rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5. Alternative rural development strategies based on larger farms**

The potential for agriculture to grow through expansion, together with dissatisfaction over the uneven pace of yield gains among African smallholders, has given rise to an active debate about smallholderfocused rural development strategies (Lipton, 2006; Hazell et al., 2010; Wiggins, Kirsten, and Llabi, 2010; Collier and Dercon, 2014). Critics argue that small farms forego economies of scale associated with skills, technology, finance, capital, and face dis-economies in terms of trading, marketing and storage, disadvantages that will become more pronounced as a larger share of the population moves to cities and as rural labor wages converge toward the higher levels found in other sectors (Maxwell and Slater, 2003; Collier and Dercon, 2014). Consequently, they argue, policies should rely more on larger commercial farms to generate productivity gains, while relying on accelerating growth in other sectors to reduce poverty among the rural poor.

The notion that agricultural policies should not neglect larger farms is not controversial. In particular, government efforts to protect tenure security, property rights and land markets (for rentals and sales) are expected to benefit smallholders currently, and set the stage for an emerging class of medium-sized commercial farms (Jayne, Chamberlin, and Headey, 2014). However, it seems unlikely that shifting budgetary resources away from smallholder farms to support large-farm programs will help this generation of rural poor, or boost global food supplies any time soon.

Farm census data show there are relatively few large farms in Sub-Saharan Africa, and there is little to suggest that market forces are driving widespread consolidation (Masters et al., 2013). In addition, there are good conceptual reasons to suggest a smallholder structure is well suited for most places in SubSaharan Africa, where land, labor, risk and credit markets are imperfect (Binswanger and Rosenzweig, 1981; Feder, 1985).

There are several reasons why Africa’s farm structure is based on small farms,
health care after the shock. In 12% of the cases, these disruptions were caused by road damages (9%) and transport issues (3%) (Kishore et al., 2018). More than half of the respondents in flood affected areas of Hanoi, Vietnam, reported not being able to access their usual health care or medication for one month after a big flood event in 2008. The main reason for this was road disruptions (69%) and transport issues (33%). In the affected areas, a higher incidence of dengue fever, pink eye, dermatitis and psychological problems was observed a month after the flood (Bich et al., 2011).

Road disruptions make it more difficult for households to access food and other essential goods, as well as potentially cause price shocks affecting the economy of an area more broadly. Having been collecting data on food losses since 1961, the Food and Agriculture Organization notes that distribution wastes tend to be larger in countries with hot humid climate and lacking reliable transportation infrastructure, with a particularly strong impact on perishable food. Baez et al. document the impact of Agatha, a tropical storm that struck Guatemala in 2010, in urban areas. They observe an increase in poverty by 18% in the aftermath of this climatic disaster, mainly because of higher food prices. This inflation, as documented by the authors, was likely to be caused by increased frictions along the food supply chain, including transportation links. Safir et al. (2013) found a 4% decrease in food consumption in areas of the Philippines recording precipitation levels below one standard deviation from the mean. The same pattern was observed after the 2015 flood in South Carolina. Even if there were no major food disruptions in the aftermath of this disaster, Cutter (2017) documents an impact on the longer-run, at the local level: the flood affected livelihoods and food security of many residents and small farmers. This impact is partly attributable to transportation disruptions: the flood caused
_Figure 4:_ W _ho is the head/chair of the committee/council?_ <!-- Start of picture text --> Minister<br>Public sector official<br>Prime Minister<br>Private sector representative<br>Co-chaired by government and private sector<br>President/ Head of State<br>0 5 10 15 20 25 30 35 40 45<br>Share of economies (%)<br><!-- End of picture text --> _Source: Reform Committee data Note: Figure based on 107 economies with a functioning business reform committee._ Notably, in terms of their appointment, in close to 40 percent of cases the chairs of the committee are nominated by the Prime Minister or the President. In other exceptional cases, the chair is nominated by members of Parliament, as is the case in South Sudan and the Islamic Republic of Iran, or elected by members of the reform committee themselves. In terms of reporting lines, the chair of the reform committee reports to the President or the head of state in close to 40 percent of the committees, while in close to 30 percent of the committees the chair reports to the Prime Minister. In 20 economies the reporting line is to the minister in charge of business regulatory reforms. It is only in close to 20 percent of the committees that the reporting lines extend to the minister in charge of the business regulatory reforms (Figure 4).

