rafmacalaba/datause-encoder-data huggingface dataset:
and the World Development Report 2015. The successes launched behavioral development economics.

2. Connections of behavioral development economics to broader social science In terms of methodology, behavioral development economics mirrors changes in the economics discipline as a whole. Hamermesh (2013) studied historical patterns in the methodologies of papers published in the top three general economic journals (Figure 1). The share of theory papers declined from 62% in 1983 to 28% in 2011. In the same period, the share of empirical papers rose from 38% to 64%. Experiments became an important part of the field for the first time in the mid-1980s and comprised 8% of the papers in 2011.

Figure 1: Methodologies of papers in the top three economics journals, 1983-2011 <!-- Start of picture text --> Share of Paper by Methodology<br>70<br>63.9<br>61.6<br>60<br>50<br>37.6<br>40<br>27.9<br>30<br>20<br>8.2<br>10<br>0.8<br>0<br>1983 2011<br>Type of Paper<br>Theory Empirical Experiment<br>Percentage of Papers<br><!-- End of picture text --> Source: Data compiled from Hamermesh (2013) 10
following main specification using two-stage least squares (2SLS): where _qi,t,h_ is the price of bond _i_ at rebalancing date _t_ , _h_ trading days before or after the rebalancing information is confirmed. For example, _h_ = 1 indicates the first trading after J.P. Morgan releases the EMBIGD’s new composition. This happens during the trading hours on the last business day of each month, meaning that _h_ = 1 falls on this day. For each rebalancing date _t_ , we consider a symmetric _h_ -day window around it. _θc_ ( _i_ ) _,t_ are country-month fixed effects, and _θb_ ( _i_ ) _,t_ are bond characteristics-month fixed effects, including maturity, rating, and bond type (sovereign or quasi-sovereign). _FIR_<sup>�</sup> _c_ ( _i_ ) _,t_ represents the flows implied by the rebalancing, instrumented with the percentage change in the theoretical index weights, _Zc,t_ . We obtain _FIR_<sup>�</sup> _c_ ( _i_ ) _,t_ by regressing _FIRc,t_ on _Zc,t_ (first stage). 1 _h∈Post_ is an indicator function equal to 1 in the _h_ days after the rebalancing and equal to 0 in the _h_ days before. **X** _i,t_ is a vector of monthly bond controls, including the bond’s face amount and (beginning-of-month) spread. Our coefficient of interest is _β_ , which captures the FIR’s effect on bond prices. In particular, how much the average log price change (or percentage change) around the rebalancing day varies with a 1 p.p. exogenous increase in _FIRc_ ( _i_ ) _,t_ . Our main specification replaces the country-month fixed effects, bond characteristics-month fixed effects, and bond controls with bond-month fixed effects.

We also estimate a leads and lags regression in which the instrumented FIR is interacted with trading-day dummies. This analysis allows us to both explore the dynamic effect of the FIR and test for parallel trends before the rebalancing.

# **3.2 Data and Summary Statistics**

We collect data from different sources to compute the FIR and our instrument. Most of the variables used in the analysis come directly from J.P. Morgan. However, one variable is not straightforward to measure: the AUM of funds that passively track the EMBIGD, _At_ . While J.P. Morgan provides data on the amount of assets benchmarked against their indexes, it does not distinguish between passive and active funds. Additionally, even if these data were available, many active funds might passively manage a significant share of their portfolios, as highlighted by Pavlova and Sikorskaya (2022).

To compute _At_ , we start with J.P. Morgan data on assets tracking the EMBIGD, which we then adjust based on an estimate of the share of passive funds. The estimation of this share involves the following steps. We retrieve data from Morningstar on the asset holdings 14
Table 1: Parameter Values and Calibration Targets

