rafmacalaba/data-use-mentions-extended huggingface dataset:
# _Vanishing Returns?_ Potential Returns and Constraints to Input Adoption among Smallholder Farmers in Uganda

Ruth Hill, Carolina Mejia‐Mantilla, Kathryn Vasilaky<sup>1</sup> JEL classification: Q12, Q16, D13 _Key words:_ Agricultural inputs (Farm inputs), Uganda, Financial returns 1 World Bank (Lead Economist, Poverty & Equity Practice), World Bank (Economist, Poverty & Equity Practice) and Cal Poly, Orfelea College of Business (Assistant Professor). We thank Johanna Fajardo for her support in the final stages. We thank Charles Wortmann for his comments, suggestions, and expert guidance in using the OFRA Response Function Database. We would like to thank Joseph Oryokot for his comments to a previous draft. All remaining errors are ours. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.
12 that we have a nested logit to express E(u1r |X) =θrpr. The search procedure consists in finding a set of θr and αi that simultaneously satisfy equations (6), (7) and (8).

To implement our estimation, the first stage model consists in estimating the probabilities to be in any part of the decision tree in 2000 and 2007, using a nested logit specification. To identify this part of the model, we need instrumental variables that enter the first stage model, but not the second stage. In our case, these instrumental variables are three: (1) the distance from kabupaten (district) to railroad station in 1930; (2) the level of rainfall in 1995-1999; and (3) unexpected rainfall in 2000.<sup>9</sup> Our rationale for using these three instrumental variables is as follows.

The first railroad line in Indonesia opened in 1867. A continuous railroad line between Jakarta and Surabaya, the two largest cities in Java, opened in 1894. Between 1900 and 1930 smaller railroad lines were constructed in Madura, Sumatra and South Celebes. In Indonesia distance to railroad lines in 1930 represents a good instrumental variable because it is related to migration costs in the past and to the need for sending migrants in the past, and therefore to the development of present day migrant social networks, but it is not correlated with the expenditure patterns of households at the time of the 2000 and 2007 IFLS Surveys. We calculated distance to railroad lines for each household using the distance from the kabupaten (district) to the nearest railroad station in 1930, using historical maps from the Indonesian Railway Authority, and then cross-checking this information with the IFLS Surveys. This type of instrument has been used before in the literature by Woodruff and Zenteno (2007) for the case of Mexico, and Adams and Cuecuecha (forthcoming) for the case of Guatemala.

Changes in rainfall have also been used before in the literature as an instrumental variable in the
outcomes. The role of quality determinants is non-negligible: overall, they account for a sizable 20 percent of the explained variation in labor market outcomes and 40 percent for graduation, mostly through T&C determinants.

**Ecuador.** Our regressions for Ecuador explain a higher share (between 27 and 37 percent) of variation in labor market outcomes than for Brazil or survey data (Table 5). The specification is not exactly comparable to that of Brazil because it does not include geographic unit fixed effects (due to small sample sizes). Still, individual and peer administrative variables account for about 40 percent of explained variation, as in Brazil (Table 5 and Figure A3). Consistent with previous results, field accounts for 10-15 percent of the explained variation and, overall, quality determinants account for 32-28 percent of it, mostly due to T&C and faculty, each of which captures about 10-15 percent of explained variation.

**_Program- and individual-level data—taking stock._** Overall, we do not explain much of the observed variation in student- or program-level outcomes. Nonetheless, quality determinants in the program-level regressions account for about 50-60 percent of the observed variation in dropout rates, ETG and formal employment, and about 20-40 percent of the individual-level regressions for academic and labor market outcomes, with the largest explanatory power generally accruing to T&C practices. Especially for academic outcomes, field and HEI characteristics are highly explanatory. Geography, in turn, is highly explanatory for labor-market outcomes. The salient role of quality determinants—together with our previous findings on the association of specific determinants to outcomes—suggests that adopting certain practices might improve outcomes for some programs and shrink their worrisome variation.

# **6 Conclusions**

34
20. Zimbabwe provides an *xample of a country where government wage regulation policies, including minimum wages, have distorted training incentives (World Bank, 1987, Vol. II, p.14). Minimum wages were adopted by the government in 1980 following independence as part of a strategy to raise the incomes of Africans at the bottom of the income distribution. Along with this, restrictions on wage increases were adopted for those at the upper end of the income distribution to reduce income disparities and manage inflation. The government also adopted job security laws restricting employers' rights to dismiss employees as an antidote to the empl -mont reduction expected with the incroase in minimum wages. The minimum wage reached its maximum in 1982, coincident with the peaking of the unemployment rate for unskilled youth. More direct evidence of the impact on training is suggested by the declining number of apprenticeships offered by **the** private sector, 2,044 in 1981 compared with 1,197 in 1985.

