rafmacalaba/datause-encoder-data huggingface dataset:
# **Is there a farm size-productivity relationship in African agriculture? Evidence from Rwanda**

# **1. Introduction**

A recurring finding in the literature on agricultural production has been the existence of an inverse relationship between the size of a farm’s operated area and its productivity. The two most common explanations focus on either a failure to properly measure key factors such as land quality or area or application of more than the optimum amounts of certain inputs by small farmers, possibly as a result of imperfections in key factor markets including those for labor, land, and insurance. Studies find this relationship to weaken with technical progress and mechanization and to be less pronounced in Africa than in other regions.

With the recent re-emergence of interest in agriculture, the extent to which small farms use resources efficiently is particularly relevant for African countries that seek to modernize their agricultural sector and make the transition from a subsistence-based to a market-driven rural economy. If they do, policy should focus on attracting investment higher up in the value chain (e.g., in agro-processing) and link smallholders to relevant market channels, e.g., via out-grower schemes but rely on the high poverty elasticity of smallholder agriculture (Ligon and Sadoulet 2008) to support growth and poverty reduction (Larson _et al._ 2012). If not, a strategy that aims to leapfrog directly to large-scale farming (Collier and Venables 2011) and a regulatory environment that reduces the scope for further subdivision and aggressively promotes land consolidation may be more appropriate.

In Rwanda, Africa’s most densely populated country, fragmentation and small farm sizes are considered a key issue by policy makers. The 2008 national agricultural household survey puts average holding size at 0.72 hectares per household (in four parcels with 0.18 hectares each on average) which with traditional technology will not generate enough revenue to allow the average household to meet subsistence needs (Republic of Rwanda 2009). To promote efficient and sustainable use of scarce land resources for agricultural development, the country put in place a three-pronged National Land Policy that (i) promotes land use planning to free up land for agricultural investors and non-agricultural development; (ii) aims to consolidate land to achieve ‘economic’ plot sizes; and (iii) prohibits any subdivision that would result in parcel sizes below one hectare. As such measures were not uncontroversial and proved to be difficult to implement in other settings, an empirical review of the underlying assumptions seems warranted.

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**Table 4: Average leverage by sector, based on all identified data (%)**

||**Levera**<br>|**ge 1 - Market**<br>|**based**<br>|**Leverage**<br>|**2 - Accountin**<br>|**g based**<br>|
|---|---|---|---|---|---|---|
|**Year**|**Electricity**|**Gas**|**Water**|**Electricity**|**Gas**|**Water**|
|1991|28.6|-2.5|n.a.|28.7|-14.2|n.a.|
|1992|26.7|0.4|n.a.|32.7|0.9|n.a.|
|1993|17.8|5.0|-21.6|25.9|21.3|-107.2|
|1994|14.2|9.2|-10.5|18.8|24.6|-16.4|
|1995|21.7|14.5|7.9|33.1|29.3|5.8|
|1996|18.7|10.1|5.8|20.1|25.7|4.9|
|1997|22.7|18.3|7.5|20.0|25.8|32.7|
|1998|36.4|23.8|24.4|24.6|28.8|36.4|
|1999|36.1|29.3|10.2|23.2|35.1|18.4|
|2000|35.1|36.7|1.8|25.3|29.6|6.2|
|2001|39.5|39.2|17.4|26.0|33.6|13.0|
|2002|44.0|39.2|26.3|31.6|38.2|7.9|

Source: Datastream and authors’ calculations.

The sample covers companies from different geographical areas and institutional backgrounds. Therefore, apart from considering sectoral differences, it is of interest to compare leverage ratios across geographical areas. Table 5 presents the average results for leverage(1) and leverage(2), calculated across countries in three geographical areas. The results are again unweighted averages and are based on all identified data. Overall, the figures show some regional differences in the way the utilities are financed. The most important results can be summarized as follows.

- _South America_ —measured by leverage(1), South American utility companies have increased their leverage levels considerably over the last 10 years. The average level of leverage has been rising throughout the period, reaching almost 60% in 2002. However, the same trend is not observed with regard to leverage(2). When book value of equity is used, the leverage has remained practically constant, at close to 30%. A slight jump can be observed from 2001 to 2002.

- _Asia_ —the average leverage for Asian utilities follows a very similar pattern to the South American utilities. The average increase in leverage(1) has not been as large over the last five years, but the trend has been very similar. _The average leverage(2) has fluctuated between 20 and 40%_ .

- _Other_ —the third group of countries consists of mainly Eastern European countries. The capital structures of utility companies in these countries have typically been characterized by low levels of debt. Both market and accounting based measures of leverage have remained low over the period, fluctuating around 0%.

