rafmacalaba/datause-encoder-data huggingface dataset:
education are based on children's recalled information, and thus may contain non-negligible measurement error (Emran and Shilpi (2021), Torche (2019)). This would tend to bias downward the estimated parents-children persistence in education and, in turn, lead to downward biased estimate of the share of the intergenerational transmission in sibling correlation. The DHS data on parents are not based on recall, and thus are much more reliable.

There are 53 countries in our sample. We use 230 waves of DHS surveys to build our data base. We exclude 42 countries where at least one DHS survey is available but the sample size is small. The cut-o for inclusion is a minimum of 1000 observations in the sample. The trade-o between country coverage and sample size is well-appreciated in the literature. For a recent analysis of intergenerational educational mobility covering a large number of countries (153), see Narayan et al. (2018), but, as noted earlier, they do not provide estimates of sibling correlation.<sup>28</sup> In each wave of DHS, our sample includes children of age 16-28 in the survey year. The exclusion of relatively older age cohorts in each wave is motivated by two considerations. First, it reduces the possibility of sample truncation due to grown-up children leaving the household for work or to start a family. Second, as noted earlier, we would like to exclude children who are born far apart as they are likely to face dierent family, neighborhood, and school environments. Among our 53 countries, there are 6 countries with fewer than 2000 observations, and 22 countries with sample size more than 5000. The total number of observations in our data set is 544624. The country level estimation samples include children from the 1960s to 1990s birth cohorts. But in many countries, the number of observations for the 1960s birth cohort is small because only a limited number of DHS surveys were administered in these countries in the 1990s and earlier. For the analysis of the evolution of educational mobility across cohorts we thus do not include 1960s observations, and focus on the three decade wise birth cohorts from 1970s to 1990s.

The estimation method adopted by Bingley and Cappellari (2019) is the method of moments. The data requirements for the analysis are more demanding because the Bingley and

> 28The price of the extensive country coverage in Narayan et al. (2018) is that in 57 countries, the sample size is less than 1000 observations, and in 19 countries less than 500 observations. There are 25 countries with more than 5000 observations. The authors are very much aware of this trade-o and report the number of observations for each estimate so that a reader can make an informed judgment. The study by Hertz et al. (2008) include 42 countries with a minimum sample size of 1047 observations (Philippines).

16
4 OFFICIAL DEVELOPMENT ASSISTANCE FROM CHINA <!-- Start of picture text --> 12.5<br>10.0<br>7.5<br>5.0<br>2.5<br>0.0<br>Devolution Energy and Petroleum ICT Transport and Infrastructure<br>Ministry<br>Loan amount (KSH Billions)<br><!-- End of picture text --> Figure 32: Ministry of Energy and Petroleum receives most loans from China (2014) Source: Estimates of Development Expenditure Government of Kenya (2014)

# **4.2.2 Chinese stands out in education aid**

China is quite active in the education sector in Kenya and provides a number of training programs, university scholarships and language programs. Recent data is hard to gather, but China spent roughly US $120,000 on volunteers and US $1.2 million on trainings in 2010 (UNESCO 2015). Education aid is a small percentage of China’s bilateral loans and aid, but education is also a small part of the overall aid budget for traditional donors (figure 31). In 2011, China built St. Francis Sivo Primary School and four other primary schools in 2012 at an estimated cost of US $400,000, supported in part by China Youth Development Foundation (UNESCO 2015).

**China focuses on scholarships, vocational and professional programs, less on the formal education sector** China’s Nanjing Agricultural University (NAU) and Egerton University began a partnership in 1994 that created fellowships for Egerton students to study and work in Nanjing. The program has trained 200 agricultural technicians to date from ten countries in East Africa (UNESCO 2015). From 2004 to 2008, China trained 697 Kenyans in short courses, focusing on agricultural technology. China offers more than 50 short courses on topics such as hydropower technology, malaria control, and hybrid rice technology (King 2010). Since 1984, China provided ten annual scholarhips to Kenyans and 40 annual scholarships in 2007. By 2013, China had more than 300 Kenyans studying in China. China offered 34 scholarships in 2015 (Ministry of Education Kenya 2015).

