rafmacalaba/data-use-mentions-extended huggingface dataset:
administrative expenses to total assets (proxy for input price of equipment/fixed capital). Z is a matrix of controls including the ratio of equity to total assets, the ratio of net loans to total assets, and the logarithm of assets (to control for bank size effects). D is a vector of year dummies. Finally, αi denote bank-level fixed effects.<sup>1</sup> The H-statistic equals β1 + β2 + β3.

To verify the condition of long-run equilibrium, the following regression is estimated: Ln(ROAit)= αi + β1 ln(W1,it) + β2 ln(W2,it) + β3 ln(W3,it) + γ ln(Z,it) + δD + εit (2) where ROA is the pre-tax return on assets. Because ROA can take on negative values, we compute the dependent variable as ln(1+ROA). We define the equilibrium E-statistic as β1 + β2 + β3 from equation (2). The test of long-run equilibrium involves testing whether E=0.

We calculate the H-statistic for Jordan over three periods. Using Bankscope data, we estimate the H-statistic for the period 1994-2006 and for the sub-periods, 1994-2001 and 20022006. For every period, we report the p-value for the test of long-run equilibrium. If p-values are larger than 0.05, then we can accept the null of long-run equilibrium.

Results on Table 6 indicate that the Jordanian banking sector operates under monopolistic competition. The H-statistic for Jordan over the period 1994-2006 is 0.19 and we are able to reject the null that the H-statistic is 0 or 1. Comparing the H-statistic for the period 1994-2001 with the period 2002-2006, we see that the degree of competition in Jordan appears to have declined over time, since the value of the H-statistic dropped from 0.34 to 0.19. Note that we are able to accept the null of long-run equilibrium, indicating that the H-statistics are valid, irrespective of the sample considered.
initial (wheat) sales earnings shares suggests that they will lose from WTO accession, whereas, after adjustment (to maize), they may end up boosting their income, possibly even lifting them out of poverty.

Robilliard, Bourguignon, and Robinson (RBR, 2003) sacrifice some of the detail attained by Ravallion _et al._ , but dig more deeply into the households’ labor market responses to external shocks. Their work highlights a different set of benefits of the two-step, micro-macro approach to poverty analysis. As in the Friedman/Levinsohn paper, the authors study the effects of the 1997 Indonesian crisis on poor households. The methodology here, however, involves linking the output of a GE model with post-simulation analysis based on a micro-simulation model. The micro-simulation model is estimated with a 33,000 person sub-sample of the 1996 Susenas survey, and offers a detailed representation of the way households generate their income.<sup>12</sup> Earnings and occupational choice equations are a function of personal characteristics, so when there is a decline in the aggregate demand for wage labor, the model predicts which individuals are forced into the informal sector. Such a shift can mean a dramatic decline in income for an individual, and indeed the entire household. Capturing this phenomenon is the real strength of the RBR micro-simulation model.

The RBR GE model relies on standard neoclassical assumptions and is linked to the microsimulation model through (a) the wage rate in each labor market, (b) income per worker in the informal, self-employed sector, (c) the number of wage workers and self-employed workers, by labor market segment, and (d) consumption prices. The micro-simulation model is solved to generate values for these linking variables that are consistent with the general equilibrium model.

The authors validate their model against the historical facts via two steps. First, they set aside the CGE model and simply impose observed historical changes in the linking variables that drive the micro-simulation model. They then compare the changes in poverty produced
Figure 5: Unweighted model-based estimates vs weighted model-based estimates (under the unit context model with weighting following Guadarrama et al., 2018) at target area level <!-- Start of picture text --> Burkina Faso Chad Guinea<br>1<br>.75<br>.5<br>.25<br>0<br>0 .25 .5 .75 1 0 .25 .5 .75 1 0 .25 .5 .75 1<br>Mali Niger<br>1<br>.75<br>.5<br>.25<br>0<br>0 .25 .5 .75 1 0 .25 .5 .75 1<br>Unweighted model estimates<br>Weighted model estimates<br><!-- End of picture text --> Table 4 presents the median, over target areas, of the coefficient of variation estimated for the model-based versus the direct estimates. Our preferred measure of uncertainty for the direct estimates is based on the Horvitz-Thompson approximation, calculated using the R SAE package, with the sum of the sample weights for each area used to approximate the domain size.

