rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6096

# **Abstract**

This paper uses Ethiopian data to explore credit rationing in semi-formal credit markets and its effects on farmers’ resource allocation and crop productivity. Credit rationing—both voluntarily and involuntarily—is found to be widespread in the sampled rural villages, largely because of risk-related factors. Political and social networks emerge as key determinants of access to credit among smallholder, peasant farmers. Significant regional variation emerges as well. In high-potential, surplus producing areas where credit is largely used for agricultural production, eliminating credit constraints is estimated to increase productivity by roughly 11 percentage points. By contrast, in low-productivity, drought prone areas where loans were rarely used to acquire inputs for crop production, the authors find no relationship between credit rationing and agricultural productivity. To be effective, efforts to improve agricultural productivity not only need to increase credit supply, but also explore the reasons for credit rationing and the availability of productive opportunities.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. 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. The authors may be contacted at dali1@worldbank.org or kdeininger@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Table S.3 – continu|ed frompr|eviouspage||||
|---|---|---|---|---|---|
|Variable|Mean|Std. Dev.|Min.|Max.|N|
|WGI Corruption_>_Median|0.51|0.5|0|1|3322|
|_Public investment management and DB controls_||||||
|Public Investment Management Index|1.92|0.6|0.27|3.53|2113|
|Missing: Public Investment Management Index|0.36|0.48|0|1|3322|
|Log of DB Start Business|3.64|0.62|1.79|5.31|3322|
|Log of DB Construction Permit|5.32|0.44|4.44|6.51|3314|
|Missing: DB Construction Permit|0|0.05|0|1|3322|
|Log of DB Register Property|3.98|1.16|0.69|6.53|3322|
|Log of DB Import+Export|4.08|0.42|2.89|5.19|3322|
|Log of DB Enforce Contract|6.28|0.28|5.35|6.89|2852|
|Missing: DB Enforce Contract|0.14|0.35|0|1|3322|

_Source_ : Authors’ analysis based on data described in the text.

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# Table 11 Expenditures on Nutrition Programs. Selected Countries

|Country|Year|As X<br>Health<br>Budzet|Domestic<br>Contrib.<br>as X GDP|Total<br>Exp.<br>as X GDP|Source|
|---|---|---|---|---|---|
|Brazil|1986|10.8|0.18|-|Musgrove (1989)<br>World Bank Docs.|
|India- state govt<br>-<br>central govt|1986-7|<br>1.86).<br>0.5)|0.16|-|Subbarao (1989)|
|Chile|1988|34.8|0.41|-|Author's<br>calculations|
|Malawib|1988|8.5|0.16|0.56||
|Philippines|1985|10.0|0.06|-||
|MCH and School Feeding<br>|Program<br>|s Only||||
|Brazil|1987|--|0.27|0.27|Mateus (1989)|
|Bolivia|1987|--|0.18|0.52||
|Chile|1987|--|0.53|0.57||
|Costa Rica|1987|--|0.29|0.29||
|Dominican Republic|1987|--|0.12|0.15||
|Ecuador|1987|--|0.19|0.22||
|Guatemala|1987|--|0.04|0.14||
|Haiti|1987|--|0.01|0.41||
|Honduras|1987|--|0.02|0.16||
|Jamaica|1987|--|0.10|0.30||
|Mexico|1987|--|0.24|0.44||
|Panama|1987|--|0.03|0.01||
|Paraguay|1987|--|0.06|0.09||
|Peru|1987|--|0.02|0.04||
|Uruguay|1987|--|0.17|0.17||
|<br>Venezuela|1987|--|0.13|0.13||

# Notes

- a X of government budget.

- b domestic contribution to WFP project only
satisfaction. In the absence of such correction, an interpretation of satisfaction data, across space or time, can be misleading.

Secondly, data on satisfaction from a random sample administered at the household level is likely to yield more representative results in most cases than a typical “user survey”, namely a survey of a sample of users of a particular type of facility. A random household sample may allow for satisfaction data to be collected from a representative sample of a country or a sub-region, or allow for the correction of selection bias arising from the household’s choice of a particular type of facility (if satisfaction is reported by only a subset of households using that facility). In contrast satisfaction reported by a sample of users of a particular type of facility can be subject to an undetectable selection bias, which would yield misleading results.<sup>22</sup> In this paper, while we estimated the reduced form models only on the sample of users of certain types of facilities, the larger household sample allowed us to check for selection bias in our results.

