rafmacalaba/datause-encoder-data huggingface dataset:
show that the evaluation of the impacts of an increase in the price of major agricultural products (like corn) is sensitive to the inclusion of both consumption and income effects, as well as to the allowance for consumption and income responses.

The paper is organized as follows. Section 2 provides a general motivation and develops the econometric model. I provide an overview of the methodology and I introduce the general equilibrium econometric model of consumption and income responses. In section 3, I apply the methods to the Mexican data and I report the results on the demand and wage income price-elasticities. Section 4 simulates the welfare effects of the increase in the price of corn taking into account consumption and income responses of Mexican households. In Section 5, I conclude.

# 2 The Econometric Model

In most studies of price reforms, such as those summarized in Deaton (1997), the analysis is based on the responses of household consumption. Standard arguments of efficiency, based on price elasticities, and equity, based on expenditure elasticities, are used to characterize optimal taxes or to indicate direction of changes in the structure of taxation. This analysis omits important aspects of the effects of any policy, namely the responses of outputs, wages, and household income. To estimate the impacts of the reforms both on the expenditure and income sides, I argue that it is necessary to estimate own- and cross-price elasticities, on the one hand, and wage-price elasticities, on the other.

Discussions about the poverty impacts of trade reforms often make the argument that supply responses are critical for the poor. Specifically, WTO reforms on agricultural trade are expected to boost production opportunities in rural areas in developing countries. Proponents of this view argue that agricultural trade liberalization will bring about increases in international prices of agricultural goods. Faced with higher permanent prices, households will choose to devote more resources to agricultural production and firms will increase their labor demand in agriculture. This higher demand may involve higher employment in rural farms (for planting, weeding, or harvesting), or it may imply higher labor demand 3
# 1 Introduction

Economic development is an uneven process, characterized by severe inequalities in growth patterns across sectors and across space (Harris, 1985). Theories of structural change typically explain the shift from agriculture to manufacturing and services that accompanies the development process as being triggered by increases in agricultural productivity ("push"), manufacturing productivity ("pull") or a combination of both (for an overview of the literature, see Matsuyama, 2008). This paper contributes to the literature by demonstrating that, even in the absence of technological progress, asymmetric sectoral output growth and employment shifts can be induced by trade. Such shifts result in spatial disparities in economic activity that are qualitatively similar to those conventionally associated with rural transformation. By highlighting the possible role of preferences as part of the explanation for these disparities, the paper also aims to contribute to the New Economic Geography literature (see Krugman, 1998, for an overview of this literature).

To demonstrate these e¤ects, we use a simple theoretical model to analyze the e¤ects of market integration on food and non-food production and the sectoral composition of employment in a poor rural setting, where utility is highly sensitive to food consumption. Our motivation for focusing on poor rural areas is twofold. At the inception of the industrial revolution, most societies were rather poor and dominated by agriculture. Currently, diversi...cation beyond agriculture is often considered a promising way out of poverty for poor rural economies. Yet, in many countries, market fragmentation constrains the growth of the rural nonfarm sector (see e.g. Loening et al, 2008). If people cannot trade, they have no choice but to produce what they need to eat.<sup>1</sup> We analyze the bene...ts of

> 1See e.g. de Janvry et al. (1991) for an analysis of peasant household behaviour when markets

1
# **<u>Table 5: Gender and Sensitivity to Shocks</u>**

||**Per Capita Exp**|**enditure, CFA**|**Food**|**–Poor**|**Abso**|**lute–Poor**|
|---|---|---|---|---|---|---|
||(1a)|(1b)|(2a)|(2b)|(3a)|(3b)|
|Estimated Shocks|-46128.2|-45872.5|0.034**|0.034**|0.035*|0.035*|
||(27948.1)|(27857.2)|(0.016)|(0.016)|(0.021)|(0.021)|
|**X**SP**X**Female Manger|32901.3|30179.18|-0.075|-0.124|-0.124|-0.195**|
||(24888.1)|(34819.7)|(0.127)|(0.079)|(0.128)|(0.081)|
|Female Manager|-72217.02<br>(50086.1)|-|-0.004<br>(0.027)|-|-0.041<br>(0.043)|-|
|R-/Pseudo -squared (%)|2.70|2.61|0.54|0.54|0.60|0.60|
|Observations|3,059|3,059|3,059|3,059|3,059|3,059|
|Fixed Effects|Eco-zones|Eco-zones|Eco-zones|Eco-zones|Eco-zones|Eco-zones|
|Logit (Marginal)|-|-|Yes|Yes|Yes|Yes|

**Notes:** Table reports estimates from regressions of per capita consumption expenditure and poverty on the shock variable, whether female household member manage social assistance conditional on receiving the assistance as well as their interactions. SP denotes social protection. Shock realizations are based on estimated shock quantities in section 2 which range between -1.12 and 7.83. The columns are differentiated by outcome variables and implied regression models. All columns include ecological zone level fixed effects. All standard errors are clustered at the region level and shown in parentheses. Significance levels are indicated by ∗∗∗ 1%, ∗∗ 5%, ∗ 10%.

