rafmacalaba/datause-encoder-data huggingface dataset:
POI-ICY RESEARCH WORKING PAPER 2435

# Summary findings

Poverty is intrinsically a complex social construct, and even when it is narrowly defined by a deficit oiF consumption spending, many thorny issues arise in setting an appropriate "poverty line." Suryahadi, Sumarto, Suharso, and Pritchett limit themselves to examining how poverty-defined on a consistent, welfare-comparable basis-changed in Indonesia during a series of crises that began in August 1997. Using various data sets and studies, they develop a consistent series on poverty's evolution from February 1996 to August 1999.

Specifically, they study the appropriate method for comparing changes in poverty between the February 1996 and February 1999 Susenas surveys.

To set a poverty line for 1999 that is conceptually comparable to that for 1996 involves a standard issue of price deflation: How much would it cost in 1999 to purchase a bundle of goods that would produce the same level of material welfare as the money spent at the poverty line in 1996?

Empirically, given major changes in the relative prices of food, the key issue is the weight given food prices in the price index.

Using different deflators produces a range of plausible estimates, but they produce two "base cases": one working forward from 1996 and one working backward from 1999.

If one accepts the official figure of 11.34 percent for February 1996, poverty increased from the immediate pre-crisis rate of about 7-8 percent in the second half of 1997 to the post-crisis rate of about 18-20 percent by September 1998 and 18.9 percent in February 1999.

If one begins from the best estimate of the poverty rate in February 1999 (27.1 percent), poverty rose by 9.6 percentage points from 17.5 percent in February 1996.

Since February 1999, poverty appears to have subsided considerably but-two years after the crisis started-is still substantially higher than it was immediately before the crisis.

This paper-a product of the Environment and Social Development Sector Unit, East Asia and Pacific Region-is part of a larger effort in the region to develop a national poverty strategy for Indonesia. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC: 20433. Please contact Patricia Sader, room MC3-556, telephone 202-473-3902, fax 202-522-1153, email address psader@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workdngpapers. Lant Pritchett may be contacted atlant_pritchett@harvard.edu. September 2000. (36 pages) _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 view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
Policy Research Working Paper 7208

# **Abstract**

There is an inherent tension between traditional norms and survey protocols for quantitative data collected in the developing world. Unexpected interactions between the interviewer and respondent can lead to interviewer effects in the data, particularly in the case of subjective or sensitive questions. This paper makes use of a unique data set available from Timor-Leste containing subjective and objective questions to study these effects. In addition to their age and gender, data were collected from the interviewers regarding their opinions on the subjective questions prior to fieldwork. Fixed effects and mixed effects logit models are used to examine the main effects and interactions between interviewer and respondent characteristics. More objective measures serve as a pseudo control group. The paper finds interviewer effects in the both subjective and objective data, but the magnitude is considerably stronger for subjective questions. The paper also finds that female respondents are more susceptible to influence based on the interviewer’s beliefs. Despite methodological shortcomings, the study highlights the need to consider more fully the impact of traditional cultural norms when conducting quantitative surveys in the developing world on topics that are outside the standard objective questions.

This paper is a product of the Poverty Global Practice 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 khimelein@ 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
Policy Research Working Paper 6500

# **Abstract**

Mali, a landlocked West African nation at the southern edge of the Sahara Desert, has introduced a program to produce biodiesel using _jatropha curcas_ , a non-edible shrub widely available throughout the country by farmers for generations as a living fence for their gardens. The aim of the program is to partially substitute diesel, which is entirely supplied through imports, with domestic biodiesel produced from a feedstock that does not have any commercial value otherwise and thus has zero opportunity cost. This paper uses a computable general equilibrium model to investigate economy-wide and distributional impacts of large-scale jatropha production on different types of lands, and conversion of jatropha oil to biodiesel for domestic consumption. It assesses impacts on agricultural and other commodity markets, resource and factor markets, and international trade. The results are fed into a detailed household survey-based micro-simulation model to assess impacts on poverty and income distribution. The study finds that the expansion of jatropha farming would be beneficial in terms of both macroeconomic and distributional impacts as long as idle lands, which have been neither used for agriculture nor protected as forests, are utilized. However, if jatropha plantation is carried out on existing agriculture lands, the economy-wide impacts would be negative although it would still help reduce rural poverty.