Table 4: Median cve for direct and model-based estimates.

|Country<br>|Burkina<br>Faso<br>|Chad<br>D<br>|Guinea<br>irect survey es<br>|Mali<br>timates<br>|Niger<br>|
|---|---|---|---|---|---|
|Sampled<br>areas|0.435|0.271<br>M|0.370<br>odel-based es|0.415<br>timates|0.425|
|Sampled<br>areas|0.167|0.115|0.121|0.164|0.155|
non-beneficiary households. In this study we concentrate on households from the third extension of the VUP, i.e. on the subsample of households located in 30 sectors where the VUP was introduced in July 2010 (financial year 2010/11).<sup>11</sup> Given, the timing of the first survey, which was conducted in November and December 2009, in the sectors where the VUP was launched prior, no baseline data are available. Limiting the sample to cohort 3 sectors comes at the expense of better external validity but allows for a better identification strategy and more robust internal validity. With the second survey conducted in November and December 2011 and the last round conducted in February 2014, we are able to consider short- to medium-term effects, 15, respectively, 30 months after the VUP was launched.

The three household surveys include a set of modules. The household survey in 2009 collected information limited to the demographic composition of the household, labor force availability, income from the main economic activity and public programs, the main expenditure types and investments, housing characteristics and household assets, access to infrastructure and services, and the _Ubudehe_ poverty category assigned to the household. In 2011 the survey tools were amended and information on income and expenditures were no longer collected. Furthermore, the information on household assets was limited to aggregate land- and livestock holdings only. The survey in 2014 included the same information as the 2009 survey, except for the income and expenditure modules. It further covered information on child care, off-farm business activities, shocks and coping, and access and use of financial services and community sensitization activities.

As discussed further below, the empirical strategy and outcome of interest is largely driven by the structure of the data. Notably, we focus on asset accumulation, particularly in the form of livestock holding over time as a welfare measure, as this is the only information which has been consistently collected throughout.

12
# **Conclusion**

This paper reports a time series of capital stock data for agriculture for 30 countries for the period 1970-2000. The capital stocks for agriculture consist of three components: fixed capital, livestock, and treestock. The data suggest that as economies grow, agricultural capital stocks accumulate, and the composition of agricultural capital changes. In particular, livestock declines as a share of total agricultural capital and capital from treestock and fixed investments in machinery, irrigation, and buildings becomes increasingly important. Moreover, the degree of the change differs between highincome and middle and low-income countries.

We review a study on agricultural productivity which highlights the important role of agricultural capital, particularly fixed capital. This finding differs from earlier studies which used incomplete measures of agricultural capital and different methodologies of estimation. We show explicitly that our findings are the result of both the improved data set on agricultural capital stocks and estimations which focus on the within transformation of the data. The results clearly show that data on tractors are poor proxies for agricultural fixed capital.

# **Acknowledgments**

This paper relies heavily on two earlier papers. Larson et al. (2000) was funded by the World Bank’s Research Support Budget (RPO 680-50). Mundlak, Butzer, and Larson (2008) was funded by the World Bank’s Research Support Budget under the research project “The Contributions of Governance to Growth in Agriculture” (RP0 94759). We benefitted greatly from the input of participants at the workshop, _Causes and Consequences of Global Agricultural Productivity,_ cosponsored by the Farm Foundation and the USDA’s Economic Research Service. We thank Keith Fuglie, who graciously shared data for the analysis and provided helpful comments on earlier drafts, Isabel Tejedo, who assisted our research and Polly Means, who greatly improving the presented figures.