The concern about selection bias in a survey limited to just users especially applies to services like health and education that present potential users with some degree of provider choice. Incorporating questions on satisfaction with basic services in household surveys is becoming increasingly popular. For example, the aforementioned CWIQ surveys, fielded in a large number of countries (mostly in Africa) combine questions on access, usage and satisfaction with basic services with those on household and community characteristics, on a nationally (or regionally) representative sample.<sup>23</sup> In cases where user surveys are the only 22 For example, if one is comparing satisfaction data from a user survey over time, a change in satisfaction levels may reflect a “real” change, or just may be a result of a changing profile of households opting to use a particular type of facility, with differences in characteristics that influence their satisfaction. 23 Numerous African countries have had at least one round of CWIQ. Some, including Ghana, Malawi, Mozambique and Rwanda have had more than one round. In Pakistan, two rounds of a survey modified from CWIQ have been fielded in 2004-05 and 2006-07, which measures satisfaction with public services.

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observations with fathers aged over 100 years or missing, or sons aged over 65 years are excluded from the samples used in the empirical analysis.

In each data set, we observe the education level and an indicator of whether the main occupation is agriculture or nonfarm activity for both the father and the son. Our main analysis of educational mobility is based on years of schooling as the measure of educational attainment. Father’s schooling is used as the indicator of parental education to avoid complications from many missing observations on mother’s schooling. In our data sets, the maximum of parental education coincides with the education level of the father in most of the cases. We define the parental occupation dummy _Di_<sup>_np_</sup> = 0 when the father of child _i_ reports agriculture as the main occupation (corresponding to _Oi_<sup>_p_=</sup><sup>_f_inthe</sup> theoretical model), _Di_<sup>_np_</sup> = 1 otherwise. This means that the households who are primarily engaged in farming with some nonagricultural sources of income are classified as agricultural occupation.

Online appendix Table A.1 shows the descriptive statistics of our main data samples from the REDS and the CFPS. In the REDS sample, we have 6887 observations, and the children’s age is 29 years on average in the survey year 1999. Fathers are 60 on average. About half of the children’s main occupation is agriculture, while 60% of the fathers also reported agriculture as their main occupation. The children attain significantly higher levels of education than the fathers, when comparing their average years of schooling (6.26 vs. 4.13.) In CFPS sample, a similar pattern is observed. We have 3,305 father-son pairs, and children’s age is about 40 years in the survey year, 2010 (29 years in 1999, same as that for India in 1999 REDS data). Fathers are aged 68 years on average in 2010. About half of the fathers work in the agricultural sector. Children receive 6.31 years of schooling on average, significantly higher than their fathers (less than 3.81 years).

While our main empirical analysis is based on the CFPS 2010 and REDS 1999, we take advantage of a number of additional data sets for exploring the economic mechanisms identified by the theoretical analysis. To understand how the relation between father’s education and household income varies by farm and nonfarm occupation in rural China we utilize the data from the Chinese Household Income Project (CHIP) 1995 and 2002. To estimate the relation between father’s education and household income in rural India, we use the data on household total expenditure from the National Sample Survey 1993.

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from the satellite can provide near real-time estimates of flow. The policy application is to provide a probability of extreme flows in these transboundary basins to inform decisions regarding water obligations and to estimate the current probability of not meeting a particular treaty obligation.

# **METHODOLOGY FOR MODELING SURFACE WETNESS WITH FLOW**

# **Surface wetness (BWI)**

We utilize a surface wetness index, the Basist Wetness Index (BWI), due to its strong correspondence with flow and availability of monthly time-series data. BWI is derived from SSM/I observations to estimate runoff (Basist et al. 1998). The BWI is based on the emissivity adjustment associated with water in the radiating surface and has a strong correlation with the upper level soil moisture (Basist et al. 2001). This surface wetness is sensitive to liquid water from multiple sources (i.e. precipitation, irrigation, snow melt, rivers, swamps, and lakes), yet the satellite observations are largely insensitive to clouds. This allows measurements to monitor water near the surface under almost all conditions at 30 square Kilometers across the globe (Owe et al. 1992) with accurate measures of the amount of liquid water on or near the earth's surface (Vinnokov et al. 1999). The index ranges from zero, which represents no water detected near the surface, to all other values, which represent the percentage of radiating surface that is liquid water. The index takes the form of a linear relationship between channel measurements (Equation 1), where a channel measurement is the value observed at a particular frequency and polarization (i.e. the SSM/I observes seven channels).

where the change of emissivity, Δε, was empirically determined from global SSM/I measurements, _Ts_ is surface temperature over wet or dry land, _Tb_ is the satellite brightness temperature at a particular frequency (GHz), _υ n_ (e.g. _υ1 υ2 υ3_ ) is a frequency observed by the SSM/I instrument, _βn_ is an empirically derived correlation coefficient: _β_ 0 and _β_ 1 are estimate coefficients.

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without a contract than their male counterparts (in Greece, Cyprus and Italy this applies only to childless females).

