rafmacalaba/datause-encoder-data huggingface dataset:
positive impacts of migration on poverty and income seen in earlier studies which are unable to use a suitable comparison group of non-migrants.

Among the fewer studies of the impact on child health outcomes, all show positive effects, although more mixed results on inputs. For example, Hildebrandt and McKenzie (2005) find lower infant mortality rates and higher birth weights amongst Mexican migrantsending families, but also that children in migrant households are less likely to be breastfed or be vaccinated. Acosta et al. (2007) find higher weight-for-age and height-for-age among children in migrant families in Nicaragua and Guatemala.

The existing literature finds ambiguous effects of migration on several other key outcomes of interest. In terms of the effect on child education, Cox-Edwards and Ureta (2003) find that migration increases school attendance rates in El Salvador, and Yang (2008) finds that increased remittances lead to more schooling in the Philippines, both consistent with higher income alleviating liquidity constraints, whereas McKenzie and Rapoport (2006) find migration lowers schooling attainment in Mexico, with boys in migrant households more likely to drop out of school to migrate, and girls undertaking more housework.

Evidence is also mixed in terms of the impact on adult employment. Funkhouser (1992) finds remittances to be associated with lower overall labor supply, but higher selfemployment in Nicaragua. Acosta (2006) finds a negative impact on female labor supply in El Salvador, but no effect on male labor supply. Yang (2008) finds higher remittances lead to households being more likely to engage in entrepreneurial activities and to spend more hours in self-employment, but to no significant effect on overall labor supply. Amuedo-Dorantes and Pozo (2006a) find remittance receipt lowers female labor supply in Mexico, and shifts male labor supply from formal to informal sector work. Woodruff and Zenteno (2007) find remittance receipt to significantly increase the amount of capital invested in microenterprises in Mexico, whereas Amuedo-Dorantes and Pozo (2006b) find a significant negative impact of remittances on business ownership in the Dominican Republic.

In this paper, we will consider these outcomes, along with other welfare outcomes such as diet, anthropometric health measures, and mental health, which are measured less often in household surveys and for which we have not been able to identify existing literature. For example, a recent submission to the Global Commission on International Migration states (Carballo and Mboup, 2005, p. 5) that “for close family and relatives left behind, the departure of migrants to seek a living elsewhere is also fraught with psychosocial difficulties”, but provides no evidence for this assertion.

8
# Up in Smoke? Agricultural Commercialization, Rising Food Prices and Stunting in Malawi

Benjamin Wood, Carl Nelson<sup>_∗†_</sup> , Talip Kilic<sup>_‡_</sup> , and Siobhan Murray<sup>_§_</sup>

## **JEL Classifications: C26, I15, O13, Q12 Keywords: Malawi, child nutrition, cash crops, tobacco, food prices Sector Boards: Agriculture and Rural Development (ARD), Health, Nutrition, and Population (HE)**

> _∗_ Corresponding author: Post-Doctoral Fellow, International Initiative for Impact Evaluation (3ie), Suite 450, 1625 Massachusetts Avenue NW, Washington, DC 20036, USA. bwood(at)3ieimpact.org

> _†_ Associate Professor, Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, USA.

> _‡_ Research Economist, Poverty and Inequality Group, Development Research Group, The World Bank, Washington, DC, USA.

> _§_ Technical Specialist, Computational Tools Group, Development Research Group, The World Bank, Washington, DC, USA.

We would like to thank Mary Arends-Kuenning, Kathleen Beegle, Marc Bellemare, Andrew Dillon, Craig Gundersen, Nancy McCarthy, Nick Minot, Lia Nogueira, Raka Banerjee, and the participants at the 2011 Midwest International Economic Development Conference, the 2012 International Conference of Agricultural Economists, and the 2012 International Agricultural Trade Research Consortium for their comments on the earlier drafts of this paper, and the Malawi National Statistical Office and the Malawi Tobacco Control Commission for allowing us access the portions of the data used in the study. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of 3ie, the University of Illinois at Urbana-Champaign, The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.

