rafmacalaba/data-use-mentions-extended huggingface dataset:
the inputs upfront. The removal of what was essentially an interest-free loan on the cost of the inputs had some financial costs for farmers. Due to the relatively small real price increase in cocoa and cotton, one cannot necessarily conclude that all export crop farmers were better off as a result of the devaluation. Cocoa farmers whose incomes also depended heavily on domestically produced staple food crops or who consumed a higher than average share of imports could well have suffered a decline in real expenditures.

On the food crop side, the real price of locally produced staple food crop prices declined. If one looks at the staple food component of the blue collar CPI, between December 1993 and December 1994, the real price of staple foods actually decreased (table 15). Between 1994 and 1995, the staple food index had essentially caught up to the overall price index. Breaking it down into the importable and nontradable foodstuffs, however, shows that most of the price increase was in the price of importables rather than nontradable foodstuffs. Between December 1993 and December 1995, the price of imported staple foods (essentially rice and wheat products) increased by 67 percent, compared to a 29 percent increase in locally produced staple foods. Over the same period, the CPI increased by 44 percent, so there was actually a decrease in the real price of locally produced foodstuffs of about 11 percent (table 15). Our analysis of the staple food price data collected across ten markets in CMte d'Ivoire shows that real staple food prices declined anywhere from 0 to 20 percent depending on the particular product between 1993 and 1995 (table 11). Only the real price of plantains remained stable, according to the market price data. In view of the decline in staple food crop prices, one would expect an increase in poverty among farners specializing in staple food crop production.

Table 15 Index of Prices of Staple Foods, **1993-1995a)**

|_Expenditure item_||_CPI price index_||
|---|---
yihdt = μYoungChildh + ωYoungChildhPreschooldt+ΓXidt + γdt + εidt (6) where YoungChildh is equal to 1 if there is a child under 10 in the household _h_ of woman _i._ The remainder of the notation is the same as in equation (5). Similarly, we find a significant negative association between the presence of young children in the household and the likelihood that a woman works, which is mitigated by preschool availability (Table 10, Columns 4-6).

# 5.2. Reduction in worker turnover

In addition to enabling labor market entry, preschool availability may increase firm productivity by increasing job stability, reducing worker turnover among women. Although the majority of studies which evaluate the impact of childcare services on maternal labor market outcomes do not analyze turnover, those that do suggest that access to childcare strengthens maternal attachment to jobs. Angeles et al. (2011) find that _Programa de Estancias Infantiles_ in Mexico, which provides access to childcare for working mothers, increased job stability among its beneficiaries. Ranganathan and Pedulla (2021) find that availability of employer-sponsored childcare increases daily work attendance, which may contribute to a reduction in turnover.

Several studies, in turn, suggest that higher turnover is likely to result in productivity losses: high turnover deteriorates firm-specific stock of knowledge and expertise directly through the loss of trained workers and through lowering incentives for the firms to invest in training of their employees. This effect is captured by TFP, as this form of human capital cannot be fully accounted for in wages (Brown and Medoff, 1978; Shaw, 2011; Strober, 1990; Yanadori and Kato, 2007; Giesing and Laurentsyeva, 2017).

We explore the likelihood of this mechanism by examining the association between preschool availability and worker turnover using SAKERNAS data. We estimate regressions (5) and (6) with dummies equal to 1 if worker _i_ changed job in year _t_ . We carry out similar analysis for the likelihood of job turnover in manufacturing and services. Table 11 shows that women experience higher job
and Xu, 2018). We find that high HCR takeovers, that is, takeovers where the target and acquirer workers have a more similar skill distribution, generate larger employment reductions at the consolidated firm in terms of both retention and hours worked. We then exploit our rich individual-level data to study the outcomes of specific workers who have similar skills to the workers at acquirers and thus are most likely to serve similar roles. We find that for a worker of a specific skill level, being acquired by a firm with a larger share of workers with that same skill level increases the likelihood of separation. For a given worker, being acquired by a firm where over two-thirds of the workforce has a similar skill set leads to a 18.9% reduction in retention compared to 9.4% for workers being acquired by where fewer than a third of workers have the same skills.

2
_._ 989<sup>∗∗∗</sup> which is comparable with the average autocorrelation coefficient for bond yields. The second factor accounts for more ˆ than half of the remaining residual variance and has an AR(1) coefficient of _휌_ = 0 _._ 965<sup>∗∗∗</sup> . These characteristics emphasize the necessity of the employed IFE method. The change in coefficients between the FE estimates in (3) and the IFE estimates (6), together with the simulation study in Appendix, Section A.2, give an idea of how strong the bias of a misspecified model is. We proceed with _퐾_ = 2.