21. Administered wage and employment policies of the public sector in many developing countries can also lead to labor market distortions affecting the efficiency of VET investments. The visibility of these policies can be a factor in determining their effect, particularly as related to the level of public sector employment. In a study of 38 developing countries, Heller and Tait show that employment in the public sector accounts for an average of 44 percent of nonagricultural employment (1983, pp.7, 42-43, also see Fallon and Riveros, 1987, p.16). The distortions arising from these policies emanate from the level of earnings paid by the public sector and the role played by it as an employer of last resort. The impact of these policies on the labor market is discussed by Psacharopoulos: 15
in US<br>and EU|
|Wang (1997)|57 cumulative<br>percentage<br>point increase<br>relative<br>to<br>baseline<br>(1996-2005)|Steady state capital<br>market closure with<br>endogenous capital<br>stock in a multi-<br>country extension of<br>the model of de Melo<br>and Tarr (1992)|Perfect competition,<br>endogenous savings,<br>CES function allows for<br>substitutability between<br>value added and<br>aggregate inputs;<br>international shipping<br>sector a la GTAP|Version 3 GTAP Database<br>aggregated into 12 regions<br>and 14 sectors;<br>35% reduction in average<br>tariffs due to accession,<br>removal of quotas on<br>textiles and apparel in US<br>and EU|
|Wang (2003)|54 cumulative<br>percentage<br>point increase<br>relative<br>to<br>baseline<br>(2000-2010)|Recursive dynamic<br>extension of the<br>model in Wang<br>(1997) with import<br>embodied technology<br>transfer|Perfect competition,<br>endogenous savings,<br>CES function allows for<br>substitutability between<br>value added and<br>aggregate inputs;<br>international shipping<br>sector a la GTAP|Version 5 GTAP Database<br>aggregated into 17 regions<br>and 25 sectors;<br>approximately 50%<br>reduction in average<br>tariffs due to accession,<br>removal of quotas on<br>textiles and apparel in US<br>and EU, efficiency gains<br>from sector-specific<br>technology transfers|
|Ianchovichina<br>and<br
# **Introduction**

The collection of survey data from war zones or other unstable security situations provides important insights into the socioeconomic implications of conflict. Data collected during these periods, however, are vulnerable to error, because conflict often limits the options for survey implementation. For example, the traditional two‐stage sample design for face‐to‐face surveys in most developing countries first selects census enumeration areas as the primary sampling unit (PSU) with probability proportional to size and then conducts a listing operation to create a frame of households from which a sample is selected. Such an approach, however, may not be feasible in conflict areas. At the first stage, updated counts are often not available, making probability proportional to size selection inefficient. Also as the second stage requires that survey staff canvas the entire selected area, it may also be too dangerous in a conflict setting. As a result, many surveys of conflict areas are limited to qualitative work or resort to non‐probability designs.

This paper uses simulations to explore several alternative sampling approaches considered for the baseline of the Mogadishu High Frequency Survey Pilot (MHFS). The baseline was a face‐to‐face household survey in Mogadishu, Somalia, conducted from October to December 2014 by World Bank and Altai Consulting. A full listing (see Harter et al, 2010 for details) was deemed unsafe in Mogadishu because the additional time in the field and the predictable movements by interviewers would increase their exposure to robbery, kidnapping, and assault, and increase the likelihood that the local militias would object to their presence. The survey needed an alternative second‐stage sample design that would minimize the time spent in the field outside the households, but also could be implemented without expensive equipment or extensive technical training. In addition, international supervisors from the consulting firm could not go to the field, necessitating a sample design in which quality could be verified ex post.

The implementing partner originally proposed a random walk procedure. While this methodology has the benefits
0.03|0.72|-0.28|10.43|
|Spouse (I)ADL Z-Score(Unable)|10036|-0.02|0.77|-0.18|9.27|

Notes: We use the following cross section surveys for analysis: 2008 CHARLS Pilot for China; 2007/8 IFLS for Indonesia; and the 2006 KLoSA for Korea. Units for housing wealth per capita are Korea (10,000,000 Won), China (10,000 RMB), Indonesia (1,000,000 Rupiahs). The definitions are slightly different given the design of the questions. Whether or not collecting pension is used as proxy for pension eligibility 34
of Callaway and Sant’Anna (2021), which accounts for staggered treatment designs and heterogeneous treatment effects under semi-balanced panel data. The approach presents event study estimates of investment of treated firms (for each cohort of AIA changes), by comparing against those firms that are never treated and not yet treated as a control group.<sup>28</sup> We also examine

> 27We are not able to estimate a regression kink design due to relatively small samples for our technology regressions.