|Parameter|Value|Target|
|---|---|---|
|_ρ_=_σ_|3|Hsieh and Klenow (2009), Broda and Weinstein (2006)|
|_β_|1<br>1.05|Interest Rate of 5%|
|_δ_|0.02|Employment-Based Exit Rate of Large Firms of 2%|
|_α_|0.467|Slope Log of Right Tail of Empl. Based Size Distribution= -0.2|
|_h_|0.25<br>_η_|Std Dev. of Employment Growth of Large Firms|
|_ωe_|_e_<sup>_ωe_ </sup>=1|Size of Entrants = 6% of Median Incumbent (Luttmer 2010)|
|_fc_<br>_fe_|0.1|Exit Rate of 5%|
|_fp_|210.95|Av. Fraction of products per frm=0.0024|
|_η_|2|Distribution of Output-Share byProduct in Multi Product Firms|

distortions will not create changes in relative prices across products, so only the elasticity of substitution across varieties within a product category matters for aggregate variables. Thus we set the elasticities of substitution to be identical to each other. We choose the discount factor so that _β_<sup><u>1</u></sup><sup>_−_1equalsarealinterestrateof5%,and</sup> normalize the size of the labor force to be equal to 1.

In terms of product attributes, we assume these are distributed Pareto, with shape parameter _η_ and lower bound _e_<sup>_λmin_</sup> = 1. Besides its analytical tractability, this parameterization is consistent with the behavior of multi-product firms in the US, in the distribution of across within particular output-shares products multi-product firms. Bernard et al. (2010) report the output-share by rank of the product, for firms of various degrees of multiple-good production (4, 6, 8, and 10 products). They run a regression of log of product rank, and the log of the output share of the product. the of this to be to 0.5. In the we They find coefficient regression equal appendix, show that this target maps into a parameter value of _η_ = 2 for the Pareto distribution of product-attributes.

Our to calibrate the cost of of also relies on strategy fixed provision goods � _f p_ � data from Bernard et al. (2010)<sup>10</sup> . They show that out of a potential of 1,440 products, the average number of products supplied by a multi-product firm is 3.5. Given that our product space lives in the continuum of measure one, this gives us a target of 3.5/1440 to be matched by the product range of the average multi-product firm in the model. Formally, _f p_ is chosen so that: imports at a 4-digit disaggregation level. 10Data taken from Table 1 in Bernard et al. (2010).

17
# **4. Data and Empirical Approach**

# _Data_

We use multiple sources of data to assess the distributional impact of fiscal policy in Turkey. The main source of information is the 2016 Household Budget Survey (HBS) collected by the Turkish Statistical Institute (TUIK). The HBS is a nationally representative household survey that collects detailed information on consumption (302 expenditure items in total), labor income, social assistance, pensions, remittances, financial income, assets, housing characteristics, accessibility to labor and financial market, health services, education opportunities, and individual characteristics such as education, health, and labor market status and experience. The survey has been collected on an annual basis since 2002. We use the 2016 round, which was the latest available HBS round at the start of the study. That year, the survey interviewed 12,092 households, encompassing 42,605 individuals.<sup>9</sup> In addition, we use the Survey of Income and Living Conditions (SILC), collected by TUIK to monitor living standards following a methodology consistent with the European Union (EU) SILC initiative. The survey aims to provide comparable data on income distribution, living conditions, access to services, material deprivations, and relative poverty. Although it does not collect data on consumption, one of the main advantages of SILC is that its sampling design allows to conduct analysis representative at subnational NUTS2 level (26 regions). We use the 2016 round, which includes 22,441 households, to allocate in-kind benefits in education across households while taking into account regional differences in spending and personnel. Annex A includes a detailed explanation of the approach.

Third, we use IPSOS Consumption Expenditure Panel, a nationally representative survey that collects detailed information on household purchases (food and beverages, cleaning products, personal care, and other products), their channel of purchase, and socio-economic status of the household. We use this data set to account for informality in consumption, by identifying the share of purchases that are made without invoice and avoid paying VAT. This allows to consider VAT evasion in a distributionally-sensitive form and represents an innovative element of the paper relative to the standard CEQ approach.

> 9 Since 2014, HBS as well as other surveys conducted by TUIK cannot produce urban/rural disaggregations, due to changes introduced in administrative divisions throughout the country.

13
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Simulations suggest that addressing Burkina’s infrastructure deficiencies —understood as improving the quantity and quality of infrastructure to the level of the African leader, Mauritius—could boost growth by 3.8 percentage points, with potential contributions coming equally from the ICT, road, and power sectors (figure 1b).