**Growing interest in Chinese language study in Kenya** China also offers language scholarships: in 2009, the Confucious Institute at the University of Nairobi offered 49 language scholarships and the China information and culture communication center sent 40 self-sponsored students to China for language study (King 2010). China is becoming a more popular educational destination for Kenyans. Even without financial assistance, Kenyans find the competitve cost of Chinese universities attractive. As China’s influence in Kenya grows, the popularity of languages studies will likely rise. The Kenya Institute of Curriculum Design and Kenyatta University, for instance,

33
Glass Barriers _Figure 1: SSCBT Is Divided in Three Distinct Activities despite Some Overlap between Brokers and Traders_ <!-- Start of picture text --> 6% (5%) of<br>brokers<br>(traders) Own-account<br>also act as<br>traders<br>traders<br>(brokers)<br>Brokers<br><!-- End of picture text --> <!-- Start of picture text --> "Absentee"<br>traders<br><!-- End of picture text --> Qualitative evidence hints at the importance of overall traffic as an explanatory factor for a predominance of brokers at a checkpoint. Busier checkpoints can indeed lead to delays and traders incur significant losses as a consequence, which fuels demand for specialized transportersbrokers who know how to get heavy carts across the border faster, where to stop for dealing with authorities and how to minimize taxes and fees. The volume of trade, both by large and small, formal and informal firms, is much larger in Poipet than in the other two checkpoints. Therefore, middlemen specialized in getting large quantities of goods through a congested checkpoint are much needed in Poipet and most SSCBTers there are brokers—see Figure 2. Conversely, brokers are almost absent in Bavet and preliminary observations, FGDs and stakeholder interviews brought evidence of no such activity in Vangtao.

Being a broker is a second best relative to the trader option: Besides being more physically demanding, brokering implies interacting with border authorities, negotiating taxes and sometimes smuggling illegal or high-tax goods. Depending on the contractual arrangement, brokers may be responsible for confiscated goods. As shown by our qualitative data, brokers would become traders, had they a better access to capital and knowledge of local demand and supply.<sup>14</sup> Their lack of knowledge of the local market is partly explained by their higher probability of being migrant workers from other provinces, which also means that they are more vulnerable to changes in the local legal environment.<sup>15</sup> In the checkpoints visited, SSCBTers were always Cambodian and Lao on the border with Thailand whereas at least a significant minority of SSCBTers was Vietnamese at checkpoints on the border with Vietnam, but still mostly coming

> 14 They explain that they do not trade for lack of capital (92%), are afraid they might get cheated or their goods might be confiscated (5%), or they “don’t know how to” (9%).

> 15 In Poipet, fees for the necessary “immigration card” increased dramatically for non-local residents of Banteay Meanchey province six months prior to the study. The process had also become stricter as birth and registration certificates were required. The regulatory change was too recent to assess whether it was generating informal arrangements or whether transporters-brokers just accepted the hike. We expect little room for negotiation as the card is issued by the Thai police and the relationship between Cambodian border users and Thai officials is notoriously poor. Moreover, the interviewees never mentioned this as an issue unless we specifically asked about travel documents.

Cambodia and Lao PDR 8
11 Although many countries may not have comprehensive surveys, the Country Economic Memorandum for Malawi (1990) demonstrates what can be done in a datapoor country. The Memorandum focuses on growth through poverty reduction. A poverty profile is constructed from several small surveys for smallholders, estate households and urban households. Next, six major factors, with supporting data, are suggested for why these people are poor. This leads to a discussion of four proposals to alleviate poverty, supported by analyses of the current situation of the labor market, social sector expenditures, and tax and transfer policies. Finally, the quantitative effects of the four poverty alleviation proposals on incomes, poverty, and growth are estimated separately, and tradeoffs and complementarities of the four approaches are discussed within one macroeconomic framework.

The UNDP and the Social Dimensions of Adjustment (SDA) project in the African region recommend analyzing the adjustment process by identifying the transmission mechanisms from macro policy to the micro economy. That is, the country situation is analyzed to identify the ways in which the different components of a macroeconomic reform affect the situation of the poor. A quantitative set of indicators, or intermediate variables, are then identified to recognize changes to the poor. These indicators reflect both the policies pursued and the structures within which they are applied. Examples of such variables would be the unskilled wage, the agricultural terms of trade for peasant producers and the price of food staples. The impacts of macropolicy on these intermediate variables could be analyzed either qualitatively (UNDP) or
other factors that affect the application rate of migrant students which reduce their probability of applying for financial aid. Previous studies in Chile have shown that financial aid application rates are heavily mediated by the student’s school (Blanco and Meneses, 2013), as information and training on how to apply is provided at the school level.

