rafmacalaba/datause-encoder-data huggingface dataset:
investment. Finally, we follow David et al. (2000) arguments in including a measure of credit market depth measured as the ratio of credit to the private sector relative to GDP to proxy for the availability of credit at the reported interest rate.

A second set of variables seeks to capture risk associated with long term investments. Following Serven (2003) we include the variance of GDP which he found correlated with physical investment.

We also include a measure of intellectual property rights that would also affect the expected quasi rents derived from innovation. Although the impact of IPR is theoretically ambiguous (Horstmann et. al 1985), Arora, Ceccagnoli and Cohen (2003) using US manufacturing survey data find that patent protection stimulates R&D across almost all industries. For this purpose we use the IPR index constructed by Park (2002).

To control for the fact that we are using a series of total R&D expenditures, which includes private and public financing of R&D, we include a measure of overall government spending over GDP as a measure of the government's capacity to mobilize resources. As possible further constraints on investment, we include measures of the availability of complementary innovation-related institutions that may also put binding constraints on new R&D projects. We include the subjective indicators from the _Global Competitiveness Report_ (GCR) published by the World Economic Forum on the quality of research institutions (universities, public research centers, etc) and the extent to which these collaborate effectively with the private sector. These considerations may also constrain the number of national innovation projects. Due to the limited time coverage of these two variables, we use the average of the available observations from the late 1990s and assume that these change little over time.

Finally, we include the Learner indicator of natural resource abundance to see if there is evidence of special barriers to R&D investment in natural resource abundant countries that may somehow explain the especially high rates of return found there.

16
Blanden, J, P. Gregg, S. Machin (2005): Intergenerational Mobility in Europe and North America, Center For Economic Performance Report, April, 2005.

Blanden, J, P. Gregg, Mcmillan (2007), “Accounting for Intergenerational Income Persistence: Noncognitive Skills, Ability and Education". _The Economic Journal_ . Vol. 117, iss. 519, pp C43‐60 Bowles, S, H. Gintis, M. Osborne Groves (2005): Unequal Chances: Family Background and Economic Success, Princeton University Press.

Chadwick L. and G. Solon (2002), “Intergenerational Income Mobility among Daughters,” _American Economic Review_ , vol. 92, no. 1, 335‐44.

Connelly, R and Z. Zheng (2003), “Determinants of School Enrollment and completion of 10 to 18 Years Olds in China”, _Economics of Education Review_ , 24 (2).

Currie, J and E. Moretti (2003), “Mother Education and the Intergenerational Transmission of Human Capital: Evidence from College Openings". _Quarterly Journal of Economics_ , v. 118, iss. 4, pp. 1495‐1532 Dearden, L and S. Machin, and H. Reed (1997), “Intergenerational Mobility in Britain”, Economic Journal, January, pp.47‐66.

Deaton, A (1997), The Analysis of Household Surveys: A Microeconomic Approach to Development Policy, World Bank.

De Brauw, A. and S. Rozelle (2008), “Reconciling the Returns to Education in O¤‐farm Wage Employment in Rural China”, _Review of Development Economics_ , v12, n1, Pages 57‐71 Deng, Quheng,Bjorn Gustafsson and Li Shi. Intergenerational Income Persistence in Urban China. _Review of Income and Wealth_ . Series, Number 3, p. 416‐‐436 Emran, M. Shahe. and F. Shilpi (2011): “Intergenerational Occupational Mobility in Rural Economy: Evidence from Nepal and Vietnam”, _Journal of Human Resources_ , issue 2, 2011.

Emran, M. Shahe. and Z. Hou (2013), “Access to Markets and Rural Poverty: Evidence from Household Consumption in China”, _Review of Economics and Statistics_ 95.2 (2013): 682‐697.

Emran, M. Shahe. and F. Shilpi (2012), “The Extent of the Market and Stages of Agricultural Specialization”. _Canadian Journal of Economics_ . Volume 40, Issue 3, p. 1125‐1153 Farre, Lidia, Klein, Roger, and F. Vella (2010), “A Parametric Control Function Approach to Estimating the Returns to Schooling in the Absence of Exclusion Restrictions: An Application to the NLSY”, Working Paper, Georgetown University.

Fields, G, D. Grusky, and S. Morgan (Eds) (2005): Mobility and Inequality: Frontiers of Research from Sociology and Economics, Stanford University Press.