At the firm level, evidence from enterprise surveys suggests that infrastructure quantity and quality are responsible for about one-third (35 percent) of the productivity performance of Burkina Faso firms (figure 2a). ―Soft‖ issues related to transport—such as customs clearance and transport logistics in general—weigh most heavily on firms’ productivity; electricity is a distant second.

**Figure 2. Infrastructure deficits constrain firms’ productivity**

<!-- Start of picture text --> a. Degree to which infrastructure contributes to firm’s increased b. Degree to which infrastructure is an obstacle to firm’s increased<br>productivity productivity, by subsector<br>Benin Mali<br>Senegal Senegal<br>Mali Mauritania<br>Mauritania Niger<br>Burkina Faso Burkina Faso<br>Niger Benin<br>0% 50% 100%<br>0% 20% 40% 60% 80% 100%<br>Percent of productivity deficit attributable to sectors Percent of productivity deficit attributable to sectors<br>Electricity Customs clearance Transportation ICT Water<br>Infrastructure Others<br><!-- End of picture text --> _Source:_ Escribano, Guasch, and Pena 2009.

# The state of Burkina Faso’s infrastructure

The country is characterized by low population density (figure 3a) and contrasting levels of poverty and wealth across states (figure 3b). Most of Burkina’s population and agricultural activity is concentrated along the Yatenga-Kadiogo-Boulgou corridor. Burkina’s territory is dominated by the Volta River Basin and Niger River Delta, whose potential remain largely undeveloped (figure 3c). Mineral resources are limited and widely dispersed (figure 3d). Meanwhile, Burkina’s geography poses challenges: a large share of the territory is arid and prone to drought and floods, the soil is infertile, and rivers are seasonal. Yet, agriculture constitutes 30 percent of the country’s GDP. Cotton is the main export commodity and driver of economic growth, making Burkina’s economy vulnerable to fluctuations in the international commodity market and to climatic and external shocks.

Burkina is also challenged by demography, with one of the fastest-growing populations in Africa. Population growth averaged 3.3 percent annually over the period 2000–08, over 30 percent above the average of other LICs, or 2.3 percent (World Bank 2009b). This rapid population increase, and the fact 3
_occur within the study sample, then this is the appropriate saturation measure._<sup>9</sup> _Alternatively, if there is a ‘gateway to treatment’ and not all eligible individuals are sampled into the study, or spillovers occur on a larger population within the cluster, then it is necessary to distinguish between the_ true _treatment saturation (the share of treated individuals in the spillover network) and the_ assigned _treatment saturation (the share of treated individuals in the study population)._<sup>10</sup> _If sampling rates or the share of the spillover population eligible for treatment are constant across clusters, the true saturation is the sampling rate times the assigned saturation. If the sampling rates are driven by cluster characteristics, then the true saturation is endogenous. In this case, the researcher can instrument for the true saturation with the assigned saturation. To streamline the remainder of the theoretical analysis, we assume that the assigned and true saturations coincide._

# **1.1 Defining Treatment and Spillover Effects**

Let _Yic_ represent the outcome for individual _i_ in cluster _c_ . In a general framework, outcomes can depend in an arbitrary way on an individual’s own treatment status, as well as the treatment status of all other individuals in the study: where _Ric ∈{_ 0 _,_ 1 _}_ indicates whether an individual complies with treatment, _Xic_ is a vector of covariates and _εic_ is an error term.<sup>11</sup> To use the RS design for causal inference requires an assumption on how the treatment status of others impacts _Yic_ . We relax the stable unit treatment value assumption (SUTVA) within clusters, but maintain it across clusters: spillovers may flow within a cluster, but do not flow between clusters. This ensures that pure control clusters provide a valid counterfactual for treated clusters and that cross cluster comparisons can identify how spillovers depend on the intensity of treatment saturation.

> 9For example, Banerjee et al. (2012) study interventions to improve performance among constables in Rajasthan police stations. Sinclair, McConnell and Green (2012) study sending social-pressure mailings to registered voters in a congressional district.

