rafmacalaba/data-use-mentions-extended huggingface dataset:
beneficiary total revenues at this same follow-up point.<sup>12</sup> These results suggest that representing women’s economic activity in terms of interdependent agency contributed to driving the economic impacts of the psychosocial interventions in the multi-faceted program.

Given insufficient power to detect differences between the two intervention conditions, we did not pre-register a comparison among them, and post-hoc analyses show no difference between the psychosocial intervention conditions on the economic composite index (difference of _d_ =0.05, SE=0.05, _p_ =.340), intrapersonal psychosocial index (difference of _d_ =-0.02, SE=0.05, _p_ =.693), or relational psychosocial index (difference of _d_ =0.04, SE=0.06, _p_ =.446) (see Tables 2 and S5). Similarly, we find no difference between these two conditions on any measure taken immediately post intervention delivery (see Table S12), with the exception of those in the Interpersonal Initiative condition being marginally less likely than those in the Personal Initiative condition to anticipate being seen negatively by those in their family and community ( β =-0.03, p=0.088).

We also pre-registered analyses on administrative data for participation rates in the business and life skills trainings that occurred shortly after the delivery of these Initiative interventions and found no difference across conditions. However, this is likely due to ceiling effects given that the median number of sessions attended by women in all conditions was 12 of 12 sessions offered (MControl=10.81; β PI=-0.09, t(2622)=-0.71, p=0.480; β II=-0.07, t(2622)=-0.62, p=0.538).

**Psychosocial outcomes.** In addition to economic outcomes, we also assessed effects on psychosocial outcomes that are important indicators of psychological and social well-being on their own
demand-pull policies stimulated non-incremental (radical) change in the California wind power industry in the 1975-1991 period also finds no evidence that demand-side policies alone encouraged more radical frontier-type technical change (Nemet, 2009).

**Figure 9. Motivation of firms introducing environmental innovations, 2006-08**

**(** **_Firms citing factors as motivations, percentage of innovative firms)_**

<!-- Start of picture text --> HUN<br>CZE<br>SVK<br>ISR<br>PRT Current or expected<br>IRL market demand<br>POL<br>EST<br>Existing<br>FRA environmental<br>ITA regulations or taxes<br>on pollution<br>DEU<br>Fiscal incentives<br>BEL<br>FIN<br>NLD<br>LUX<br>SWE<br>0% 10% 20% 30% 40% 50%<br><!-- End of picture text --> Source: OECD, based on Eurostat (CIS-2008) and national data sources, June 2011. Note: The Community Innovation Survey (CIS-2008) was the first to introduce a voluntary module on environmental innovation. An ̳environmental innovation‘ is defined as an innovation that leads to more environmental benefits than alternatives. The environmental benefits can be the primary objective or the result of other innovation objectives. ̳Innovative firms‘ refers to firms with any of the four types of innovation (product, <u>process, organizational and marketing).</u> However, for technologies that are more mature, a greater emphasis on demand-side policies may be more effective in spurring firms to introduce more incremental innovations. Based on firm responses in 16 countries as part of the 2008 EU Community Innovation Surveys, Figure 9 shows that existing or future environmental regulations, followed by market demand from customers, are identified by firms in most countries as the main driver of introducing environmental innovations. The availability
# **Table 1. Bribes in selected countries**

||Faced|Reported|Bribery|Firms|Bribes as|CPI|
|---|---|---|---|---|---|---|
||bribery|corruption|depth (%|expected to|a share of||
||(% of<br>firms)|as a major<br>constraint (%<br>of firms)|of firms)|give bribes “to<br>get things<br>done” (%)|sales (%)||
|Sweden|1.9|2.7|1.5|-|-|84|
|Poland|5.1|18.6|4.5|14.5|-|60|
|Guatemala|7.3|64.6|4.2|12.9|12.4|28|
|Argentina|9.3|50.0|6.2|14.7|6.5|39|
|Brazil|11.7|68.8|8.4|12.5|6.6|37|
|Peru|16.5|51.2|15.9|15.5|7.2|37|
|Germany|-|3.8|-|-|1.1|81|
|All countries|18.0|32.7|14.1|22.5|-|43|
|LAC|8.7|36.3|6.3|10.0|-|43|

_Source:_ WBES and BEEPS 2005.

