rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure C5: Distribution of poor population across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Share of poor population (%)<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: WorldPop 250m is used for the DOU and DB methods. Each boxplot shows the distribution of the share of the poor population over different geographic areas in 20 countries.

Figure C6: Comparison of urban shares of poor populations between official and DOU/DB urban definitions <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>100 BGD 100 100<br>EGY EGY EGY<br>80 80 80<br>60 60 60<br>TCDETH TZA GAB TCD ETHGABTZA<br>UGA NERUGA NER UGA<br>40 TZA AGOGABCOL 40 MWI LSO AGO COL 40 LSOMWI AGOCOL<br>ETH CIV BFA SENMRC TI V BFAMRTSENCIV<br>20 BFANERVNMGINMWITCDGNBSENLSOMRTGHA 20 GINGNBBGDGHA 20 GINGNBGHA BGD<br>VNM VNM<br>20 40 60 80 100 20 40 60 80 100 20 40 60 80 100<br>Share of poor in
_“Have there been times in the past 12 months when you did not have enough money to buy the food that you or your family needed?”_ A simple yes or no answer is recorded. For simplification, we refer to this as the “food insecurity” indicator rather than a more cumbersome term such as “unaffordability of food.”<sup>8</sup> What are some of the strengths and weaknesses of this question? The strengths include a focus on access rather than availability, a recall period (12 months) capable of capturing seasonality and other short-run food price movements, and large cross-country and multiyear coverage. This last strength is a significant advantage in the absence of more regular economic or nutritional surveys, but there are also limitations with subjective data. Unlike simulation approaches, for example, subjective data do not provide much information about the mechanisms or magnitudes of welfare impacts. However, there are some indications that the simple yes/no indicator used here may not lead to much loss of information in practice. The GWP has data for Africa in which a similar question is asked that allows for five different answers based on the frequency of deprivation. Those data show a similar trend to the dichotomous indicator (see fig. S.1 in the supplemental online appendix, available at http://wber.oxfordjournals.org/).

A more significant problem is that the definition of food needs is not universal. For a well-off or well-educated family accustomed to a high-quality diet, “food” may mean a food bundle of

> 8 We note that there are other welfare indicators measured by Gallup, including a question pertaining to hunger rather than food affordability as well as a general life satisfaction question (scaled from one to ten). In earlier versions of this paper, we considered the hunger variable, but the sample size for that indicator was much smaller, and trends in that variable could not be significantly explained by economic growth or food inflation. The life satisfaction question
and related work on African politics. We then present new data on cabinet size in Sub-Saharan African countries, building on prior work by Arriola (2009), and assess their association with a series of governance measures related to corruption and the quality of public policy. Analyzing cross-national patterns as well as within-country variation over time, we find a robust correlation between cabinet size and several measures of governance in this sample. The conclusion summarizes policy implications and explores possible avenues for further research.

# **2. From cabinets to governance**

Scholars of African politics have highlighted a link between clientelism and cabinet appointments. Van de Walle (2012) distinguishes mass clientelism and elite clientelism. Mass clientelism relies on the practice of using state resources, or patronage, to provide jobs and services for mass clienteles, and usually involves party organizations and electoral politics. This type of clientelism requires considerable resources and is therefore likely to take place in richer countries, also in mature Western democracies. For poorer countries, on the other hand, clientelism is more likely to be limited to a narrower clientele, so-called elite clientelism. In SubSaharan Africa, the characteristic of elite clientelism is _prebendalism_ , or the strategic political 2
‘version’ of RUSLANA, meaning that it was last updated by BvD in 2012, and spans the 1999‐2011 period. Due to relatively poorer availability of employment data in years prior 12 to 2003 and in 2011, we limit our analysis to 2004‐2010. In addition, all observations with negative or missing values for any of the following variables used on our analysis—age, operating revenue, tangible fixed assets and number of employees—were dropped; observations with missing profit value were also dropped. Outlier values (outside the 1‐99 percentile range) of key variables (such as the level and change in employment and revenue) were Winsorized (top‐coding and bottom‐coding the 1 percentile tails).

# **3.3 Measuring shocks to industrial sectors**

We classify large deviations from the trend annual growth rate of a manufacturing sector as a shock episode. For each 4‐digit level sector, a global trend growth rate was measured using long‐term, cross‐ country data on industrial output.<sup>13</sup> A sector is defined as being in ‘surge’ (resp. ‘slump’) when the difference between its actual sales growth rate and the trend growth rate is larger (resp., smaller) than a pre‐specified cutoff value (resp., the 75<sup>th</sup> and 25<sup>th</sup> percentile values of the distribution of the trend growth

> 11 Concerns around exit and panel definition are addressed in Section 4.1.

