rafmacalaba/datause-encoder-data huggingface dataset:
follow-up I and follow-up II respectively. The coefficients β1 and β2 will measure the intent-to-treat effect of being assigned to the DOT treatment compared to the control group. y0i is the baseline value of the outcome variable. X′0i is a vector of baseline control variables. time is a dummy variable taking the value of zero for the follow-up I period and one for the follow-up II period. In cases when a control variable is missing, its value is set to zero and a dummy variable is included for whether the variable is missing. εit is the error term.

# **3.2.2** **_PI/BSED study_**

The regression specification for the PI/BSED study is also an intention-to-treat (ITT) analysis using the one follow-up round of data and includes the lag of the dependent variable. For outcome Y we then estimate the following equation for firm i at time t: Where yit is the outcome variable measured at follow-up I. PIi and BSEDi are dummy variables taking the value of one if the entrepreneur was assigned to the PI training or the BSED training treatment groups, respectively. The coefficients β1 and β2 will measure the intent-to-treat effect of being assigned to the PI or BSED training groups respectively, compared to the control group who did not receive training. y0i is the baseline value of the outcome variable. X′0i is a vector of control variables. In cases when a control variable is missing, its value is set to zero and a dummy variable is included for whether the variable is missing. εit is the error term.

E1 and E2 will provide the intent-to-treat (ITT) effect which is the effect of being offered to participate in the training among the experimental sample.<sup>8</sup> For the outcomes for which we have follow-up data only we

> 8 In addition to intention-to-treat (ITT) analyses we ran a two-stage least squares (2SLS) estimation to estimate the local average treatment effect (LATE) where we instrument the actual participation in the training program with the random assignment to the treatment group. This measure of the treatment-on-the-treated (TOT) gives an estimate that controls for non-compliance with treatment assignment (i.e. for the lower than 100% take-up). LATE estimates are not presented in the regression tables since they did not offer any new information than that provided by the ITT estimates.
are the most deprived, closely followed by the South East, where over and close to 70 percent of the children have no access to water from a protected source.<sup>25</sup> State level estimates of severe water deprivation from the MICS survey are highest for Cross River. Despite the MICS estimates generally being lower than the GHS, still half the children in Cross River are considered severely water deprived. Also, in Taraba, Bayelsa and Ebonyi over a third of the children are considered severely water deprived. Differences of up to 30 percentage points of the share of children being considered severely water deprived between states in the same geopolitical zone underline the importance to look beyond regional difference.

Figure 31: State Level Water Deprivation <!-- Start of picture text --> Water Deprivation - by State - MICS<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis

> 25 Further conditioning water access to being directly drinkable water, such as bottled or sachet water, to consider a child not water deprived, would raise water deprivation levels to over 90 percent in almost all states. Though as discussed in WB (2017) Box 3.2 even sachet water may not convincingly quality as directly drinkable water due to seasonal high levels of contamination detected.

42
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24
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18
The present paper aims to provide causal evidence of the effects of maternal mental health on children’s human capital accumulation in developing countries. We study the under-explored relationship between maternal depression and child cognition, a dimension of child development that has been extensively documented as a crucial determinant of life outcomes (Becker 1964; Currie and Thomas 1999; Feinstein 2003; Cunha et al 2005). We focus our analysis in the context of Peru, a developing country with a high prevalence of maternal depression.

To shed light on the issue, we conduct our analysis using information from the Young Lives (YL) survey in Peru, a rich longitudinal household survey that follows households with at least one child born between 2001 and 2002 (index child). For our analysis, we use YL’s first three rounds: a baseline round in 2002, when the index child was 6-20 months old, the first follow-up when the child was 4 - 6 years old, and the last round in 2009-2010, when the index child was 7 - 8 years of age. The YL also has the novelty that includes questions related to maternal mental health and child vocabulary, along with a wealth of information on child, family and community characteristics.

