rafmacalaba/datause-encoder-data huggingface dataset:
returns to productive investments in adults (see Heckman 1999; Currie 2001). This suggests that interventions that benefit children should receive high priority. In addition, targeted social protection measures that relate to youth employment, family planning, and preschool programs could help improve the employment prospects of young people.

# Table 5.1: Poverty Profile by Four Measures of Poverty (P0) Dispersed Rural Areas <u>of Argentina, 2003</u>

||Consumption<br>Poor|Consumption<br>Indigent|Income<br>Poor|Income<br>Indigent|
|---|---|---|---|---|
|Gender: Male|45.4|20.4|54.4|32.7|
|Female|64.4|27.0|54.2|35.5|
|Age: <25|100.0|100.0|82.3|61.4|
|25–44|46.5|24.3|62.2|39.5|
|45–65|54.4|22.9|56.6|34.1|
|>65|37.1|8.9|29.6|15.4|
|Literacy: Literate|47.2|20.3|53.1|31.2|
|Illiterate|60.8|33.8|65.6|50.5|
|Years of schooling:|||||
|None or less than 1|66.0|31.5|64.3|43.9|
|1–4|56.6|25.2|54.4|33.4|
|5–8|45.8|20.5|56|34.6|
|9–12|30.3|10.7|38.2|14.9|
|More than 12 years|0.0|0.0|40.7|29.2|
|Labor status: Economically Inactive|52.2|43.6|43.5|23.4|
|Economically Active|61.4|17.9|95.5|77.7|
|Employed|46.8|20.7|53.4|31.7|
|Work position: Wage-worker|53.3|23.2|59.4|33.6|
|Self-employed|43.1|18.7|45.9|25.9|
|Pieceworker|50.9|26.0|69.8|46.2|
|Employer|14.8|4.4|38.3|24.1|
|Work sector:|||||
|Agriculture & Livestock|43.3|24.5|51.2|31.9|
|Industry|51.6|5.2|57.7|20.0|
|Commerce & Services|54.9|25.7|54.9|24.8|
|Other Sectors|57.6|0.0|69.4|33.4|
|Public Administration|62.6|23.4|69.2|25.2|
|Work condition:|||||
|Formal|38.6|21.2|44.8|18.8|
|Informal|50.1|14.6|56.7|38.8|
|Family size: 1-–3 members|23.2|4.1|31.7|20.9|
|4–5 members|44.2|14.8|56.1|34.5|
|More than 5 members|80.5|47.4|76.6|44.8|
|Land tenure: No land|45.0|12.9|56.9|33.0|
|0–1 ha|74.9|23.8|70.5|40.6|
|1.1–10 ha|62.8|18.4|55.1|30.5|
|10.1–35 ha|26.4|30.8|40.1|29.3|
|35.1–100 ha|8.9|30.5|35.9|20.1|
|100.1–250 ha|16.4|13.3|33.4|16.8|
|More than 250 ha|11.4|2.6|24.8|9.3|
|Source: Own calculation based on RHS|2003.||||

25
Table 8. Policy Simulation: favoring largest cites versus smallest ones (1⁄2 standard deviation (.4) decrease in inter-city transport costs in 2000)

|Comparison|Total urban inco<br>baselinei|me relative to the<br>ncome (%)|(b-a %p)|
|---|---|---|---|
||Favoring largest<br>cities (a)|Favoring smallest<br>cities (b)|,|
|1 largest vs. 88 smallest|102.072|102.763|0.691|
|2 largest vs. 104 smallest|103.761|104.458|0.698|
|3 largest vs. 109 smallest|105.227|105.550|0.323|
|4 largest vs. 112 smallest|106.072|106.413|0.341|
|5 largest vs. 113 smallest|106.651|106.715|0.064|
|6 largest vs. 115 smallest|107.020|107.517|0.497|
|7 largest vs. 116 smallest|107.679|108.033|0.354|

38
_page 25_

# Appendix **III:** Country-by-country Regression Results.

Sample period: 1965-1989. For each dependent variable, two regressions are carried out.