> 12 The gaps in year 2011 data are likely due to updating lags in the official databases which BvD uses to compile RUSLANA.

> 13 Only countries in the same per capita income quartile as Russia were included, on the assumption that they would share similar technological levels and global markets with Russia.

7
> The relative changes are derived from _DPCMREL_ i,c = _PCMPCM_ ( _pre_ ( _crisis_ − _crisis_ )i,c )i,c − 1

> for the price-cost margins and _DESTREL_ i,c = _ESTEST_ ( _pre_ ( _crisis_ − _crisis_ )i,c )i,c − 1 for the EST. The

dummies control for industry-fixed effects. On the country level, we use either dummies or country-level variables. Note that country dummies control, among others, for the severity of the crisis and the consequent impact on average demand. As a robustness check, we include country-level variables to control for a misspecification due to possible omitted institutional differences which impact the dependent variables and are correlated with the interaction terms in Equation (1). Pre - crisis level _Y_ i,c is either _PCM_ ( _pre_ − _crisis_<sup>)</sup> i,c or _EST_ ( _pre_ − _crisis_<sup>)</sup> i,c and controls for country-industry differences in the level of PCM (EST) before the crisis. Note that using pre-crisis values helps in avoiding endogeneity issues. The interaction term consists of the external dependence measure of _young_ firms, taken from RZ, and a pre-crisis institution index. This is the average of the institution index of interest in the pre-crisis period, being law and order, accountability, corruption or a composite of the three. Institutions are better for higher values of the indices. We also use the pre-crisis values of the indices to avoid endogeneity problems.

According to Hypothesis 1, we expect the coefficient of the interaction term, β 2 , to be negative and significant only when we use a corruption index in the interaction term. This means that for more dependent industries in less corrupt countries, the drop of its PCM during a crisis is significantly deeper. This implies that the depressing effect of weakening demand was not compensated for by the uplifting effect of a significant drop in output and competition, causing a larger slide of the PCM. Hence we
, this association between prices and the JHIC score does not identify the structural hedonic parameter in the presence of patient sorting. While we cannot address patient sorting fully, we can assess the sensitivity of our estimates to select features of the patient population in each facility, as shown in Column 2, Table 2. Here, in addition to the machine-selected controls from Column 1, we also include patient wealth, education, self-reported health status and distance traveled to the health facility, all characteristics that are likely correlated with the demand for higher quality care. Although these variables are positively associated with OOP payments, there is virtually no change in the price premium for higher quality as measured by the JHIC score, which retains its strong statistical significance.

Vertical differentiation requires a positive price-quality correlation in the private sector (which we find) but not necessarily a quantity-quality correlation as some facilities could be niche highend facilities. Nevertheless, we do find a positive, but insignificant correlation between market share and the JHIC score in the private sector. We can also ask whether the positive valuation of quality extends to patients visiting public sector clinics. Since prices in the public sector are administratively determined and therefore uncorrelated with the JHIC score, a positive valuation of quality should show up in demand and we indeed find a strong quantity response with a 1 SD increase in the JHIC score associated with a 3.1 percentage points increase in outpatients among public facilities.

These results strongly suggest that (a) consumers placed a premium on safety as measured by

> 19Throughout the paper, unlicensed refers to facilities that do not have a license or have an expired license.

14
domestic work, Panel B indicates that in 2 out of the 10 countries (Argentina and Uruguay) the gender gap increased (in absolute value); in 2 countries, the gender gap barely changed (Mexico and Guatemala); and in the remaining 6 countries the gender gap declined. Panel C shows that gender differences in time devoted to personal activities—including personal care and leisure, social and study—for the 8 countries with information. Over the last years, the gap increased in 4 countries (Guatemala, Mexico, USA and Uruguay) and declined in the remaining 4 countries (Argentina, Iraq, South Africa and West Bank and Gaza).

# **4. How do time use patterns vary according to individual and household characteristics?**

To analyze gender differences in time use patterns according to individual and household characteristics, we focus on the average number of hours devoted to market work and unpaid domestic work.<sup>9</sup> Consistent with previous sections, the gender gap is calculated as the difference between the number of hours that men and women allocate to each activity.

> 7 Time use patterns for these countries were analyzed in the 2012 WDR using previous rounds of surveys. The time difference between the survey data used in the WDR and those used in this study ranges between 4 and 12 years. 8 In this exercise, the gender gaps corresponding to our study were calculated for the sample of men and women aged 18-65 to match the sample definition used in the 2012 WDR.

> 9 Time use for self-care and leisure, social and study activities are not included in the analysis given that they do not present a clear pattern by gender.

