rafmacalaba/datause-encoder-data huggingface dataset:
such as unionization and labor laws that offer safeguards to (permanent) workers against indiscriminate layoffs. As a result, employers may resort to measures such as wage renegotiation or reduction in the number of working hours to be able to manage the negative impact of unreliable electricity provision on their activities. Substantiating these arguments requires granular data on the average working hours by the workers of firms in our sample, as well as data on the workers’ wages. However, data on these measures are unavailable in the WBES. Nonetheless, I use data on total labor cost and labor cost per worker as proxies for wage and explore its relationship with outages experienced by firms.

Interestingly, the effects on total labor cost and labor cost per worker are negative and statistically significant. A percent increase in the number (hours) of outages experienced by firms is associated with a 1.1% (0.6%) reduction in labor cost (column 10 (12)). Similarly, the labor cost per worker reduces by 1.15% (column 14) and 0.64% (columns 16) respectively for every percent increase in the number and hours of outages experienced by a firm: perhaps an indication of lowering wages in response to the negative effects of outages on productivity.

I also explore how the effects of outages on firm performance vary across firm size,<sup>50</sup> and energy intensity. In Table A13 for instance, the results suggest that the negative effects of outages on firm performance pertain largely to small firms. The effects on medium and large firms are not statistically significant albeit negative. These results are perhaps indicative of the ability of large and medium-sized firms to invest in abatement technologies such as the use of generators or reliance on captive power generations to mitigate the unreliable grid electricity supply on their operations. Similarly, the results in Tables A14 and A15 also suggest that the outage impacts on firm performance and labor demand is largely concentrated among firms in high energy-intensive sectors. This is plausibly due to the critical role of electricity in the daily operations of firms in such sectors.

> 50Definition of firms are as follows: small firms (¡20 employees), medium (20-99 employees), and large (100 and above employees)

35
- **68** -

The level of aggregation has Lmportant implications for the evaluation of the econometric models used. In an aggregate time series (for a single industry in a particular country) the data is all likely to be drawn from rather homogenous capital stock i.e. the composition of the capital wll not change rapidly. The parameters of the model are thus likely to be very similar at all data points. As opposed to this the model will typically pick up short run responses to fuel price changes unless some explicit dynamic adjustment is included. In a cross section there is likely to be a wider spread of technology and hence a greater chance that the true **parameters** of the cost function vary between observations. If the observations are for countries (each aggregating over plants and industries) the averaging process may reduce this difference. At the same time differences in response between countries will be associated with persistent and long term differences in relative prices (due to local market conditions) so that the model would be more likely to capture long run substitution. Panel data (as used by Orlffin) will zombin **the** features of **time** series and cross section models. For those **cross** section **studies where** the unit of observation is a plant there can be very sharp **differences in** technology associated with the age of the capital stock. However tlv potentlal rlchness of such a sample might allow the use of a "vintage approach to the production or cost function, which would essentially attempt to allow for the **age** of capital. The issue of separability would be crucial ln determining whether separate cost share functions could be estimated for each vintage. Problims of Usain EstLwates of lnter-Fuel BWbstLtutsbliLt For the policy purposes considered by thli paper the key Lssue is the response of fuel use to a tax or subsidy induced price change. In order to encourage the use of gas as opposed to coal (say) in thermal power generation, a government could impose a tax on coal or give a subsidy to gas. This would be expected to change the fuel mix and hence the pollution characteristice of the power sector.
recently been shown to predict childhood height (Spears, 2012c) and childhood cognitive achievement (Spears and Lamba, 2012).<sup>8</sup> We take this historical data from the Indian Census. Census data are available only at 10 year intervals (1971, 1981, 1991), so we interpolate between Census rounds in order to match the adults we study with the IMR and open defecation rate in their district, in the year of their birth. Our preferred interpolation between census rounds is linear, but results are robust to using log-linear interpolation, or instrumenting for linear interpolation with log-linear interpolation to reduce measurement error, as we will show. We are constrained to use a smaller, younger sample when studying the effect of early-life sanitation because sanitation data are not available in the 1971 census.

Table 1 presents summary statistics for the IHDS and census data that we use. Our average workers are poor – earning about 10 rupees an hour – and were exposed to threatening early-life disease environments, with infant mortality of 113 deaths per 1,000 births and only about 17 percent sanitation coverage, on average. Because district-level census records are not available for IMR in the 1961 census, the workers we study are relatively young, with an average age of about 26 years.

# **3 Empirical Results**

Figure 2 verifies that men who were born in district-years with worse infant mortality and sanitation earned lower wages as adults in the IHDS in 2005. Panels A and B plot locally weighted kernel regressions depicting a clear downward trend. Of course, wages are different, on average, in different places and for different age groups, for reasons other than effects of disease. Therefore, Panels C and D plot residuals of wages against residuals of our disease measures, in both cases after controlling for year-of-birth and state-times-urban fixed effects. The visible downward trend remains, in an initial suggestion of the effect we will estimate.