Inspired by the literature that links the exposure to shocks during pregnancy, maternal mental health and children’s outcomes, we employ an instrumental variables (IV) approach as estimation strategy. This approach helps us to address potential simultaneity bias in the estimation of the effect of maternal depression on child’s vocabulary. We exploit the richness and longitudinal nature of the data to better capture the dynamic nature of maternal mental health on child cognitive development at 5 and 8 years of age. In particular, we instrument maternal depression with experience of a shock (loss of crop or livestock) at baseline (when the child was in utero or recently born). Given the richness of the YL data, there are several potential exogenous shocks that can serve as instruments for maternal depression or additional controls that can be included in our model. Since the IV estimator is biased when the number of instruments is large, we use IV-LASSO to select the instruments that we include in our model. This method helps to also avoid overfitting the model or omitted variables bias by selecting the vector of control variables that fits better to the data. We also strengthen the robustness of the analysis by considering variations of the indicator used for maternal depression, exploring heterogeneous effects of household characteristics, such as mother’s education, household wealth, and the presence of a male partner and some of his characteristics.

3
# WPS4116

International Remittances and the Household: Analysis and Review of Global Evidence Richard H. Adams, Jr. Development Research Group (DECRG) MSN MC4-402 World Bank 1818 H Street, NW Washington, DC 20433 Phone: 202-473-9037 E-Mail: <u>radams@worldbank.org</u>

## **Abstract**

This paper examines the economic impact of international remittances on countries and households in the developing world. To analyze the country-level impact of remittances, the paper estimates an econometric model based on a new data set of 115 developing countries. Results suggest that countries located close to a major remittance-sending region (like the United States, OECD-Europe) are more likely to receive international remittances, and that while the level of poverty in a country has no statistical effect on the amount of remittances received, for those countries which are fortunate enough to receive remittances these resource flows do tend to reduce the level and depth of poverty. At the household level, a review of findings from recent research suggests that households receiving international remittances spend less at the margin on consumption goods – like food – and more on investment goods – like education and housing. Households receiving international remittances also tend to invest more in entrepreneurial activities.

## World Bank Policy Research Working Paper 4116, February 2007

_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. Policy Research Working Papers are available online at http://econ.worldbank.org._

## -- Forthcoming in Journal of African Economies, December 2006
**Table 3.7: New connections in a three year period before and after the Peace Accord**

<u>(Number of new connections)</u>

||Electricity|Piped<br>water<sup>♠</sup>|Sanitation<sup>♦</sup>|
|---|---|---|---|
|**National**||||
|1993-1996|208,518<br>|240,069<br>|281,106<br>|
|1997-2000<br>|329,734<sup>***</sup>|352,336<sup>***</sup>|350,418<sup>**</sup>|
|% change|58%|47%|25%|
|**Urban**||||
|1993-1996|92,823|109,453|134,692|
|1997-2000|105,009|128,593|109,792|
|% change|13%|17%|-18%|
|**Rural**||||
|1993-1996|115,695<br>|130,616<br>|146,414<br>|
|1997-2000|224,725<sup>***</sup>|223,743<sup>***</sup>|240,626<sup>***</sup>|
|% change|94%|71%|64%|
|**Extreme poor**||||
|1993-1996|13,662<br>|24,253<br>|27,979<br>|
|1997-2000|33,135<sup>***</sup>|43,091<sup>**</sup>|38,674<sup>*</sup>|
|% change|143%|78%|38%|
|**All Poor**||||
|1993-1996|95,296<br>|108,754<br>|132,815<br>|
|1997-2000|180,842<sup>***</sup>|184,682<sup>***</sup>|176,028<sup>**</sup>|
|% change|90%|70%|33%|
|**Non-poor**||||
|1993-1996|113,222<br>|131,315<br>|148,255|
|1997-2000|148,892<sup>*</sup>|167,654<sup>*</sup>|174,390|
|% change|32%|28%|18%|
|**Indigenous**||||
|1993-1996|87,785<br>|105,547<br>|114,052|
|1997-2000|142,414<sup>***</sup>|153,789<sup>***</sup>|137,572|
|% change|62%|46%|21%|
|**Non-indigenous**||||
|1993-1996|117,976<br>|133,965<br>|166,007<br>|
|1997-2000|186,392<sup>***</sup>|195,611<sup>***</sup>|209,926<sup>*</sup>|
|%change|58%|46%|26%|