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Policy Research Working Paper 7662

# **Abstract**

Devolving property rights to local institutions has emerged as a compelling management strategy for natural resource management in developing countries. The use of property rights among fishing cooperatives operating in Mexico’s Gulf of California provides a compelling setting for theoretical and empirical analysis. A dynamic theoretical model demonstrates how fishing cooperatives’ management choices are shaped by the presence of property rights, the mobility of resources, and predictable environmental fluctuations. More aggressive management comes in the form of the cooperative leadership paying lower prices to cooperative members for their catch, as lower prices disincentivize fishing effort. The model’s implications are empirically tested using three years of daily logbook data on prices and catches for three cooperatives from the Gulf of California. One cooperative enjoys property rights while the other two do not. There is empirical evidence in support of the model: compared to the other cooperatives, the cooperative with strong property rights pays members a lower price, pays especially lower prices for less mobile species, and decreases prices when environmental fluctuations cause population growth rates to fall. The results from this case study demonstrate the viability of cooperative management of resources but also point toward quantitatively important limitations created by the mismatch between the scale of a property right and the scale of a resource.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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://econ.worldbank.org. Corresponding author may be contacted at sri@acumenllc.com.

_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
Finance_ 61 (4): 1553–604.

- CGAP (Consultative Group to Assist the Poor). 2010. “Technology Program Focus Country: India.” http://www.cgap.org/.

- Chawla, M., G. Betcherman, and A. Banerji. 2007. _From Red to Gray: The “Third Transition” of Aging Populations in Eastern Europe and the Former Soviet Union._ Washington, DC: World Bank.

- Chen, S., M. Ravallion, and P. Sangraula. 2010. “The Developing World Is Poorer Than We Thought But No Less Successful in the Fight against Poverty.” _Quarterly Journal of Economics_ 125 (4): 1577–625.

- Clausen, B., A. Kraay, and Z. Nyiri. 2011. “Corruption and Confidence in Public Institutions: Evidence from a Global Survey.” _World Bank Economic Review_ 25 (2): 212–49.

- Cole, S., T. Sampson, and B. Zia. 2011. “Prices or Knowledge? What Drives Demand for Financial Services in Emerging Markets?” _Journal of Finance_ 66 (6): 1933–67.

- Collins, D., J. Morduch, S. Rutherford, and O. Ruthven. 2009. _Portfolios of the Poor: How the World’s Poor Live on Two Dollars a Day._ Princeton, NJ: Princeton University Press.

- Cull, R., A. Demirguc-Kunt, and J. Morduch. Forthcoming. _Banking the World: Empirical Foundations of Financial Inclusion._ Cambridge, MA: MIT Press.

- Deaton, A. 2008. “Income, Health, and Well-Being around the World: Evidence from the Gallup World Poll.” _Journal of Economic Perspectives_ 22 (2): 53–72.

- Demirguc-Kunt, A., and L. Klapper. 2012. “Measuring Financial Inclusion: The Global Findex Database.” Policy Research Working Paper 6025, World Bank
# **Table 13. Standardized effects of the determinants of occupational attainment. Youths from 18-35 years of age of ENCEL 2017.**

|VARIABLES|EFF|ECTS||
|---|---|---|---|
|Social origin on occupational attainment||||
|Direct effect|-|||
|Indirect effects|0.23||***|
|Via education||0.09|***|
|Via youth’s cognitive abilities and education||0.02|**|
|Via first employment and education||0.12|***|
|Total effect (direct + indirect)|0.23||***|
|Provider’s cognitive abilities on occupational attainment||||
|Direct effect|-|||
|Indirect effects|0.03||**|
|Via youth’s cognitive abilities||0.03||
|Total effect (direct + indirect)|0.03||**|
|Youth’s cognitive abilities on occupational attainment||||
|Direct effect|0.08||***|
|Indirect effects|-|||
|Total effect (direct + indirect)|0.08||***|
|Education on occupational attainment||||
|Direct effect|0.16||***|
|Indirect effects|0.26||***|
|Via youth’s cognitive abilities||0.04|**|
|Via first employment||0.22|***|
|Total effect (direct + indirect)|0.42||***|
|First occupation to occupational attainment||||
|Direct effect|0.32||***|
|Indirect effects|-|||
|Total effect(direct +
26 level of imports as the NTM in place, and use this as the basis of aggregation. Measurement methods of the trade impact of NTMs generally use a partial equilibrium approach and, in addition to utilizing either the direct or indirect approach (see Section 2.3.1), rely on either price or trade volume data.<sup>14</sup> Examples of the indirect approach using price data involve some of the first attempts to assess the trade impact of NTMs by comparing the domestic price of a good with its international price to obtain an estimate of the price gap (Andriamananjara et al., 2004 or Bradford, 2003). The underlying idea is that non-tariff measures, just like tariffs, drive a wedge between the two prices. A simple expression of the tariff equivalent (TE) of NTMs calculated through the price gap approach is: where �� is the domestic price, �� is the world price (both net of wholesale and retail margins), _t_ is the ad valorem tariff and _c_ is the international transport margin also expressed in ad valorem terms. Thus, the severity of NTMs as barriers to trade is inferred from any remaining price gap (after controlling for direct trade barriers). In contrast, Dean et al. (2009), while also using data on international price gaps, utilizes the direct approach which requires observable data on NTM incidence. This paper estimates the impact of observable NTMs on price gaps (controlling for a number of factors, including observed differences in local markups, tariffs and transport costs). Obviously a key issue in these approaches involves obtaining the relevant price information.