Regression 1: In yj, = uxi + P(L) In **xk,** + e,1, j identical across countries. Results are in the form: Y variable TOT Coef TOT T-stat Debt Coef Debt T-stat IntRate Coef IntRate T-stat R<sup>2</sup> a N P-value Regression 2: In yt = **a;** + 3 P(L) In xt + ej,, ,B unrestrcted across countries. Results are in the form: Country TOT Coef TOT T-stat Debt Coef Debt T-stat IntRate Coef IntRate T-stat R<sup>2</sup> a N P-value The P-value in regression I refers to the Lagange Multiplier test of the null hypothesis of no countryspecific effects; the P-value in regression 2to the F-test of the null hypothesis of coefficient stability across countries. Where a coefficient and its T-statistic are not reported, the corresponding variable was not included in the regression.

|REALEXRA|-0.651|-28.7|0.325|6.07|
|---|---|---|---|---|
||0.675|0.249|431|4.21e-17|
|Algeria|-0.447|-6.82|||
|Bahrain|-2.53|-13.6|||
|Congo|-0.0493|-0.257|||
|Ecuador|-0.263|-1.89|0.166|1.87|
|Egypt|-0.818|-3.69|||
|Gabon|-0.629|-6.29|||
|Indonesia|-0.467|-5.25|||
|hran|-0.691|-11.5|||
|Iraq|-0.866|-12.3|||
|Kuwait|-0.728|-13.4|||
|Mexico|-0.637|-2.29|0.178|1.53|
|Nigeria|-0.731|-8|0.489|4.99|
|Oman|-0.853|-15.5|||
|Saudi Arabia|-0.783|-14.8|||
|Syria|-0.214|-1.55|||
|Tobago|-1.14|_-5_|||
|Emirates|-0.577|-9.97|||
|Venezuela|-0.359|-5.48|0.354|3.28|
||0.804|0.203|431|2.7e-14|
|CONS|0.368|12.5|||
||0.277|0.325|431|5.81e-07|
|Algeria|0.488|6.03|||
agricultural productivity growth on labor allocation and wages, we develop a simple model where household members can be employed in three types of activities: own farming, wage labor in agriculture and household production (non-traded and non-marketed services).<sup>3</sup> There are two types of households in the farm economy who di¤er in two dimensions: some households are endowed with more labor, and the technology for home production (degree of diminishing returns to labor) is also di¤erent between the two groups. The model yields the standard predictions of a positive e¤ect on agricultural wages, and a reallocation of labor from home production to market work in response to a positive agricultural productivity shock. There are two interesting implications of the model particularly relevant for our empirical analysis: (i) while wages respond positively to an agricultural productivity shock, the extent of the wage increase is lower the higher is the reallocation of labor from home production to market work (own farming and wage labor), (ii) the response of the quantity of hired labor depends critically on the di¤erences in the strength of diminishing returns to labor in home production across labor-surplus and labor-de...cit households. In particular, it is possible that the amount of labor hired through the labor market may go down in response to an increase in agricultural productivity. Thus a focus on the response of wage and hired labor alone may lead to misleading conclusions regarding the impacts of agricultural growth.

We test the predictions of the model using an upazila (subdistrict) level panel data set from Bangladesh. To understand the implications of agricultural productivity, we exploit variations in rainfall across upazilas and over time, and implement a procedure that focuses

> 3Given the focus on the interactions between home production and agriculture, we abstract away from non-farm production in a village. We, however, emphasize that the predictions regarding agricultural wage and labor allocation are robust; they remain intact in a more general model including non-farm production.