- _Overall_ : the Asian and South American utility sectors appear to be characterized by very similar levels of leverage. In Eastern European countries, the utility companies seem to be significantly less debt-financed.

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distribution. As an additional measure, we provide standard errors to capture the measure of uncertainty around linking assessments.

_5.1.2. Differences in underlying populations._ Since PISA measures 15-year olds, and TIMSS measures grades (4 and 8), it is possible that the underlying populations do not perfectly match. However, despite occasional diverging results, Rindermann and Stephen (2009) have found the correlation between PISA and TIMSS to be high. While overall the results are relatively consistent, we account for some of the differences that do exist by using TIMSS as our default anchor reference assessment, rather than averaging across both TIMSS and PISA.

An additional concern in ensuring similarity of the underlying populations is non-participation. Since ISATs and RSATs are sample-based and designed to be representative at the countrylevel, in principle, doubloon countries should ensure the underlying populations are similar. However, if randomly sampled schools and students do not participate for non-random reasons, this would jeopardize the representativeness of the sample of a given test at the country level. As an assurance against this, ISATs and RSATs have strict rules on threshold participation rates. For example, PISA requires an 85 percent participation rate at the school level, and an 80 percent participation rate within school at the student level. If these benchmarks are not met, the results are excluded or caveated appropriately. This is a safeguard to ensure ISATs and RSATs accurately represent the nation’s underlying population.

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although the result is not significant at conventional levels. Columns 3 and 4 report hours of paid work as the dependent variable and, as expected, they exhibit the reverse pattern, though results are again not significant.<sup>40</sup> As before, female labor supply does not vary by ability or treatment.

<u>5.5 Loan Uptake and Repayment</u> We now turn to the impacts on loan demand and repayment. We use administrative data on 542 loans disbursed after November 2006 that matured from February 2007 to November 2007, the period before the loan lottery and on 1,815 loans disbursed from November 2007 to November 2008, the period during which the lottery was available until 5 months after the lottery. We have repayment data from February 2007 to November 2009, at which point all loans had matured.

Table 8 presents the results on loan uptake (column 1), loan size (column 2) and default (columns 3-4) before the lottery. We measure default as the percentage of the due amount that had not been repaid by the 20<sup>th</sup> of the month (column 2) and at maturity (column 3).<sup>41</sup> While there are no treatment effects before the lottery, the loan lottery leads to an increase in the loan size among winners. The effect sizes are however small. The ITT estimate is an increase of Rs 1,307 or USD 22.

The model predicts that repayment should be lower among clients offered business training as increased business knowledge increases the probability of business success and thus repayment. However, default among controls both by the 20<sup>th</sup> of the month and at maturity is very low at less that 2 percent. During and after the lottery period, loan sizes are higher among lottery winners but there is no increase in default. The model can rationalize the same default among lottery winners by assuming that the high return – high investment technology is as risky as the low return – low investment one. However, contrary to the results, the model does predict that repayment should be lower among those that were offered business training.

> 40 Hours spent in agricultural activities show a similar pattern to that of columns 1 and 2 but the results are not statistically significant (not shown).

> 41 NRSP uses the repayment of loans by the 20th of each month when an installment is due as an early warning signal. Repayment by the 20<sup>th</sup> of the month is used to trigger the bonus scheme of credit officers (see Giné, Mansuri and Shrestha (In Progress) for details on an evaluation of two incentive schemes in NRSP).

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LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 12. Liberia could further expand GSM coverage on a commercial basis**

<!-- Start of picture text --> % of population<br>coverage gap efficient market gap existing access<br><!-- End of picture text --> Source: Mayer and others 2008.

# Financing Liberia’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Liberia needs to expand its infrastructure assets in key areas (table 12). The targets outlined below are purely illustrative, but they represent a level of aspiration that is not unreasonable. They are standardized to allow for cross-country comparisons of the affordability of meeting them and can be modified or delayed as needed to achieve financial balance.

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2018; Laczko _et al._ , 2009; Raleigh _et al._ , 2008). While informal coping mechanisms may be good enough for dealing with shorter-term impacts of the weather in subsistence agricultural contexts, those same circumstances are poorly suited to deal with longerterm effects of climate change (Collier _et al._ , 2008). Examples of such coping mechanisms are holding of low-return liquid assets or diversification of crops and economic activities. In addition, households can sell productive assets (Deaton, 1992) or seek to enhance their families’ human capital (Jacoby & Skoufias, 1997). Compared to these remedies however, migration often has superior potential to increase migrants’ welfare gains (Clemens, 2011).