35
<!-- Start of picture text --> +<br>no<br>Lam<br>5<br>£<br>D<br>wi<br>Cc<br>Re)<br>NN<br>@<br>a<br>Ke)<br>9<br>©<br>z<br>o*-<br>faa)<br>fo)<br>1993 1996 1999 2002 2005 2008 2011<br>Year<br><!-- End of picture text -->
states, depends only upon the present state and not on any past states—lagged productivity must be included in the regression model for consistency (Fernandes, 2007). Fourth, the inclusion of industry, sub-national location, and country fixed effects controls for time trends and unobserved sub-national location-, industry-, and country-specific characteristics that might affect the correlation between productivity growth and changes in the business environment.

Fifth, as described in the previous section, merging the AMADEUS and BEEPS manufacturing datasets on country, sub-national location, firm size, and year mitigates the endogeneity between firm productivity and business environment indicators. The econometric analysis in this paper treats BEEPS variables as exogenous determinants of firm productivity; however, firms can be proactive in reducing the constraints they face in the business environment, producing a simultaneity bias in the estimation exercise. For example, a wellmanaged firm with high productivity growth may have worked with authorities to secure a more reliable power supply or to relax hiring and firing restrictions. Statistically, a balance must be struck so that the set of variables, on which the AMADEUS and BEEPS manufacturing datasets are merged, is large enough so that resulting average values not only mitigates the endogeneity problem but also retain sufficient variation for regression analysis. To the extent that sub-sample groupings as defined are sufficiently aggregated so that individual firms are less likely to influence averages but varied enough so that heterogeneous “pockets” of business environments are reflected, using year-specific averages of BEEPS indicators taken across firms in the same country, sector, sub-national location, and size groups is a valid way to instrument out the simultaneity problem (Bastos and Nasir, 2004).

Sixth, in order to mitigate the problems of multicollinearity in the full model regression, principal component analysis (PCA) is used to reduce the dimensionality of the BEEPS data and construct indicators that summarize various dimensions of the business environment. In the BEEPS database, there are typically several variables that address a particular issue that affect the productivity and growth of firms. Several questions, for example, collect information on the quality of infrastructure, namely the number of days of power outages or surges from the public grid, the number of days of insufficient water supply, and the number of days of unavailable mainline telephone service. Inclusion of two or more highly correlated explanatory variables in a regression model generally leads to difficulties in ascertaining the effects of individual factors 15
<!-- Start of picture text --> Figure 5. Mothers of tribal children are less likely to get health care<br>80<br>72.3<br>70 63.1 64.2 63.1 61.9<br>60 54.8<br>50 44.3 48.2 47<br>40.5 41.2<br>40<br>32.2<br>30<br>20<br>10<br>0<br>Three or more antenatal visits Pre-natal care provided by doctor Ever used contraception<br>Scheduled Castes Scheduled Tribes Other Backward Classes Other<br>Source : NFHS-3<br>in last 3 years<br>Proportion of married women who gave birth<br><!-- End of picture text --> Medical practitioners and others often cite the reasons for poor outcomes for women as being “lack of demand” or “ignorance”. Nagda (2004) for instance attributes poor health outcomes to the reluctance of tribal women to accept modern treatment. Using his research from Rajasthan he elaborates how about 86 percent of tribal deliveries in the state are performed at home and over three-fourths are conducted by untrained _dais_ or other personnel largely because acceptance of antenatal care is “not necessary or customary” in tribal societies. Rather, the decision about the nature of treatment is taken at the community level (mostly by traditional healers known as _Bhopas_ ) based on traditional health care systems and a deep understanding and observation of nature.

However, low demand for health or nutrition services is often disguised as demand for public employment, food security and access to roads. And what is the extent to which quality of health services, attitude of medical providers and issues of trust influence demand? There is a deep rooted cultural chasm and mistrust between the largely non-tribal health providers and the tribal residents, with several reports of the former treating the tribal patients with little dignity, if any (Bharat et al 2003, Pallavi 2004). These stereotypes of tribal peoples are not peculiar to India. Colloredo-Mansfeld (1998) for instance documents the “pernicious images of disease, irrationality, and “dirty Indians” used by White-mestizos to characterize Ecuador’s indigenous population and to explain the latter’s poverty (Colloredo-Mansfeld, 1998: 186).