18
.176]<br>[2.016]<br>[2.411]<br>_NREGAd,t × Rainy_<br>2.345<br>2.389<br>3.099<br>3.498<br>[2.257]<br>[2.184]<br>[1.987]<br>[2.351]<br>Observations<br>59130<br>59130<br>59130<br>59130<br>Worker Controls<br>No<br>Yes<br>Yes<br>No<br>District Controls FEs<br>No<br>No<br>Yes<br>No<br>SGRY Control<br>No<br>No<br>No<br>Yes<br>_Source_: Author’s analysis based on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data f<br>penditure data is from the Ministry of Rural Development, Government of India.<br>_Notes_: All estimates are of the order10_−_2. Each column presents results from a separate regression. All r<br>fxed effects. The dependent variable in panel A is the log of nominal daily earnings in casual work. The<br>real daily earnings in casual work. The sample is composed of all adults aged 18 to 60 interviewed from Jul<br>June 2008. Real earnings are defated using the monthly, state-level price index for agricultural labourers fro<br>is a dummy variable equal to one for early districts during July 2007 to June 2008. Dry is a dummy variab<br>the year. Rainy is a dummy variable equal to one for the second two quarters of the year. Worker controls<br>group, education levels, caste, religion and marital status. District controls are rainfall, agricultural produc<br>rized as casual (agriculture
d’Ivoire, 55 percent in Peru, and 50 percent in Ghana. In the Mexican urban labor force survey used in this paper, 65 percent of households are followed for all five quarters. Thomas et al. (2001) report that the follow-up rate in the second round of the Indonesia Family Life Survey would have been 84 percent instead of 94 percent if they had not tracked households which move. Failure to follow households which move is likely to understate mobility in both the OLS and IV estimates, since it appears likely that households which move dwellings are likely to have experienced greater income changes than households which stay put. Although correction for attrition is possible under certain structural assumptions, most studies of mobility do not attempt to address this issue.<sup>10</sup> We therefore now investigate how much of the difference between our pseudopanel estimates and panel data estimates is due to non-random attrition rather than measurement error. We begin by examining the determinants of who attrits. Table 3 presents marginal effects from probit estimation of two types of attrition. Column 1 considers households which attrit after only one round of interviews. These household heads are younger, less likely to be married, have smaller household sizes and larger incomes than household heads who remain for two or more waves of the survey. However, while these differences are significant given the large number of observations, the magnitude of the effects is rather small. In Columns 2 through 5, we look at households which appear in the first two quarters of the survey and examine the determinants of attriting before their full five quarters are completed. This allows us to examine whether attrition is related to the change in income experienced by the household between the first two waves. We find that both the change in income or log income, and the absolute value of this change, are positively associated with subsequent attrition from the panel. However, a one standard deviation change in either the change in income or absolute value of the change in income is associated with less
**–** **<u>Table 9: Financing Parameters in Sierra Leone, 2014 17 Averages</u>**

||Share of total<br>government spending as<br>% of GDP|Share of education in<br>total government<br>spending|
|---|---|---|
|Sierra Leone|11.8|13.8|
|**Median for LICs**|**15.6**|**16.2**|
|**75**<sup>**th**</sup>**percentile for LICs**|**21.9**|**18.7**|

_Source:_ World Bank calculations based on UIS and IMF data. _Note:_ LIC = low-income country.

How rapidly could Sierra Leone increase its public spending on education? The underlying drivers of public education spending growth can provide useful benchmarks. It is not realistic to expect Sierra Leone to reach the levels of the 75<sup>th</sup> percentile countries in five years. Table 8 shows that over a given five-year period, fast-spending-growth low-income countries expanded the overall size of their government sector as a share of GDP by 2.4 percentage points and increased education spending’s share of the government budget by 2.4 percentage points. These figures show that these rates of growth are feasible within five years and suggest that Sierra Leone should be able to increase its public education spending from 1.6 to 2.3 percent of GDP over a five-year period. A less ambitious expansion path would be to try to emulate median countries rather than 75<sup>th</sup> percentile countries. In this case, Sierra Leone would aim for the average growth of spending in low-income countries by increasing its public education spending as a share of GDP from 1.6 to 1.7 percent
)|(0.43)|(0.42)||||
|Constant|-1.330|-1.330|-1.330|-0.904|-0.904|-0.904|
||(4.90)**|(4.85)**|(4.80)**|(6.84)**|(6.83)**|(4.84)**|
|Observations|45,199|45,199|45,199|109,817|109,817|109,817|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 12
# WPS3606