1
Committee of the League, forerunner of the World Health Organization, which advised the League’s Secretariat on drug related matters. The most important and specialized of these bodies within the new control regime was the Advisory Committee on the Traffic in Opium and Other Dangerous Drugs, known as the Opium Advisory Committee (O.A.C.), which in turn created the Opium Control Board to assist it in its duties.

From this institutional foundation, the League went on to incrementally develop a comprehensive control regime. Knowledge and operational gaps in the system were identified and addressed through follow up conferences and the introduction of new conventions. This process of building up the control system proceeded with two conferences in Geneva in 1924 that sought to address the problems encountered by the O.A.C. in developing a comprehensive picture of the ‘legitimate’ medical drug market.

# _The Geneva Convention_

The Geneva Convention of 1928 expanded the manufacturing control system by establishing compulsory drug import certificates and export authorizations that were to be administered by national authorities and which were required for all drug transactions between countries. This sought to prevent countries importing or exporting drugs beyond medical and scientific requirement. In order to determine the level of legitimate medical drug requirements, parties to the Convention were to provide annual statistics estimating production, manufacture and consumption requirements for opiates, coca, cocaine and, for the first time in drug control, cannabis. This information was to be supplemented by quarterly statistics detailing the volume of plant based and manufactured drugs imported and exported and estimated figures for opium smoking. A new drug control organ, the eight-person Permanent Central Opium Board (P.C.O.B.), which replaced the Opium Control Board, assumed responsibility for processing the statistical information. The P.C.O.B. had the authority to request explanations from national governments if they failed to submit statistical information or if stated drug import or export requirements were overshot. The Board could also recommend an embargo of drug exports or imports on any country that exported or imported in excess of stated production levels or medical need. This extended to countries that were not party to the Convention, universalizing the control system. Aside from refining the institutional structure and remit of drug 14
have meaningful variation in project outcomes across both countries and task managers. Our headline finding here is that task manager fixed effects are at least as important as country fixed effects in accounting for the variation in project outcomes, suggesting a strong role for various task managerspecific characteristics in driving project outcomes.

The results in this paper have important implications for aid effectiveness in general, and for IDA in particular. The first is basic and not very new, though it is confirmed by the updated and expanded work in this paper: targeting aid to countries with better policies and institutions pays off, as rates of project success are significantly higher in countries with good policy, as measured by the CPIA ratings. However, the very large heterogeneity in project performance within countries suggests that policies to improve aid effectiveness could focus more on project-level factors as opposed to country-level factors. These include those that make individual projects difficult to restructure or cancel outright even after early indications of problems arise; those that contribute to project size and complexity; and those that underlie the large differences in project performance across task managers that we observe in the data.

The rest of this paper proceeds as follows. In the next section we review related literature that has also considered the World Bank project-level outcome data we work with here. In Section 3 we describe the project-level data in detail. Section 4 contains our main empirical results, and Section 5 discusses countries, projects, and task managers. Section 6 offers policy implications and conclusions.

# **2.  Related Literature**

This paper is not the first to study the correlates of individual World Bank project outcomes. In earlier contributions, Isham, Kaufmann and Pritchett (1997) and Isham and Kaufmann (1999) studied the determinants of project-level estimated ex-post economic rates of return. Both of these papers focused primarily on country-level factors affecting project returns, notably the role of democracy and civil liberties in the first, and the role of sound macroeconomic policies in the second. Subsequent papers have similarly focused on country-level determinants of project performance. For instance, Dollar and Levin (2005) estimate a series of cross-country regressions of country-average project success ratings on a set of country-level explanatory variables; they emphasize the role of differences in institutional quality in driving cross-country differences in average project performance. Guillamont and Laajaj (2006) focus on country-level volatility in accounting for project-level success, while Chauvet, Collier, and Duponchel (2010) emphasize country-level conflict measures. In addition Dreher, Klasen, Vreeland and Werker (2010) focus on the effect of political influence in the project approval decisions (as proxied 4
be associated with worse outcomes for women and their children, including increased health risks as well as potentially “worse” marriage matches.<sup>9</sup>

# **IV. Empirical Methodology**

# _IV.1 Specification_

We are interested in the relationship between outcomes in wave 5 (including education, occupation, and marital status) and the level of child labor intensity (which we measure through mean child labor hours in waves 1 to 4). An OLS regression of the form where _Yi,t_ are outcomes in wave 5, _Ti,t_ –10 is mean child labor hours in waves 1 to 4, and _Xi,t_ are household and community-level controls, is most probably not suitable for estimating a causal relationship. The principal concern is omitted variable bias. The child labor decision is likely to be correlated with both household- and child-level covariates, not all of which will be observable to the researcher. For example, though we can control for parents’ education we cannot control for their discount rates. At the child level, we have few covariates other than age and sex, and thus, for example, cannot control for ability. Reverse causality is less of a concern because the outcome is measured 10 years after child labor intensity.