> 8Although latrines are a normal good, more likely to be owned by richer people, we are studying districtlevel sanitation rates, which are not well explained by wealth in South Asia. Even the richest people in India live near people who defecate in the open (Spears, 2013), which would have been even more the case in the 1970s and 1980s. Moreover, open defecation is not identical to failure to own a latrine: many, many people in rural India choose to defecate in the open despite owning a latrine, perhaps provided by the government or bought for the use of some members of the household, and certainly despite being able to afford one (Coffey _et al_ , 2014). Exposure to open defecation in South Asia is not a mere proxy for socioeconomic status.

8
# Improving the Quality of Survey Estimates from Longitudinal Studies:

# An Application to LSMS Panel Surveys

Piero Falorsi<sup>1</sup> , Paolo Righi<sup>2</sup> , Giulia Ponzini<sup>3</sup>

## JEL codes: C81, C83, C87, Y80

Keywords: Panel surveys, attrition, tracking, Generalized Weight Share Method, survey methods.

> 1International Consultant, Development Economics Data Group, World Bank, 1,2 Italian Insititute of Statistics (ISTAT), 3 Development Economics Data Group, World Bank;

This paper was produced with financial support from the 50x2030 Initiative to Close the Agricultural Data Gap, a multipartner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030. For more information on the Initiative, visit 50x2030.org.
> - 19-

Table 1--Nutritional status of preschool children (0-6 years) (N-1078)

||Mean Weight<br>for-Height<br>(Percent<br>of<br>Standard)|- Percent<br>Below<br> 80%oWeight-fo<br> <br>Height<br>|Mean Height-<br>r for-Age<br>(Percent<br>of<br>Standard)|<br>Percent<br>Below<br>90% Height-<br> <br>for-Age|
|---|---|---|---|---|
|Punjab|||||
|Attock<br>Faisalab|93.8<br>ad 95.5|5.9<br>4.8|91.6<br>93.0|60.2<br>31.6|
|Sind|||||
|Badin|95.8|8.7|89.2|59.5|
|NWFP|||||
|Dir|97.9|6.2|90.9|44.3|
|Baluchista|n||||
|Kalat|99.3|3.4|85.9|80.5|
|All areas|97.3|5.8|89.9|51.3|

Source: IFPRI Rural Survey of Pakistan (Round 3), 1986/87.
Policy Research Working Paper 10686

# **Abstract**

This paper reviews the main findings from 113 studies produced between 1990 and 2023, focusing on the impact of migration on various child groups affected through the migration path, including left-behind, migrant (voluntary and forced), and native children. The findings reveal that migration influences children’s outcomes in complex and context-dependent ways, and it interacts dramatically with household demographics and public policies. Key results include the following: (i) left-behind children benefit from remittances but experience dramatic declines in their cognitive and non-cognitive development due to parental absence; (ii) immigrant children generally fare better than those in their origin countries but still underperform compared to native children in host countries; and (iii) the impacts of migration on native children largely depend on the adjustment of public service supply to meet increased demand. In cases where education services expand to meet rising demand, the effect on native children can be minimal or even positive. This paper emphasizes the need for more experimental or quasi-experimental research to examine the effectiveness of programs that support migrant and minor host children, and it calls for longitudinal data collection to better understand the challenges and needs of migrant children, particularly in developing countries.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at sandrarozo@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
29 that party characteristics themselves account for the differences in political budget cycles between younger and older democracies.

In addition, country income itself may be a source of political budget cycles. Since richer democracies exhibit older parties, it could be the component of party age that is correlated with country income that drives the significant interaction with the election dummy. At the end of this section, we report evidence that this is not the case: poor countries do not exhibit larger political budget cycles, and the effect of parties on political budget cycles is unaffected by the addition of an interaction term with income and elections.

To examine the effect of democratic age, we extend Brender and Drazen’s dummy for new democracies across time. We then construct a second interaction term consisting of this New Democracy dummy variable and elections. We then re-estimate our main specification, column four in Table 1, additionally controlling for this new interaction term and the new democracy dummy. This allows us to assess whether the party effects observed earlier are the spurious result of correlation with the age of democracy. If the earlier party age results in Table 1 are, in fact, the spurious result of the correlation of party age with democratic experience, then the party age-election result should be insignificant in this new regression. If, on the other hand, differences in political party characteristics account for the larger political budget cycles in younger relative to older democracies, then the estimated magnitude of the age of democracy interaction should decline. The results reported in Table 4 support the latter interpretation.