2
|P131118|2014|2016|4/21/2016|4 Active|Ethiopia|
|---|---|---|---|---|---|
|P131118|2014|2017|5/4/2017|5 Active|Ethiopia|
|P131118|2014|2018|4/5/2018|6 Active|Ethiopia|
|P131118|2014|2019|1/23/2019|7 Active|Ethiopia|
|P131118|2014|2019|5/16/2019|8 Active|Ethiopia|
|P131118|2014|2020|10/3/2019|9 Active|Ethiopia|
|P131118|2014|2020|4/9/2020|10 Active|Ethiopia|
|P131120|2013|2014|12/1/2013|1 Closed|Congo,Democratic Re|
|P131120|2013|2014|4/9/2014|2 Closed|Congo,Democratic Re|
|P131120|2013|2015|12/9/2014|3 Closed|Congo,Democratic Re|
|P131120|2013|2015|6/27/2015|4 Closed|Congo,Democratic Re|
|P131120|2013|2016|8/3/2015|5 Closed|Congo,Democratic Re|
|P131120|2013|2016|2/8/2016|6 Closed|Congo,Democratic Re|
|P131120|2013|2017|10/25/2016|7 Closed|Congo,Democratic Re|
|P131120|2013|2017|2/28/2017|8 Closed|Congo,Democratic Re|
|P131120|2013|2020|6/17/2020|9 Closed|Congo,Democratic Re|
|P131124|2012|2013|5/27/2013|1 Closed|Pakistan|
|P131124|2012|2013|6/26/2013|2 Closed|Pakistan|
|P131124|2012|2014|12/31/2013|3 Closed|Pakistan|
|P131124|2012|2014|3/30/2014|4 Closed|Pakistan|
|P131124|2012|2015|7/12/2014|5 Closed|Pakistan|
|P131124|2012|2015|1/15/2015|6 Closed|Pakistan|
|P131124|2012|2016|7/20/2015|7 Closed|Pakistan|
|P131124|2012|2019|5/9/2019|8 Closed|Pakistan|
|P131126|2013|2015|1/15/2015|1 Closed|Armenia|
|P131126|2013|2016|9/18/2015|2 Closed|Armenia|
|P131126|2013|2016|6/10/2016|3 Closed|Armenia|
|P131136|2012|2013|6/12/2013|1 Closed|Indonesia|
|P131136|2012|2015|2/2/2015<br>|2 Closed|Indonesia|
|P131138|2013|2013|6/18/2013|1 Closed|Europe and Central As|
|P131138|2013|2014|12/28/2013|2 Closed|Europe and Central As|
|P131138|2013|2014|6/25/2014|3 Closed|Europe and Central As|
|P131138|2013|2015|12/23/2014|4 Closed|Europe and Central As|
|P131138|2013|2015|6/17/2015|5 Closed|Europe and Central As|
|P131138|2013|2016|12/8/2015|6 Closed|Europe and Central As|
|P131138|2013|2016|6/16/2016|7 Closed|Europe and Central As|
|P131138|2013|2017|12/29/2016|8 Closed|Europe and Central As|
|P131141|2013|2013|5/16/2013|1 Closed|Chile|
|P131141|2013|2014|6/22/2014|2 Closed|Chile|
|P131141|2013|2016|11/13/2015|3 Closed|Chile|
|P131177|2012|2014|7/8/2013|1 Closed|Pakistan|
|P131177|2012|2015|4/8/2015|2 Closed|Pakistan|
|P131194|2013|2013||0 Active|Djibouti|
|P131194|2013|2014|10/12/2013|1 Active|Djibouti|
|P131194|2013|2014|4/28/2014|2 Active|Djibouti|
|P131194|2013|2015|7/12/2014|3 Active|Djibouti|
|P131194|2013|2015|12/24/2014|4 Active|Djibouti|
scription, with the prescription-purchase gap mostly driven by individuals declining to purchase available treatment (77 percent of cases) rather than – stock outs.<sup>15</sup> This could be a consequence of credit constraints a reason to subsidize treatment in the first place – but may also be a sign of misalignment between doctor prescriptions and patient preferences.