_Notes_ : Based on household recall of the year in which they were first connected The null hypothesis of equality of the number of users before and after Peace Accord is rejected at:<sup>***</sup> 99% level.<sup>**</sup> 95% level,<sup>*</sup> 90% level.

- ♠ Piped water in dwelling or yard. ♦ Includes toilets and latrines.

_Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística - Guatemala 19
Not only the fact of importing intermediate inputs can help exporters’ performance but it is also important to investigate whether the amount imported matters. Table 2 also presents the results from estimating a variant of Eq. (1) where instead of the exporter-importer status dummy we include the logarithm of the value of imported inputs (plus one unit so as to keep exportersonly whose imports are zero in the regressions). The estimates show evidence of stronger export performance in terms of higher total exports, faster export growth, larger numbers of destination countries, and higher relative unit values for exporting firms that import relatively more intermediate inputs.<sup>5</sup>

**-** **<u>Table 2. Baseline Export Performance Premia for Exporter Importers</u>**

||**Log (Export**<br>**Value)**<br>**(1)**|**Export**<br>**Growth**<br>**(2)**|**Nb. of**<br>**Destinat.**<br>**(3)**|**Avg. Relative**<br>**Unit Values**<br>**of Exports**<br>**(4)**|**Log (Export**<br>**Value)**<br>**(5)**|**Export**<br>**Growth**<br>**(6)**|**Nb. of**<br>**Destinat.**<br>**(7)**|**Avg. Relative**<br>**Unit Values**<br>**of Exports**<br>**(8)**|
|---|---|---|---|---|---|---|---|---|
|||||**Al**|**l**||||
|**Dummy for Exporter‐Importers**|0.440***<br>(0.023)|0.154***<br>(0.026)|0.718***<br>(0.062)|0.161***<br>(0.022)|||||
|**Log (Import Value +1)**|||||0.054***|0.022***|0.098***|0.021***|
||||||(0.002)|(0.003)|(0.007)|(0.002)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Firm Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|53,881|33,663|53,881|53,881|53,881|33,663|53,881|53,881|
|**R‐squared**|0.869|0.309|0.851|0.820|0.870|0.310|0.852|0.821|
|||||**Agribu**|**siness**||||
|**Dummy for Exporter‐Importers**|0.495***<br>(0.062)|0.056<br>(0.063)|0.776***<br>(0.172)|0.144***<br>(0.035)|||||
|**Log (Import Value +1)**|||||0.053***|0.005|0.106***|0.018***|
||||||(0.006)|(0.006)|(0.018)|(0.004)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Firm Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|4,277|2,909|4,277|4,277|4,277|2,909|4,277|4,277|
|**R‐squared**|0.841|0.420|0.808|0.782|0.842|0.420|0.810|0.783|
|||||**Appa**|**rel**||||
|**Dummy for Exporter‐Importers**|0.503***<br>(0.059)|0.195***<br>(0.065)|0.943***<br>(0.142)|0.312***<br>(0.060)|||||
|**Log (Import value +1)**|||||0.066***|0.026***|0.128***|0.044***|
||||||(0.006)|(0.007)|(0.017)|(0.007)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Firm Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|10,818|6,717|10,818|10,818|10,818|6,717|10,818|10,818|
|**R‐squared**|0.857|0.420|0.828|0.934|0.859|0.421|0.830|0.934|

Notes: Robust standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

One could be concerned that the premia in export performance in Table 2 reflect simply the size of the exporting firm and the conjecture would be that larger firms simply perform better,

> 5 Again the exception to the pattern is the insignificant export growth premia for exporter-importers in the agribusiness sector.