_Note_ : The CPI is measured from 0 to 100, and a higher number means a lower perception of corruption. BEEPS =The Business Environment and Enterprise Performance Survey; CPI = Corruption Perception Index
's de 850 A “eo 29<br>& Wan. ° °<br>°<br>¢ eee?00 8° %o. 6 00 ° 000 o<br>x=<br>no 11 12 13 14 15 16 17<br>E Log Population Size (Inh.) of Agglomeration<br>1980 or Before === Early1990s =—————= c.~2010<br><!-- End of picture text --> <!-- Start of picture text --> a<br>2<br>ke}F x a ,<br>fe}g ot oe<br>i ° =n ” *s, re<br>@ “=e, . » ooonaaae<br>rs N “sen0° ‘ee<br>5<br>Fa<br>et> !<br>3<br>uw£ ©<br>1 2 3 4 5 6 7 8 9<br>City Population Size Category<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>fe}2 rey~<br>eaeot<br>&<br>2 Q<br>2 s<br>5 © -<br>5 Oe<br>2°> ooeTM Pot ite L DT Trt se<br>a0 eoneee<br>usE ee=<br>1 2 3 4 5 6 7 8 9<br>City Population Size Category<br><!-- End of picture text -->
instance a child grown up in a wealthy household, with parents employed in highly ranked jobs, might have been pushed to pursue a high profile career and, at same time, might have been used and educated to a healthy life-style with a vegetable-rich diet and regular sport practice, and thus in a position to reduce the probability of subsequent chronic conditions.

To address these empirical problems, we look at the siblings living in those households surveyed by the Egypt Household Health Utilization and Expenditure Survey (EHHUES) 2002, a cross section composed of about 33,000 observations. Siblings are more similar in terms of genetic endowment, preferences and family background than any two randomly matched individuals. Therefore any pair of siblings shares a common family fixed effect that we can control for by means of within-siblings estimates. Of course, especially among siblings of rather different ages, it is likely that an important idiosyncratic component remains uncontrolled for (Griliches, 1979), as family background itself might change overtime (Ermish and Francesconi, 2001). To avoid this problem, within-siblings estimators have been often applied to subsamples of twins (Ashenfelter and Krugman, 1994; Ashenfelter and Rouse, 1998), as twins share exactly the same family background on top of an almost identical genetic endowment. Unfortunately the number of twins is too small in our data to be of any use. Thus, to check the robustness of our identification strategy, we approximated the ideal case of twins by carrying out our analysis on the restricted set of siblings whose age differs by at most two or three years, obtaining results closely in line with those obtained from the unrestricted sample.

A second empirical issue that we have taken into account is the fact that the indicator of chronic diseases is self-reported and therefore subject to possibly relevant measurement errors (responsible for an attenuation bias in the estimates).

The remainder of the paper is organized as follows. Section 2 presents the theoretical framework from which the empirical models are derived. Section 3 describes which variables are included in the model
household consumption from surveys and HFCE in SSA is equal to 0.18 and is not significantly different from zero, compared to 0.87 for the rest of the regions. In other words, in our sample of consecutive spells, private consumption as measured by national accounts has no predictive value for household consumption as measured from surveys in SSA.<sup>16</sup> As mentioned earlier in this paper, the lower GEP in SSA should not be a surprise, given that elasticities tend to be lower in countries with high baseline poverty rates (and hence a given percentage point reduction in poverty will be smaller in relative terms). Although the regressions in Tables 4-6 control for both baseline poverty rates and baseline mean welfare, one could still argue that these controls are insufficient and that the result of lower elasticities in SSA is an artifact of the way the GEP is calculated.<sup>17</sup> While this argument can be valid, it cannot be extended to the regressions of survey means on GDP per capita or HFCE (presented in Table 7 and Table 8), as both the dependent (survey means) and key independent variable (GDP per capita or private consumption expenditure per capita) have a similar low base in SSA. As such, we consider the results in Table 7 and Table 8 as our strongest results, suggesting that economic growth (measured by either GDP per capita or HFCE per capita) has a lower passthrough to household consumption as measured from surveys in SSA relative to other regions. And given that poverty in most countries in SSA is measured based on household consumption expenditures from surveys, it suggests that GDP per capita growth must have a weaker effect on poverty reduction in SSA relative to other regions, independent of SSA’s higher baseline poverty rates.

