rafmacalaba/datause-encoder-data huggingface dataset:
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# **1 Introduction**

In economics, health sciences, and other disciplines, data on the outcome variable of interest is often costly to collect. The measurement of poverty for instance relies on household consumption expenditure data. Collecting this data involves long questionnaires administered over an extended period of time which substantially adds to the data collection costs. Individual-level data collection that involves physical examinations also tends to be costly. This has implications for the sample size and the frequency with which the data is collected. In many developing countries for example, particularly in Sub-Saharan Africa, estimates of poverty, malnutrition and health are obtained highly irregularly and as a result are often outdated.<sup>1</sup> The demand for bigger and more frequent data has motivated researchers to explore ways to substitute predicted data for real data. Consider the application to poverty measurement by Douidich et al. (2015) where household consumption poverty is predicted into annual labor force surveys in order to increase the frequency of poverty estimates from once every 7 years to every year.<sup>2</sup> Similar applications can be found in Stifel and Christiaensen (2007) and Christiaensen et al. (2012). Prediction methods have also been used to dramatically expand the sample size of poverty and health data. A prominent example of this is the small area estimation of welfare where the outcome variable of interest (i.e. consumption poverty) is predicted into a population census which covers (almost) all members of the population unlike typical household surveys. This allows researchers to obtain estimates of poverty at a highly disaggregated level such as districts, communities, and towns. These small-area estimates are often plotted in the form of a map known as a poverty map. The small-area estimation, which provides the methodological foundations for poverty mapping, was pioneered by Elbers et al. (2003) and extended by e.g. Tarozzi and Deaton (2009), Tarozzi (2011) and Elbers and van der Weide (2014). Fujii (2010) has modified the approach to obtain small areas estimates of the prevalence of

> 1For example, across the 26 low-income countries in Sub-Saharan Africa over the period between 1993 and 2012, the national poverty rate and prevalence of stunting for children under five are on average reported only once every five years and once every ten years in the World Development Indicators.

> 2This approach could of course be expanded to other outcome variables of interest (such as health outcomes), while surveys other than labor force surveys could be considered to further extend the frequency of estimates. The same approach could also be adopted to construct a measure of consumption poverty that alleviate concerns of comparability over time when the original consumption data is deemed incomparable due to changes in the questionnaire. See for example the debate on the comparability of poverty estimates in India documented in e.g. Deaton (2003), Deaton and Dr`eze (2002), Kijima and Lanjouw (2005), Tarozzi (2007), and Deaton (2005).

2
sample of 42,527 women aged 15-54 living in 29,498 households located in 612 municipalities with populations less than 100,000 across all 32 states.

The ENADID enables us to construct two indicators of child health outcomes, which are infant mortality and birth weight. Infant mortality is defined in the standard way as a live birth dying during the first year of life. Mothers are asked more detailed information about their last two births since January 1<sup>st</sup> 1994, including the birth weight in kilograms of the baby. The initial sample is first used to examine whether women in migrant households are any more or less likely to have had a child since 1 January 1994 than women in non-migrant households. Finding no difference, we then condition on fertility and study infant mortality and birthweight for babies born after this date for the remainder of our analysis. After dropping missing values, this results in a main sample of 16,593 children born to 12,767 mothers in 12,396 households in 601 municipalities across all 32 states.

Data on birthweight are only available for 12,974 children due to both non-response and to this data only being collected for the last two children born since 1 January 1994 in each household. Furthermore, there appears to be some misreporting at the tails, with reported birthweights ranging from 0.5 kilograms to 6.5 kilograms. We trim the top and bottom 1% of birthweight observations to reduce this measurement error, leaving a sample of 12,117 children for our birthweight analysis. Non-reporting of birthweight is more common for less educated mothers, and conditional on education, slightly more prevalent in households without a migrant compared to households with a migrant. We will examine the sensitivity of our results to these reporting differences.

We classify households according to whether or not they had at least one member aged 15 and over who had migrated to the United States prior to 1 January 1994. We then are able to look at the impact of previous migrant experience on subsequent child health measures. This timing allows us to avoid concerns that child health outcomes and the migration decision are both the result of contemporaneous shocks, such as poor weather or disease outbreaks. The ENADID survey asks whether each member of the household has ever been to the United States in search of work, and whether they have ever lived in the U.S. These questions are asked of all household

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Crucially however, these devolved staff remain provincial employees and therefore, despite these formal changes the nazim has limited administrative control over the local bureaucracy. The officers in the local government — the DCOs, EDOs, and junior officers — continue to be formally posted and managed by the province, and this administrative control permeates downwards to the frontline service providers.