In Eastern Europe, as well as in Israel, workers with either ethnic minority or immigrant background have a significantly higher propensity to work without a contract than otherwise similar native workers<sup>26</sup> . In Eastern Europe, this effect is less pronounced for those with only one parent being immigrant, as well as for immigrants from EU countries. In Spain and Portugal, all immigrants, both from EU and non-EU countries, face a substantially higher risk of informality than native workers (the marginal effect is 13 to 15 percentage points); however, this is not the case for second generation immigrants. In Italy, Greece, and Cyprus we found a significantly higher informality risk only for immigrants not covered by the free movement of labor provisions (the marginal effect is 21 percentage point), as well as for „mixed‖ second generation immigrants (13 percentage points). With regards to Italy this result should be interpreted carefully because the Italian data cover only year 2006, and the sub-sample of informal employees is pretty small.

In the UK, Ireland, Austria, and the Netherlands, immigrants not covered by the free movement of labor provisions constitute the only group among population with immigrant or minority background featuring a significant informality effect (5.6 percentage points above native workers). It is worth noting that belonging to this group is clearly an exogenous variable – unlike having an individual work permit.

Remarkably, in Nordic countries, as well as in Germany, Switzerland, France and Belgium, none of the minority or immigrant groups, after controlling for other observable characteristics, features a higher informality rate than the native employees. See, however, Hazans (2011a; 2011b) for a simultaneous analysis of informal dependent employment, informal self-employment, unemployment and inactivity.

In Eastern Europe (but not elsewhere), workers who are return migrants are more likely to be employed informally. This is consistent with the idea that a substantial part (although not necessarily a majority) of return migrants might be ―negatively selected‖ out of homecountry‘s labor force, and their return is an evidence of not being successful abroad as well. It is enough if the proportion of such low-productivity workers among return migrants would be substantially higher than the informality rate among stayers, which is about 5% in the Eastern Europe, and such a situation is quite a likely outcome; Hazans (2008) shows that about 25% of return migrants are negatively selected.

> 26 This result does not apply to Hungary, Romania, and Slovenia. However, at least for the former two countries, this might be due to data limitations: It is well documented (see e.g. Kahanec et al., 2010; Kahanec and Zimmermann, 2011), that informal employment is common among Roma population across Eastern Europe. Yet this minority group is severely under-represented in ESS samples for Hungary and Romania.

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**Table 2**

**Explanatory Sectoral Policy Variables Influencing Power Sector Performance** **<mark>(</mark> Ex** **<mark>p</mark> ected Relationshi** **<mark>p)</mark>**

|||**Expec**<br>|**ted Sign **<br>|
|---|---|---|---|
|**Variables**|**Definition**|**Performance**|**Environmental**|
|||**indicators**|**indicators**|
||**DEGREEOF VERTICALUNBUNDLING**<br>|||
|**Partial Vertical Unbundling**|= 1 from the year of separation of generation from transmission<br>and distribution|**+**|**-**|
||= 0 otherwise|||
|**Full Vertical Unbundling**|= 1 from the year of separation of transmission from generation<br>and distribution<br>|**+**|**-**|
||= 0 otherwise|||
||**DEGREE OF DISAGGREGATION**<br>|||
|**Degree of disaggregation**|= the reverse of the Hirschman-Herfindal index (HHI),<br>computed separately for the generation, transmission, and<br>distribution segments; in the case of generation is expressed<br>in installed generating capacity (MW); in the case of<br>transmission expressed in terms of km line length without<br>discrimination between transmission voltage levels; in the<br>case of distribution expressed in terms of total retail sales<br>(MWh)<br>**DEGREE OF PRIVATE SECTOR PARTICIPATIO**<br>|**+**<br>**N**|**-**|
|**Share of private sector**<br>**participation**|= the percentage of private sector participation expressed in<br>installed generating capacity (MW) in the generation<br>segment; expressed in terms of km line length in the<br>transmission segment; and expressed in terms of total retail<br>sales(MWh)in the distribution segment|**+**|**-**|
||<br>**REGULATION**<br>|||
|**Introduction of a regulatory**<br>**agency**|= 1 from the year of establishment of a regulatory agency<br>= 0 otherwise|**+**|**+**|

Table 3 introduces the key control variables related to GDP per capita and power system size, with a particular attention to Group A and Group D, the interaction between the groups with the policy variables, as well as additional indicators, including the financial crisis for Latin America, capital investment, and the share of fossil fuels in generation.

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gender balance. These difference between countries are explained by the prevalent type of work (e.g. construction and repair vs. personal and household services) performed by shadow employees in different countries (see below).

