rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 3: IV results

||Inversions in frst two layers|
|---|---|
|Mean PM2_._5|-0.0075<sup>_∗∗_</sup><br>(0.0033)|
|N|6,142|
|**Relative Efects:**||
|1SD_↑_PM2_._5_→_“X”SD∆GDP change|-0.41|
|**Kleibergen-Paap rk Wald F statistic:**|5.92|
|**Weather controls:**||
|Temperature|Y|
|Precipitation|Y|
|Relative humidity|Y|
|Surface pressure|Y|
|Wind speed|Y|
|Wind direction|Y|
|**Fixed efects:**||
|Fiscal year|Y|

Notes: The outcome is the first difference within a district of the log of annual GDP measured at the district level. We instrument for annual average of PM2 _._ 5 measured at the district level during a fiscal year from April to March and weighted by population with inversions and inversion strength. Pollution data comes from the Van Donkelaar group. Inversion data comes from the MERRA re-analysis data and measures the incidents when temperature at atmospheric levels above the surface is higher than at the surface by district for each fiscal year. Inversions are measured as the share of days with an inversion. Inversion strength is the average difference in temperature when there is an inversion. All inversion data and weather controls are population weighted averages across districts. The unit of observation is the district year. In all columns we control for the first difference of the number of days in one of fifteen precipitation bins, the number of days the mean temperature is in one of twenty-five bins, a second order polynomial of relative humidity and surface pressure, four wind speed bins, ten wind direction bins, and interactions of relative humidity and the square of relative humidity with all of our
]|[0.140]|[0.126]|
|Community, social|-0.087|-0.147|-0.256**|-0.386***|
|& personal services1996|[0.124]|[0.127]|[0.107]|[0.097]|
|Observations|9,583|9,583|9,583|9,558|
|R-squared|0.595|0.604|0.638|0.715|
|1996 Controls|Yes|Yes|Yes|Yes|
|2011 Controls|-|-|-|Yes|
|Province FE|-|Yes|-|-|
|MunicipalityFE|-|-|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The coefficients presented correspond to the sum of the coefficient for the share of employment in given sector in 1996 (e.g. mining) and the interaction of the homeland indicator with the share employment in the sector in 1996. The dependent variable is the difference in log per capita income for 2011-1996. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 1019, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. 2011 controls include: same as 1996 except for per capita income (which is excluded). All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***
Policy Research Working Paper 10958

# **Abstract**

This paper studies how credit guarantee and employment protection programs interact in supporting firms during crises, using real and financial administrative data for all firms and a quantitative macroeconomic model. Low interest rates encourage riskier firms to demand government-backed loans, while banks tend to reject those applications. The credit demand outweighs this screening response, expanding indebtedness. Given the opportunity cost of shutting down, the employment program’s take-up is unrelated to risk. The employment program mitigates the credit program expansion by supporting firms and enabling banks to screen them. Counterfactuals show how policy ingredients, including interest rate caps, limit macroeconomic risk.

This paper is a product of the Development Research 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 fhuneeus@bcentral.cl, jkaboski@nd.edu, mlarraine@uandes.cl, sschmukler@worldbank.org, and mavera@cmfchile.cl.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
the transaction data. Columns 1 and 3 of Table 1 report data on the sample of 131 matched agents, while Columns 2 and 4 report data from the remainder of the 1,119 unmatched agents. Column 5 reports the p-values for group differences between the sample of matched and unmatched female agents. Column 6 reports analogous p-values for male agents. Finally, Column 7 reports p-values of differences between male and female matched agents.

The comparisons in Columns 5 and 6 indicate significant differences between the matched and unmatched samples with respect to age of the agent and their business, as well as the frequency of some types of transactions. Significant differences in the age of agent and the business should not come as a surprise since we obtained registration records for the first agents that signed up, which therefore, would tend to be older.

