rafmacalaba/datause-encoder-data huggingface dataset:
# **Productivity effects of land rental markets in Ethiopia: Evidence from a matched tenant-landlord sample**

# **1. Introduction**

It is well recognized that there is considerable potential for the agricultural sector to help improve labor productivity in rural areas through technology, integration into supply chains, and better functioning of factor markets including those for land and labor to facilitate off-farm employment (World Bank 2007). In the context of economic growth, low-cost and flexible mechanisms to bring land to its most productive use can help in the transformation of rural economies by allowing those with little comparative advantage in agriculture to take up non-agricultural employment and thus lay the basis for a more diversified growth (de Janvry _et al._ 2001). Given their greater flexibility and limited upfront capital requirements, rental markets allow greater flexibility than sales and allow owners working off-farm to continue enjoying the benefits of land ownership (Otsuka and Hayami 1988) while at the same time drawing on non-agricultural income (Deininger and Jin 2006). There is, however, concern that, especially if other markets do not work well, contractual arrangements adopted in such markets may fail to fully realize their potential. However, few empirical studies have explored productivity impacts of different forms of land rental, making it difficult to assess the scope and possible nature of policies to help realize such potential more fully. Doing so for the case of Ethiopia‟s highlands, where rental markets are quite active, is the goal of this paper.

We use a sample of some 1,700 households and their 800 rental partners to address two questions that are critically important for productivity and welfare. First, the extent to which „Marshallian inefficiency‟ affects productive efficiency of sharecropping and whether parties can alter associated impacts. Second, whether landlords‟ choice of partners with greater agricultural ability affects the productivity impact of land rental market and the extent to which policy can help to encourage productivity-enhancing outcomes.

Use of within-household fixed effects estimates for owner-cum-tenants to answer the first question suggests that output on sharecropped plots is significantly lower, by between 16% and 25%, than on owned plots cultivated by the same household. If landlords share in provision of inputs, the inefficiency will disappear. Landlords‟ monitoring capacity, i.e., being a farmer or having off-spring live in the village, attenuates but fails to eliminate the inefficiency arising from sharecropping contracts.

While earlier analyses provided estimates of the magnitude of the „Marshallian inefficiency‟ comparable in magnitude to those obtained here, lack of information on partners in rental markets made it impossible to ascertain whether this was due to the contractual form, or to tenants‟ inherent productivity. Analysis of pair fixed effects for properly chosen plot samples, i.e., plots owned and cultivated by either the landlord 2
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# Stages of Diversification Redux

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Morogoro. In this section we summarize some key features of private schools in Morogoro and contrast them with those of public schools. Therefore, unless explicitly mentioned, data for this section come from the 2015 Morogoro data. In some places, these data are supplemented with 2013 IE data (details in Section 2).

Roughly half of the private schools in our sample are owned and managed by a faith-based organization, a third are owned and managed by a private business and the rest fall under the category of community/NGO/Trade Union schools.

Figure 8: Types of private schools in Urban Morogoro

|Type ofprivate schools|Percentage|
|---|---|
|Faith based/religious organization|54%|
|Private business|38%|
|Community|3%|
|Charity/NGO|3%|
|Trade Union|1%|

Source: Authors' calculations using 2015 Morogoro data.

Private schools are newer than public schools by an average of 6 years. Nearly 45 percent of public and approximately 61 percent of private schools in the sample were established between 2005-2014.

# 6.1 Size and Capacity

In the 2015 Morogoro data we find that private schools have significantly higher capacity for additional enrollment at the preprimary/primary level and slightly higher capacity for additional enrollment at the secondary level. Nearly 86 percent of private pre-primary/ primary schools have capacity for additional enrollment, compared to only 27 percent in public schools. At the secondary level, 83 percent of private schools have capacity for additional enrollment, compared to 65 percent in public schools. Around 86 percent of private schools admit every student who wishes to attend their school but only 42 percent of public schools admit every student who applies.