Table 8 illustrates the coefficients of applying for financial aid and migrant status. We also control for school fixed effects and cluster errors at the school level. The results indicate that migrant students are less likely to apply for financial aid, which reduces their overall probability of transitioning into higher education in Chile.

Table 8: OLS: Financial Aid Applications

|VARIABLES|OLS|
|---|---|
|Migrant|-0.232***<br>(0.0147)|
|Forced Migrant|-0.269***<br>(0.0210)|
|Constant|0.653***<br>(0.000882)|
|Observations|236,841|
|Number of Schools|3,343|
|School FE|Yes|
|R-squared|0.002|

Higher education regressions for students in 12th grade, N=236,841 Standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **Regression discontinuity design**

While college selection test registration and financial aid applications are highly predictive of higher education enrollment, this section explores the causal impact of financial aid on higher educational enrollment. Previous papers have analyzed the impact of financial aid on college and higher educational enrollment in Chile (Solis, 2017; Meneses 19
6

# FIGURE 2

<!-- Start of picture text --> 30 30<br>20 ~~~~~total norminal subs'iOlis 340 Gilliam Foantse,<br>In 0989 present-valu: 160l Billioni Forinta<br>1989 ~~~~~~~2017 2025<br>-eO- bond rates (% Subsidies.<br>Subsid les ~ifl<br>4 191_ 30<br>_<br>1.5 10 . .- iP lP - - -C; up - . llUilNiNon<br>25<br>1989 2008<br>| n-bond rates (%) subsidies In % GDP<br><!-- End of picture text -->
# **1. Introduction**

From its inception, the Penn World Tables (PWT), building on the International Comparisons Program (ICP) of the United Nations, has sought to compare the _standard of living_ of individuals in different countries. That is, the term “real GDP per capita” as reported in the PWT is intended to represent the ability to purchase goods and services by a representative agent in the economy. The same is true of benchmark comparisons as published by the United Nations, Eurostat or OECD. But this expenditure-side interpretation of real GDP is quite different from the uses to which benchmark ICP and PWT data are frequently applied, such as in the “millions” of growth regressions (Sala-i-Martin, 1997) where “real GDP” is intended to reflect the production side of the economy.<sup>1</sup> The goal of this paper is to carefully distinguish the production or output-side measure of real GDP, denoted _real GDP_<sup>_o_</sup> , from the expenditure-side measure, denoted _real GDP_<sup>_e_</sup> . Real GDP<sup>o</sup> reflects the production possibilities of an economy, whereas real GDP<sup>e</sup> measures real expenditures which includes an adjustment for the trade balance. The reason these concepts were not distinguished in the ICP and PWT is that these projects treat the net foreign balance in an unsatisfactory way. While there may have been some data justifications for that treatment in benchmark studies of the 1970s, this is no longer the case. The treatment of exports and imports proposed in this paper will not only remove the ambiguity presently surrounding real GDP<sup>e</sup> and real GDP<sup>o</sup> in the ICP and PWT, but provides a rich new international measure, namely the difference between them. Essentially, these two concepts differ by the _terms of trade_ in the economy, i.e. the prices at which goods are exported and imported.

In section 2, the distinction between real GDP on the output-side and expenditure-side is set out conceptually, followed by a discussion of how they can be separately measured in time series data. In order to incorporate these concepts into the PWT, however, we need to have a _cross-country_ measure of their difference. To achieve this, we propose a measure of the purchasing power parity for outputs (PPP<sup>o</sup> ) rather than expenditures (PPP<sup>e</sup> ). Currently the cornerstone of the ICP and PWT is the PPP for final expenditures (PPP<sup>e</sup> ), used to deflate nominal national income to obtain real GDP<sup>e</sup> . If instead we want to deflate nominal GDP to