Panel B of Table 1 reports the additional characteristics from registration records for the sample of matched agents. According to Column 7, male and female matched agents are similar along most characteristics. The only statistically significant difference is in the mean number of opening hours per day (female agents were open 10.7 hours and males 12.3 hours on average, p = 0.042). Both male and 5
Since the proposed taxfree amounts will apply to all taxable incomes, a larger share of the benefits are expected to benefit households at the top of the distribution, pointing to efficiency gains that could be possible if the proposed tax break were better targeted. As shown in Figure 11, the poverty and inequality reducing impact per zloty spent is

|Figure 11. Inequality and Poverty-reducing efficiency Family<br>andTax-free allowance|500+|
|---|---|
|Family 500+ & PIT<br>allowance at PLN<br>Family 500+ & PIT<br>allowance at PLN||
|Family 500+<br>5000<br>8000||
|PLN 22.2<br>1.2<br>0.14<br>ine||
|PLN 29.7<br>PLN 41.2<br>0.0<br>0.2<br>0.4<br>0.6<br>0.8<br>1.0<br>0.00<br>0.02<br>0.04<br>0.06<br>0.08<br>0.10<br>0.12<br>Change in Gini relative to Basel<br>(per PLN spent)|Change in extreme poverty<br>(per PLN spent)|
|Redistributive efficiency<br>Poverty-reducing efficiency||
|Source: WorldBankestimates based on HBS2014.||

lower for scenarios that include the tax-free allowance compared to the Family 500+ program alone. About 48 percent of the foregone revenue will benefit households in the top 40 percent of the distribution, while only 30 percent will benefit the bottom 40 percent. In relative terms, the tax break represents 4 to 7 percent of disposable income for the poorest 40 percent, but only 2 to 4 percent for the top of the distribution. These results point to potential efficiency gains
Policy Research Working Paper 5472

# **Abstract**

Capital is a fundamental component of agricultural production, and the accumulation of capital is key to growth in agriculture and the process of development. Unfortunately, cross-country data sets on agricultural fixed capital are rare. Using a common methodology that allows comparisons across countries, as well as over time, this paper introduces a data series on fixed capital in agriculture, based on national accounts data. The fixed capital measure differs remarkably from the Food and Agriculture Organization’s data series on tractors, which has been widely utilized as a proxy for agricultural fixed capital. The authors construct comparable measures of capital in livestock and tree stock. They examine the evolution of the capital stocks from 1970 to 2000, paying particular attention to the changing composition of agricultural capital, as well as differences in the accumulation of capital for high-income and middle and lower-income countries. Using the capital measures in agricultural productivity analyses, the data yield estimated input elasticities substantially different from those found previously in the literature. The authors show explicitly that this is due to the improved data set on agricultural capital stocks, as well as the methodology used in the study.

This paper—a product of the Agriculture and Rural Development Team, Development Research Group—is part of a larger effort in the department to examine the determinants of agricultural growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at DLarson@worldbank.org. This study was supported by the Bank’s Research Support Budget under the research project, “The Contributions of Governance to Growth in Agriculture”.

_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
support to store, process and analyze the data. The way one would use these third-party organizations to access data is by engaging them as consultants who do all the work of negotiating with either MNOs and/or regulators in a specific country to access the data and carry out the analysis.

# **Technologies and Platforms**

Since data generated from mobile phones are large due to the high frequency and scale on which the data are generated, it falls into the category of big data, and dealing with it requires non-traditional technologies and frameworks. In this regard, it is worthwhile describing best practices in terms of the tools and techniques required to store, process and analyze big data.

_Data storage:_ With the advent of cloud computing, handling large scale data sets is not as daunting as it used to be for example 20 years ago. Now, organizations can choose to store and process their data on their premises or store it in the cloud. The advantage of cloud storage is that organizations do not have to pay for maintenance costs, and the processing and storage is highly elastic in that it can easily be scaled up or down as need be. This is not the case with on-premise infrastructure, which is static and inflexible. However, for some countries, shipping off their data to the cloud is not acceptable due to security concerns. There are many cloud computing service providers to choose from. Luckily, all the technology solutions we discuss here are agnostic to a specific cloud solution and so would work equally well on any cloud platform. For both on-premise and cloud storage, the state-of-the-art software framework for handling large-scale data sets is the Apache Hadoop software library (or in short Hadoop). Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. One of the core modules for Hadoop is the Hadoop Distributed File System (HDFS); a distributed file system that provides high-throughput access to application data. There are several programming models which
correlations (column 6) – doubling firm size is associated with approximately 0.047 point increase in management score. Going across columns, this relationship appears to be stronger for manufacturing and particularly low-tech. Services, and especially knowledge intensive services, have a weaker relationship of management capabilities with firm size. This result is analogous to the finding in Mexico where the relationship between firm size and management is weaker in services relative to that in manufacturing (Bloom et al., 2019b).