Unsurprisingly, migrants believe that these welfare gains can often be realized by moving to rapidly growing cities because of their capacity to offer alternative sources of income such as wage labor (Porter, 2012). That this belief may be reasonable is evidenced by Henderson _et al._ (2017) who find a link between climate and urbanization in a large panel of districts and cities across Sub-Saharan Africa. Crucially, the key dimension of heterogeneity is whether cities are likely to have manufacturing for export. In regions where cities presumably offer manufacturing jobs, drier conditions lead to an increase in both urbanization and total urban incomes. This research builds on earlier studies such as Barrios _et al._ (2006), who estimate an increase in national urban share of 0.45 percent for a 1-percent reduction in national rainfall. Specifically, they find that shortages in rainfall have increased rates of urbanization in Sub-Saharan Africa but not elsewhere in the developing world. They report that this association has strengthened since colonial independence, convincingly arguing that the end of the colonial era coincided with lifting of movement restrictions previously imposed on native populations.

# **3 Data and Empirical Strategy**

To investigate how adverse climatic conditions affect urban extent over the long term, we build a novel panel dataset that covers the entire globe between 1985 and 2014.<sup>1</sup> We rely on the PRIO-GRID v2.0 data structure (Tollefsen _et al._ , 2012) that divides the Earth’s surface into 0.5°cells (corresponding to about 55 _×_ 55 km per cell). These cells serve as our 1The time period under investigation was determined based on data availability at the time of writing.

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**o,Pbcrlnft and R~rch**

# **Sodoooonom** Date

The most direct way of comparing output in different countries is to use the official exchange rate to convert GDP in one country's prices into the prices of another country - and in multicountry comparisons to convert it into a common currency, such as the U.S. dollar. But exchange rates mainly indicate the purchasing power of currencies over tradables, not the average purchasing power of currencies over aU goods and services. And even for tradables, the use of exchange rates is problematic because of currency fluctuations and capital movements.

With the measurement of comparative real output across countries intertwined with assessments of purchasing power, the question becomes: what is the best way to make those assessments? Most purchasing power parities have been developed for the components of final demand - for consumption, investment, and so on. This expenditure approach is useful for looking at an entire economy, but it cannot be used directly for analyzing individual sectors because it does not show real product by industry.

The method essentially takes the value of output in national prices and uses unit values from census data to standardize output unifonrly and consistently. So, rather than use a common conversion factor (the exchange rate) it uses a standardized basis of valuation.

Applied to the manufacturing sectors of Brazil, Mexico, and the United States, the revaluation of output in national and U.S. prices provides a sounder basis for constructing relative indicators of roductivity. It also reveals much about trade protection policies and their incidence on different sectors of the economy.

The approach shows, in addition, which data are anomalous and which analytically useful in industrial census. It thus provides important lessons for the development of the Bank's data base - on how to increase its reliability and improve its relevance to operations. It also shows how new insights might be gained by exploiting some official sources which, though rich in detail, often remain untapped by intemational agencies.

The production approach used here looks at - the industry of origin and provides a basis for growth accounting, comparative structural anaylsis, studies of technological performance, and work on labor productivity and total factor productivity.

This paper is a product of the Socioeconomic Data Division, Intemational Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Estela Zamora, Room F7-136, ext. 33706.

The PPR Working Paper Series disseminates the fmdings of work under way in the Bank's Policy, Planriing, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The fndings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Barnk.

Copyright @ 1988 by the International Bank for Reconstniction and DevelopmentrThe World Bank
Figure 9 plots tax evasion and output per worker from the model against the data for African and Latin American countries. Here, the output per worker is normalized by the output per worker of Chile. As previously, the data on GDP per worker are from the World Bank’s World Development Indicators. The reported value of the GDP per worker is normalized by the levels in Chile in both the model and the data. As previously, each circle represents one country and corresponds to a combination of the output per worker from both the model and the data. As it can be seen, the model’s predicted values are highly correlated with the data, with a regression coefficient of around 0.92 for tax evasion and 0.20 for GDP per worker. These coefficients are statistically significant at the 1 and 10 percent level respectively. Using Chile as a benchmark economy, the model explains 35% of the variation of tax evasion and around 10% of the dispersion in GDP per worker across African and Latin American countries. These findings are comparable to those obtained from the calibration to the United-States, where the model explains 35% of the variation of tax evasion and 49% of the variation of GDP per worker across African and Latin American countries.

With the calibrated model on the Chile economy, we conduct three tax neutral counterfactual experiments using Guinea as in the quantitative analysis. Especially, we examine what happens in terms of tax evasion if both the level of distortionary infrastructures and policies can be improved to the average level of the sample and by 50 percent respectively. The approach consists of examining the effects of the change separately in experiments 1 and 2 before combining them in experiment 3. Experiments 1 and 2 lead respectively to a decline in sales not reported for tax purposes by 26.72 and 18.34 percent. In these experiments, the output per worker increases up to 13.20-fold respectively. The third experiment leads to a decrease in firms’ tax evasion by 40.16 percent, while the output per worker is nearly 5-fold as large as in the benchmark situation of the country. The counterfactual experiments are in line with the findings from the calibration to the U.S. economy.