Two other issues need to be addressed before we can have reasonable confidence in the results presented so far. The first relates to surveys not adequately capturing rich households. One might argue that if richer households in
# **Table 6 Summary Statistics**

|**Variable**|**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
||**1980**|**-1989**||||
|**Log of GDP per capita**|71|8.52|0.95|6.05|10.09|
|**Openness Index**|71|50.91|31.11|7.53|142.98|
|**Log of Distance to the US**|71|8.45|0.55|6.98|9.15|
|**English**|71|0.41|0.50|0.00|1.00|
|**Military conflict**|71|0.38|0.49|0.00|1.00|
|**Communism index**|71|0.14|0.35|0.00|1.00|
||**1970**|**-1979**||||
|**Log of GDP per capita**|72|8.34|0.93|6.20|10.01|
|**Openness Index**|72|51.10|36.09|7.16|189.99|
|**Log of Distance to the US**|72|8.43|0.56|6.98|9.15|
|**English**|72|0.42|0.50|0.00|1.00|
|*
# I. **Introduction**

Economic policy makers have traditionally held the view that, due to capital market imperfections, there is a shortage of long term finance and that this acts as a barrier to industrial performance and growth. This belief has led to the establishment of special term lending institutions often supported by governments and other agencies, particularly in developing countries. Long tern finance is thought to allow firms to invest in more productive technologies, even when they do not provide an immediate payoff, without the fear of premature liquidation. However, short term finance may have better incentive properties compared with long term finance, because it allows suppliers of finance to monitor and control firms more effectively, with favourable effects on firms' performance.

Although progress has been made in investigating the nature of capital market imperfections, which state interventions are supposed to provide a remedy for, the consequences of these interventions on firms' behaviour and performance have not been as thoroughly analysed. Ultimately the twin issues of the determinants and the consequences of the maturity structure of debt cannot be satisfactorily resolved at the theoretical level. An empirical analysis of these issues is both justified and necessary. Using data from a panel of Indian public limited companies for the period 1980-89 this paper empirically examines the determinants of the maturity structure of debt held by firms and the consequences of maturity on their performance, in particular on their productivity.

This study is, in our view, particularly interesting for the following reasons. Firstly, India during the period of the study had a repressed financial system where financial institutions were largely government owned and a major portion of credit was allocated according to state policy. This was a typical situation in LDC's before the introduction of financial liberalization measures in the 80's and early 90's. In India significant reforms of the financial system have taken place since 1991, but
**Table 7.** Risk Exposure Limits Imposed on Licensed MFIs, 2002

|_Item_|_Benin:_<br>_credit unions and authorized_<br>_MFIs_|_Ghana:_<br>_licensed banks, NBFIs,_<br>_and credit unions_|_Tanzania:_<br>_licensed banks_<sup>_a_</sup>|
|---|---|---|---|
|Ceiling on total loans|200 percent of total deposits|No ceiling specified|No ceiling specified|
|Unsecured loans ceiling|No ceiling specified|No ceiling specified|10 percent of core capital|
|Single borrower limit|10 percent of total deposits|� Unsecured loans: 10 percent of<br>net worth<br>� Secured loans: 25 percent of net<br>worth for banks; 15 percent of net<br>worth for NBFIs and S&Ls|25 percent of core capital|
|Connected lending limit|20 percent of total deposits|� Unsecured loans (individual): 0.67<br>percent of net worth<br>� Secured loans (individual): 2<br>percent of net worth<br>� Unsecured loans (aggregate): 10<br>percent of net worth|Less than or equal to the value of the<br>officer’s shares and deposits; in total<br>less than or equal to 25percent of the<br>bank’s net worth|
|Ceiling on fixed assets|No ceiling specified|No ceiling specified|10 percent of net worth|

a. Data for Tanzania are as of June 2003. _Source:_ Gallardo and Randhawa (2003); Ouattara (2003); Steel and Andah (2003).

