure, one could explore the justification for the presence of SOEs and, as a result, be alerted to the risks of distortive consequences or unintended consequences of SOE presence. A natural conclusion is that sectors with little or no economic rationale should be the first to be considered for SOE divestiture/privatization, given that the well-documented performance gap and distortionary effects of SOEs (the “cons”) cannot be compensated for by a specific economic benefit (the “pros”) (Figure 1).

> 18 SOEs may operate with a surplus or with systematic losses that impact the government’s budget or balance sheets through numerous transmission channels: taxes and dividends on the revenue side, subsidies and transfers on the expenditure side, government loans to SOEs generating interest receipts, government takeover of SOE debt, and valuation effects, which tend to be correlated with SOE performance (Soler and Sy 2021).

> 19 For example, Szarzec, Dombi, and Matuszak (2021) analyze a micro-level data set on the aggregate economic weight of SOEs in European countries according to leading business indicators, and on that basis assess the overall growth effects of SOEs and their relationship with governmental institutional quality, finding that better institutions entail a more favorable growth effect of SOEs. Böwer (2017) argues that the negative productivity aspects of SOEs in general could spill over to affect the economy at large. Mattera and Silva (2018) focus on firms in the steel sector across countries, finding not only that SOEs are associated with poorer economic performance and higher levels of indebtedness than private enterprises, but also that they have contributed to increasing overcapacity. Ehrlich et al. (1994) and Karpoff (2001) corroborate the exposition above by
standards is such that you are not vulnerable to falling into poverty, then you probably belong to the middle class. The question that arises is on what is the “upper threshold” of this middle class. Or in other words, what is the level of consumption that distinguishes the middle class from the top strata, the upper class, or the elite?

Here the literature offers a number of suggestions. Based on their analysis of household consumption data from 13 developing countries including India and Pakistan, Banerjee and Duflo (2008) propose from $2 to $4 per person per day as lower and upper thresholds respectively for the middle class. A global study of the middle class conducted by the Asian Development Bank defined the middle class in Asia (including Nepal) as those with consumption between $2 and $20 per person per day.<sup>1</sup> In our case, the lower threshold is determined by the vulnerability line which translates to roughly $3 per person per day in 2014 in 2011 PPP terms. For the upper threshold, we inspect the consumption distribution and pick a value that comes to about $8 per person per day in 2014. Even though this choice is somewhat arbitrary, it is between the global upper thresholds of $4 and $20 per person per day. Besides, given how thin the consumption distribution is at that level, the effect of the choice has minimal impact on the size of the middle class. In other words, the size of the middle class is much more sensitive to the choice of the lower threshold than the upper threshold.

With the definition of the vulnerable and the middle class clarified, we can now examine the relative sizes, trends and the various characteristics of these groups in Nepal. The results presented in Figure 5 show that as the proportion of the poor has declined in Nepal, there has been a corresponding growth in the size of the vulnerable population from 28 percent in 1995/96 to 45 percent in 2010/11
is smoother and shifted to the right compared to the distribution of reported percentage juvenile values.

This procedure assumes that misreporting of percentage juvenile is constant within trip. This assumption would be violated if vessels differentially underreport percentage juvenile in the most desirable fishing grounds, which could occur if vessels want to prevent the closure of productive fishing grounds they have discovered. I support this assumption by matching at the set-level the percentage juvenile reported to vessel owners with the percentage juvenile reported to the regulator. I perform this matching for SNP sets only since there is no percentage juvenile reported to vessel owners for non-SNP sets. I calculate the percentage juvenile reported to the vessel’s owner minus the percentage juvenile reported to the regulator, and regress that difference on tons reported to the vessel owner and trip fixed effects. A positive coefficient on tons implies that vessels underreport percentage juvenile by a greater amount to the regulator when they have a more productive set, relative to other sets on the same trip. The coefficient on tons is in fact slightly negative (-0.0002) and not different from zero, supporting my assumption of constant within-trip misreporting.

Finally, I shift the length distribution of each set up or down in half-cm increments until the absolute difference between the implied percentage juvenile (updated percentage of individuals that are less than 12 cm) and the corrected percentage juvenile is minimized. I use the resulting length distribution to calculate the corrected number of individuals caught by each set. The number of juveniles caught by each set is the corrected number of individuals times the corrected percentage juvenile. Figure C1b displays the corrected length distribution data for all sets. The procedure described in this section preserves the resolution of the electronic logbook data while ensuring that my main outcome of interest—juvenile catch at a given location and time—is not systematically manipulated.

37