Figure 9: Filled capacity in public and private schools, by level

|Primary|Private|85%|
|---|---|---|
||Public|175%|
|Secondary|Private|65%|
||Public|107%|

Source: Authors' calculations using 2015 Morogoro data.

Private schools are smaller—the average number of students in public schools is 661 compared with 247 in private schools. Size comparison by education level is provided in Figure 10. On average, private schools have half as many students in these levels as public schools, though the number of students for both public and private schools is widely distributed around the respective means.

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(Eq 6) _Number of workers_ = _worker per 1000 population_ * _population in 2030_ /1000 To obtain estimates for the other cadres, we apply the standardized approach adopted by the team and which was previously applied to the need‐based worker estimates. The income group‐specific multiplier<sup>2</sup> was multiplied with a constant ratio of 3.517 and the physician density, according to the following formulas according to the method applied by WHO (2016):

|High income|Other cadresper 1000 = 0.373 *(3.517)*physiciansper 1000|
|---|---|
|Upper middle income|Other cadresper 1000 = 0.406 *(3.517)*physiciansper 1000|
|Lower middle income|Other cadresper 1000 = 0.549 *(3.517)*physiciansper 1000|
|Low income|Other cadresper 1000 = 0.595 *(3.517)*physiciansper 1000|

The resulting density of other cadres was then multiplied by population size (see Eq 15) to obtain the absolute number of workers in other cadres.

# **A5. Calculating worker surplus or shortages**

The surplus or shortage of workers for each country _c_ and year _t_ is calculated as the difference between what is demanded what is supplied: (Eq 7) _Difference (Surplus/Shortage)ct_ = _Workers suppliedct_ – _Workers demandedct_

# **A6. Aggregation by region and income level**

We report projected health worker demand, supply, and shortage estimates by regions and income levels as defined by the World Bank (see data.worldbank.org/about/country‐and‐ lending‐groups).

Demand projections were only made for 165 countries for which sufficient data for input parameters were available. In reporting aggregate shortages, we only include countries for which there is a projected demand‐based shortage in 2013 or in 2030. In other words, countries that are estimated to have health worker surpluses in 2013 or in 2030 are not counted toward shortage totals in respective years (i.e. a zero shortage is assumed). This is consistent with the assumption that there are no transnational movements of workers (i.e. shortages and surpluses do not net out across countries), following the approach of WHO (2006).

> 2 The multiplier was determined via a separate methodology within the needs‐based worker threshold analysis (WHO 2016).

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general equilibrium effects of internal forced displacement in the Colombian economy. As found by Calder ́on-Mej ́ıa and Ib ́a ̃nez (2015), for instance, other parts of the economy are also affected by these migration shocks. Particularly, the authors document that as a consequence of internal forced displacement native workers in the informal sector receive lower wages. Additionally, the positive population shock has also been shown to be inflating housing prices for low-income individuals and reducing them for high-income individuals (see Depetris-Chauvin and Santos, 2018). These another potential effects may end up interacting in interesting ways worth exploring with more detail.

# **References**

- Abadie, A. and J. Gardeazabal (2003). The economic costs of conflict: A case study of the basque country. _American economic review 93_ (1), 113–132.

- Accetturo, A., M. Bugamelli, and A. R. Lamorgese (2012). Welcome to the machine: firms’ reaction to low-skilled immigration. _Bank of Italy Temi di Discussione (Working Paper) No 846_ .

- Altindag, O., O. Bakis, and S. Rozo (2018). Blessing or burden? The impact of refugees on businesses and the informal economy. _SSRN. Working Paper N. 3188406_ .

- Altonji, J. G. and D. Card (1991). The effects of immigration on the labor market outcomes of less-skilled natives. In _Immigration, Trade, and the Labor Market_ , pp. 201–234. University of Chicago Press.

- Amaral, P. S. and E. Quintin (2006). A competitive model of the informal sector. _Journal of Monetary Economics 53_ (7), 1541–1553.

- Banerji, A. and S. Jain (2007). Quality dualism. _Journal of Development Economics 84_ (1), 234– 250.