# **_Liquidity Risk_**

64. Credit unions and licensed MFIs in Benin and licensed banks, and deposit-taking NBFIs in Ghana and Tanzania are required to maintain primary liquidity reserves against deposit liabilities
Policy Research Working Paper 9385

# **Abstract**

This paper investigates the local effects of mining on the quality of public services and on people’s optimism about their future living conditions. It also assesses the mediating role of local institutions and local governments’ taxing rights in shaping the proximity-to-mine effects. The empirical framework connects more than 130,000 respondents from the Afrobarometer survey data (2005‒2015) to their closest mines based on the geolocation coordinates of the enumeration areas (EA) and data on the mines and their respective status from the SNL Metals & Mining. The geo-referenced data are matched with new indicators on local governments’ taxing rights across the African continent. The results suggest that citizens living near an active mine are less likely to approve government performance in key public goods and services—including health, job creation and improving living standards of the poor. On the mediating role of local governance and local taxing rights, the findings point to a negative effect of local corruption, yet a positive effect of local authorities’ discretion over tax and revenues. However, the positive marginal effect of local taxing powers tends to reduce in environments with poor quality of local governance, high incidence of bribe payment and low level of trust in local government officials. Residents of mining communities with low corruption and comparatively high-level of raising revenue ability have the highest rate of positive appraisal compared to the other scenarios.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 mk4415@columbia.edu or at r.vincent@maastrichtuniversity.nl.

_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
and 108 rural health centers in the nine control districts. The surveys included general facility assessments, health provider interviews, and direct clinical observation of consultations with children under-5 and adults aged 40 and above. When present, a single health house affiliated with each rural health center was randomly selected for assessment.

In the catchment area of each of the chosen health centers, two villages were randomly selected to be included as a sampling unit for the household survey.<sup>8</sup> Within these villages, 4,345 households with women who had live births in the 24 months before the survey were randomly selected for the household survey. In a third of the catchment areas in each district, households with adults aged 40 and above were also selected for a total of 1,668 such households.

A follow-up survey was conducted from March to July 2018 in the same health facilities and the same villages, using the same survey tools. The only change in survey methodology concerned the direct observations. In the baseline survey, less than 30% of the target number of consultations have been observed because few patients arrived in the facilities in the winter months, and care was mainly provided through home visits. Therefore, the follow-up facility-based survey was conducted in the spring and summer months, and the enumerators also observed consultations provided through home visits.

In six rural health centers included in the baseline sample, a follow-up survey was not conducted because they were under renovation, closed, or downgraded to health house status. Those catchment areas were removed from the analysis sample. Table A2 in the appendix presents sample characteristics and some key outcomes from the baseline survey of the analysis sample.

# Empirical specification

Our primary empirical specification is the following difference-in-differences model used for estimating the impact of the PBF intervention on outcomes of interest:

> impact on results. If the program had immediate impacts, this would diminish the differences between baseline and follow-up surveys and our estimated impacts.

> 8 In catchment areas with a health houses, one of
0.90|0.80|

Results from household-level EBP models using geospatial auxiliary data. Sources: Edochie et al. (forthcoming), Newhouse et al. (2022), Merfeld and Newhouse (2023) Pfefferman and Sverchkov (2007) proposed a bias correction for out-of-sample areas when the probability of selection of an area into the sample is known, but first stage sampling probabilities are rarely available to analysts, and this correction has not yet to our knowledge been implemented in any small area estimation software package. Estimating a separate model with inverse probability weighting for out-of-sample areas may have the potential to improve the accuracy of out-of-sample estimates, by giving greater weight to areas that were less likely to be sampled, based on observable characteristics, when estimating model parameters. As discussed in more detail below, tree-based machines learning methods are also more robust to this source of bias and therefore tend to generate more accurate outof-sample predictions. Until either the use of tree-based machine learning or explicit bias correction becomes routine, however, predictions for out-of-sample areas based on publicly available geospatial indicators should be treated with great caution.

11
- Dix-Carneiro, R., Goldberg, P. K., Meghir, C., & Ulyssea, G. (2021). _Trade and domestic distortions: the case of informality_ (Working Paper No. 28391). National Bureau of Economic Research.

- Dix-Carneiro, R., & Kovak, B. K. (2019). Margins of labor market adjustment to trade. _Journal of International Economics_ , _117_ , 125–142.

- Flaaen, A., Shapiro, M. D., & Sorkin, I. (2019). Reconsidering the consequences of worker displacements: Firm versus worker perspective. _American Economic Journal: Macroeconomics_ , _11_ (2), 193–227.