# **5 Limitations**

## **5.1 Lack of biodiversity**

For the sake of consistent, global coverage, the wealth data made the deliberate decision to calculate aggregated numbers on the country level. This prevents decompositions beyond what Table 6 presents. One interesting component that affects all renewables is biodiversity. The wealth of species diversity and ecosystem services is currently only accounted for indirectly. These are restricted to terrestrial areas and do not include the blue economy. Loss of biodiversity and collapse of the ecosystem is ranked as top five global risks (WEF, 2020a). Regulators, pension funds and banks are paying more and more attention to the effects on the financial sector (Schellekens et al., 2019; van Toor et al., 2020).

## **5.2 Low data frequency**

A further limitation imposed by the wealth data is its annual frequency. Seasonal effects and other business cycle dynamics that possibly affect bond prices cannot be addressed. The immediate impacts of droughts and floods are not discernible. One promising possibility to address this issue is to include high-frequency and objective geospatial data, such as remote-sensing satellite data or weather station measurements (WWF et al., 2020). This type of data, which has the added benefit of being exogenous by nature, would
identify “predicted (potential) beneficiary households” by applying the PMT targeting formula to the baseline data collected in the treatment and control groups. In practice, there are differences between the PMT score predicted from the baseline survey and the actual PMT score calculated from the targeting registry census. As such, the set of predicted beneficiaries does not fully overlap with actual beneficiaries in the treatment groups. This approach should thus be interpreted as providing intent-to-treat estimates. Figure A.1 illustrates that the PMT score from the registry census accurately predicts beneficiary status: the probability of being a beneficiary drops sharply around the eligibility cut-off. This shows that the PMT selection procedure was faithfully implemented based on the registry census data. However, the “predicted” PMT score based on the baseline survey variables does not perfectly match the PMT score from the registry census: the likelihood of being predicted beneficiary is strongly associated with the PMT score from the registry census, but no sharp drop is observed at the cut-off.<sup>21</sup> Table 1 (Panel C) illustrates the composition of the sample by “predicted” beneficiary status for all baseline households included in the follow-up sample. (Note that predicted beneficiary status cannot be estimated for households in the booster sample since they were not interviewed at baseline. Booster sample households are not included in specifications involving comparisons with the control group.) The effect of the cash transfers can be separated from the effect of the behavioral change component by comparing “predicted beneficiaries” between the treatment and control groups. ITT estimates are obtained through the following regression model based on follow-up data on the sub-sample of “predicted beneficiary households” (PB=1): _i_ refers to an individual or household (depending on whether the outcome of interest is a household-level or individual-level outcome), _Y_ is the outcome of interest, _Z_ is a set of randomization strata fixed effects, and _ε_ is the error term. Robust (White-Huber) standard-errors are clustered. This specification compares outcomes between observations in cells H
regions use community clinics whereas those who live in richer regions bypass community clinics and may prefer to use private health services or hospitals even for the first line of care, causing greater neglect of community clinics in richer regions. If this happens to be the case, then the poorest of the poor in relatively higher income regions are likely to be left out to a greater degree than the poorest of the poor in relatively low‐income regions.

However, this assessment only gives us a partial picture. To understand the depth of the problem, a more extensive analysis would be necessary. The simplicity of the rapid assessment survey affords representativeness at the cost of depth of information. Therefore, while this analysis provides a preliminary account of the state of WASH in community clinics, it does not sufficiently establish a baseline against which progress towards SDGs can be measured. The DGHS an MoHFW will benefit from adopting the core survey components designed to assess SDG indicators detailed in the WHO/UNICEF (2018) guidelines, which can then be adjusted to fit the Bangladeshi context.

10
<!-- Start of picture text --> Share of Elderly in Formal and Informal Employment<br>In European countries, by region<br>2004-2009<br>East North South West<br>Employee with a contract Employee without a contract<br>Informal self-employed<br>Elderly: Persons aged 55+<br>Source: Calculation with ESS data<br>30<br>20<br>10<br>0<br><!-- End of picture text -->

**Figure 6 Share of elderly in formal and informal  employment**

Figure 7 demonstrates that the share of young workers among informal employees is close to 20% or even higher in all European countries except Bulgaria, Czech R., Cyprus, and Portugal. Especially high proportions of 15-24 year olds among those working without contracts are found in Slovenia (close to 60%), Russia (40%), Slovakia, Poland, Italy and the Netherlands (35 to 30%). By contrast, the share of youth among formal employees fluctuates around (in most cases, below) 10%; only in Austria and Israel it is closer to 20%. The share of youth among informal employees is substantially higher than among their formal sector counterparts in all countries except Portugal, Cyprus and Israel.