On the other hand, there are also signs of patient-driven overprescription. First, patients over-estimate their malaria risk: 55 percent of respondents report suspecting malaria before they see the provider; among those with a valid home-based RDT, this proportion is 61 percent, even though only 25 percent actually test positive. Second, the share of patients prescribed treatment exceeds providers’ average estimate of true malaria prevalence (48 percent) by 13 percentage points (see Appendix Table B2). Finally, 57 percent of health workers report feeling pressure from patients to prescribe unnecessary medication, with over half mentioning antimalarials specifically.<sup>16</sup>

# **2 Theory**

In order to investigate the role of patients pressuring health workers to prescribe, we use a simple model of a doctor-patient interaction to predict differences between two experimental pricing conditions. In both conditions, we provide vouchers that make simple malaria treatment free of charge to the patient, while keeping the clinic’s and doctor’s financial incentives constant. In the first condition, which we refer to as “Patient Voucher” or “ _PV_ ”, both the doctor (she) and the patient (he) know that simple malaria treatment is free. In the second condition (“Doctor Voucher” or “ _DV_ ”), only the doctor knows about the discount going into the consultation, and she can decide whether to reveal this information to the patient. In section 3, we explain how we operationalized these pricing treatments. The remainder of this section introduces the model and outlines the key testable predictions that identify patient- and doctor-driven demand. We leave the full model and formal analysis for Appendix A.

> 15Our home survey found that just 6 percent of individuals who declined to purchase at the clinic obtained an antimalarial from another source after leaving the clinic.

> 16Appendix B gives additional detail on the health worker survey.

12
# **<u>Table 2. Incidence and characteristics of administrative reallocations</u>**

||**All**||**By re**|**gion**||
|---|---|---|---|---|---|
||**China**|**N & NE**|**Coastal **|**Central **|**SW**|
|**Total and illegal reallocations**||||||
|Share of villages w.at least one reallocation since 2000 (%)|28.67|31.50|40.32|23.23|19.41|
|Average share with land reallocation before RLCL (%)|13.18|13.78|18.96|12.11|8.69|
|Average share with land reallocation after RLCL (%)|8.31|10.27|9.89|6.32|6.36|
|Total area affected by reallocation per village since 2000 (mu)|745.74|1417.57|382.21|655.02|443.56|
|Total number of hhs affected by reallocation per village|151.9|221.6|115.0|137.3|131.7|
|Share of villages affected by illegal reallocations before RLCL (%)|6.15|5.75|12.10|4.04|2.74|
|Share of villages affected by illegal reallocations after RLCL (%)|3.46|4.00|6.99|1.01|1.27|
|**Rationale for reallocations (affected villages only)**||||||
|Population change (%)|45.42|43.48|47.78|50.00|41.51|
|Land taking (%)|24.88|22.22|22.67|13.64|37.78|

_Source:_ Own computation based on 722 villages from the 2005 NBS/WB survey.

24
# **3.1 Putting Together a Large Panel of Micro and Small Firms**

We draw on information on firms from both multi-purpose household and firm panel surveys in order to construct a large panel with which to examine death rates. Our resulting dataset combines data from 16 surveys in 12 developing countries, and includes a total of 14,426<sup>4</sup> firms at baseline, with a longitudinal dimension that is able to cover the dynamics of these firms in the short, medium and long term. Table 1 lists the surveys underlying the dataset, appendix A.1 discusses more details of each survey and how firm death was measured in each case.

Our inclusion criteria for using multi-purpose household panel surveys was to include nationally representative surveys that included a detailed non-farm business module, had low rates of attrition, a relatively large sample size, and enabled us to measure firm survival and death rates over at least three or more waves. This yielded four surveys. The first two are the Mexican Family Life Survey (MxFLS) and the Indonesian Family Life Survey (IFLS), both of which provide large samples of households. These surveys occur at low frequency, enabling us to measure firm death over horizons of three, four and a half, and eight years in Mexico, and seven, eight, and fifteen years in Indonesia. We examined the set of LSMS surveys to determine the ease at which they could be used to construct household firm panels. This led to the inclusion of the Nigeria General Household Survey (NGGHS), which allows measurement of firm death over 0.5, 1.5, 2, and 2.5 year horizons. Finally, we also include data from the Townsend Thai Project. This is a smaller sample, but resamples households annually, over an extended period of time, enabling firm death to be measured over periods from one to seventeen years.