- Foster, L., Grim, C., & Haltiwanger, J. (2016). Reallocation in the great recession: cleansing or not? _Journal of Labor Economics_ , _34_ (S1), S293–S331.

- Fr ́ıas, J. A., Kaplan, D. S., Verhoogen, E., & Alfaro-Serrano, D. (2022). Exports and wage premia: Evidence from Mexican employer-employee data. _Review of Economics and Statistics - forthcoming_ .

- Garin, A., & Silv ́erio, F. (2022). How responsive are wages to demand within the firm? evidence from idiosyncratic export demand shocks. _Review of Economic Studies - forthcoming_ .

- GC Britto, D., Pinotti, P., & Sampaio, B. (2022). The effect of job loss and unemployment insurance on crime in Brazil. _Econometrica_ , _90_ , 1393–1423.

- Gerard, F., & Gonzaga, G. (2021). Informal labor and the efficiency cost of social programs: Evidence from unemployment insurance in Brazil. _American Economic Journal: Economic Policy_ , _13_ (3), 167–206.

- Gerard, F., Lagos, L., Severnini, E., & Card, D. (2021)
|Croatia|1|8|3|3|15|
|---|---|---|---|---|---|
|Ethiopia|2|1|0|1|4|
|Georgia|1|0|0|1|2|
|Ghana|3|1|0|0|4|
|Indonesia|1|3|1|1|6|
|Kenya|4|1|0|1|6|
|Kosovo|2|0|0|2|4|
|Peru|15|1|0|0|16|
|Romania|4|2|1|3|10|
|Solomon Islands|1|1|0|0|2|
|South Africa|1|3|1|2|7|
|Ukraine|16|4|0|1|21|
|Uruguay|1|0|1|3|5|
|Total|72|32|7|24|135|

_Source_ : World Bank Infrastructure SOE Database.

# **4 Fiscal Injections to SOEs**

# **_4.1 A Typology of Fiscal Injections to SOEs_**

We created the following measures of fiscal injections to support infrastructure SOEs:

- **Operational subsidies:** Operational subsidies are the fiscal transfers governments send to SOEs to cover shortfalls for the year and, often, to compensate the SOE for specific QFOs. They also include extraordinary transfers to cover unexpected losses, shortfalls in capital investments, etc. These subsidies can account for a large share of assets, especially when they cover equity shortfalls (that is, are used to recapitalize an SOE). In some countries, they are made regularly, especially when governments compensate SOEs ex ante for specific QFOs and mandates.

- **Government injections of equity:** Governments can bail out and recapitalize SOEs by injecting equity. These equity injections increase
such as health and education.<sup>7</sup> This scheme, which requires Venezuelans to apply for it, covers migrants already living in Colombia with either regular or irregular status.

However, we know very little about the public’s preferences for specific policies towards Venezuelan migrants. To be sure, Proyecto Migración Venezuela has collected data on public opinion towards Venezuelans in Colombia. Using data collected in September 2020, Figure 3 shows the percentage of individuals agreeing to different statements. At the time of that survey, a large percentage of Colombians perceived Venezuelans as a threat to employment (i.e., taking jobs away) (57%) while the majority also perceived them as a fiscal burden (58%). Yet a majority of respondents also agreed that Venezuelan migrants should be given access to healthcare (72%) and employment (54%), while a strong plurality thought they should be granted legal documents to access health and labour markers under the same conditions as Colombians (48%).<sup>8</sup> However, these observational results have several potential limitations, including difficulty in interpreting whether Colombians prioritise some policy aspects over others. A survey experiment is therefore well-suited to elicit Colombians’ views towards realistic policy options and understand their likely drivers.

Figure 3 - Attitudes towards Venezuelan migrants, September 2020 <!-- Start of picture text --> Threat to employment<br>Fiscal Burden<br>Should be given access to employment<br>Should be granted access to health<br>Should be given legal documents<br>0 20 40 60 80<br>Percent<br><!-- End of picture text --> Note: Authors's calculation using data from Proyecto Migracion Venezuela, Revista Semana 2020. Percentage of respondents agreeing with the statements.N(2020-9)= 2,289

> 7 https://www.cancilleria.gov.co/newsroom/news/presidente-duque-anuncia-decision-historica-crear-estatutoproteccion-temporal

> 8