> 1  There are many other applications where expenditure-side real GDP is used when output-side real GDP is really needed. Recent examples are Bernard and Jones (1996), Jones and Hall (1999) and Acemoglu and Ventura (2002).
# **1 Introduction**

Estimates of income inequality are conventionally derived from household income and expenditure surveys. Due to the sizeable cost of collecting accurate data on household standards of living the sample size of these surveys generally constitutes less than half a percent of the total population. Unfortunately, the rich are often missing or under-covered, either due to non-response or under-reporting of income or both; see the recent literature on top income shares (e.g. Atkinson et al., 2011). Surveys still permit accurate estimation of median income and measures of poverty, even when data on top incomes are poor or are missing altogether, since the rich make up a small percentage of the total population. For the estimation of income inequality however, having good data on top incomes is crucial.

A remedy that has gained considerable traction recently is to estimate the top tail of the income distribution using data from income tax records. This estimate of the top tail can then be combined with an estimate of the bottom part from the household survey to obtain an estimate of the complete income distribution (Atkinson, 2007; Alvaredo, 2011; Alvaredo and Londo ̃no V ́elez, 2013; Diaz-Bazan, 2014; Anand and Segal, 2015).<sup>1</sup> Income tax records denote the ideal source of data as far as top incomes are concerned. For lower incomes tax records may be less reliable, here the household income survey arguably denotes the ideal data. When household survey and tax data are combined in this way, the Gini index for (i) the United States in 2006 increases from 59 to 62 (Alvaredo, 2011), (ii) Colombia in 2010 from 55 to 59 (Alvaredo and Londo ̃no V ́elez, 2013), and (iii) Korea in 2010 from 31 to 37 (Kim and Kim, 2013).

For all the pros of income tax records, the availability of the data is unfortunately rather limited, particularly in developing and emerging economies. The World Top Incomes Database (Alvaredo et al., 2015) for example includes no countries from the Middle East and North Africa region. Furthermore, data derived from tax records are less useful in places where tax evasion is more pervasive, as is the case in many developing countries. It should also be noted that combining household survey data and tax records is not without complications because the two data sources use different income definitions (disposable versus taxable) and have different units of analysis (households versus tax units, which could be individuals).

In the absence of data from tax records, this study explores the feasibility of using data on house prices to estimate the top tail of the income distribution.

1Diaz-Bazan (2014) generalizes the method of Atkinson (2007) and Alvaredo (2011) by allowing for a more general choice of the cut-off level for joining up the distributions. Morelli et al. (2015) review the literature attempting to combine household surveys and tax data in rich countries.

2
agricultural sectors in rural Vietnam during the period 2002-2008, however nonfarm employment did not have a significant impact on agricultural income.