25
implement an empirical approach that deals with simultaneity and to appropriately identify all relevant correlates of radicalization. A more recent paper by Benmelech and Klor (2016) draws the opposite conclusion. It argues that poor economic conditions and human development conditions do not drive participation in the Islamic State (ISIS), instead ideology and difficulties of assimilation into homogeneous Western countries play a key role.

The most relevant to our analysis is an article by Haddad (2004) who relies on survey data from Lebanon to assess Palestinian refugees’ and Lebanese Muslim perceptions of suicide bombings. Except for their finding that support for suicide attacks is stronger among women than men, Haddad’s findings are consistent with ours as they indicate that support for terror attacks is stronger among low-income people, those living in camps and therefore experiencing hardship, and among people committed to their beliefs as part of political Islam.

In sum, the existing literature associates violent extremism with economic and social deprivations or with religious and ideological beliefs, but does so based mostly on the findings of country-case studies. It is problematic to generalize the findings in such studies as they employ data that are not harmonized and comparable across countries. The lack of rigorous empirical evidence on attitudes towards terrorist attacks and more generally the causes of radicalization is striking and reflects the fact that a better understanding of these issues requires detailed crosscountry data at the individual level, which unlike macro data, are difficult to collect or gain access to. This paper is the first to utilize such data to study the factors affecting attitudes towards terrorist acts in a cross-country framework.

The remainder of the paper is structured as follows. Section 2 defines the concept of radicalization and describes the data used to measure the extent of radicalization in a country and the main variables of interest. Section 3 discusses the methodology, the identification issues, and 7
- Bernanke, Ben, 1983, Nonmonetary Effects of the Financial Crisis in the Propagation of the Great Depression. _American Economic Review_ 73 (3), 257-276.

- Bernile, Gennardo, Jianfeng Hu, and Yuehua Tang, 2015, Can Information be Locked Up? Informed Trading Ahead of Macro-news Announcement. _Journal of Financial Economics_ forthcoming.

- Bertrand, Marianne, Francis Kramarz, Antoinette Schoar, and David Thesmar, 2008, Politicians, Firms and the Political Business Cycle: Evidence from France, University of Chicago Working Paper.

- Boycko, Maxim, Andrei Shleifer, and Robert W. Vishny, 1993, Privatizing Russia, _Brookings Papers on Ecomomic Activities No 3._

- Caballero, Ricardo, J. and Pierrer Yard, 2010, Future Rent-seeking and Current Public Saving, _Journal of International Economics_ 82(2), 124-136.

- Campante, Filipe R. and Quoc-Anh Do, 2014, Isolated Capital Cities, Accountability and Corruption: Evidence from US States, _American Economic Review_ 104(8), 2456-81.

- Carvalho, Daniel R., 2014, The Real Effects of Government Owned Banks: Evience from An Emerging Market, _Journal of Finance_ 69, 577-609.

- Claessens, Stijn, Erik Feijen, and Luc Laeven, 2008, Political Connections and Preferential Access to Finance:the Role of Campaign Contributions. _Journal of Financial Economics_ 88, 554-580.

- Chodorow-Reich, Gabriel, 2011, The Employment Effects of Credit Market Disruptions: Firm-Level Evidence from the 2008-9 Financial Crisis, _Quarterly Journal of Economics_ , 129(1), 1-59.

- Cohen, Lauren, Joshua Coval, and Christopher Malloy, 2011, Do Powerful Politicians Cause Corporate Downsizing?, _Journal of Political Economy_ 119, 1015-1060.

- Cole, Shawn, 2009, Fixing Market Failures or Fixing Elections? Election, Banks and Agricultural Lending in India. _American Economic Journal:Applied Economics_ 1(1), 219-250.

- Degeorge, Francois, Dirk Jenter, Alberto Moel, and Peter Tufano, 2004, Selling Company Shares to Reluctant Employees: France Telecom's Experience, _Journal of Financial Economics_ 77, 169-202.