Steering boards are decision making bodies, usually made up of high-level advisors who set the agenda and monitor and offer advice on broader objectives. A working group, on the other hand, has a more precise objective and consists of members who oversee the work and ensure its success. The membership of both steering boards and working groups may consist of different stakeholders, including representatives from the public sector, the private sector, think tanks, the research community, trade associations, and chambers of commerce.

As expected, the public sector is
The price, PINTA(A) is used in the producer’s first order condition over the intermediate aggregate input and the primary factor aggregate input.

The original SAM data does not include a rebate account. Taxes paid by the activity to the government are net of subsidies. The rebate is a subsidy on the use of intermediate inputs. When we use rebates explicitly in the model, the tax payment from the activity to the government must be adjusted upward to add back in the subsidy payment. The indirect tax rate, ta(A), must be recomputed to take into account the adjustment to the SAM.

The value added tax revenue paid to the government (VATREV) becomes: Much of the analysis of tax policy reform depends on the initial tax rates in the base data. We use the following tax rates as found in the SAM for South Africa: 8
directed to the regulator. They control for the exogenous factors embodied in both inter-firm and intra-firn comparisons. Box 6.1 presents several examples that could be -obtained relatively easily from international statistical sources.<sup>27</sup>

**Box 6.2. Management indicators in the rail industry.**

|_ Prices<br>Average passenger fare (revenues per passenger-km)<br>Average freight price (revenues per ton-kxn)<br>**.***<br>ality<br>oservice<br>2 S<br>Average train-speed (in passengers<br>and freight)<br>Delayed arrivals or depatures (as % of scheduled)<br>Commercial<br>% of lost or damaged freight<br>Average passenger load factor<br>Traffic density (trains per hour)<br>* Pollution and safety<br>Rate of fuel usage (per train-kin)<br>Level of noise<br>Level of emission of pollutants<br>Number of accidents or incidents|
|---|
|* Labr~produc~tivity<br> <br>|
|Passengers-km<br>per-employee<br>Tons-km<br>per empoe<br>Passenr train-km per,employee<br>Freigh**t**rains-km per employee<br>Operational<br>Total trains-km per emploeee<br>*CapiWa productiit<br>Numberikms. traveled by locomotives<br>Locomotive<br>availability (in °)<br>Tons-km per wagon-km<br>Wagons-km<br>per wagon<br>Tonskm per wagon|
|Efficiency<br>Costs per employee.-<br>I<br>..<br>Costs per unit of capital<br>Financial<br>Unit cost (per passenger-km.<br>ton-km, train-km)<br>* Profits<br>Revenues/costs<br>.______________________________<
female teachers to teach in rural areas and sensitize students, parents, and community members on girls’ education (U.S. State Department).<sup>10</sup>