# **IV. Political Accountability**

Citizens can hold policy-makers accountable through two channels: first, through actively contacting policy-makers, either individually or collectively in the form of interest groups, with specific demands, sometimes backed up with financial contributions; and second, through the ballot box by voting out officials who were unable to satisfy these demands. How has devolution impacted these two channels of enforcing accountability in Pakistan?

# _Citizen contacting_

Any visit to a local government office will reveal that the degree of access of citizens to their representatives has increased considerably after devolution. This increase was in many ways inevitable – with hundreds of union councilors in all parts of a district, citizens now have multiple channels for conveying their problems to the government. Anecdotal evidence suggests that on average 50-60 people meet with councilors on a daily basis, and if one multiplies this by the number of councilors it is clear that the quantum of information flowing upwards from citizens to elected representatives has increased by several orders of magnitude as compared to the predevolution set-up (World Bank, 2004).

Figure 2 provides some survey-based confirmation of this perception. In 2005, approximately 25% of households reported having some contact with their union councilors over the preceding twelve months (left panel), up from 22% in 2001. Over 60% of the households stated that they would approach a union councilor or nazim for their problems, as compared to only 10% who said that they would approach members of the provincial or national assembly (MPA or MNA), again reflective of the increase in accessibility of policy-makers after devolution.

**Figure 2: Citizen contacting of union councilors**

<!-- Start of picture text --> Households reporting contact with union councillors (percent) Who households would contact, 2004 (percentage)<br>35 100%<br>30 90% MPA/MNA<br>80%<br>25 70% Community members<br>20 60%<br>50%<br>15<br>40%<br>10 30% Union nazim or councillor<br>5 20%<br>0 10%<br>Punjab Sindh NWFP Balochistan Pakistan 0%<br>2002 2004 1<br> Source : DTCE (2005)<br><!-- End of picture text --> The survey also revealed that most of the reasons for contact with union councilors cited by male and female respondents alike, related to personal issues, such as financial support, the issuance of identity cards, a police problem, or some form of dispute. Those who contacted for reasons of service delivery did so regarding mainly about water, roads, and electricity. Less than 2 percent 6
Table A.4: The choice of how much to export: before and after the crisis