This paper is a product of the Environment and Energy Team, Development Research 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 authors may be contacted at gtimilsina@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
# TABLE 1 (30)

|LEGAL|**SITUATION OF**|INDIGMO<br>(1990)|US AREAS**IN**|BRAZIL||
|---|---|---|---|---|---|
|Legal<br>Situation|No of<br>Indigenous<br>Areas|W<br>|Area in<br>Hect|W|Popula-<br>tion|
|Not<br>Identified|90|17.11|37,598|<br>0.05|<br>6,802|
|Identified|80|15.21|11,651,331|14.72|<br>40,774|
|Interdicted|67|12.74|30,007,419|37.92|<br>17,329|
|Delimited|93|17.68|10,264,111|12.97|<br>46,969|
|Demarcated<br>and<br>Confirmed|136|25.86|16,321,220|20.62|<br>91,364|
|Regularized|60|11.41|10,853,773|13.72|<br>32,378|
|TOTAL|526|100.0|79,135,452|100.0|235,616|

# DEFINITIONS:

Not Identified: Lands known to be occupied by indigenous peoples but awaiting formal indentification as Indigenous Areas (lAs) by the National Indian Foundation (FUNAI).

Identified: IAs formally identified by FUNAI.

Interdicted: IAs where a formal decree has been issues by FUNAI announcing indigenous occupancy and intent to proceed with delimitation and demarcation.

Delimited: lAs where technical studies have been conducted by FUNAI and boundaries have been formally noted on government maps.

Demarcated: IAs where the actual physical demarcation of boundaries has been done and confirmed by presidential decree.

Regularized: IAs whose titles have been registered in the federal, state and municipal registries.
**_Table 5. Relationship between staffing predicted performance and IEG outcome rating_**

||||**Dependen**|**t variable: IE**|**G 1-6 outco**|**me rating**|||
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
||0.085***|||0.044||0.050||0.067|
|Preparation TTL|(0.025)|||(0.030)||(0.038)||(0.041)|
|||0.144***||0.039||-0.009||-0.003|
|1<sup>st</sup>hlf ii TTL|||||||||
|a supervson||(0.028)||(0.039)||(0.050)||(0.052)|
|2<sup>nd</sup>hlf ii TTL|||0.162***|0.126***||0.131**||0.130**|
|a supervson|||(0.028)|(0.038)||(0.051)||(0.054)|
||||||0.054|0.031||0.075|
|Practice Manager<sup>1</sup>|||||(0.042)|(0.049)||(0.055)|
||||||||0.013|-0.092|
|Country Director<sup>1</sup>|||||||(0.042)|(0.060)|
||2.427***|2.216***|2.263***|2.691***|1.875***|2.567***|1.814***|2.395***|
|Constant|(0.339)|(0.303)|(0.306)|(0.348)|(0.376)|(0.488)|(0.276)|(0.527)|
|Observations|2,038|2,560|2,513|1,946|1,723|1,222|2,540|1,129|
|Adjusted R-squared|0.122|0.116|0.115|0.130|0.124|0.134|0.107|0.149|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS and controlling for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects. (1) Practice Manager and Country Director at project preparation.

27
# **Notes**

1 Angel 2011.

2 An economist would argue that the land price can qualify as high only when compared with something else, such as the price of construction or the median household income. But for most urban planners land price is always too high.

3 For Stockholm, see Cervero (1998); for Seoul, new towns see Lee and Ahn (2005).

4 McKenzie and Rapino 2011.

5 Prud’homme and Lee 2009.

6 OECD 2011.

7 Cervero 1998.

8 In a polycentric city, all else equal, the maximum travel distance of a worker located in the periphery would have to equal the diameter of the built-up area (assumed to be a circle), rather than the radius, if jobs are concentrated in the center. Alternatively, speeds would have to double to cover the same area.

9 To my knowledge, only the government of South Africa taxes itself for the land it occupies (see figure 2).

10 The acquisition price for eminent domain is fixed at “jantri rates,” which are administrative land values fixed by the Gujarat state government for tax purposes. It is usually much below the market value, even with the 30 percent bonus usually added to it.

26
# **Table 3(a): Probits for alternative measures of subjective poverty in Tajikistan**