The elderly are over-represented among informal employees in all Western European countries except Ireland, but also in Poland, Czech R., Denmark, Norway, Israel and Portugal (Figure 7).

26
# **10. References**

Aten, B., A. Heston and R. Summers. 2002. Penn World Table Version 6.1, Center for International Comparisons at the University of Pennsylvania (CICUP).

Barro, Robert J. and Jong-Wha Lee. 2001. “International Data on Educational Attainment: Updates and Implications.” _Oxford Economic Papers_ 2001; 53: 541-563.

Bhagwati, J. 1968. The Theory and Practice of Commercial Policy: Departures from Unified Exchange Rates. Special Papers in International Economics. New Jersey: Princeton University Press.

Baumol, W.J., R.R. Nelson and E.N. Wolff. 1994. The Convergence of Productivity, Its <u>Significance and Its Varied Connotations. Oxford: Oxford University Press.</u> Beck, T., R. Levine and N. Loayza. 2000. “Finance and the sources of growth.” _Journal of Financial Economics_ . 58(1-2): 261-300.

Butkevicius, A., A. Kadri and J. Mayer. 2002. “Dynamic Products in World Exports.” United Nations Conference on Trade and Development Discussion Paper No. 159. Geneva: UNCTAD.

FUSADES (Fundación Salvadoreña para el Desarrollo Económico y Social). 2004. “Cuarta estrategia económica y social 2004-2009 oportunidades, seguridad y legitimidad: bases para el desarrollo.” El Salvador: FUSADES.

Ganslandt, M and J.R. Markusen. 2000. Standards and related regulations in international trade: A modeling approach. _Quantifying the impact of technical barriers to trade: Can it be done?_ 95135. Studies in International Economics, Ann Arbor: University of Michigan Press.

Grubel, Herbert G. and P.J. Lloyd. 1975. Intra-Industry Trade: The Theory and Measurement of <u>International Trade in Differentiated Products. New York: John Wiley & Sons.</u> Gwartney, James and Robert Lawson (2004). “Economic freedom of the world: 2004
with information from the credit bureau slightly boosts performance, suggesting that the information gathered by the bureau is only slightly complementary to that collected by our approach.

A robustness check assesses models trained instead with 10 fold cross validation in Table S2, which exploits more of the sample but is more computationally demanding. This improves performance of the CDR models in particular, which are more complex and thus data hungry. Their performance is also likely to improve with additional tuning.

Our method also performs better than credit bureau models when assessed with alternate metrics of performance that are informative for our decision problem; in particular, the H-measure designed to overcome some weaknesses of AUC (Hand, 2009) and out of sample R<sup>2</sup> (see Table S3). ROC curves are also presented in Figure S4 and comparisons of scores to actual repayment in Figure S5, for the main models.

# _Out of Time_

When implemented, a model trained on past data will be used to predict future repayment. As a robustness check, we assess the out-of-time performance of all models by training and testing on different time periods. To do this, we construct an offset version of the dataset. The sample of individuals is split into two; the early group that was transitioned before the median date, and the late group after the median. Then, the phone data is evenly divided, into an early and late period. We construct offset versions of our indicators using only transactions occurring in that half of the data (up to the date of each transition). Because these offset indicators are constructed on a shorter panel, they capture less information than our full indicators. The model is trained on the early group, with phone indicators derived from the early period of phone data, and test it on the late group, with indicators derived from the late period of phone data, with results in the last column of Table 3. Because there is only one late period to test on, out of time results are
Policy Research Working Paper 10598

# **Abstract**

This paper introduces a new disaggregated and harmonized dataset on public procurement and budget execution by Brazilian subnational entities, which currently covers half of Brazilian municipalities and spans the years 2003–21. This dataset provides key information that was previously unavailable from aggregate data, such as the identities of suppliers, details on purchases of goods and services, and granular information on the life cycle of each expenditure action. It then uses these data to provide new stylized facts about local public finance. First, it shows that about one-quarter of government purchases are locally procured and discusses implications for efficiency. Second, it demonstrates that close to 15 percent of payments exceed the 30-day threshold and that payment timeliness is systematically correlated with the income level of the municipality.