||OLS|OLS|OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|---|---|
|Crisis period?|16.129**|-17.683|14.907|-23.251|15.714**|-40.104|25.639|
|(1=Yes)|(6.648)|(22.975)|(9.398)|(14.931)|(6.845)|(34.221)|(17.344)|
|Subj. fnancial||0.300|-0.105|||||
|constraint||(3.789)|(1.144)|||||
|Subj. fnancial||-1.455|0.376|||||
|constraint _×_ Crisis?||(4.008)|(1.605)|||||
|Unable to obtain a||||-6.569|-1.985|-73.944**|12.357|
|loan? (Yes=1)||||(6.475)|(1.931)|(35.157)|(10.117)|
|Unable to obtain a||||2.477|0.667|56.265|-13.257|
|loan? (Yes=1) _×_ Crisis?||||(6.912)|(2.887)|(42.760)|(23.487)|
|Foreign shock|0.195***||0.195***||0.195***||0.196***|
|(%∆GDP _×_ %Export)|(0.011)||(0.011)||(0.011)||(0.015)|
|Foreign shock|-0.087***||-0.086***||-0.086***||-0.087***|
|(%∆GDP _×_ %Export) _×_|(0.025)||(0.025)||(0.025)||(0.027)|
|Crisis?||||||||
|Domestic shock|-0.037***||-0.037***||-0.037***||-0.039***|
|(%∆GDP _×_ (100-%Export))|(0.002)||(0.002)||(0.002)||(0.003)|
|Domestic shock|-0.022***||-0.022***||-0.021***||-0.019**|
|%∆GDP) _×_ Crisis?|(0.005)||(0.005)||(0.005)||(0.009)|
|Capacity|0.058**|0.116*|0.058**|0.106*|0.056**|0.002|0.064**|
||(0.024)|(0.068)|(0.023)|(0.063)|(0.023)|(0.102)|(0.031)|
|Capacity _×_|0.025|-0.029|0.027|-0.025|0.025|0.052|0.018|
|Crisis?|(0.056)|(0.089)|(0.056)|(0.085)|(0.055)|(0.128)|(0.074)|
|Log(Labor|-2.686***|-3.442***|-2.683***|-3.546***|-2.715***|-3.947***|-2.781***|
|Productivity)|(0.681)|(0.788)|(0.682)|(0.782)|(0.679)|(0.982)|(0.787)|
|Firm Age|-0.125*|-0.258***|-0.125*|-0.252***|-0.124*|-0.226***|-0.125*|
||(0.064)|(0.066)|(0.064)|(0.065)|(0.064)|(0.074)|(0.071)|
|Small Firm? (Yes=1)|-8.815***|-10.603***|-8.838***|-10.983***|-8.906***|-11.664***|-8.573***|
||(2.075)|(2.342)|(2.074)|(2.308)|(2.069)|(2.754)|(2.513)|
|Foreign Ownership?|15.045***|14.746***|15.066***|14.618***|14.986***|13.922***|15.145***|
|(1=Yes)|(2.832)|(2.945)|(2.831)|(2.915)|(2.834)|(3.102)|(2.889)|
|Industry F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year F.E.|Yes|Yes|Yes|Yes|Yes|No|No|
|Mean dep. var.|43.293|43.293|43.293|43.293|43.293|43.293|43.293|
|F stat||||||12|71|
|Kleibergen-Paap LM test||||||0.091|0.149|
|Hansen J stat||||||0.000|0.015|
|Endog. test||||||0.425|0.000|
|N|4958|4958|4958|4958|4958|4958|4958|

The dependent variable is the _Share_ ( _ExportedSalesit_ ). Robust standard errors are reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level. Under the null hypothesis of the Kleibergen-Paap LM test, the structural equation is underidentified, that is instruments are not relevant. Under the null hypothesis of the Hansen J test, the instruments are valid. The null of the endogeneity test is that the endogenous regressor(s) can be treated as exogenous.

24
impact according to the basic achievement model that also includes the independent impact of economic institutions – column 4 of Table 4.1.

**Figure 4.4: Improved GDP with Moderately Strong Knowledge Improvement (0.5 s.d.)**

<!-- Start of picture text --> 40%<br>30%<br>20%<br>10%<br>0%<br>year<br>10-year reform 20-year reform 30-year reform typical education spending<br>2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2080<br>percent additions to GDP<br><!-- End of picture text --> The figure indicates how much larger the level of GDP is at any point after the reform policy is begun as compared to that with no reform. In other words, the estimates suggest the increase in GDP expected over and above any growth resulting from other factors.

Obviously, for any magnitude of achievement improvement, a faster reform will have larger impacts on the economy, simply because the better workers become a dominant part of the workforce sooner. But, the figure shows that even a 20- or 30-year reform plan has a powerful impact on GDP. For example, a 20-year plan would yield a GDP that was five percent greater in 2037 (compared with an economy with no increase in educational quality). The figure also plots 3.5 percent of GDP, an aggressive spending level for education in many countries of the world. Five percent of GDP is significantly greater than the typical country’s spending on all primary and secondary schooling, so that it is truly a significant change that would permit the growth dividend to more than cover _all_ of primary 45
||Table 15. Paying for Research<br>|
|---|---|
|Exchange|Subsidize Research?|
|NSE India Emerge|Exchange has a third-party research organization provide research on<br>companies listed on SMEplatform. Exchange bears the cost.|
|JSE AltX|Yes, 2 or 3 reports each year|
|GreTai|No|
|Bovespa Mais|Yes, two reports each year, by two different firms during the first two years<br>after the IPO|
|WSE NewConnect|—|
|BIST|No|

_Source:_ WBG compilation of exchange information. _Note: — =_ not available.

# **Box 2. The Case of Bovespa Mais**

BM&FBOVESPA launched Bovespa Mais in 2005. Nine companies are listed, and only two issued IPOs. Some problems are the following:

- Bovespa Mais applied the rules of the main board to the SME segment, although companies had up to seven years to comply with the financial rules.