**Table 5.3: Income effect of the BTA**

||Δ log of real<br>income per capita|Δ log of real nonfarm<br>income per capita|Δ log of real farm<br>income per capita|
|---|---|---|---|
||(1)<br>|(2)<br>|(3)|
|Change in provincial tariffs|-4.209<sup>***</sup>|-5.405<sup>***</sup>|0.276|
||(1.246)<br>|(1.063)|(2.672)|
|Household head (HH) ethnicity-1|-0.532<sup>***</sup>|||
||(0.015)|||
|HH age-1|0.215<sup>***</sup>|0.326<sup>***</sup>|0.030|
||(0.029)|(0.043)|(0.031)|
|HH gender-1|-0.001|0.006<sup>***</sup>|0.001<sup>*</sup>|
||(0.001)|(0.001)|(0.001)|
|HH vocation-1|-0.011|-0.086<sup>***</sup>|0.120<sup>***</sup>|
||(0.012)|(0.015)|(0.022)|
|HH high school-1|0.210<sup>***</sup>|0.313<sup>***</sup>|-0.076<sup>*</sup>|
||(0.027)|(0.033)|(0.039)|
|HH middle school-1|0.126<sup>***</sup>|0.167<sup>***</sup>|0.066<sup>*</sup>|
||(0.024)|(0.036)|(0.034)|
|HH primary school-1|0.094<sup>***</sup>|0.114<sup>***</sup>|0.091<sup>***</sup>|
||(0.020)|(0.030)|(0.025)|
|Spouse vocation-1|0.072<sup>***</sup>|0.073<sup>***</sup>|0.074<sup>***</sup>|
||(0.015)|(0.022)|(0.019)|
|Spouse high school-1|0.278<sup>***</sup><br>|0.333<sup>***</sup><br>|-0.111<sup>**</sup><br>|
||(0.025)|(0.032)|(0.052)|
|Spouse middle school-1|0.169<sup>***</sup>|0.239<sup>***</sup>|0.055|
||(0.020)|(0.029)|(0.035)|
|Spouse primary school-1|0.097<sup>***</sup>|0.090<sup>***</sup>|0.099<sup>***</sup>|
||(0.018)<br>|(0.021)<br>|(0.026)|
|Household size-1|0.062<sup>***</sup>|0.059<sup>***</sup>|0.025|
||(0.013)|(0.020)|(0.019)|
|No of working age members-1|0.009<sup>***</sup>|0.008<sup>***</sup>|0.015<sup>***</sup>|
||(0.001)|(0.001)|(0.001)|
|Log of real income per capita-1|0.109<sup>***</sup>|0.090<sup>***</sup>|0.069<sup>***</sup>|
||(0.008)|(0.008)|(0.013)|
|Log of real nonfarm income per capita-1||-0.450<sup>***</sup><br>(0.013)||
|Log of real farm income per capita-1|||-0.409<sup>***</sup>|
||||(0.014)|
|Constant|3.861<sup>***</sup>|2.689<sup>***</sup>|<br>3.062<sup>***</sup>|
||(0.137)|(0.120)|(0.242)|
|_N_|19835|13801|14889|
|adj._R_<sup>2</sup>|0.476|0.395|0.353|

Notes: - Standard errors in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01

- Control variables are Household head (HH)’s ethnicity, HH age, HH gender, HH vocational education dummy, HH high school dummy, HH middle school dummy, HH primary school dummy, spouse vocational education dummy, spouse high school dummy, spouse middle school dummy, spouse primary school dummy, household size, and number of working age members. All of these household characteristics are reported in 2002. Standard errors are clustered at province level.

23
to guarantee that an infinitesimal increase in an explanatory variable leaves the sum of income shares unchanged, that is equal to 1.

The regression is written as _Dijt_ = β 0 + β 1 _Gjt_ + β 2 _INFjt_ + β 3 _REFjt_ + β 4 _EXPjt_ + β 5 _DEMjt_ + β 6 _DIjt_ + β 7 _DSjt_ + (1) + β 8 _DDj_ + _eijt_ where subscripts _i_ , _j_ and _t_ denote respectively decile, country, and time (year), D = decile share,<sup>10</sup> G = real growth rate, INF = annual inflation, REF = the average unweighted EBRD reform index, EXP = total government expenditures as percentage of GDI, DEM = value of _Polity2_ variable from the Polity database (ranging from -10 for complete dictatorship to +10 for full democracy), DI = dummy variable for whether survey is income- or expenditures-based, DS = dummy variable for survey reference period (monthly, quarterly, semiannual and annual), DD = country dummy and eijt = error term.<sup>11</sup> To control for inter- and intra-country heteroskedasticity, the regressions are run with robust (Huber-White) standard errors.<sup>12</sup> Since reforms were, in almost all cases, influenced or imposed from abroad, being at first mostly of the Washington Consensus type favored by the World Bank, EBRD and the IMF, and later of the “milder” type favored by the European Union, their exogeneity seems patent. It is very unlikely that they were responding to domestic income distribution concerns.

> 10 In regressions, decile share are expressed as a multiple as the mean rather than as the percentages of the total.  Thus, the bottom decile’s share of (say) 3 percent of total income is translated as 0.3 mean incomes. This can be interpreted as the average income of the bottom decile normalized by the mean.

> 11 Inflation is defined, as usual, as ln (1 + annual inflation rate); EBRD index runs from 1 to 4.33. Real growth rate, annual inflation, government expenditures as a share of GDI are all from the World Bank database (World Development Indicators). _Polity2_ variable is from PolityIV database (accessible at http://www.systemicpeace.org/polity/polity4.htm), and EBRD index is compiled from various annual EBRD reports.

> 12 We perform Breusch-Pagan test of independence to see whether correlations are zero or not. The null hypothesis of no correlation is rejected.

14