# **Access to health facilities is much poorer for tribals and their children seem to succumb to**

**this lacuna.** One of the key issues in tribal areas is poor physical access of tribal communities to health facilities.  In most states in India, Scheduled Tribes are physically isolated, concentrated in certain regions and districts and in hilly and forested areas that make communication and access to services difficult even in normal circumstances.  Poor coverage of all weather roads makes transportation in emergencies virtually impossible, even if health centers were attended by medical personnel.  A longitudinal study in the tribal dominated Bolangir district in Orissa found for instance that villagers took a young child to the hospital only when his or her condition was critical given difficulty with transport (poor road condition) and the expenditure associated with

15
Policy Research Working Paper 6725

# **Abstract**

Evidence-gap maps present a new addition to the tools available to support evidence-informed policy making. Evidence-gap maps are thematic evidence collections covering a range of issues such as maternal health, HIV/ AIDS, and agriculture. They present a visual overview of existing systematic reviews or impact evaluations in a sector or subsector, schematically representing the types of interventions evaluated and outcomes reported. Gap maps enable policy makers and practitioners to explore the findings and quality of the existing evidence and facilitate informed judgment and evidence-based decision making in international development policy and practice. The gap map also identifies key “gaps” where little or no evidence from impact evaluations and systematic reviews is available and where future research should be focused. Thus, gap maps can be a useful tool for developing a strategic approach to building the evidence base in a particular sector. This paper provides an introduction to evidence-gap maps, outlines the gap-map methodology, and presents some examples.

This paper is a product of the Public Sector Evaluation Department, Independent Evaluation Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mgaarder@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
identifier codes, and then combine the school with the National Census data sources using unique locality identifier codes.

To identify PEC schools, we use administrative data on PEC coverage in 2001, 2002, and 2003.<sup>2</sup> One key variable for our analysis is the definition of the treatment variable. Specifically how should one define coverage by PEC? The construction of this variable plays a critical role if one seeks to evaluate the impact of PEC. We choose to work with two alternative definitions of what constitutes participation in PEC. The first treatment variable (T) is based on a strict criterion, requiring that a school must have received PEC funds in all three school years covered by the school census data we were able to access. Thus, T=1 identifies schools that received a PEC grant in school years 2001, 2002, and 2003 is classified in the treatment group, whereas T=0 identifies schools that did not receive a PEC grant in the same school years (the control group).<sup>3</sup> Based on the strict definition of treatment, there are 1,767 schools in the treatment group and 65, 457 in the control group, each school observed in 2000 and in 2003.

Given that the number of schools covered by PEC increased significantly in 2002 and 2003 we also constructed an alternative treatment variable (T2) based on a less strict criterion. The treatment variable T2=1 identifies schools that received a PEC grant in any of the three school years, while T2=0 identifies the schools that did not did not receive a PEC grant in any of three school years (the control group).<sup>4</sup> Based on the less strict definition of treatment, there are 9,244 schools in the treatment group and 65, 457 in the control group, each school observed in 2000 and in 2003.

The main outcome variables we examine are the average (across all six grades) dropout, failure and repetition rates in the school. To measure dropout, failure, and

> of three questions (math) or three of four questions (language) in a subject.

> 2We identify a school year by the calendar year in which it began, so 2001 refers to the 2001-2002 school year.

> 3 Thus schools that received a PEC grant in one or two of the three school years since the start of PEC in 2001 are excluded from the analysis when the variable T is used as a measure of treatment.

7
**Table 13. Explaining cross-country variation in efficiency, Single Input-Single Output**