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>LV BG PL EE SK UA HU SI CZ RO RU FI NO DK SE GR CY IT IL PT ES UK FR IE BE DE AT NL CH<br>East North South West<br>Employees with contracts Informal self-employed Employees without contracts<br>% Male<br><!-- End of picture text -->

**Figure 4 Gender composition of formal and informal employment, 2004-2009.**

_Source:_ Calculation with ESS data.

By contrast, in _all_ European countries the proportion of males among the informally self-employed is higher than among formal employees; the ratio varies between 1.2 and 1.5 in continental Europe, whilst it is 1.6 in the UK and 1.8 in Ireland<sup>20</sup> . Possible explanations include a higher risk tolerance among males (see Ekelund _et al._ (2005), Le _et al._ (2010) and references therein), and the heritage of the social norms which considered business as a ̳nonfemale‘ occupation. The fact that informal self-employment is dominated by males is consistent with the finding of the survey of undeclared work in the EU (Riedmann and Fischer 2008: p. 24) that undeclared work of any type is done mostly by males, both in the EU as a whole and in every country except Spain, France, and Italy.

**_Age._** The age composition of informal workforce is also markedly different for its two components: In all parts of Europe, the young workers are over-represented among employees without contracts but (with exception of Russia) under-represented among informal selfemployed (Figure 5). On the other hand, workers aged 55+ are over-represented among informal self-employed (less so in Central and Eastern European countries), while the degree of involvement of the elderly as no-contract employees varies by country (Figure 6). This finding can be used to support both the exclusion and the exit arguments explaining informal self employment. The exclusion story might refer to workers which were in early 1990s displaced by privatization, de-industrialization or retrenchment in the public sector; being

> 20 Higher prevalence of (and/or preference for) self-employment among males is reported by Georgellis and Wall (2005), Flaquer  and Escobedo (2009), Macieira (2009),  Leoni and Falk (2010).

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17 making investment decisions (granting or denying public support to banks, for example); defining specific terms and conditions for support; negotiating contracts; designing reporting requirements and systems; monitoring performance against contract terms and other benchmarks; and enforcing contracts. These tasks likely will have to be performed over a period of time measured in years. The institutional arrangements will have to provide the managerial and organizational capacity necessary to carry out this work professionally.

In each country, pract:ical means need to be found to accommodate politicians' need to provide leadership in a crisis and to feel comfortable in their ability to oversee government's response, while at the same time ensuring that the work is done expeditiously, professionally, consistently, sustainably, and in a fair and objective manner. This paper elaborates one means that may have broad applicability: specifically, explicit delegation of the work of crisis management to a technically competent government team, a _crisis management team,_ where the ongoing role of the political leadership is to oversee the work of the team."<sup>1</sup> Clearly delegating responsibility to a crisis management team has, several key advantages. It publicly identifies the locus of responsibility for dealing withl the crisis. It consolidates decisionmaking and promotes the government's ability to speah; with one voice, two objectives that can be critical to the effectiveness of government's response.

To provide for oversight, the crisis management team would best report to some form of governing body. This governing body might consist of representatives of senior political interests and perhaps the private sector. The governance arrangements should be designed so that the crisis management team is seen as operating largely independently of inappropriate political interference. It should operate transparently, reporting publicly on the results of its analyses, on its decisions, and on its actions, to the extent practical. The combination of reporting to the governing body and to the public would allow interested politicians, affected parties, the public at large, and the domestic and international media to judge the performance of the team; over time it would contribute to restoring confidence and building public support for resolution actions. It also would allow politicians to deflect some or much of the potential criticism and lobbying that 11 This section is based on advice provided by the World Bank to the Korean govermnent at the outset of the crisis.
**Figure 7: Cost differences within countries: Transportation cost per school or facilitation visit across Kenyan education programs (2011 USD)**

<!-- Start of picture text --> Cost (2011 USD)<br>$33<br>$32 $32 $32 $32<br>$31<br>$30<br>$29<br>$28<br>$27 $27<br>$26<br>$25<br>$24<br>$23<br>Extra teachers & Girls' merit Textbooks Uniforms<br>Streaming scholarships<br><!-- End of picture text --> Source: Data from J-PAL (2014a).

**Figure 8: Simulated cost-effectiveness of improving learning using a remedial education program, using local data on the cost of community teachers**

<!-- Start of picture text --> Nepal (simulation)<br>Benin (simulation)<br>Madagascar (simulation)<br>Remedial education, India (actual estimate)<br>Guinea (simulation)<br>Niger (simulation)<br>0 1 2 3 4 5<br>Additional SD per $100<br><!-- End of picture text --> Source: Data adapted from J-PAL (2014a). Calculations by authors. Notes: Unlabeled bars are the cost-effectiveness of other learning interventions, illustrated in Figure 1.

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