_Second,_ as noted in the descriptive in Section 3, regressions confirm that firm age in Croatia is negatively correlated with management score, that is, adoption of structured management practices is lower among older firms (column 2). Following similar analysis in Bloom et al. (2019b) on Mexico and the work of Hsieh and Klenow (2014) regarding the life cycle of firms, this finding appears to be indicative of poor allocative efficiency among firms in Croatia where clearly, market selection is not operating perhaps due to the lack of pro-competitive conditions, especially in services. Nevertheless, the finding in Croatia is somewhat similar to that observed in a survey of manufacturing firms in Russia such that firms also do not improve with age (Grover and Torre, 2019), however, in Croatia this negative association is primarily driven by the services sector.

_Third,_ demonstrate that management score is positively and significantly correlated with the likelihood being an exporter, and having higher a share of tertiary educated workers (columns 3-4). Exporters are more likely to be better managed, and significantly so in manufacturing. More precisely, the management score of a manufacturing exporter in Croatia is higher by 7 percent than a non-exporter. This result is mainly driven by high-tech manufacturing firms, where the same difference is likely to around 14 percent, thereby suggesting that management capabilities can be improved among other firms in all sectors. Column (5) uses an indicator for ownership of firms. It takes value 1 if a firm is family-owned and run by a family
groups (Lavado et al., 2018; GoA Decree, 2004). Hence, one in five Armenians reports that financial barriers are the reason for forgoing needed care. In addition to these financial barriers, the perception that PHC is of poor quality and gaps in regulation contribute to foregoing of PHC for specialist and emergency care (Andreasyan et al., 2016).

These gaps in service delivery contribute to challenges in detecting and managing NCDs in Armenia, including hypertension and diabetes. In a nationally representative survey, one out of three people above the age of 15 years has undiagnosed hypertension. In addition, only 43.5 percent of the population above 15 years have had their blood pressure measured by a health provider, screening for hypertension. There were higher rates of screening among women compared to men, and in Yerevan compared to the provinces (Andreasyan et al. 2016). Similarly, only 24 percent of individuals aged 15 years and above had their blood glucose levels measured in the past 12 months, of whom 20 percent had been measured by a physician (Andreasyan et al. 2016).

5
district-level characteristics. Unlike the case for gender and social minorities, we see instead evidence of strong complementarities between educational attainment and district characteristics in terms of rural non-farm employment – particularly for salaried workers in the service sector. Thus, in districts with relatively high per capita bank branches or bank credit (Tables A9 and A10, Appendix A.1), rural workers with secondary education have an additional 6 percentage point higher likelihood of being employed in non-farm activities, driven almost exclusively by an additional 4 percentage point increase in the likelihood of being employed as a salaried worker (across both the manufacturing and service sectors).

# **3.3 Rural Non-Farm Employment and Household Consumption**

This section examines whether rural non-farm employment affects household consumption using the same specification outlined in equation (1). We have two outcomes of interest: logged household monthly per capita consumption, and a dummy which equals 1 if the household falls below the poverty line (BPL).<sup>9</sup> A key distinction from the specifications undertaken till now is that the unit of observation for the consumption regressions is the household, instead of the individual. To this extent, we aggregate the individual level employment data to create binary indicators for farm and non-farm employment types and sectors at the level of household. To account for mechanical increases in consumption due to price inflation, we deflate all consumption values to 1993 prices using the aggregate annual wholesale price index.

Table 6 documents the impact of household and district characteristics on household consumption. The outcome of interest in columns (1)-(3) is logged per capita monthly household consumption; in columns (4)-(6) a dummy equaling 1 if the household is situated below the poverty line. Columns (1) and (4) include dummies indicating whether any household member is engaged in non-farm activities. Columns (2) and (5) disaggregates non-farm employment by employment type, while columns (3) and (6) does likewise for employment
On the other hand, when it comes to revenues, our linear specification might not appropriately capture the potential nonlinear nature of tax elasticities. Given that we are dealing with a large sample of diverse countries, it would be impossible to incorporate more precise specifications of tax elasticities for each country. Our approach mimics that of the academic literature that typically uses government expenditures or government consumption to measure volatility in fiscal policy (see, for example, Fatás and Mihov 2013).