We address concerns with the OLS specification using an instrumental variables strategy. Our instruments, S _i,t_ –10, are indicators of household agricultural shocks and rainfall shocks in waves 1 to 4. Thus our basic specification is a two-stage least squares procedure of the form:

> 9 Younger mothers are more likely to suffer from micronutrient deficiencies and be unaware of the health risks associated with pregnancy; they are also more likely to have children soon after marriage with increased risk of maternal and infant mortality (World Bank, 2007). Younger ages at marriage may result in curtailed education for girls, although it is difficult to ascertain the causality. In any case, a younger bride may be less able to assert power and authority in her marriage especially given that women marry men who are on average several years older.

9
**Table 2a. First stage regression results**

||Fem<br>|ale<br>|M<br>|ale<br>|
|---|---|---|---|---|
||**_Year 1990_**|**_Year 2000_**|**_Year 1990_**|**_Year 2000_**|
|Lanuae|0.483*|0.407*|0.373*|0.279*|
|gg|_(0.042)_|_(0.035)_|_(0.038)_|_(0.033)_|
|Bd|0.401*|0.393*|0.407*|0.425*|
|orer|_(0.045)_|_(0.037)_|_(0.043)_|_(0.036)_|
|Distance|-0.488*|-0.334*|-0.492*|-0.344*|
||_(0.021)_|_(0.018)_|_(0.021)_|_(0.018)_|
|Colonial|0.660*|0.029|0.627*|0.058|
|link|_(0.045)_|_(0.041)_|_(0.045)_|_(0.041)_|
|Di|0.457*|0.550*|0.467*|0.553*|
|aspora|_(0.007)_|_(0.007)_|_(0.006)_|_(0.006)_|
|OBS|35,910|35,910|35,910|35,910|
|RSQR|0.908|0.915|0.847|0.885|

Notes: (*) significant at 1% level.

# **Table 2b. Second stage regression results**

|||Fem|ale|||M|ale||
|---|---|---|---|---|---|---|---|---|
||**High**|**-skill**|**Low**|**-skill**|**High**|**-skill**|**Low**|**-skill**|
||**_Year 2000_**|**_Year 1990_**|**_Year 2000_**|**_Year 1990_**|**_Year 2000_**|**_Year 1990_**|**_Year 2000_**|**_Year 1990_**|
||0.663|0.553|0.484|0.454|0.577|0.518|0.153|0.258|
|Language|_(0.030)_|_(0.045)_|_(0.040)_|_(0.049)_|_(0.030)_|_(0.042)_|_(0.042)_|_(0.051)_|
|d|0.281|0.223|0.808|0.606|0.515|0.222|0.844|0.488|
|Borer|_(0.036)_|_(0.056)_|_(0.043)_|_(0.057)_|_(0.040)_|_(0.059)_|_(0.047)_|_(0.062)_|
||-0.247|-0.348|-0.381|-0.524|-0.163|-0.280|-0.395|-0.585|
|Distance|_(0.013)_|_(0.017)_|_(0.019)_|_(0.021)_|_(0.014)_|_(0.017)_|_(0.021)_|_(0.023)_|
|Colonial|0.496|0.708|0.211|0.644|0.498|0.588|0.349|0.653|
|link|_(0.030)_|_(0.044)_|_(0.041)_|_(0.051)_|_(0.034)_|_(0.049)_|_(0.047)_|_(0.058)_|
||0.394|0.342|0.537|0.463|0.419|0.387|0.542|0.495|
|Diaspora|_(0.005)_|_(0.006)_|_(0.007)_|_(0.008)_|_(0.005)_|_(0.007)_|_(0.007)_|_(0.008)_|
|Destination|0.911|0.911|1.084|1.084|0.989|0.989|1.148|1.148|
|fixed effect|_(0.016)_|_(0.016)_|_(0.020)_|_(0.020)_|_(0.018)_|_(0.018)_|_(0.022)_|_(0.022)_|
|OBS|30,|419|30,|419|30,|419|30,|419|
|RSQR|0.8|93|0.8|98|0.8|71|0.|871|

Notes: All estimates are significant at 1% level.