This paper is a product of the Development Impact Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at tscot@worldbank.org.@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_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
crisis, by January 1, 2001, the date that masked data from June of 2000 was first made available to the public, the exercise of unilateral market power in California had already resulted in more than $5 billion in overpayments to suppliers in the California electricity market as measured by BBW (2002). Consequently, a long time lag between the date the data is produced and the date it is released to the public has an enormous potential cost to consumers that should be balanced against the benefits of delaying the data release.

The usual argument against immediate data release is that suppliers could use this information to coordinate their actions to raise market prices through sophisticated tacit collusion schemes. Although the immediate availability of information on bids, schedules and actual unit‐level production could allow suppliers to design more complex state‐ dependent strategies for enforcing collusive market outcomes, it is important to bear in mind that coordinated actions to raise market prices are illegal under US anti‐trust law and under the competition law in virtually all countries around the world. The immediate availability of this data means that the public also has access to this information and can undertake studies examining the extent to which market prices difference competitive benchmark levels as described in BBW (2002). Keeping this real‐time data confidential prevents this potentially important form of public scrutiny of market performance from occurring.

12
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autocorrelation data available for the study populations being considered. We see from Table 2 that autocorrelations are often in the 0.2-0.3 range for household income and consumption. In rural Ghana, the autocorrelation in Boozer et al (2010)’s data is 0.32 when wives report on both their own and their husband’s consumption, but increase to 0.58 to 0.66 when husbands and wives report separately on consumption and the results are combined. The autocorrelations are lower for the poor – in both Mexico and Argentina the autocorrelation of labor income is around 0.2-0.3 for individuals with first period labor income below the median, compared to autocorrelations of 0.5-0.8 for those with this income above the median. This is consistent with the evidence in Collins et al. (2009) that incomes for the poor are very volatile.

The autocorrelation will typically be lower in the more homogeneous samples typical of many randomized experiments than it is in general samples of the population. It will also be lower if one stratifies the randomization or uses fixed effects and thereby controls for time-invariant reasons why one household or firm may have higher income, expenditure or profit levels than another. To see this in a simple example, write the error term , where _X_ is a characteristic that is used to stratify the randomization on, or to restrict an experiment only to individuals with certain values of _X._ Then: And Controlling for _X_ via stratified randomization, or restricting the sample only to those with certain values of _X_ will therefore reduce the variance of , which increases the power of an experiment. However, it also reduces the autocorrelation (by removing a factor which would otherwise cause the error term to be correlated from one period to the next), thereby increasing the value of more measurements.

# **2.4. What should one do if the autocorrelations are not constant?**

The empirical evidence therefore suggests that the assumption that the autocorrelation is constant over different time
Furthermore, progress in reducing IPV differs between Boko Haram affected areas and the rest of Nigeria. Controlling for differences in individual and household characteristics, we find that BH insurgency is associated with slower progress towards preventing and eliminating women’s experiences of physical and sexual IPV. This finding implies that conflict may not always increase the prevalence of IPV but may set back progress in lowering IPV that could otherwise have been achieved.

Our findings contribute to two literatures. First, we extend the literature that quantifies the association between conflict and rates of IPV using nationally representative data. In addition, we analyze the association between conflict and indicators of behavior change that are associated with IPV risk; controlling behavior of male partners and women’s ability to influence household decision-making. We find controlling behaviors of husbands/partners and reductions in women’s autonomy in household decision-making are heightened in locations that are impacted by the BH insurgency, indicating that the BH insurgency adversely affects women’s agency and exacerbates behaviors that are often pre-cursors to IPV. Second, in analyzing IPV we expand the research literature examining the effect of conflict on human development in Nigeria. UNDP (2018) points to the destruction of social and economic infrastructure in the conflict affected areas. Measures of well-being – including food security, gender equality, access to health, education, clean water and sanitation - captured in the various reports and community surveys conducted in the region have all worsened. Research to date has focused on health outcomes and access to maternal health services. Ekhator-Mobayode and Asfaw (2019) and Howell et al. (2018) find the BH insurgency reduced anthropometric measurements of children under five. And Chukwuma and EkhatorMobayode (2019) find that the BH insurgency decreased the probability of any antenatal care visits and decreased the probability of delivery at a health center by a skilled health professional.

We use a careful empirical strategy employing a quasi-experimental methodology to explore these conflict-related impacts. To estimate the effect of the BH insurgency on IPV we spatially link geo-referenced data on conflict events from