These household panel surveys were not designed to track businesses over time, and do not link the businesses reported from one survey wave to the next. We therefore consider households that only operate one enterprise, and follow Vijverberg and Haughton (2004), McCaig and Pavcnik (2016) and Kraftt (2016) in using information on firm age, business ownership, and sector to attempt to link firms over time.<sup>5</sup> Since these household panels also contain data on individuals who are not operating firms in the first period, they enable us to also capture information on new firms that households start between one survey round and the next. We then pool together multiple survey rounds to get a larger sample, Appendix A.1. details these procedures in more detail.

> 4 This number refers to the total number of unique firms we observe.

> 5 The majority of households who operate a business operate only one business. In the 2000-round of the IFLS these are 80 percent, in the 2002-round of the MxFLS 93 percent, in the 2010-round of the NGGHS 58 percent, and in the 1997-round of the Townsend Thai Project 83 percent.

7
elected officials. That is, relying on chiefs as local patrons weakens councilors’ incentives to appeal to a broad base of voters. Of course, which exactly of these mechanisms are at play cannot be answered by the research tools used here – but suggest potential areas for supplementary qualitative research. In contrast we find that inter-elite relationships that are defined by competition are beneficial to both the provision of local public goods and citizens’ perceptions of these improvements. These findings fit well with previous literatures documenting the benefits of political competition on local development outcomes (Acemoglu and Robinson 2006; Acemoglu, Reed, and Robinson 2013)

# **_Conclusion_**

This paper has attempted to address gaps in the existing political economy literature by examining how decentralization shapes power relations among new and traditional elites, and how these relationships, in turn, have affected the provision of local public goods. In postconflict Sierra Leone, decentralization has layered new political institutions onto existing informal governance structures that have traditionally ordered rural life. Using household surveys as well as surveys of new local elites created through decentralization, we present evidence that inter-elite competition results in improvements of local development outcomes, whereas inter-elite collusion is deleterious for development.

This research presents clear policy implications – as well as several potential areas for future research. First, to date very little attention has been paid to the ways in which decentralization empowers new local authorities in areas of traditional governance. Given this, the success of decentralization policies likely hinges on the ways in which existing elites react to the creation of new local authorities. If existing elites have a history of co-opting local institutions, as Paramount Chiefs do in Sierra Leone, then the design of these new institutions would likely benefit from additional mechanisms that provide autonomy to new decision-making bodies so that they may operate free from the influence of traditional elites. On the other hand, when decentralization creates new institutions that successfully foster effective inter-elite competition, the theoretical benefits of devolved authority are more likely to unfold.

Finally, the survey-based evidence we have presented here suggests several potential areas for future work. Extensions of this research would benefit from additional questions on future rounds of the DSS surveys, including more detailed questions about the nature of chiefs’ 29
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_Source_ : Shah (2007)

**<u>Table 9: Better Practices in  Simpler  and Transparent Equalization Grant Design</u>**

|**Grant objective**|**Grant design**|**Examples of better**<br>**practices**|**Examples of practices**<br>**to avoid**|
|---|---|---|---|
|**Fiscal capacity**<br>**equalization:**||||
|To enable provincial/Local<br>fiscal capacity to provide<br>comparable levels of public<br>services at comparable<br>burdens of taxation per<br>capita|General non matching<br>fiscal capacity equalization<br>transfers|Fiscal equalization with<br>explicit standard that<br>determines total pool as<br>well as allocation (Canada,<br>Denmark, and Germany)|General revenue sharing<br>with multiple factors (Brazil<br>and India); fiscal<br>equalization with a fixed<br>pool (Australia, China)|
|**Expenditure need**<br>**equalization:**||||
|To enable provincial/local<br>governments to meet<br>minimum or average<br>standards of merit public<br>services|Conditional nonmatching<br>output-based bloc<br>transfers with conditions<br>on standards of service<br>and access<br>Conditional capital grants<br>with matching rate that<br>varies inversely with local<br>fiscal capacity|Road maintenance and<br>primary education grants<br>(Indonesia before 2000,<br>Finland)<br>Education transfers<br>(Brazil, Canada, Chile,<br>Colombia) Health transfers<br>(Brazil, Canada, Finland)<br>Capital grant for school<br>construction (Indonesia<br>before 2000), highway<br>construction matching<br>grants to states and local<br>governments (United<br>States)|Comprehensive<br>expenditure need<br>equalization as in Australia<br>Conditional transfers with<br>conditions on spending<br>alone (most countries), pork<br>barrel transfers (USA e.g.<br>$200 million earmark in<br>2006 for a “bridge to<br>nowhere” in Alaska), ad hoc<br>grants<br>Capital grants with no<br>matching and no future<br>upkeep requirements|