**Figure 2. Histograms of Out-of-sample log ratios, Female migrants, 2000**
countries and data sets is essential before this methodology can be fully validated and used in operations.

The paper consists of five sections. Section II provides the basic theory and analytical framework. Section III provides the country background, a description of the data and the empirical results including robustness tests. Section IV discuss further extensions in other contexts and Section V concludes.

# **II. Analytical Framework**

Where consumption data are either incomparable across two survey rounds or missing in one survey round but not the other, but other characteristics (x�) that can help predict consumption data are available in both survey rounds, we can apply survey-to-survey imputation methods. In particular, we apply Dang, Lanjouw, and Serajuddin’s (2017) imputation framework, which builds on earlier survey-to-census imputation studies (Elbers, Lanjouw, and Lanjouw, 2003; Tarozzi, 2007).<sup>3</sup> We briefly describe this imputation method before discussing its extensions to the refugee context.

Let _xj_ be a vector of characteristics representing the main observable factors that determine a household’s consumption, where _j_ indicates the survey type. More generally, _j_ can indicate either another round of the same household expenditure survey, or a different survey (census), for _j_ = 1,

> 3 Elbers _et al._ (2003) provide a method that imputes household consumption from a survey into a population census to measure poverty, which is commonly known as “poverty mapping”. Adapting this approach for survey-to-survey imputation, Christiaensen _et al._ (2012) impute poverty estimates using data from several countries, including China, Kenya, the Russian Federation, and Vietnam; other studies analyze data from Uganda (Mathiassen, 2013). Compared to previous studies, Dang _et al._ ’s (2017) method provides a more explicit theoretical modeling framework, with new features such as model selection and standardization of surveys of different designs (e.g., for imputing from a household survey into a labor force survey). This technique has recently been applied to data from several African countries (Beegle _et al._ , 2016), India (Dang and Lanjouw, 2018), Tunisia (Cuesta and Ibarra, 2017), and Vietnam (Dang _et al._ , 2019).

5
India – surveyed over four periods from the 1950s to the 1980s, and the village of Sugao in Satara district in Maharashtra state in western India surveyed over three periods from the 1940s to the 1970s. Both villages have been the subject of close qualitative and quantitative examination over several decades (Bliss and Stern 1982, Dandekar 1986, Lanjouw and Stern, 1998).

Our analysis in Palanpur shows that examining caste differences on the basis of the conventional inequality decomposition yields a relatively modest “contribution” of caste differences to overall inequality. This is at odds with what is actually known about the way caste has figured in the evolution of Palanpur’s economy and society – knowledge based on first-hand observation as well as detailed data covering all households in the village in four annual rounds of intensive data collection between the late 1950s and early 1980s. Palanpur is a poor, agricultural village which has been relatively untouched by globalizing forces and is therefore imbedded within inter-linked systems of political, economic and social power. These differences are revealed to be quite acute when caste differences are explored on the basis of the ELMO (2008) adaptation. The statistical analysis based on ELMO lines up much more clearly with what the more detailed, field-based analysis has suggested – that caste based inequality is large and durable. A further finding from this study is that a crude breakdown of the village population into a “Scheduled Caste/Non-Scheduled Caste” partition may not do so well in capturing the importance of certain key group differences in a given setting. We also document that while caste based inequality in Palanpur is significant, preliminary analysis based on other criteria for group differentiation – education of household head, and household landownership – fail to reveal sizeable between group differences, whether based on the ELMO statistic or the standard between group decomposition.