The first two columns use the original database from Brender and Drazen (2005) and their New Democracy dummy variable. Column one excludes the party variables and shows, as in their results, that political budget cycles are larger in new democracies. The second column adds the party age variables. The party age variable remains significant and of a similar magnitude as in Table 1. In contrast, the new democracy interaction falls in magnitude and loses significance. Columns 3 and 4
Table A5: Heterogeneity in Effects of ACASI on Placebo Questions (Liberia)

|(1)|(2)|(3)|(4)|
|---|---|---|---|
|Farm work<br>(past year)|Market visit<br>(past week)|Maize/Rice<br>(next week)|Meat<br>(next week)|
|**Panel A. Primary education completion**||||
|SI _×_ Primary Educ (_β_)<br>0.07|0.00|-0.01|-0.06|
|(0.05)<br>|(0.05)<br>|(0.03)<br>|(0.06)|
|SI _×_ No Primary Educ (_γ_)<br>-0.00|0.11***|-0.04***|0.01|
|(0.02)|(0.03)|(0.01)|(0.03)|
|Primary Educ<br>-0.11*|0.01|-0.03|-0.06|
|(0.06)|(0.07)|(0.03)|(0.07)|
|FTFI _×_ No Primary Educ mean<br>0.82|0.66|0.98|0.68|
|_p_-value (_β_ =_γ_)<br>0.159|0.097|0.194|0.337|
|Observations<br>1,259|1,260|1,226|1,101|
|**Panel B. Access to mobile phone**||||
|SI _×_ Mobile (_β_)<br>0.08**|0.05|-0.03*|0.02|
|(0.03)|(0.04)|(0.02)|(0.04)|
|SI _×_ No Mobile (_γ_)<br>-0.03|0.11***|-0.04***|-0.03|
|(0.03)|(0.03)|(0.02)|(0.04)|
|Mobile<br>-0.07**|0.09**|0.00|-0.00|
|(0.03)|(0.04)|(0.01)|(0.04)|
|FTFI _×_ No Mobile mean<br>0.83|0.63|0.98|0.69|
|_p_-value (_β_ =_γ_)<br>0.017|0.293|0.677|0.313|
|Observations<br>1,259|1,260|1,226|1,101|
|**Panel C. Able to read/write in English**||||
|SI _×_ English (_β_)<br>0.02|0.04|-0.02|0.01|
|(0.04)|(0.05)|(0.02)|(0.05)|
|SI _×_ No English (_γ_)<br>0.01|0.11***|-0.04***|-0.02|
|(0.03)|(0.03)|(0.01)|(0.03)|
|English<br>0.00|0.03|-0.01|0.01|
|(0.04)|(0.05)|(0.02)|(0.05)|
|FTFI _×_ No English mean<br>0.81|0.66|0.98|0.69|
|_p_-value (_β_ =_γ_)<br>0.861|0.203|0.512|0.597|
|<br>Observations<br>1,259|1,260|1,226|1,101|
|**Panel D. Age**||||
|SI _×_ Below-median Age (_β_)<br>-0.01|0.09**|-0.02|0.01|
|(0.03)|(0.04)|(0.02)|(0.04)|
|SI _×_ Above-median Age (_γ_)<br>0.03|0.08**|-0.05***|-0.02|
|(0.03)|(0.04)|(0.02)|(0.04)|
|Below-median Age<br>0.04|-0.03|-0.01|0.04|
|(0.04)|(0.05)|(0.02)|(0.05)|
|FTFI _×_ Above-median Age mean<br>0.82|0.68|0.98|0.70|
|_p_-value (_β_ =_γ_)<br>0.399|0.816|0.277|0.607|
|Observations<br>1,259|1,260|1,226|1,101|

Note: Regressions include individual controls (including all variables in Table 1). Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

28
misrepresentations, particularly with regard to destination countries (destinations are recorded only by two-letter codes, which leads to possible confusions between CN-China, CHSwitzerland and SZ-Swaziland etc.). Last but not least, non-revenue earning export transactions may not be monitored by customs authorities with the same level of diligence as revenue-earning imports.

In order to allay some of these concerns, the data cleaning process has dealt with the following issues: irregularities in product codes (those missing have been excluded and the different HS revisions have been harmonized), destinations (we have not changed designations that we suspect to suffer from an improper coding of country names, but we have excluded transactions with non-standard codes), firm licenses (those missing or having the generic codes have been excluded), and USD transaction values (those having a zero, missing or showing abnormal exchange rates to the LAK value have been excluded, but several transactions that were obviously off by a factor of 1,000 were corrected).

# **3 DESCRIPTIVE EXPORT CHARACTERISTICS**

The data were first scrutinized for various effects and trends related to exporter behavior that have been identified elsewhere in the literature. This analysis provides a better understanding of the structure and composition of Lao exports. To achieve this, we require the use of some concepts and instruments developed elsewhere in the literature.. After a brief introduction to these tools, we will then use them to describe some aspects of the data and we will conclude this section with a descriptive look at the survival of exporters.