# **5. Estimation methodology**

Our data set consists of a four-year panel of 405 clusters. Our dependent variable is the average share of girls aged 5-15 who attend school in the community, measured as a fraction between zero and one. Standard linear regression models are not well suited for such estimation since they may produce predicted values greater than one. We instead follow a method proposed by Papke and Wooldridge (2008), to deal with fractional response dependent variables in a panel data context. This approach will allow us to control for cluster/community unobserved heterogeneity possibly correlated with the explanatory variables, in particular water infrastructure (in our case, time to the water source). We impose bounds on the proportion of girls attending school by using a Probit functional form for the mean proportion. This gives us the following model: where i = 1,...,C represent clusters, t = 1 to 4 the survey year, and Φ ( ) . the standard normal cumulative distribution function. The dependent variable, _sit_ , represents the proportion of girls aged 5-15 attending school, where 0 ≤<sup>_s_</sup> _it_ ≤ 1. The **x** it vector is the set of explanatory variables, _ci_ stands for cluster-specific unobserved heterogeneity, possibly correlated with some explanatory variables, and _vit_ is the time-varying error term. Following Papke and Wooldridge (2008) we make the following assumption along the lines of Chamberlain (1980): 1 4 where **~~x~~** _i_ ≡ ∑ **x** _it_ is the vector of cluster means (calculated over the four time periods) and 4 _t_ = 1 2 _ai_ **x** _i_  Normal ( 0, σ _a_ ) .

Inserting (2) into model (
poverty incidence rates across countries, as they are compared against a fixed standard, although this comparison still suffers from limitations.

**To estimate this measure, one can divide the household's overall energy expenditure by the household income or household expenditure.**<sup>**27**</sup> The measure can be estimated as a share of household expenditure or income. Still, income measures are usually not captured accurately in household budget surveys, as the instruments are generally designed to capture consumption and expenditure patterns rather than income sources. Therefore, it is recommended that these measures work appropriately and are reliable when deciding how to scale energy expenditure in the case of the relative measures.

# Measure M/2 and 2M

## **Another relative expenditure-based measure, next to the 10-percent measure, is the so-called 2M**

**measure.** The "2M" indicator captures abnormally high energy expenditure. It represents the percentage of households whose energy expenditure share (in income or consumption) is more than twice the national median share (Energy Advisory Hub, 2022).  This metric gauges the burden of energy bills on households in relation to their incomes or overall expenditure, using the national median as a reference. Therefore, poor households that limit their energy consumption due to budgetary constraints may not be captured in this measure.

**In contrast, the M/2 measure follows the concept of hidden energy poverty and measures abnormally low absolute energy expenditure.** M/2 is an indicator that captures abnormally low energy expenditure, consisting of the share of households whose absolute energy expenditure is below half the national median. The M/2 measure draws from the "hidden" energy poverty concept, which argues that households might decrease their energy consumption so much that it becomes abnormally low and could result in negative health outcomes (Nguyen, 2022). Notice that households living in highly efficient buildings that technically require less energy may be considered energy poor under this definition.

*
Inequality in income and consumption is a concern for policy makers. Source and group decompositions have been developed to better understand the determinants of inequality and the policies which could be implemented to reduce it. Unfortunately, the methodologies available for group decompositions are less advanced than those available for source decompositions. This paper goes some way to bridge the gap. Two extensions to Yitzhaki and Lerman's (1991, hereafter YL) group decomposition of the Gini index are derived. They are applied to data from Bangladesh to analyze the inequality between land owning, education, and occupation groups, and to estimate the impact of targeted transfers by group on the Gini for the overall population. It is well known that the Gini index is not additively decomposable into within and between group components. The attractiveness of YL's decomposition along a unique dimension (say by education groups) lies in that the remainder of the decomposition has an intuitive interpretation as a measure of stratification or overlap between groups. Unfortunately, without an extension to take into account several dimensions at once, the YL decomposition is able to account for only a small part of total inequality. This is because when the groups are - defined along a unique dimension, the within group component which remains unexplained - typically contributes the most to the overall Gini (from 92 to 97 percent in the decompositions reported by YL). To account for a larger part of total inequality through the stratification and between group components of the decomposition, and to measure inequality by subgroups along one dimension (say land owning class) within groups defined along another dimension (say education level), a multidimensional extension is needed. This extension is provided.