41
Table 4: 2SLS for the Probability of Engaging in Self-employment by Education

||OLS||2SLS||
|---|---|---|---|---|
|Sample|(1)<br>All|(2)<br>All|(3)<br>Low education<br>(Primary Completed)|(4)<br>High education<br>(Higher than Primary)|
|Ln Pop|-0.058<sup>_⇤⇤⇤_</sup>|-0.045<sup>_⇤⇤⇤_</sup>|-0.075<sup>_⇤⇤⇤_</sup>|0.001|
||(0.006)|(0.010)|(0.015)|(0.010)|
|Addis Ababa|Yes|Yes|Yes|Yes|
|Control|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|Mean|0.48|0.48|0.64|0.29|
|Underidentifcation||18.62|17.11|19.94|
|Weak identifcation||73.71|62.05|83.99|
|N|73,955|73,955|38,017|35,938|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. Control variables are demographic: gender, age (ten years increment), marital status, and household size, and education: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, post-secondary, and adult education.

The main results in columns 2 and 4 show that the estimated probability change of formal jobs with respect to the town population is 2.5 percentage points (book account) and 5.1 percentage points (license), respectively.

# **5.3 Other Labor Outcomes**

## **Labor Force Participation and Unemployment**

Table 6 presents the labor force status of individual workers; whether the individual is active in the labor force or not. Given the di↵erence in the motivation of labor force participation between single/married and male/female, we estimate OLS and 2SLS models for each of their combinations separately. The 2SLS results in columns 5 to 8 do not show clear link between the town population and labor force participation, except for female. A log change in town population is associated with 3.6 percentage point increase in labor participation for single women. The coefficient estimate is also positive for married women but not statistically significant. That said, the chance of unemployment among active labor forces is likely to be higher in larger towns.

Panel B, Table 6 summarizes the estimation results of OLS and 2SLS for individual workers’ 16
|**Tabl**|**e B5: Rule of Law - cont**|
|---|---|
|**Code**|**Concept Measured**|
|**Non-**<br>|**representative Sources**|
|**ADB**|Property Rights|
|**AFR**<br>**ASD**|Based on your experiences, how easy or difficult is it to obtain help from the police when you need it?<br>Rule of Law|
|**BPS**|Fairness, honesty, enforceability, quickness and affordability of the court system<br>Property right protection|
||How problematic is organized crime for the growth of your business.|
||How problematic is judiciary for the growth of your business.|
||How problematic is street crime for the growth of your business.|
|**BRI**|Enforceability of contracts|
|**BTI**|Rule of Law|
||Private Property|
|**CCR**|Rule of Law|
|**CPIA**|Property rights|
|**FRH**|_Rule of Law_: Considers judicial/constitutional matters as well as the legal and de facto status of ethnic minorities.|
|**GII**|Executive Accountability<br>Judicial Accountability<br>Rule of Law<br>Law Enforcement|
|**IFD**|Access to land|
||Access to water for agriculture|
|**LBO**|Trust in Judiciary|
||Trust in Police|
||Have you been a victim of crime?|
|**VAB**|Trust in Justice|
||Trust in Police|
||Trust in Supreme Court|
||Have you been a victim of crime?|
|**WCY**|Tax evasion is a common practice in your country|
||Justice is not fairly administered in society<br>Personal security and private property are not adequately protected<br>Parallel economy impairs economic development in your country|
||Patent and copyright protection is not adequately enforced in your country|

77
Luxembourg, Austria, France, Denmark, Sweden and the Netherlands, the largest effect is observed in the fourth quintile.

_Figure 5: Composition of automatic stabilizers_ <!-- Start of picture text --> LU DK SE EE CZ BG RO NL FI BE LT ES LV CY PT PL SI HR FR HU DE SK EL MT AT IT IE<br>Unemployment Social assistance Other Taxes SIC<br>6<br>4<br>2<br>0<br>-2<br><!-- End of picture text --> Note: See Figure 5 in the Appendix for results by income groups. Other benefits include housing benefits, family and education benefits, health benefits as well as old-age and survivor benefits. Source: Own calculations based on EUROMOD I4.0+.

Finally, Figure 4 shows that additional COVID-19-emergency reforms to taxes and benefits (red bars in Figure 4) play only a small role in most countries. The effect of additional policies is particularly important in Ireland, accounting for a 4 percent increase in household income. COVID-19-emergency policies also play a role in Austria (1.6 percent), Czechia (1.6 percent) and Italy (1 percent), whereas they contribute by less than 0.6 percent in all other countries. In general, their effect is concentrated in the lower part of the distribution (Figure A4 in the Appendix).