) Source: Boadway and Shah (2009) 26
Table 1: Summary statistics on our main variables

||Mean|SD|Min|Max|N|
|---|---|---|---|---|---|
|_Panel A: Firm-level controls_||||||
|Firm’s longevity (Years)|29.72|16.77|3.00|351.00|31406|
|Firm size (Small=1, Medium=2 or Large=3)|1.81|0.77|1.00|3.00|31406|
|Own website (No=0, Yes=1)|0.42|0.49|0.00|1.00|31406|
|Foreign private participation (%)|7.75|24.93|0.00|100.00|31406|
|_Panel B: Country-level controls_||||||
|Regulatory Quality, Percentile Rank (0-100)|42.67|18.55|5.21|91.75|31406|
|Financial development index|0.28|0.14|0.03|0.70|31406|
|Bank concentration (%)|61.49|20.04|22.60|100.00|31406|
|Net ODA received (% of GNI)|2.42|4.16|0.01|37.37|31406|
|Infation, consumer prices (annual %)|7.58|8.58|-1.05|84.86|31406|
|GDP growth (annual %)|4.34|4.44|-25.91|14.01|31406|
|GDP per capita (constant 2015 USD)|3976.76|3208.01|302.07|14086.02|31406|
|_Panel C: Outages related variables_||||||
|Outages frequency (occurrences per month)|7.31|8.69|0.00|31.00|16821|
|Outages intensity (hours per occurrence)|4.45|6.98|0.00|96.00|16416|
|_Panel D: Performance related variables_||||||
|Log revenue-based TFP (TFPR)|2.46|1.50|-3.23|8.83|31406|
|Cost of inputs per unit of sales (2009 USD)|0.40|0.22|0.00|0.99|31406|
|Sales per labor cost (2009 USD)|11.71|21.32|0.11|646.50|31406|
|Capacity Utilization (%)|73.48|21.12|0.00|100.00|30714|
|Losses due to Outages (% of annual Sales)|8.12|11.85|0.00|100.00|12813|

Asiedu et al. (2021) find that the probability of facing power constraints is lower for state-owned firms with majority ownership. However, a high level of private participation in a firm could reveal significant levels of firm attractiveness or performance.

Further details on the firm-level variables can be found in the Appendix (Table 16).

# **4.2.2 Country-level data**

At the country level, we control the quality of regulation, financial development, bank concentration, foreign aid, inflation, economic growth, and the level of wealth. Indeed, Agostino et al. (2020) establish strong evidence that better local institutions (the rule of law and government efficiency) help small and mediumsized firms become more productive in Europe over the period 2010-2014. We also have evidence of a positive effect of foreign aid on firm sales growth (Chauvet and Ehrhart, 2018). Furthermore, financial development and greater competition between banks (strong bank concentration) favor the performance of firms ( Chauvet and Jacolin, 2017; Lee et al., 2020). Finally, Bambe et al. (2022) show that inflation targeting increases the growth and productivity of firms in targeted countries compared to non-targeted countries. Indeed, inflation targeting improves the performance of developing countries by reducing the level and volatility of inflation (Lin and Ye, 2009).

Further details on the country-level variables can be found in Appendix Table (16).

11