The second contribution of the paper consists in deriving the impact of marginal changes in income or consumption by group on the Gini for total income or consumption. This extension
determine which Article XXIV agreements can be considered to be “deep”. The data set on the content of RTAs maps 52 provisions in 279 PTAs notified to the WTO and signed between 1958 and 2015. It also includes information about the legal enforceability of each provision. We define agreements as deep when they cover more than 20 provisions, hence, the variable _deepijt_ equals one if a trade agreement covers more than 20 provisions and zero otherwise, _shallowijt_ is one if at most 20 provisions are covered. The richness of the data allows for distinguishing between the simple count of all provisions and those that are legally enforceable. For the second type of depth measure, we classify trade agreements to be deep when they contain at least 20 legally enforceable provisions. These definitions are comparable to Hofmann et al. (2019) and Mattoo, Mulabdic, et al. (2022).

One drawback of the DTA data is their coverage: only agreements that are still in force in 2015 are included. This is mostly an issue for deepening agreements as the shallower ones

> 6 https://www.usitc.gov/data/gravity/itpde.htm

> 7 http://rtais.wto.org/UI/PublicMaintainRTAHome.aspx.

> 8 The World Bank’s World Development Indicators are available at https://databank.worldbank.org/source/ world-development-indicators, we use GDP, constant 2010 US$ (time series: NY.GDP.MKTP.KD).

> 9 http://ptadb.wto.org/.

12
.5|31.3|14.0|18.5|
|_West_|5.1|6.3|5.5|7.1|15.8|17.8|14.5|16.4|8.9|10.1|
|_East_|5.2|3.8|6.7|5.3|14.8|13.6|13.4|11.9|7.9|6.9|
|_North_|2.7|2.6|2.7|2.7|10.7|11.1|10.3|10.6|6.6|6.7|
|_Total_|5.8|8.0|6.6|8.6|17.4|19.3|15.7|17.4|9.4|10.4|

|**_B. L_**|**_ower bound_**<br>Non-empl<br>were rece<br>share of<br>Recent e|**_estimates o_**<br>oyed popul<br>ntly inform<br>current ex<br>mployees|**_f the pop_**<br>ation me<br>ally empl<br>tended la<br>T|**_ulation engag_**<br>mbers who<br>oyed, as a<br>bor force<br>otal|**_ed in informal employm_**<br>Total population rece<br>employmen<br>Extende<br>for|**_ent during_**<br>ntly engag<br>t as a shar<br>d labor<br>ce|**_the last y_**<br>ed in info<br>e of<br>Popul<br>aged
results using De la Roca and Puga’s (2017) preferred measure of metro/urban size as our main independent variable of interest – i.e. the number of people within 10 km of the average person in the metro area or district. Table 2, as well as Tables A1 – A10 in Appendix A that contain various robustness check to our main specification, show the estimated agglomeration wage premia when relying on seven different ways to define Indonesia’s metro areas – namely, Duranton’s algorithm based on commuting flow thresholds of 10 percent and 7 percent; the AI; the cluster algorithm using both the UC (i.e. T<sup>�</sup> � = 300 people per km<sup>2</sup> ; T<sup>�</sup> � = 5,000) and the HDC (T<sup>�</sup> � = 1,500 people per km<sup>2</sup> ; T<sup>�</sup> � = 50,000) sets of thresholds; and the NTL approach using the 25<sup>th</sup> and 80<sup>th</sup> percentile thresholds.<sup>33</sup> We focus on these seven based on our discussion in 32 We derived our temperature and rainfall measures from 2013 data produced by the University of East Anglia’s Climate Research Unit (https://crudata.uea.ac.uk/cru/data/hrg/). Meanwhile, we derived out elevation and ruggedness variables using data that were originally generated by NASA's Shuttle Radar Topography Mission (SRTM). Terrain ruggedness at the central point of a grid cell is defined as the mean of the absolute differences in elevation of the central point between the central grid cell and its eight adjacent grid cells, i.e., grid cells in the north, northeast, east, southeast,
|||||||
|(D) % teachers|No|No|Yes|No|No|Yes|Yes|Yes|No|No|Yes|No|No|Yes|Yes|Yes|
|management|||||||||||||||||
|(E) School FE|No|No|No|No|No|No|No|Yes|No|No|No|No|No|No|No|Yes|