We start by examining the determinants of fiscal policy volatility. Table 2 displays a first set of exploratory results. We present three specifications each for primary expenditures (columns (1) to (3)) and government consumption (columns (4) to (6)) as our indicators of fiscal policy volatility. We start with the simplest possible specification in columns (1) and (4). Results show that the dummy variables for EMDEs and commodity exporters are significant and explain as much as 30 percent of the cross-country volatility in fiscal policy. That is, even after controlling for EMDEs, commodity exporters by themselves display more volatility. This suggests that commodity dependence is a source of volatility by itself. Of course, these dummy variables represent a diverse

> 15 The energy index is taken from the World Bank Commodities Prices Data. We also examined the extent to which fiscal policy volatility comoves with non-energy prices. Since the energy and non-energy indexes are highly correlated over the sample period, we chose to include only the energy index in Figure 6 for simplicity.

10
In all SP studies, the main challenge is to persuade the respondent to seriously consider a proposal, typically to be delivered at some point of time in the future. We found respondents to be willing to look ahead and earnestly consider the commodity—improved water supply. This is presumably because water supply is a serious issue that commands people’s time and attention and because the scenario we presented was credible. The credibility of the scenario could stem from the fact that many households seemed to have heard about plans to expand `the` water supply network in the area. The local population’s willingness to understand and consider our proposed scenarios was a valuable signal regarding the content of the survey instrument and the feasibility of the conjoint approach.

# **_Survey administration_**

The data come from a 2003 survey of 1800 households in the Greater Negombo and the coastal strip from Kalutara to Galle in Southwest Sri Lanka. This survey was implemented to gauge current water and sanitation conditions, estimate household demand, and assess access and affordability of poor households before reform measures were implemented. A 3-stage stratified random sampling approach was used to select our sample (see Pattanayak et al. 2004 for more details on how we selected our sample). Our survey instrument was developed through a series of focus groups (in Kandy, Galle, and Kalutara); several purposive discussions with households, the National Water Supply and Drainage Board officials, and the Water Sector Reform Unit staff; and 120 pre-tests in Negombo, Kalutara and Peradeniya. The final 69-page survey contained 7 modules, with a split sample design on the contingent valuation and conjoint questions to gauge household demand. The seven modules included: location and policy priorities; water sources; water treatment, storage and hygiene; sanitation and sewerage; contingent valuation; family roster and health; and a socio-economic profile.

Enumerators were selected from a pool of recent Peradeniya graduates and trained using a mix of lectures, role-plays and field trials in the final survey area. Each survey was conducted as
**Table 11: Observed and simulated mean accepted wages**

||Benchmar|k<br>Same_gend<br>er|frictions|wage_dist|H_prod|preferences|cost|
|---|---|---|---|---|---|---|---|
|pt_m|1.3|1.3|1.3|1.3|1.3|1.3|1.3|
|ft_m|0.7|0.7|0.7|0.7|0.7|0.7|0.7|
|pt_w|1.2|1.3|1.2|1.3|1.2|0.0|0.0|
|ft_w|0.6|0.7|0.6|0.7|0.6|0.0|0.0|

_Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey. _Note:_ Abbreviations: FT= employed full-time; PT= employed part-time. This table presents the converted values in 2011 USD PPP dollars.

**To understand the results of these counterfactual experiments, it is important to recall findings from the estimated parameters.** For example, the parameters indicated that husbands have a labor market advantage in wages offered, as well as a higher probability of finding or losing jobs. Moreover, the leisure preferences of husbands are weighted more heavily than for wives in the instantaneous utility, husbands’ search costs are higher, and wives are on average more productive in home production.

**Figure 8: Mean time in home production by labor market state (%)**

<!-- Start of picture text --> Males without children Males with children<br>60 60<br>50 50<br>40 40<br>30 NP 30 NP
crisis.

Note that statistical data are needed in both models to calculate the expected cash flows per player in each action combination. However, whereas in the efficiency approach the mere existence of statistical data is sufficient, the strategic approach goes much further by allowing each player to _actively_ make a decision. This decision considers the distribution of cash flows between the players as determined by the distribution rules. Distribution rules create incentives that can only be captured by applying strategic considerations. However, it is also important to note that the strategic approach no longer leads to an optimum from a social point of view since the project with the lower NPV is selected.

8