|Independent<br>Variable|Gross<br>Primary<br>Enrollment|Net<br>Primary<br>Enrollment|Gross<br>Secondary<br>Enrollment|Net<br>Secondary<br>Enrollment|Literacy of<br>Youth|Average<br>Years of<br>School|First<br>Level<br>Complete|Secondary<br>Level<br>Complete|Life<br>Expectancy|Immuni-<br>zation<br>DPT|Immuni-<br>zation<br>Measles|
|---|---|---|---|---|---|---|---|---|---|---|---|
|WAGE|-.00117***|-.00357*|-.00172**|-.00680*|-.00189**|-.00570*|-.00470**|-.00546*|.00065|-.00052|-.00049|
|GOVEXP|-.00387*|-.00546*|-.00340*|-.00455**|-.00387*|-.00696*|-.00566*|-.00765*|-.00269**|-.00078|-.00227***|
|PUBTOT|--|--|--|--|--|--|--|--|-.00213*|-.00150*|-.00135***|
|GDPPC|-.00002*|-.00002*|-.00001*|.00002**|-.00002*|-1.5e-6|-.00001|-7.7e-6|7.6e-7|-.00001*|-.00001*|
|URBAN|.00167*|.00143***|.00168*|.00037|.00187*|.00532*|.00551*|.00555*|-.00018|.00099**|.00088|
|AIDS|-.04471**|-.08731**|-.02204|.01243|-.02974|.12717***|.1211***|.11041|-.05473|-.01108|-.02730|
|GINI|-.06688|.01507|-.19326**|-.42311|-.18484***|-.44658**|-.34402|-.45870**|.22118|.09510|.08692|
|EXTAID|-.00094|-.00196**|-.00021|-.00106|-.00054|.00089|-.00025|-.00006|-.00224***|-.00155|-.00324**|
|CONS|1.02996*|1.1282*|1.0472*|.84138*|1.0697*|.76791*|.70009*|.81705*|.79193*|.78734*|.84384*|
|# of Obs|79|44|79|34|72|71|71|71|118|118|118|
|(# ofCountrs)|(51)|(30)|(51)|(20)|(46)|(45)|(45)|(45)|(69)|(69)|(69)|
|Wald Chi2(6)|83.91|66.09|46.72|55.31|44.27|64.13|45.53|61.94|50.83|123.97|35.01|
|(Prob>Chi2)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|(.00)|

Note: * 0.01 significance level, ** 0.05 significance level, *** 0.10 significance level, and insignificant otherwise 33
Moreover, it is common that such multi-purpose surveys will be fielded over 12 months, to account for seasonality in consumption expenditures. In this case, the recall period will vary across households depending on the month during which the household is interviewed. In the case of surveys from year-long fieldwork, such as the three examined here, there is variation up to 11 months between the time of the harvest and the data collection, and recall periods for input use are several months longer.

Even if all sample households are surveyed once over a very short period just after harvest, events early in the season will be reported with a recall of several months (e.g. fertilizer application). This raises concerns about the reliability of information reported for earlier events. Substantial recall effects resulting from this gap in time could introduce bias or measurement error into data collected further from events of interest, and the problem may be further aggravated by variable recall periods across sample households.

The objective of this paper is to investigate the extent of recall bias, using data from three national household surveys conducted in East and Southern Africa with fieldwork over about 12 months. We use the variation in recall period and random assignment of households to month of interview (and, therefore, recall period) to examine several important agricultural indicators within the three datasets. Specifically, we explore whether we find any differential in reporting of input use and harvest amounts for main crops based on the time elapsed between events and reporting. In general, we find little evidence of any significant recall bias, in terms of under/over reporting. The findings suggest that farmers’ reports of harvest, crop sales, and input use are not significantly different when collected more than 8 months later as opposed to just after the harvest. Although this is _not_ evidence that agricultural data are not of poor quality, it does address at least one aspect of data collection – length of recall period – which could compromise data quality.

The next section briefly reviews the literature on survey reporting errors, with attention to the types of bias most relevant for agricultural household surveys. Section 3 discusses the data and empirical approach. Results are presented in Section 4. Section 5 concludes.

3
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- Gindling, Thomas H and David Newhouse (2014). “Self-employment in the developing world”. In: _World development_ 56, pp. 313–331.

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- Reinhold, Steffen and Kevin Thom (2013). “Migration Experience and Earnings in the Mexican Labor Market”. In: _Journal of Human Resources_ 48.3, pp. 768–820.

22
2.

# Figure E.4

<!-- Start of picture text --> Net Exports Per Worker<br>Honduras - 1965<br>10- TROP<br>_<br>100<br>a 50ao<br>PETRO A FOR LAB CAP MACH CHEM<br>-o - & | | |<br>a,5<br>100<br><!-- End of picture text -->

<!-- Start of picture text --> Net Exports Per Worker<br>Honduras- 1988<br>TRO<br>~~~~~~~~~~~~~~~TROP<br>300<br>0 200<br>ANL<br>-s PETRO MAT FOR CER LAB CAP MAGH CHEM<br>26 -I(I<br>-200<br><!-- End of picture text --> <!-- Start of picture text --> Net Exports Per Worker<br>Nlcargua- 1988<br>S0 'ROP<br>60<br>_0<br>20<br>PETRO MAT FOR ANL I LAB CAP MACH CHEM<br>0II<br>60<br><!-- End of picture text --> <!-- Start of picture text --> Net Exports Per Worker<br>Nicargua- 1965<br>'50<br>,DO<br>TROP<br>50 ~ ~ ~ ~ ~ ~~ ~ ~~~~~~0CER<br>PETRO MATFR ANL LAB CAP MACH CHEM<br>o ~<br>50-II<br>100.4<br>' 50<br><!-- End of picture text -->