- Bovespa Mais does not have an advisor model.

Brazil has a large number of medium-sized companies with clear financing needs that wanted to go public but were considered too small to list and raise capital on the main exchange. In 2011 Bovespa Mais was reorganized to target these companies.

The key issue for Bovespa Mais is investor demand, and it has faced challenges in developing a domestic investor base for SME markets. It is working to increase the number of local investors on the main board as well. Retail investors reduced their participation in the stock markets during the 2008–2009 financial crisis. Domestic institutional investors are traditionally buyers of government bonds; however, as interest rates are falling, they are now looking at different asset classes including SMEs. Seventy percent of investors in main board IPOs are foreign and want large opportunities and high liquidity.

In 2012, BM&FBOVESPA visited seven countries (Australia, Canada, China, Poland, Republic of Korea, Spain, and the United Kingdom) to learn from their experiences of building SME exchanges. Based on the findings, in 2013 Bovespa Mais undertook a review of its regulations and operations, which came into effect in 2014.

This was driven by and supported by a group of regulatory bodies including BM&F BOVESPA, the CVM, the Securities and Exchange Commission, the National Development Bank of Brazil (the main source of capital for SMEs), the Federal Innovation Agency (which funds companies with investment in innovation), and the Brazilian Industrial Development Agency.

In July 2014, a tax exemption until 2023 for investors acquiring stocks of SMEs directly or through funds investing a minimum of 67 percent in SMEs was published, completing a set of measures that also included creating a closed-end fund dedicated to SMEs, expanding the limits for PE funds to invest in listed companies, eliminating the obligation for companies to publish in newspapers and offerings publications, a restricted offering to qualified investors, and a simplified prospectus.

34
cipal rotated factor from factor analysis. 74 Following equations 2 to 5, the determinants are grouped into four categories. e Basic characteristics x°: demographics including gender, age, and age squared of the individual; household size (number of persons) and the share of individuals in these age groups (under 6, 7 — 14, 15 (omitted category), and 50 plus); marital status (married or single with widow/widower as omitted category); education (index ranging from 0 to 4 (no schooling (omitted category), lower primary (year 1 — 3), upper primary (year 4 — 6), lower secondary, and post-lower secondary (including university))); parental schooling indicator (equal to one if either father or mother or both went to school). e Economic characteristics x° Housing conditions (index of type of dwelling, floor area, drinking water, electricity, and sanitation derived using factor analysis); per capita value of assets as the sum of estimated values of land, livestock, and savings; *° six occupation categories (unemployed, non-coffee farmer, coffee farmer, pensioner, student, and wage earner (omitted category); monthly per capita aid assistance during the last 12 months; ”° access indicator of dwelling to paved road (equals to 1 if no access during rainy season; if access with more than 10 minutes distance; if access with between 5 to 10 minutes distance; and 4 if access with less than 5 minutes distance); welfare ratio (per capita value of consumption during the last month relative to the poverty line);?’ and non-household Aldeia average of the welfare ratio. e Social characteristics z* User group (share of adult household members participating in community groups during the last 12 months); number of radios or television sets owned by the household; indicator variables for the four most common mother tongues (Bunak, Macasae, Mambae, and Tetun, with other mother tongues as omitted category); indicator variables for speaking Tetun and Portuguese; an indicator variable for having the mother tongue that is the most prevalent in the sub-district; and the average 4 The results are unchanged if the housing index is replaced by the estimated rental value of housing. 25 Land value is based on the household’s estimated sales value of land. Livestock value is the sum of young and old livestock (buffalo, bali cow, cow, horse, pig goat, sheep, chicken, other) evaluated at the median price estimate of farmers. Savings is the sum of cash holdings in Rupiah and foreign currency, deposits with Indonesian banks or other institutions, gold, silver or other precious metals, and jewellery. 26 Aid assistance is the sum of the estimated value of 19 items (credit /cash, vaccination for livestock, cattle, pigs, chicken, seeds, fertilizers, fruit trees, agricultural tools, non-agricultural tools, fishing nets, mosquito nets, mosquito net repair kits, contraception, housing repair kits, building materials, rice, corn, and other food) received over the last 12 months from NGOs, the church, or other organizations. 27 The derivation of the consumption measure and poverty line is discussed in detail in World Bank (2003).