## **_3.1 Concepts and Instruments_**

One way to look at the export activity of firms at an aggregate level is to gauge the “quality” of their products. Several indicators or proxies have been put forth that do this, but of particular interest and popularity have been the concepts of PRODY and PATH, first proposed by Hausmann, Hwang and Rodrik (2005) in their seminal paper “What you export matters”, and further developed in Hausmann and Klinger (2007). As part of their analysis, Hausmann, Hwang and Rodrik define the related concepts of PRODY (a measure of the “productivity” or “income potential” of a given product), EXPY (an aggregate measure of the productivity or income potential of a country„s overall export basket, essentially the weighted average of PRODYs), and PATH (a measure of the scope for future diversification from a product, based on the minimum of probabilities that a country exporting one product will export another). The authors use these tools to construct a framework through which a country„s current and potential export performance can be assessed.<sup>2</sup> In this paper we focus on PRODY and PATH, but we will also make use of the concept of revealed comparative advantage (RCA), defined as:

> 2 See Record and Nghardsaysone (2009) for a fuller application of the Hausmann-Hwang-Rodrik methodology to the Lao data.

_Exporting from a small landlocked economy_ _5_
# **Migration and Education Decisions in a Dynamic General Equilibrium Framework**

# **I. Introduction**

Following a first wave of interest in the 1970s,<sup>1</sup> the literature on the impact of migration on sending countries was recently revived with the rise of international migration – and related financial flows – in the 1990s of skilled workers in particular<sup>2</sup> . A first branch of the literature<sup>3</sup> , using micro data (such as household budget surveys), generally concludes that there is a statistically significant impact of migration and remittances on economic welfare, investment and education. A second branch<sup>4</sup> , using global general equilibrium models, underlines the importance of remittances in the newly emerging global finance architecture, and suggests potentially large welfare impacts of lower restrictions on migration flows. A third branch, though, estimating reduced forms on cross-country samples, is more nuanced on the net impact of remittances on economic growth<sup>5</sup> .

The empirical literature is nevertheless less abundant when it comes to the analysis of migration and brain drain issues at the country-wide level, where policy decisions are most generally made. Indeed, micro-based approaches cannot capture the macro-economic impact of migration decisions, nor can they consistently assess the interdependency of important macro-economic phenomena influenced by migration and remittances<sup>6</sup> . For instance, while migration possibilities and remittances might affect education decisions at the micro level, they can also simultaneously affect relative prices (e.g, the skill premium, the cost of education, or the real exchange rate) – hence the environment in which micro-economic decisions are being made. Furthermore, microbased approaches are generally static (often in the absence of panel data), limiting the possibility to assess the long term impact of investment and migration decisions on long term macro-economic or demographic outcomes, which will in turn affect the migration and education decisions of next generations. At the other extreme of the spectrum, global

> 1 Bhagwati and Hamada (1974).

> 2 Docquier and Marfouk (2006).

> 3 See Ozzden and Schiff (2006) for a compilation of contributions on these topics.

> 4 World Bank (2006).

> 5 See Chami et al. (2008) and Beine et al. (2001).

> 6 Throughout this note, remittances are defined as the sum of transfers and capital flows influenced by emigration flows.

2
**Figure 5. Population out of the labor force, by** **<u>gender, age, and locality</u>**

<!-- Start of picture text --> 100%<br><!-- End of picture text --> <!-- Start of picture text --> 80%<br>60%<br>40%<br>20%<br>0%<br>Male Female Male Female Male Female Male Female Male Female Male Female Overall<br>15-24 25-34 35-44 45-54 55-64 Share in LF Population<br>Sudan Urban Rural<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15. Note: LF means “Labor Force” Being a housewife and full-time student are the main reasons why women and youth are out of the labor force, respectively. More than two-thirds (70 percent) of women reported being out of the labor force because they were either a housewife or homemaker , compared to only 2 percent for men. Among youth, 64 percent reported the reason as being full-time students. Nearly half of the household heads were in the labor force, but only 52 percent were employed (Figure 6).

**Figure 6. Reasons for being out of the labor force**

<!-- Start of picture text --> 100%<br>Income recipient<br>80%<br>24%<br>2% Pensioner/retired<br>36.6%<br>60% 70% Disabled/too sick<br>40% 79% 93% 89% Too old<br>73%<br>64%<br>60%<br>48.5% No hope to find job<br>20%<br>21% Full time<br>9%<br>0% homemaker/housewife<br>Male Female 15-24 25-34 35-44 45-54 55-64<br>Overall Gender Age Group<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15.

The overall unemployment rate declined from 13 percent in 2009 to 11 percent in 2014, with variation by gender and location. The decrease in unemployment among the poorest quintile and rural areas 12