An application of the indirect approach which uses information on trade volume is undertaken by Chen and Novy (2012). This paper uses a gravity model to infer bilateral trade frictions from observable trade data by comparing actual trade flows and predicted trade flows in the absence of non-tariff measures. They then show that NTMs such as product standards are highly correlated with these inferred trade frictions
For the purposes of the evaluation exercise, we treat the census-based EBP estimates as the gold standard. The census-based estimates are compared to: (a) small area estimates under both survey weighted and unweighted versions of model (1) with the outcome defined at household level and the geospatial covariates defined at grid cell level; (b) small area estimates under an area level (Fay-Herriot) model, with geospatial covariates aggregated at the target area level; and (c) small area estimates under a grid cell-level model where both the outcome and the geospatial covariates are defined at grid cell level. It is important to note that the survey data was collected from the same harmonized WAEMU survey instrument as the survey data for the other four countries we consider in this paper.

Figure 3 and Table 3 summarize the results of these comparisons. Across all Communes in Burkina Faso, we find a high correlation equal to 0.799 between the estimates under the household-level model with geospatial covariates and those derived under the household-level model with census covariates. However, there is a large difference in this correlation between in-sample and out-of-sample Communes. For the 234 Communes included in the sample, which comprise 84 percent of the population of Burkina Faso according to WorldPop estimates, the correlation between the survey and census-based estimates is 0.879. In contrast, the correlation for the 117 non-sampled Communes is 0.457. The in-sample correlation is also remarkably similar to findings from other contexts (Masaki et al., 2022; Newhouse et al., 2022; Van der Weide et al., 2022). The correlation for out-of- sample areas meanwhile, is significantly lower than the out-of-sample correlation of 0.7 reported between geospatial and census-based estimates in Mexico (Newhouse et al., 2022). This may be explained by differences in the nature of the geospatial covariates used in Mexico, which could lead to better out-of-sample predictions, as well as differences in the country context. Perhaps, the lower out-of-sample correlations in this case could be explained
this perception contrasts with the reality facing most firms.

# **6. Discussion and External Validity**

Our baseline sample was a random sample of informal firms in the two largest cities in Sri Lanka. As with all microeconomic studies, there is a question of external validity. We present evidence here to suggest that our results from Sri Lanka are likely to be informative of constraints to formalizing firms in other countries.

First, we note that the pattern of informality with regard to firm size, and what firms say are the potential advantages and disadvantages of formalizing are similar to those we see in other countries for which data exists. Appendix Figure 1 compares municipal government and tax authority registration rates by firm size in Sri Lanka, Bangladesh and Mexico. In all three countries, the data are representative of firms in the largest urban areas.<sup>10</sup> Sri Lanka has tax registration rates which lie between those in Mexico

> 10 The Sri Lankan data come from the Sri Lankan Longitudinal Survey of Entrepreneurs, which draws a random sample of 2255 firms from household listings in 31 cities outside the northern province. In Mexico, the data are from the 2002 version of the National Microenterprise survey, conducted in urban areas with a sample drawn from a

20
>-10<br>40 -15<br>Food Budget Share<br>Food Engel Curve Slope -20<br>20<br>-25<br>0<br>5<br>100<br>0<br>80<br>-5<br>60<br>-10<br>40<br>-15<br>Formal Food Budget Share 20<br>Formal Food Engel Curve Slope -20<br>0<br>-25<br><!-- End of picture text --> This figure combine two sources: data from the core-sample of 31 countries and data from the Global Consumption Database (GCD) which adds 58 developing countries not included in the core-sample. Panel A shows each country’s food budget share, plotted against log per capita GDP. The average food budget share in the core sample is 49%, while the average in the GCD sample is 48%. Panel B shows the countryspecific slope of the food Engel curve, plotted against log per capita GDP. The average slope in the core sample is 12.5, while the average in the GCD sample is 13. The lines correspond to local polynomial fits. GDP per capita is in constant 2010 USD (Source: World Bank WDI). Panels C and D are constructed similarly to Panel A and B, but for formal food expenditure which can only be measured in the core sample.

41