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- Wahba, Jackline (2007). “Returns to Overseas Work Experience: The Case of Egypt”. In: _International migration, economic development and policy_ , pp. 235–258.

- (2015). “Selection, Selection, Selection: the Impact of Return Migration”. In: _Journal of Population Economics_ 28.3, pp. 535–563.

- Wahba, Jackline and Yves Zenou (2012). “Out of Sight, out of Mind: Migration, Entrepreneurship and Social Capital”. In: _Regional Science and Urban Economics_ 42.5, pp. 890–903.

- World Bank, the (2018). _Moving for prosperity: Global migration and labor markets_ . The World Bank.

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**Table 2: Data Transparency and Sovereign Bond Spreads: Linear and Conditional Impact, fixed effect**

||||dependent va<br>1995|riable: spread<br>-2018|||
|---|---|---|---|---|---|---|
|Variables|[1]|[2]|[3]|[4]|[5]|[6]|
|GDP per capita (constant 2010 US$)|-0.640***|-0.753***|-0.696***|-0.415**|-0.543***|-0.583***|
||(0.196)|(0.152)|(0.154)|(0.198)|(0.156)|(0.156)|
|Growth (annual %)|-0.0316***|-0.0288***|-0.0293***|-0.0290***|-0.0274***|-0.0284***|
||(0.00624)|(0.00544)|(0.00544)|(0.00612)|(0.00537)|(0.00541)|
|CPI Inflation (average consumer prices)|0.0561**|0.0677***|0.0585**|0.0704**|0.0656***|0.0497**|
||(0.0282)|(0.0237)|(0.0237)|(0.0277)|(0.0233)|(0.0235)|
|Primary Balance to GDP (%)|0.00360|0.00156|0.00421|0.00520|0.00563|0.00377|
||(0.00762)|(0.00710)|(0.00709)|(0.00751)|(0.00708)|(0.00701)|
|Current Account Balance to GDP (%)|0.00199|-0.000538|-0.00152|0.00162|-0.00164|-0.000941|
||(0.00455)|(0.00429)|(0.00428)|(0.00447)|(0.00425)|(0.00427)|
|Public Debt to GDP (%)|0.0124***|0.0145***|0.0159***|-0.0444***|0.0101***|0.0147***|
||(0.00157)|(0.00132)|(0.00131)|(0.0159)|(0.00201)|(0.00152)|
|VIX Index|0.508***|0.600***|0.580***|0.548***|0.649***|0.605***|
||(0.0571)|(0.0573)|(0.0576)|(0.0565)|(0.0574)|(0.0573)|
|US 10-year Treasury Bond Yield|-0.386***|-0.273***|-0.254***|-0.294***|-0.201**|-0.196**|
||(0.0824)|(0.0789)|(0.0786)|(0.0832)|(0.0792)|(0.0796)|
|ICRG Index|-1.933***|-2.062***|-1.945***|9.165**|-1.398***|-1.506***|
||(0.391)|(0.297)|(0.297)|(4.589)|(0.373)|(0.316)|
|Transparency Index (WB)|0.226|||9.737**|||
||(0.215)|||(4.326)|||
|Transparency Dummy (IMF) Subscription||-0.352***|||2.892**||
|||(0.0679)|||(1.386)||
|Transparency Dummy (IMF) Standard compliance|||-0.311***|||4.141***|
||||(0.0594)|||(1.359)|
|Transparency (WB) x ICRG||||-2.550**<br>|||
|||||(1.052)|||
|Transparency (IMF) Subscription x ICRG|||||-0.892***<br>||
||||||(0.334)||
|Transparency (IMF) Standard compliance x ICRG||||||-1.110***<br>|
|||||||(0.323)|
|Transparency (WB) x public debt||||0.0139***|||
|||||(0.00380)|||
|Transparency (IMF) Subscription x public debt|||||0.00717***||
||||||(0.00203)||
|Transparency (IMF) Standard compliance x public debt||||||0.00357*|
|||||||(0.00193)|
|Constant|16.51***|18.85***|17.72***|-27.19|14.39***|14.89***|
||(2.600)|(1.758)|(1.786)|(19.00)|(2.116)|(1.898)|
|Observations|560|723|723|560|723|723|
|R-squared|0.440|0.506|0.506|0.467|0.522|0.519|
|No. Countries|57|61|61|57|61|61|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 24