> 10For example, Gine and Mansuri (2012) sample every fourth household in a neighborhood, and randomly offer treatment to 80 percent of these households. This causes the true treatment saturation to be 20 percent rather than the assigned 80 percent. Other examples include unemployed individuals on official unemployment registries form a small portion all unemployed individuals in an administrative region (Crepon et al. 2013); neighborhoods eligible for infrastructure investments comprise only 3 percent of all neighborhoods (Craig McIntosh, Tito Alegria, Gerardo Ordonez and Rene Zenteno 2013); and malaria prevention efforts target vulnerable individuals, who account for a small share of total cluster population (GF Killeen, TA Smith, HM Ferguson, H Mshinda, S Abdulla et al. 2007).

> 11As is standard, _Ric_ is only observed for individuals with _Tic_ = 1.

7
Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Here common controls and constant terms are omitted for brevity. All regressions include both country fixed effects and year fixed effects. The dependent variable at the second stage are different measures of informality: share of self-employment (in percent of total employment; survey-based), MIMICbased output informality (in percent of GDP), and DGE-based output informality, calibrated to the share of self-employment (percent of total employment). Commercial bank branches are per 100,000 adults. GDP per capita is in constant 2010 U.S. dollars. See sections 3 and 4 for detailed data descriptions and empirical methods. Data are between 2000 and 2018.

28
that can indirectly affect the food supply. For instance, some of the non-edible components, upon further processing, may be used as feed for livestock, which is an important source of calories for human consumption. Meanwhile, the FAOSTAT production data reports the total production quantity of the crop, which includes both edible and nonedible components of the crop. In the present study, we assume that the relative ratio of edible and non-edible components for a given crop remains similar over the years. As such, the indirect contribution of calorific content from non-edible components should not severely bias the results.

# **4. Results**

## **_4.1 Production and yield patterns_**

Figure 3 plots the global area, calorific production, and calorific yield across the 144 crops over the sample period, indexed to 100 in 1961. Global crop production nearly quadrupled over the past six decades on a calorific basis. Most of the increase reflects yield advancements, with cropland expansion experiencing much smaller growth. Figure 3 shows that the aggregate global yield has risen from approximately 4,330 _KCal/ha_ in 1961 to almost 11,000 _KCal/ha_ in 2021. Such growth, amounting to a 158% increase, closely mirrors the 152% increase in the global population over the same period.

Figure 4 further shows that much of the yield growth was driven by the Big-4 commodities (maize, wheat, rice, and soybeans), whose yields almost tripled from 4,826 _KCal/ha_ in 1961 to 14,323 _KCal/ha_ to 2021. In contrast, the yield of the “other” category (which includes the remaining 140 crops) grew by less than 100% over the sample period, with their 2021 calorific yield only half that of the Big-4.

Table 1 provides a more detailed decomposition of the area, yield, and production growth of the Big-4 versus other crops. We compute the average area, yield, and production for the first (1961-63, Panel A) and last (2019-21, Panel B) three years of the data, along with their respective shares. The middle panel shows their growth rates (calculated as logarithmic change) between the two periods, while the lower two panels present the contribution of area and yield to the total production growth. Figure 5 summarizes the decomposition analysis based on Table 1.

Three key findings emerge from the decomposition analysis. Firstly, more than two-thirds of global food production growth over the past six decades came from yield increases, while the remainder reflects area expansion. Secondly, the Big-4 commodities contributed nearly 60 percent to production growth with the rest of the growth attributable to all other crops. Thirdly, about two-thirds of the 60 percent production growth of _— 12 —_
- 26 -

# Table 6

# JOINT CONTINGENCY TABLE OF ASSOCIATION BETWEEN ACHIEVEMENT OF ID OBJECTIVES AND ACHIEVEMENT OF OVERALL PROJECT OBJECTIVES, ALL SECTORS (Review Years CY87-CY88)

# By Number of Projects

# Overall Achievement

|Un|satisfactory<br>Satisfacto|ry|
|---|---|---|
|Negligible<br>ID|47<br>29|76|
|Achievement|||
|Substantial|10<br>99|109|
||57<br>128|185|