- Dinc, Serdar, 2005, Politicians and Banks: Political Influences on Government-Owned Banks, _Journal of Financial Economics_ 77, 453-479.

Faccio, Mara, 2006, Politically Connected Firms, _American Economic Review_ 96(1), 369-386.

- Fang, Hanming, Quanlin Gu and Li-An Zhou, 2014, The Gradient of Power: Evidence from the Chinese Housing Market, NBER working paper No.20317 _._

- Fisman, Raymond, 2001, Estimating the Value of Political Connections, _American Economic Review_ 10951102.

- Fisman, Raymond, and Yongxiang Wang, 2015, The Mortality Cost of Political Connections, _Review of Economic Studies, forthcoming_

27
Table 8 Tradable and Non-tradable Prices

|--------------------------------<br>Year|--------------------<br>PT|PN|PTN|PXN|PMN|
|---|---|---|---|---|---|
|--------------------------------|--------------------|||||
|1970|42.6|44.4|96.1|75.3|54.8|
|1971|41.7|46.1|90.4|65.1|55.6|
|1972|44.7|48.1|93.0|68.7|56.2|
|1973|55.0|51.1|107.6|99.1|61.7|
|1974|66.5|60.5|109.9|115.5|84.3|
|197;.|68.2|65.1|104.9|101.3|87.6|
|1976|71.9|65.3|109.1|97.4|_91.2_|
|1977|_'.8.2_|69.9|109.0|93.7|92.4|
|1978|83.6|76.1|109.9|93.1|'l.5|
|1979|90.0|85.5|105.2|99.0|94.2|
|1980|100.0|100.0|100.0|100.0|100.0|
|1981|109.4|105.8|103.4|97.3|111.7|
|1982|110.3|119.9|92.0|79.9|101.1|
|1983|113.6|125.7|90.4|77.3|91.0|
|1984|113.2|126.3|89.6|76.2|91.3|
|1985|112.5|132.0|85.2|74.9|95.7|
|1986|116.6|133.0|87.7|77.4|89.3|
|1987|122.0|138.6|88.0|79.3|91.3|
|1988|131.9|139.9|94.3|91.2|99.1|

----------------------------------------------------

Source: Computed from NESDB, Px and Pm from IFS.

- PT = Price of tradables PN =Price of non-tradables PTN = Real exchange rate

PXN = Relative prices of exportables to non-tradables PWMN =Relative prices of importables to non-tradables 4-
|Hu and Schlosser<br>(2015)|Higher and<br>increasing SRB<br>(indicator of<br>higher and<br>increasing<br>prenatal sex<br>selection)|Girls’<br>mortality<br>and<br>nutritional<br>outcomes|Indian census and<br>pooled individual<br>data from NFHS<br>(1992-93, 1998-<br>99, 2005-06)|Quasi-experimental study:<br>Triple difference-in-difference<br>that exploits geographic<br>variation in the prevalence of<br>prenatal sex selection over<br>time, and changes in outcomes<br>for boys v/s girls across states<br>over time|No reduction in girls’<br>mortality or reduction in<br>son preference in states<br>with increasing prenatal<br>sex selection|Reduction in<br>surviving girls’<br>malnutrition<br>(underweight and<br>wasting). Prenatal<br>sex selection does<br>not lead to a<br>selection of girls<br>into households<br>with more<br>resources, but<br>girls are more<br>likely to be born<br>into families with<br>weaker son<br>preference. Also,<br>rising SRB leads<br>to larger reduction<br>in family size and<br>increase in<br>breastfeeding<br>duration for girls|
|---|---|---|---|---|---|---|
|Kalsi (2015)|Access to sex<br>selective<br>abortion in 1985|Girls’<br>university<br>attendance|Individual level<br>data from 1991-<br>2010 Taiwan<br>Family<br>Income/Expenditu<br>re Survey<br>(children of<br>college-age born<br>between 1978 and|Quasi-experimental study:<br>Difference-in-difference that<br>exploits timing of legalization<br>of abortion, birth-order of the<br>child (order 2 or higher), and<br>sex of the child|N/A|After abortion<br>legalization, the<br>average rate of<br>university<br>attendance for<br>birth order 2 or<br>higher girls<br>increased by<br>approximately 4.5|
||||1992)|||percentage points|

17
facilitation. This means that in terms of our effective sample, i.e. the number of observations for which data are available across all variables, the zero problem in fact only affects 159 observations. Although this is just 6% or so of the effective sample, we still believe it is important to ensure that our results are robust in this sense.