||(1)||(2)||
|---|---|---|---|---|
||Probit for pl<br>oneself on s|acing<br>tep 1|Probit for placi<br>at or below vi|ng oneself<br>gnette 1|
||Coeff.|s.e.|Coeff.|s.e.|
|log per capita real consumption|-0.685***|0.082|-0.674***|0.067|
|_Household Head Demographics_|||||
|female headed household|0.076|0.087|0.083|0.075|
|age of hh head|0.003|0.003|0.002|0.003|
|_Ethnicity (Reference: Tajik)_|||||
|Uzbek|-0.079|0.082|-0.073|0.068|
|Russian|0.137|0.228|0.381*|0.219|
|Other|0.615***|0.194|0.543***|0.178|
|_Education (Reference: No Education)_|||||
|Primary|-0.105|0.174|0.021|0.154|
|<br>Basic|0.024|0.159|0.122|0.144|
|General Secondary|-0.114|0.157|0.030|0.142|
|Special Seconday|-0.179|0.174|-0.028|0.155|
|Technical Secondary|-0.147|0.185|0.058|0.159|
|Higher Education|-0.290|0.182|-0.146|0.160|
|<br>Graduate School|||||
|_Household Characteristics_|||||
|log household size|-0.491***|0.080|-0.561***|0.071|
|no. of elderly (65+)|-0.031|0.074|0.018|0.061|
|any migrant(s) in the hhs|-0.017|0.074|0.035|0.062|
|no. of employed|0.001|0.034|0.017|0.026|
|Agriculture / Fishing / Forestry|-0.153|0.115|0.038|0.091|
|Manufacture / Mining|-0.250|0.164|-0.190|0.143|
|<br>Services|n.a.|n.a.|-0.457**|0.232|
|Construction|-0.110|0.149|0.061|0.117|
|Public Administration / Education /<br>Health|-0.080|0.123|-0.106|0.110|
|Sales and Services|-0.211*|0.120|-0.182*|0.096|
|Other|0.027|0.170|0.182|0.144|
|_Landholding (Reference: No Land)_|||||
|Small Holding|-0.107|0.096|-0.069|0.081|
|<br>Medium Holding|-0.260**|0.124|-0.210**|0.103|
|<br>Large Holding|-0.328***|0.125|-0.214**|0.099|
|_Geography_|||||
|Sogd Urban|-0.028|0.143|-0.015|0.119|
|<br>Sogd Rural|0.214|0.137|0.247**|0.116|
|<br>Khatlon Urban|0.091|0.183|0.441***|0.138|
|Khatlon Rural|0.473***|0.132|0.405***|0.111|
|RRP Urban|0.486***|0.165|0.371**|0.147|
|RRP Rural|0.499***|0.125|0.583***|0.104|
|Gbao Urban|-0.328|0.344|-0.353|0.263|
|Gbao Rural|-0.175|0.146|0.101|0.116|
|Pseudo R<sup>2</sup>|0.099||0.087||

_Note:_ *** p<0.01, ** p<0.05, * p<0.1; Col (1): there were no households with heads in services.

21
across surveys, the same procedure was used to deflate expenditures spatially, and overall price levels were stable over this period—the national currency in Ecuador at the time of both surveys was the US dollar. We therefore do not have a satisfactory explanation for the secular downward shift in the Engel curve in our evaluation sample or in the rural sierra in general.

More importantly for our analysis, however, the lower panel of Figure 1 shows that the food Engel curve for lottery winners at follow-up is everywhere above that for lottery losers. Note that this is a very different pattern of what we find at baseline. Figure 1 thus suggests that BDH transfers led households to shift expenditures towards food.

# B. <u>Parametric results</u>

We next turn to two parametric specifications of the data. In the first of these, we use only data from the follow-up survey: (1) Wht= Zhδ + f(ln(x/n))ht + **X** htβ + ψ c + εht, where Wht is the food share at the time of the follow-up survey; Zh is a dummy variable that takes on the value of one if a family was a lottery winner; f(ln(x/n))ht is a flexible formulation of per capita expenditures. In practice, we use a quadratic in expenditures—we consistently fail to find evidence that higher-order terms are important (see Attanasio and Lechene 2002, and Blundell, Banks, and Lewbell 1997 for a similar parametrization of total expenditures in Engel curve analysis); **X** ht is a vector of demographic controls, including the log of household size, and indicator variables for the number of family members in 5 age ranges (0 to 5, 6 to 17, 18 to 44, 45 to 64, and 65 or older) interacted with gender; ψ c is a vector of 45 canton fixed effects; εht, finally, is the regression error term. Standard errors are corrected for within-parish correlation. The parameter of interest is δ, which tests for differences in the food Engel curve between lottery winners and lottery losers. Note that this is a “lottery effect” that is exogenous as it relies only on the random assignment.

There are a number of considerations that require discussion in this specification. The first of these is that estimation of (1) above allows the BDH to have an effect on the intercept but not the slope of the Engel curve. In practice we consistently fail to find evidence of BDH effects on the slope of the Engel curve, and therefore use this more parsimonious specification. A second concern is measurement error. Measurement error in per capita expenditures would bias the linear and quadratic terms in ln(x/n)ht above, and could also bias estimates of the program effect on the Engel curve if per capita expenditures at follow-up are not orthogonal with participation in the BDH program. We therefore also present estimates in which total per capita expenditures (and its square) are instrumented with per capita non-food expenditures (and its square).