_Notes_ : The authors’ estimate is based on data from _Prova Brasil_ , Census of Education, and IBGE. Wild bootstrap p-values. 95% CI in brackets. ***, **, and * indicate significance at the 1, 5, and 10% critical levels. Standard error in parentheses and clustered at the municipality level. All regressions are weighted by fifth-grade enrollment at the school level. Math performance on a scale from 0 to 350 (SAEB scale). reading performance on a scale from 0 to 325 (SAEB scale). The columns DiD refer to the differences-in-differences estimates and the columns TD show the triple difference-in-differences estimates. The sample of municipalities and schools included in the analysis are detailed in Table 2. (A) municipal fixed effects. (B) students’, teachers’, schools’, and principals’ characteristics. (C) the percentage of teachers that work in a state and a locally-managed school simultaneously. (D) the percentage of teachers working in a state-managed school that implemented the intervention of managerial practices. (E) schools’ fixed effects. All TD estimates exclude state-managed schools in which the state government implemented managerial practices intervention.
benchmarked their performance: with IAEP they assess performance at the end of the primary cycle in science and math; with TIMSS the focus is on science and math, for students in grade 8, in parallel to education reforms; and with PISA and NAfKE they assess structural diagnostics of skills at the end of the compulsory school stage.

Early IEAP results in 1991 were alarming, as Jordan ranked 18 among 19 countries. IAEP II provided crucial data on educational performance but also allowed the country the opportunity to learn assessment techniques (sample selection, test administration, implementation monitoring). Thus IAEP was instrumental in building national capacity for conducting surveys of student achievement. Jordan’s students ranked near the bottom in IAEP II. The results came as a shock. Almost 75 percent of students in mathematics and 67 percent of students in science scored lower than the international average. Jordan ranked third from the bottom in both subjects among the 20 participating countries.

While the impact of the assessment results of the education reforms and projects was expected to take time before showing results, after serious interventions and follow-ups on the gaps in their 3
services are offered.

# **2 Ethiopia – A High Fertility Country**

In 2005 Ethiopia’s current total fertility rate (TFR), the predicted number of children a woman will have during her reproductive life, was estimated at 5.4, in effect adding about 2 million people a year to Ethiopia’s population of about 74 million in 2007.<sup>10</sup> Population growth resulting from such high fertility is believed to come at a high cost to living standards. Already in 1999, the average land holding per rural person was estimated at only 0.21 ha, down from 0.5 ha in the 1960s. This, coupled with lack of agricultural productivity growth, has contributed to a (rapidly growing) core group of five to seven million who are chronically food insecure. Spatial resettlement of about two million people from the highlands to the lowlands, adopted in 2003 as one of a series of policy measures to tackle the problem of chronic food insecurity in many highland weredas, is unlikely to provide a sustainable solution (World Bank 2007). The high fertility and population growth rates are not unique to Ethiopia. There are about 20 countries that have a TFR higher than 5, almost all very poor (World Bank 2010).

Cognizant of the challenge population growth posed, the government of Ethiopia adopted a population policy in 1993. The overall objective was to harmonize the country’s population growth rate with that of the economy, specifically to achieve a TFR of 4 by 2015. One of the major strategies to do so was to increase the contraceptive prevalence rate to 44 percent by 2015 by expanding access to family planning (Transitional Government of Ethiopia 1993). Ethiopia has historically had among the lowest contraceptive prevalence rates in Sub-Saharan Africa. According to the first-ever national survey on fertility and family planning in 1990 only 4 percent of women of reproductive age were using some family planning methods and less than 3 percent were using modern contraceptives (Transitional Government of Ethiopia 1993