Analysis of inequality in the village of Sugao in Satara district, Maharashtra, in contrast, finds that the ELMO decomposition yields relatively little additional insight into the 6
**Table 3:** Electricity Consumption Models

|**Variable**|**Model (1)**<br>Log<br>Electricity|**Model (2)**<br>Log Electricity|**Model (3)**<br>Log Electricity|**Model (4)**<br>Log Electricity|**COVID-19**<br>**Model**<br>Log<br>Electricity|
|---|---|---|---|---|---|
|**Trend**|0.00025***<br>(0.00)|0.00025***<br>(0.00)|0.00025***<br>(0.00)|.00025***<br>(0.00)|.00025***<br>(0.00)|
|**Holiday**|||-.064***|-.066***|-0.066***|
||||(0.01)|(0.01)|(0.01)|
|**Cooling**||||.029***<br>(0.00)|0.029***<br>(0.00)|
|**Ramadan**||||. 046***<br>(0.00)|046***<br>(0.00)|
|**Friday**|||-.049***|-.049***|-.049***|
||||(0.00)|(0.00)|(0.00)|
|**Saturday**|||-.016***<br>(0.00)|-.016***<br>(0.00)|-.016***<br>(0.00)|
|**End of data**|Dec 2019|Dec 2019|Dec 2019|Dec 2019|Oct 2021|
|**Weekly FEs**|No|Yes|Yes|Yes|Yes|
|**Weekday FEs**|Yes|Yes|Yes|Yes|Yes|
|**2020/2021 daily FEs**|No|No|No|No|Yes|
|**No. of observ.**|3,621|3,621|3,621|3,621|4,274|
|**R-squared**|0.84|0.90|0.91|0.93|0.95|

_Note:_ Standard errors are in parentheses. FE = fixed effects. *p < 0.1, **p < 0.05, ***p < 0.01.

# **7. Tracking the Economic Impact of COVID-19 with Electricity Consumption**

# _7.1 COVID-19 Infections and Government Restrictions_

Bangladesh has been impacted severely by the COVID-19 pandemic. The first three cases were confirmed on March 8, 2020, and from then until the end of 2021, the country went through three waves of infections and a ripple. The first wave started in March 2020, intensified in April, and peaked in June with around 3,000 registered infections a day. Daily infections then started to decline slowly but steadily and halved by October. The ripple was smaller than the first wave, with cases increasing again until the end of November 2020, to around 2,100 registered cases a day. The ripple was followed by a sharp decline to just a few hundred registered infections in January and February 2021. The second wave started in early March 2021 and peaked at more than 7,000 registered cases a day in early April. The spike tapered 13
observations should not be missing systematically. For each pollutant, table S1.5 in supplementary appendix S1, provides time series of share of missing observations in each month between 2004 and 2006. Eye balling the data does not suggest a pattern to missing observations. We examine this more thoroughly in two ways.

First we examine whether there is any association between data being missing and the timing of metro expansions. To do this we use data for our entire study period, 2004–2006, and estimate equation (1) by replacing the outcome variable with an indicator variable for whether the pollutant measure is missing. In this specification we include all six discontinuity dummies (representing the six metro expansions undertaken in this period), and also include season fixed effects. Results are presented in tables S1.6a and S1.6b for _ITO_ and _Siri Fort,_ respectively, in supplementary appendix S1. At _ITO_ , for _NO_ 2 and _PM_ 2.5 there is some evidence that observations are less likely to be missing in summer. However, given that for each pollutant some extensions are positive, some negative, and some insignificant, we conclude that the extensions by themselves are not systematically associated with the pollutant measure being missing.

Next we investigate whether observations are more likely to be missing on either high or low pollution days. This is done using the entire series for _CO_ and _PM_ 2.5 at _ITO_ . The last column of table 2 shows that _CO_ at _ITO_ has 14 percent missing observations, while the corresponding figure for _PM_ 2.5 is 42 percent. Given these shares of missing observations, we use a specification similar to equation (1), wherein missing status for _PM_ 2.5 is regressed on whether or not _CO_ is above the CPCB prescribed limit of 2000  _g m_ / 3 (proxy for high pollution). Other right hand side variables are the same except that we drop the discontinuity dummies and the time polynomial, and once again include season fixed effects. The results are shown in appendix table S1.7 in supplementary appendix S1. The coefficient on 22