# **Decomposition results for the most affected by the crisis**

Figure 6 shows the same decomposition but zooms in on those severely affected by market income shocks, leading to quite pronounced decompositional differences compared to the total population (shown in Figure 4).

By definition, the role of market income changes is more pronounced for those more severely hit by the pandemic. However, there are notable differences between the overall population and those severely affected. While Scandinavian countries as well as Bulgaria show comparably low drops in market incomes overall, the role of market incomes is significantly more pronounced for those severely affected. The difference to the total population is largest in Germany with a negative contribution of market incomes of close to 70 percent. While these differences are sufficiently mitigated in Denmark, disposable incomes still decrease by close to 10 percent or more in Bulgaria, Finland, Sweden and Germany.

In a number of countries, automatic stabilizers are more important for the group of severely affected than overall. This is especially the case in Finland, the Netherlands, Denmark, Germany and Latvia. At the same time, the Netherland is an interesting case where those severely affected benefit very little from the monetary compensation scheme.

16
WPS4027

# **Bank Efficiency, Ownership and Market Structure Why Are Interest Spreads So High in Uganda?**

## **Thorsten Beck and Heiko Hesse** *

**Abstract:** Using a unique bank-level dataset on the Ugandan banking system over the period 1999 to 2005, we explore the factors behind consistently high interest rate spreads and margins.  While foreign banks charge lower interest rate spreads, we do not find a robust and economically significant relationship between privatization, foreign bank entry, market structure and banking efficiency. Similarly, macroeconomic variables can explain little of the over-time variation in bank spreads.  Bank-level characteristics, on the other hand, such as bank size, operating costs, and composition of loan portfolio, explain a large proportion of cross-bank, cross-time variation in spreads and margins. However, time-invariant bank-level fixed effects explain the largest part of bank variation in spreads and margins.  Further, we find tentative evidence that banks targeting the low end of the market incur higher costs and therefore higher margins.

JEL Classifications: G21, G30, O16 Keywords: Foreign Bank Entry; Financial Sector Reform; Bank Efficiency; Financial Intermediation; Uganda World Bank Policy Research Working Paper 4027, October 2006 _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._

* Beck: World Bank, <u>TBeck@worldbank.org. Hesse: Nuffield College, University of</u> Oxford, <u>heiko.hesse@nuffield.ox.ac.uk. We would like to thank Alexander Al-Haschimi,</u> Steve Bond, Martin Cihak, Robert Cull, Michael Fuchs, Dino Merotto, Richard Podpiera, Rachel Sebudde and seminar participants at Oxford for useful comments and suggestions and are grateful to the Bank of Uganda for sharing the data with us and to Edward AlHussainy for help with the data. Also, funding from the ESRC under grant number PTA051-2004-00004 is gratefully acknowledged by Heiko Hesse.
1.5% of the total _rabi_ surplus generated by the borewell, similar in magnitude to the average fixed transaction cost of 1.8%. For the same reason as before, the contracting distortion is greater for borewell owners with smaller plots. Interestingly, comparing the right and left panels of the figure, the impact of groundwater market frictions on area irrigated is, in percentage terms, roughly triple its impact on welfare.

Finally, shutting down uncertainty altogether by means of counterfactual scenario (3) results in modest gains in both welfare (in this case, more so for larger plots) _and_ in area irrigated.<sup>34</sup> Thus, in reference to the reduced form regressions in Table 2, our model suggests that groundwater market distortions are far more important than the precautionary planting motive in explaining the variation in irrigated area due to groundwater supply uncertainty.

# **6.2 Drip irrigation and land fragmentation**

Based on agricultural experiments in India, water use efficiency is 2-3 times greater under drip than under flood irrigation (Narayanamoorthy 2006). Taking the conservative estimate of a doubling of irrigation efficiency, we use the model, through a doubling of the effective discharge parameter _λ_ , to simulate the gross return to drip adoption<sup>35</sup> Based on the four-district estimation sample, the average borewell owner would realize a 53% surplus gain by converting his plot to drip irrigation, with an interquartile range of 44-65%.