## **MEASURING MICROFINANCE ACCESS: BUILDING ON EXISTING CROSS-COUNTRY DATA**

## **Patrick Honohan**<sup>1</sup>

### _Abstract_

Given the acknowledged need for a new effort to expand the set of available data on direct access to financial services, including a focus on access by those at low income, the paper provides a selective review of the diverse sources of data that exist and considers how best to build upon them. It proposes a basic framework within which the analysis of the interesting questions: (i) how does access affect poverty and productivity and (ii) what hinders access, can be considered. The existing and potential contribution of user surveys, both household and business, as well as from surveys of providers and their regulators, and of experts is discussed with an assessment of their relative strengths.

Prepared for the UNDP, World Bank and IMF Workshop _Data on the Access of Poor and Low Income People to Financial Services_ Washington, DC, October 26, 2004

### World Bank Policy Research Working Paper 3606, May 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Financial Sector Operations and Policy Department & Development Economics Research Group, World Bank.  I am indebted to Bob Annibale, Christina Barrineau, Thorsten Beck, Stijn Claessens and Asli Demirguç-Kunt for helpful comments, and to Ying Lin for excellent research assistance.
has been accompanied also by difficulties along other characteristics, such as reduced depth and access to finance and in some cases also reductions in efficiency, particularly in financial markets.

Overall, the data from the Global Financial Development Database suggest that the key disparities among countries in terms of the nature of their financial systems have somewhat subsided in the aftermath of the crisis, as financial sectors in many medium- and low- income countries were relatively more isolated from the global turmoil, and therefore less affected by the global liquidity shocks. In addition, financial institutions on average rebounded faster than markets, showing improvements in depth and efficiency after the crisis. This seems to have been the case so far for example for Brazil and other Latin American countries (de la Torre, Ize, and Schmukler 2011), China, and many Sub-Saharan African countries (see, for example, World Bank 2012). The medium-term effect of the crisis on financial systems still remains to be seen.

> 14 To put this in a more anthropomorphic perspective, the tallest adult person on Earth is less than 5 times taller than the smallest person (www.guinessworldrecords.com).
|-|BBB+|BBB|BBB|
|3|Russian Feder-|RUS|-|BBB|BBB-|BBB|
||ation||||||
||South Africa|ZAF|-|A|BB+|BBB|
||Hungary|HUN|-|BBB|BBB-|BBB-|
||India|IND|-|BBB-|BBB-|BBB-|
||Indonesia|IDN|-|BBB|BBB-|BBB-|
||Philippines|PHL|-|BBB|BBB-|BBB-|
||Portugal|PRT|Yes|BBB|BB+|BBB-|
||Romania|ROU|-|BBB|BBB-|BBB-|
|4|Brazil|BRA|-|BBB|BB-|BB+|
||Turkey|TUR|-|BBB|BB-|BB+|

Figure 2 shows the wealth composition of the countries studied. Natural capital, or natural wealth, is further divided into renewable and non-renewable resources. The term “renewables” refers to natural capital that can produce benefits in perpetuity under sustainable management. It stands in contrast to “non-renewables”, which refer to extractable, subsoil assets. We do not use the term renewables in the sense of renewable energy sources.

Different from other data sets on natural resources, which account for stock or flows of a particular resource, the wealth data employs a forward-looking approach. Human capital, for instance, is calculated as the discounted expected lifetime earnings of an individual, which is either working age or still in school. A similar rationale applies to the valuation of natural resources. A country’s fossil fuel wealth is calculated as the discounted value of future resource rents, until this non-renewable resource is depleted. Renewable resources distinguish themselves in that their discounting horizon depends on the rate of extraction versus replacement. For instance, forest 6
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey complexity increases. He uses his model to analyze how changes in relative labor supply between 1998 and 2012 affected the wage distribution. He finds that the skill premium of workers with secondary and tertiary education with respect to those with primary or no education decreased considerably. However, in line with the work of Ferreira et al. (2017), the overall effect of changes in labor supply are negligible, since the reduction in returns to skills is followed by an endowment effect that increases inequality. Haanwinckel (2018) also models the impact of changes in demand and minimum wage, which we discuss in later sections.