# By Percentage of Projects

# Overall Achievement

|Un|satisfactory Satisfactor|y|
|---|---|---|
|Negligible<br>ID|25%<br>16%|41%|
|Achievement|||
|Substantial|5%<br>54%|59%|
||30%<br>70%|100%|

Note : Does not include 'partial" ID achievement category. Source: OED Database
Policy Research Working Paper 5385

# **Abstract**

This study examines the relationship between transport infrastructure and agriculture in Sub-Saharan Africa using new data obtained from geographic information systems (GIS). First, the authors analyze the impact of road connectivity on crop production and choice of technology. Second, they explore the impact of investments that reduce road travel times. Finally, they show how this type of analysis can be used to compare cost-benefit ratios for alternative road investments in terms of agricultural output per dollar invested.

The authors find that agricultural production is highly correlated with proximity (as measured by travel time) to urban markets. Likewise, adoption of high-productive/ high-input technology is negatively correlated with travel time to urban centers. There is therefore substantial scope for increasing agricultural production in Sub-Saharan Africa, particularly in more remote areas. Total crop production relative to potential production is 45 percent for areas within four hours’ travel time from a city of 100,000 people. In contrast, it is just 5 percent for areas more than eight hours away. Low population densities and long travel times to urban centers sharply constrain production. Reducing transport costs and travel times to these areas would expand the feasible market size for these regions.

Compared to West Africa, East Africa has lower population density, smaller local markets, lower road connectivity, and lower average crop production per unit area. Unlike in East Africa, reducing travel time does not significantly increase the adoption of high-input/highyield technology in West Africa. This may be because West Africa already has a relatively well-connected road network.

This paper—a product of the Sustainable Development Division, Africa Region—is part of a larger effort in the department to improve the global knowledge base on African infrastructure as part of the Africa Infrastructure Country Diagnostic. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pdorosh@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
that, if the households have a higher participation rate (in particular in casual wage activity) than rich households, non-farm income can reduce rural inequality.<sup>3</sup> China is a good candidate for a country case study that examines the impacts of non-farm activity on rural income inequality. The objective of this paper is to examine whether and why non-farm activity reduces overall household income inequality in rural areas of two Chinese provinces, Hebei and Liaoning, in 1995/1997.<sup>4</sup> The oversupply of agriculture labor, the relatively limited quantity of arable land, and the obsolete agricultural technology in China result in its low level of farm income per capita. The economic reforms, in particular the implementation of the Household Responsibility System (HRS), in the late 1970s, not only stimulated the incentive of the farmers and contributed to the increase of the agricultural productivities, but also legitimized the rural redundant labor to leave land and participate in non-farm activity. Since then, the non-farm sector has played an increasingly important role in absorbing the surplus agricultural labor and reducing rural poverty.<sup>5</sup> Based on the data from the Living Standards Measurement Study (LSMS), we study rural household income determination. We examine the impacts of participation in non-farm activity on rural income inequality in two ways: First, we assume non-farm income as an exogenous transfer to total income to decompose the Gini index; second, we assume non-farm income as a potential substitute for farm income to take household choices into account and simulate household income. The results suggest that non-farm activity reduces rural inequality. The poor households would have suffered a relatively larger income loss than the rich households if they were not allowed to participate in non-farm activity.

The paper is structured as follows: section 2 briefly reviews the development of non-farm activity in rural China; section 3 describes the income composition and characteristics of households in the provinces of Hebei and Liaoning, based on the LSMS data; section 4 discusses two analytical methods, namely, the decomposition of the Gini index and the simulation of household income; section 5 studies the determination of non-farm activity participation and the distribution of the household income with and without non-farm activity; and section 6 presents the conclusions.

> 3 See the results obtained by Adams (1994); Chinn (1979); de Janvry and Sadoulet (2001); Lachaud (1999); Stark et al. (1986).

> 4 See Annex 1 for more information on the location of the provinces of Hebei and Liaoning.

> 5 See Ravallion (2003); Ravallion and Chen (2004).

2