> 28The results are similar if we only use never treated firms as controls.

17
professional, clerical, and skilled manual job groups; the other categories exactly describe the occupation groups included. Ideal number of children is censored at 20; no women in the Demographic and Health Survey report having more than 18 children, and non-numerical responses � are assigned the maximum value. Years of Schooling _izy_ is the predicted level of schooling, instrumented with the combined intensity measure, ∆ _Izy_ . All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age. Each estimate is from a unique regression, and the second-stage estimate in column 5 is generated using a tobit model. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.38
# **2 Assessing Trade Impacts of PTAs and Infrastructure Investments**

In this paper, we employ a four-step strategy to investigate the trade implications of the AfCFTA and of the improvements in transportation infrastructure. The results of the first two stages are outlined in this section: i) estimating a structural gravity model where the ambition of _signed_ trade agreements explains bilateral trade intensity; ii) giving a functional form to the _unobserved_ bilateral frictions in relations between African countries and trading partners by making them dependent on transport time as measured in our data set. The third stage, which quantifies the impact of planned infrastructure investments on transport time, is presented in the section 3. The final stage, simulating the general equilibrium effects of policy and infrastructure integration in Africa, is discussed in section 4.

## **2.1 The Effects of Different Types of PTAs on Trade**

In this section, we estimate the trade impact of the three clusters of PTAs identified in Fontagn ́e et al. (2023) in a gravity framework in order to recover the partial effect on trade of existing PTAs associated with the different clusters.This estimation is performed on the worldwide sample of countries and the obtained elasticity is used in the trade policy counterfactuals addressing the economic impact of integration among African countries.<sup>22</sup> To this end we estimate the following structural gravity equation with PPML on panel data: Where X _ij,t_ includes both intra-national and international manufacturing plus agricultural trade flows on 1-year intervals from 1986 to 2018.<sup>23</sup> Bilateral flows are normalized with total absorption at destination (X _.j,t_ ), as to mitigate the differences in the penalization of large and small trade flows of the PPML estimator (Eaton, Kortum & Sotelo 2013, Head & Mayer 2014).

Our main variable of interest, PTA _ij,t_ , is sliced across clusters: PTA<sup>_z_</sup> _ij,t_<sup>.
significant positive impact on all household public goods, including education as well as food, and significant negative impacts on adult goods.

Another strand of literature looks at the impact of the wife’s bargaining power on subjective measures of the wife’s decision-making powers. For instance, Anderson and Eswaran (2009) find that the wife’s landholdings and her independent income earnings both have statistically significant impacts on various measures of autonomy. Doss et al. (2014) also look at the impact of both women’s sole ownership of and joint ownership of land on women’s self-reported level of input into several farm and non-farm decision-making processes. In the three African countries included in their study, both sole and joint land ownership increase women’s input into decisionmaking across most of the domains considered, though neither has a statistically significant impact using the Indian data. Interestingly, results from a study of Brazil’s Bolsa Familia program, which provided cash transfers to women in poor households, indicate that such transfers have positive impacts on women’s autonomy over many decisions only in urban, and not rural, areas (de Brauw et al., 2014).

A number of empirical papers have also tested whether household outcomes are Pareto efficient in rural, developing country settings, an assumption underlying the hypotheses of the collective bargaining models. While much of the evidence from developed countries supports Pareto 6
The companion study of social exclusion and land administration in Orissa considers further aspects relating to encroachment on commons, particularly in relation to the land rights of tribal communities (Mearns and Sinha 1998). For example, Land alienation through indebtedness and the mortgaging of land has long been of concern in scheduled tribal areas, and persists in spite of legislation designed to prevent it. Given the weak bargaining power of tribal communities, the most promising policy options to mitigate some of the practical consequences of these forms of social exclusion concern public awareness-raising and access to information. Some NGOs in Orissa have been effective in pursuing public interest litigation in order to defend the land rights of tribal communities against various forms of encroachment by others.