Finally, in contrast with Haanwinckel (2018), Jaume (2018) develops a Ricardian economy with a unique good that is produced by the combination of infinitely many occupations that also vary in complexity. Again, more educated workers have comparative advantages over less educated ones that increase with respect to the complexity of occupations, which ensures positive assortative matching between workers and occupations. In his model, an increase in overall education has direct and indirect effects. First, with an increased relative supply of more educated workers, their wages reduce, making it more profitable for firms to employ them on less complex tasks. At the same time, the wages of less educated workers increase both because their relative supply reduced, but also because the higher demand for the occupation they perform, a consequence of increased production due to more complex occupations.<sup>8</sup> Hence, inequality should decrease as a consequence of educational expansion. He calibrates his model using representative data for the whole Brazilian economy in 1995 and the ISCO-88 3-digit occupation classification. Based on this model he then estimates the effects of education expansion between that year and 2014. His model predicts 85% of the decline in wage inequality during the period.

Hence, Jaume (2018) and Mak and Siow (2018) seem to agree between themselves and the previous literature
**<u>Table 8: Average property price per parcel and effective registration rate</u>**

||**Mean la**<br>|**nd price**<br>|**Effective r**<br>|**eg. fee (%)**<br>|
|---|---|---|---|---|
|**Decile**|**Rural (USD)**|**Kigali(USD)**|**Rural**|**Kigali**|
|1|23|985|99.5|5.27|
|2|58|2,216|69.4|1.83|
|3|92|3,498|42.2|1.15|
|4|131|4,787|29.9|0.84|
|5|171|6,245|22.6|0.64|
|6|225|8,165|17.2|0.49|
|7|292|11,101|13.3|0.36|
|8|396|15,865|9.8|0.26|
|9|666|26,487|6.0|0.16|
|10|2,696|171,785|2.3|0.06|

Note: The price of Kigali is from official registry for the period 2013-2015 on about 25,000 parcels. The exchange rate was 647, 682 and 720 Rwanda Franc per USD for 2013, 2014 and 2015, respectively. The data for the rural areas is from the 2015 World Bank survey of 3600 households. For this analysis, 10% of rural parcels with reported prices less than the official registration fee were dropped.

23
# **2 Public investment quality index: Estimation and properties**

# **2.1. Data and methodology**

**_Project Performance Ratings Database._** We build our public investment quality (PIQ) index using publicly available data from the World Bank Project Performance Ratings Database. These ratings are based on the evaluation of World Bank projects carried out impartially by the World Bank’s Independent Evaluation Group (IEG).<sup>6</sup> The database reports the results of project assessments aimed at evaluating the relevance of project objectives and design, project outcomes relative to stated objectives, and the efficiency of resource use in achieving the project’s objectives. The ratings are based on data from IEG’s Implementation Completion Report Reviews — these are desk reviews of the World Bank’s Implementation Completion and Results Reports (ICR) of lending — projects and Project Performance Assessment Reports (PPARs) which are independent fieldbased project evaluations based on site visits and interviews with stakeholders. The World Bank Project Performance Ratings Database covers 5,900 projects in 150 countries over the period 20002022.<sup>7</sup>

**_Recap of the IEG project evaluation methodology._** At the end of every project funded by the World Bank, the evaluation team prepares evaluation reports and ratings. During the IEG evaluation process, the team conducts an independent, desk-based critical review of the evidence, results, and ratings of the completed project. Using evidence from the ICR and an interview with the last task team leader, IEG establishes its own ratings of the project based on the World Bank evaluation criteria. These ratings employ a harmonized methodology and a set of standardized scales, ensuring full comparability across countries and over time. The IEG methodology provides a granular metric based on six potential scores, ranging from “Highly unsatisfactory” to “Highly satisfactory.” Before being publicly disclosed on the portal, project review reports, along with evaluation ratings, undergo thorough reviews and approvals