12
<!-- Start of picture text --> Cox stratified survivor estimate<br>Covariates at the mean, with 95% Cl<br><!-- End of picture text --> <!-- Start of picture text --> 0 10 20 30 40<br>Marriage duration in years<br>Stratification var: Husband's father marital status<br>———- Monogamous ———_ Polygamous<br>note: loglog based Cl<br><!-- End of picture text -->
The next section lays out the data we use and the methods by which they were collected. After exploring the characteristics of female‐owned firms in our study sample, and comparing them with male‐ owned firms in terms of productivity and profits (section three), we move on to examine the relative roles of the three groups of factors mentioned above in determining which women cross over and which do not (section four). Once in business, challenges such as access to finance and lack of networks and sometimes a hostile environment are the most prevalent ones, together with limited technical and managerial skills – as detailed in section five. Section six draws on the findings to identify some areas for testing future policy interventions aiming to promote female participation in male‐dominated sectors of the economy, as well as to support women who wish to cross over.

# **Section II: Data and methods**

This paper draws on baseline data collected in the first semester of 2011 from a sample of entrepreneurs in urban Uganda from within and just outside Kampala, which are mostly part of the Katwe Small Scale Industry Association (KASSIDA). The data were collected as part of an evaluation of the impact of a skills and managerial training program on business performance. The baseline survey included a total sample of 326 women and 409 men, of which only 30 women (crossovers) owned businesses in male‐ dominated sectors. Table 1 shows the distribution of women and men across different industries and indicates the gender concentration, with crossover sectors identified in bold.<sup>7</sup> We use this data set (hereafter the KASSIDA data set), which has detailed information on assets, inputs, other costs, sales, and profits, to compare the performance of crossovers with both other women‐owned businesses in traditional female sectors and male‐owned businesses in the same sectors.

> 7 The sample size in table 1 is larger than in the analysis that follows as we do not observe all variables for all firms listed here.

5
in-time supply, which favors the trade sector and could lead to a positive marginal effect of net exports (h') over the medium-term, though in the short run the marginal effect (h’) is likely to be negative due to leakage of part of the fiscal stimulus to foreign markets through imports and decline in exports resulting from the global recession. The contribution of net exports to China's GDP growth in 2009 was dominated by the effect of the stimulus and amounted -3.5 percentage points (a substantial positive growth impulse for the rest of the world) compared with +2.6 percentage points in 2007 and +0.9 percentage point in 2008 (IMF 2012e).

Nevertheless, the overall dynamics of policy reforms of the last few years favor the export sector which could determine the medium- to longer-term dynamics. More specifically, the relocation of the labor-intensive tradable sector of East Asian economies to China and high corporate saving rates in China as a result of its dual-track approach to reform resulted in a significant increase in China’s trade surplus in general and with the United States in particular (Lin and Treichel 2012). China’s comparative advantages in the international market no longer narrowly concentrate on low-end labor-intensive products. China has a growing share of the market for some technology- and capital-intensive products. In 2009, for example, its trade with the United States included both high-value and low-value items (Kemp 2010). The largest trade surpluses in its total exports of $295 billion were in computer and electronic products ($95 billion), miscellaneous manufactures ($33 billion), apparel ($26 billion), leather ($18 billion), and electrical equipment and appliances ($17 billion). High-value export items include products produced and assembled in China by Western companies as joint ventures with local firms, as well as products designed and built by Chinese companies (for example, Haier in appliances and Lenovo in computers).

# **_<mark>Strong Countercyclical Nature of Subnational Government Expenditure</mark>_**

The strong countercyclical subnational government spending in China resulted in exceptionally large fiscal multipliers. This pattern of spending is in contrast to procyclical government expenditures at the subnational levels in most countries.

As in many countries, subnational governments in China are the major public sector employers, responsible for the delivery of most public services. More than 80 percent of total government expenditure is carried out by subnational governments. The countercyclicality of local 13