10
in January in any given year is more likely to start kindergarten or primary school one year later than a child who was born in December of the preceding year, despite an age difference of only one month. We also conduct a series of robustness and placebo analyses to test the validity of the instrument as well as estimation results.

Our paper makes several new contributions to the literature. First, while there is a considerable literature on the impacts of childcare subsidies for richer countries (e.g., see Akgunduz and Plantega (2018) for a recent review), only a handful of studies consider the developing country context. The effects of childcare on parental employment can vary significantly between the former and the latter countries because of their systematic differences in childcare and labor market institutions. For example, the self-employment rate ranges between 30 and 80 percent of the employed labor force in developing countries (World Bank, 2013). Our study on Vietnam—the poorest of those studied in the current literature—thus adds to this growing literature.

Furthermore, the empirical findings on the effect of childcare on parental employment are not conclusive. While most recent studies find a significant, positive effect of childcare use on women’ labor supply (e.g., <mark>Bauernschuster and Schlotter, 2015; Martínez and Perticará, 2017),</mark> a number of other studies do not (e.g., Cascio, 2009; <mark>Havnes and Mogstad, 2011). Reviews by Blau and Currie (2006) and A</mark> kgunduz and Plantega (2018) show a large variation in the elasticity of maternal employment to childcare costs across different studies, resulting from differences in samples of women and children, estimation methods, and country contexts. The few existing recent studies on middle-income countries offer mixed evidence as well. Focusing on women with at least one child age 4 in Argentina, Berlinksi, Galiani, and McEwan (2011) find a positive impact for childcare use on women’s labor force participation (LFP) and work hours. Yet, Li (2017) recently observes no such effects on urban Chinese women.<sup>2</sup> We add to these studies by investigating nationally representative household surveys for Vietnam and all children age 1-5.

> 2 Li (2017), however, finds some positive impact on women’s LFP for informal childcare provided by grandparents. Other studies indirectly examine the impacts of childcare use through the presence in the household of young children or older people (Maurer-Fazio et al., 2011) or available childcare facilities in the community (Du and Dong, 2013). For earlier studies on childcare and women’s employment for developing countries, see, e.g., Lokshin, Glinskaya, and Garcia (2004) for Kenya, Hallman et al. (2005) and Quisumbing, Hallman, and Ruel (2007) for Guatemala, and those reviewed in Akgunduz and Plantega (2018).

3
Table 2 and the precipitation data in Table 3. Note that there is a wide range of climates across the 11 countries in the sample.

It is not self-evident how to represent monthly temperatures and precipitation data in a Ricardian regression model. The correlation between adjacent months is too high to include every month. We explored several ways of defining three-month average seasons. Comparing the results, we found that defining winter in the northern hemisphere as the average of November, December and January provided the most robust results for Africa. This assumption in turn implies that the next three months would be spring, the three months after that would be summer, and August, September and October would be fall (in the north). These seasonal definitions were chosen because they provided the best fit with the data and reflected the mid-point for key rainy seasons in the sample. We adjusted for the fact that seasons in the southern and northern hemispheres occur at exactly the opposite months of the year. We also explored defining seasons by the coldest month, the month with highest rainfall, and solar position, but found these definitions did a poorer job of explaining current agricultural performance.

Soil data were obtained from FAO (2003). The FAO data provide information about the major and minor soils in each location as well as slope and texture. Data concerning the hydrology were obtained from the University of Colorado (Strzepek & McCluskey 2006). Using a hydrological model for Africa, the hydrology team calculated flow and runoff for each district in the surveyed countries. Figure 1 depicts the distribution of estimated long run flow (in m<sup>3</sup> ) across the continent. Data on elevation at the centroid of each district were obtained from the United States Geological Survey (USGS 2004). The USGS data are derived from a global digital elevation model with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).

The literature has made it clear that irrigation and water availability is an important variable in crop production. Irrigated land is generally considered to be of the highest value. However, in Africa most agricultural areas rely on rain (nearly 80%). We explore in this analysis the effect of irrigation on the climate response functions of farmers in different regions of Africa. The irrigation variable is based on plot specific data on water sources. If any primary plot on a farm was using water sources other than rainfall, such as surface water resources, ground water or stored water, in any season of the survey year, the plot was assumed to be irrigated. Table 1 provides a breakdown of where irrigation is employed by country based on the 10