# **RECORDS OF RIGHTS IN LAND**

The question of records of rights in land has been relatively neglected in contemporary literature on land reforms and agrarian relations in India<sup>29</sup> . Remarks are often made in passing that records of rights in land are inaccurate, out of date, and wilfully manipulated by large landlords with the connivance of rent-seekingpatwaris, but it is rarely regarded as a subject of policy concern in its own right. Successive Five-Year Plans have underlined the need for the updating of land records as mandated by legislation, but implementation has been woefully inadequate, not least because land administration is itself a non-plan activity and therefore receives little investment. Primary records of rights in land are maintained _by patwaris_ at village level, and are usually recorded in local languages and dialects using various and non-standard measures. The physical documents, including village maps, deteriorate over time through continuous handling, and become illegible owing to marginal notations following mutations, to the extent that these are recorded at all. Legislative requirements for regular land settlements and updating of land records are rarely met.

It is widely acknowledged that the costs involved in carrying out land transactions in India are enormous. Under the Transfer of Property Act 1882 a seller of land is obliged to produce documents of title but these are private
63.9 percent probability that a flood of at least this intensity will occur once within a 25-year period. But this also leaves the possibility for this event to not occur at all, or to occur several times. In comparison, a 1-in-100-year flood is a more extreme event with a lower probability but higher intensity — that is, it affects a wider area and has a greater depth.

As time passes, more climatic data become available, which will update the empirical probabilities associated with certain natural shocks. As the impacts of climate change increase, intense shocks will probably become more frequent. This means that there is significant uncertainty around the probability 5
series which allows us to precisely analyze the liberalizing trends over time as opposed to investigating cross‐country difference only in case we use alternative services policy reform measures. Note, however, that these reform proxies capture (anti‐competitive) restrictiveness of regulations, but they do not capture enforcement of existing regulations at the EU level. We also use a second set of regulatory measures that vary over time and which is country‐sector specific, namely the Foreign Direct Investment (FDI) restrictiveness indicators, and also comes from the OECD. Assessing the effect of these measures is important as many foreign service suppliers enter markets through a foreign affiliate. Again, the FDI restrictiveness indicator does not pick up the enforcement of existing EU regulations.

The NMR indexes vary on a scale between 0 to 6 with lower values indicating lower levels of restrictions and higher values reflecting the most restrictive conditions in services sectors. In all, they measure the restrictiveness of competition in services in domestic economies. Moreover, each overall index is made up of several sub‐indexes analyzing different aspects of reforms. With these distinctions, one is able to make a difference between the different _types_ of regulations in services as provided in Table 1. Some reform measures are purely related to the entry of domestic as well as foreign firms in the market, whereas others are comprised of barriers connected to anti‐competitive rules and practices specifically targeted to the conduct of the firm, or firm operations. We regroup these sub‐indicators together so as to create this difference of regulatory type and use them separately in our regressions next to an overall index measure of reform that summarizes both entry and conduct. Table A1 in the annex shows in detail which sub‐barriers are taken up for each of the two types of indexes. We follow the OECD in constructing these indexes in the sense that we use equal weights across indicators, which the OECD does for its overall index as well.

As said, next to the NMR this paper also exploits the OECD’s FDI
with publicly-available time-series data on school closure policies and mobility in those countries.

The firm-level data was collected as part of the Future of Business (FoB) survey which is administered on Facebook to business owners and managers with a Facebook Business Page (see global descriptive report Facebook Data For Good (2020)).<sup>2</sup> The data are a repeated cross-section of approximately 25,000 firms across 50 countries surveyed every month over six months in 2020 and in the last two weeks of September 2021 for a total sample size of 154,458 firms.<sup>3</sup> The survey was designed specifically to examine how childcare and domestic responsibilities affect business performance and included questions on gender, family characteristics, childcare arrangements, time spent on childcare and domestic responsibilities, as well as core firm-level outcomes such as business closure and revenues.<sup>4</sup> In our main analysis we trace through the effects of school closure policies to increased care and firm closure, controlling for a mobility index in the country that captures the contemporaneous economic impact of the COVID-19 shock on business demand. Accounting for movement in the country helps us to isolate the impact of the childcare shock from an overall COVID-19 related demand shock on business outcomes.<sup>5</sup> We also control for a country’s wealth using 2019 level of GDP in each country. In the paper we show that school closures lead to higher firm closures and increased responsibilities within the home and this effect is only present for women entrepreneurs with children under 18. Women entrepreneurs with older children or no children, are not differentially affected by school closure policies than male entrepreneurs.

Our contributions are threefold. First, we contribute to a growing literature documenting

> 2In May 2020, the Future of Business (FoB) survey was adapted from its standard bi-annual approach to run six monthly survey waves to provide timely insights on the impact of the COVID-19 pandemic on small businesses and to take the pulse of their