Our approach to this problem follows Santos Silva and Tenreyro (2006).<sup>7</sup> First, we express (5) in non-linear form (i.e., prior to taking logarithms): The notation _X_ 0 _ijk_ is designed to emphasize the fact that the trade flow data include zeros. The essential difference between (5) and (6) relates to the error term, which we have relabeled ω _ijk_ in (6). Equation (5) assumes that the error is additive in a log-linear specification, or alternately that _k_ exp (ε _ij_ ) is multiplicative in the original non-linear specification. On the other hand, equation (6) more naturally assumes that the error is additive in the original non-linear specification. If (6) represents the “true” model, then the OLS estimator derived by log-linearization will generally be inconsistent (see Santos Silva and Tenreyro 2006 for details).

The first order conditions for estimation of (6) by weighted nonlinear least squares are identical to those for maximum likelihood estimation using the Poisson model for count data (Gourieroux et al. 1984; Davidson and MacKinnon 2004). Estimated coefficients from Poisson can still be

> 7 The most common alternative is the Heckman sample selection model (e.g., Francois and Manchin 2006; Helpman et al. 2006). However, reliance only on the non-linearity of the inverse Mills ratio for identification implies a strong distributional assumption that may often be rejected in practice (Davidson and MacKinnon 2004, 488-489). At the same time, overidentification via variable exclusion has tended to rely on unconvincing assumptions.

- 14 -
Gibson and McKenzie (2010) we find that 90 percent of Micronesians and 95 percent of Tongans with bachelor degrees in their sample earned these degrees abroad, as did 28 percent of Ghanaians and 49 percent of Papua New Guineans with advanced degrees such as a masters or doctorate.

# **Question 7: Do High-Skilled Workers Remit, Invest, and Share Knowledge Back Home?**

The benefits that a sending country might receive from high-skilled immigration are usually listed in terms of return flows of income, investment and expertise from migrants back to the sending country. These are the kinds of benefits that underlie the claim by India‘s prime minister that modern India is benefitting from its high-skilled out-migration and the resulting global Indian diaspora. But how common are such benefits? As we have seen, brain drain rates are highest from countries with small populations and those experiencing political instability and poor prospects for career success. Thus while India‘s annual remittances have now reached $55 billion (World Bank, 2011) and India‘s high-skilled migrants have shaped the development of its impressive information technology sector (Saxenian, 2006), the experience of a country with a population of over one billion and a fast-growing economy may have few lessons to offer the small and/or unstable countries for which brain drain is at most an issue. New research offers a more mixed picture of the potential benefits of brain drain.

Consider first remittances, the most visible channel through which migrants contribute to households back home. Global remittance data at the micro-level are not currently publicly available, so we use a database of 33,000 immigrants in 11 OECD countries put together by Bollard et al. (2010). In this sample, skilled migrants remit more than less-skilled migrants, with the average tertiary-educated migrant remitting about $1,000 per year. We take the 6,318 migrants in their sample with tertiary education who come from countries with at least 50 skilled migrants in the database. We then remove survey fixed effects, and then, in Figure 2, plot the proportion of tertiary educated migrants who remit against Gross National Income per capita in the sending country. There are two important results. First, for most sending countries, less than half of tertiary-educated migrants send remittances. Second, there is a strong negative correlation between income levels and the likelihood that skilled migrants remit. In this sample, the likelihood of remitting is significantly higher for countries with higher brain drain rates, but once we control for per capita income, this relationship is no longer significant.

15