The interesting question, however, is how these gains are achieved, since the gross return reflects the full range of endogenous responses to increased irrigation efficiency, including more intensive cultivation of the borewell plot, increased water sales, and switches between alternative transfer arrangements. To gauge the nature and extent of these responses, Table 8 reports the predicted transition probabilites pre- to post-drip adoption. Thus, we see that of those starting in unconstrained self cultivation ( _U_ ), 62% remain unconstrained postdrip, 13% move to autarky ( _A_ ), meaning that they now fully cultivate their own plot, and 25% start selling water, virtually all under the seasonal contract ( _C_ ). The most dramatic transition is, of course, from autarky, where virtually no one remains post-drip, to _P_ and _C_ (mostly the latter). Overall, the model predicts that the proportion of borewell owners

> 34Indeed, since log( _VU_ 0<sup>_/VU_) = log(</sup><sup>_l_</sup> _U_<sup>0</sup><sup>_/lU_),thepercentagegainsaremathematicallyequivalent.</sup>

> 35Equation (17) is a simplification for purposes of exposition. In practice, through suitable adjustments to _λ_ , we only allow for an increase in irrigation efficiency on the borewell plot area, not on the area of other plots that may be (or could be) irrigated by the borewell.

33
However, before this willingness to save can be harnessed towards actual gains on the ground, we need to better understand the challenges that the poor might face in setting money aside as savings. Banerjee and Duflo (2007) have documented that a huge proportion of the poor still lack access to formal banking services. Chaia et al. (2009) estimate that 80 percent of the entire adult population in SSA was unbanked at the beginning of the 2000s. Expectedly, the penetration numbers are much worse for those living below the poverty line: Gaul (2011) calculates the absolute difference between the population living below the poverty line and the population with access to financial services, and finds that the numbers are as high as 80 million for Nigeria and 48 million for Congo. While this is a somewhat crude and imprecise way to measure financial inclusion, it does underline the fact that a vast majority of the very poor continue to be bypassed by financial institutions. Data from IMF statistics also reflects this lack of access to financial services. The number of deposit accounts as a percentage of population is a meager 19 percent on average (excluding South Africa) (Figure 5). Again, the caveat applies that these are supply-side data that do not control for multiple and dormant accounts and actual formal bank penetration is likely much lower.

The lack of access to formal financial institutions drives the poor to save in sub-optimal ways. For instance, the widely prevalent practice of saving through Rotating Savings and Credit Associations (ROSCAs) in Africa comes with a significant risk of the ROSCA itself collapsing before all the members have „won‟ the draw. Similarly, in an RCT based in rural Kenya, Dupas and Robinson (2011a) offered savings accounts to 163 microentrepreneurs. These accounts provided no interest and entailed a withdrawal fee, which effectively means that they had a negative interest rate. They find that 53 percent of those who were offered this account chose to save through it, implying that they could not access a better way to channel their savings.

19
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# **2. Survey Methodology**

# _2.1_ **_Target Population_**

In both Benin and Malawi, the survey of traders targeted input traders, food crop traders, and cash crop traders at both the wholesaler and retailer level. In addition, a market-level survey was conducted in order to obtain information on the marketing environment for each of the selected markets in which trader surveys were conducted.

In both countries, food crop traders are primarily independent traders working on their own account, who could be identified by their location in the market. With the exception of cotton, this is also true for cash crop traders in Benin, who often trade in both food and cash crops such as pineapples. However, in Malawi, in the case of tobacco trading, field observations revealed a similar structure of traders operating as agents on behalf of very large tobacco traders. Conversely to the trade of inputs, these traders acted as buying agents, thus buying tobacco from farmers on the account of a large company and delivering the purchased tobacco to the auction floor. Thus, in the case of cash crop trading in Malawi, the survey targeted these buying agents.

# _2.2_ **_Survey Site Selection and Sampling Frame_**

The survey sites for the trader surveys in both countries are market towns in which a good sample of agricultural traders existed. These market towns were selected on the basis of their importance in the agricultural economy of the country, in terms of flows and volumes of the three types of products: major food crops, cash crops, and agricultural inputs. An additional criterion used to select survey sites was the availability of secondary price data for the market towns. According to these criteria, 24 markets were identified in Benin and 40 markets were identified in Malawi (see Table 1).

In both Benin and Malawi, a sample population of 800 retail and wholesale traders was targeted, broken down into 200 input traders, 200 cash traders, and 400 food crop traders. Due to the absence of reliable census information on the population of traders in both countries, the first step in drawing a random _5_