rafmacalaba/datause-encoder-data huggingface dataset:
second stage, households from chosen census enumerations are sampled. Every quarter, 1,020 households are part of the survey each month. These 1,020 households are interviewed every quarter. Each household completes a diary that collects information on its respective expenditures twice a month. Interviewers visit each household twice a month to collect this information. They also gather additional data on a limited set of household characteristics and a broad measure of household income. The sample is generally representative at the national level and at the residence level (urban-rural) but not at lower levels of geographic disaggregation.

**Expenditure data in the HBS follows the classification of COICOP, developed by Eurostat, and allows us to identify energy-related spending components clearly.** We refer to the variables detailed in Table 2 to measure energy spending. To distinguish between different types of energy expenditures, we rely on the 4-digit categories of spending components, which is the most disaggregated category detailed in the HBS codebooks. Table 2 indicates that we can identify spending on electricity, natural gas and town gas, liquefied hydrocarbons, butane, propane and similar, liquid fuels, coal, other solid fuels, and heat energy.

**We define energy spending shares, our target variable, as the ratio of energy spending over household income, as observed in the HBS.** To calculate the energy expenditure share, we aggregate the different energy spending components at the household level and divide the resulting overall energy expenditure by household income. We do not scale energy spending or household income by the number of household members or the adult equivalent of household members, as they would cancel out in any case. The resulting indicator is the energy spending share in overall income at the household level. Table 2 reveals that the average energy spending share observed in the HBS 2019 in Bulgaria is 13.1 percent. The largest energy spending component is on electricity (59.3 percent of all energy expenditure), followed by other solid fuels (27.5 percent) and heat energy (9.3 percent).

**There are several definitions of energy poverty from a monetary perspective.** One stream of literature defines the energy poor as all households that spend more than 10 percent of their total expenditure on energy (Boardman, 1991). Thema and Vondung (2020) explore two additional energy poverty indicators. The first one is based on the national median of the energy spending share (often

15
as claims on North and South production, i.e.

k − v ⋅ e v ⋅ e k = = η ⋅ a (1− η) ⋅ a * η ⋅ a + (1− η) ⋅ a * equilibrium.

A little bit of additional algebra shows that in equilibrium the world distribution of capital stocks is implicitly determined by:<sup>10</sup> Equations (11) and (12) describe the demand for North and South capital, and Equation (13) is the market-clearing or world adding-up constraint. Note that the interest rate is lower than the marginal product of capital, as investors demand a premium for holding production risk. The demand for North capital is higher than the demand for South capital as a result of the technological gap, i.e. π≥1. In the absence of this gap, both countries would have the same capital per person in equilibrium.

Figure 3 plots South’s net foreign asset position i.e. NFA* = <u>(1−(1η−)η⋅</u> a) ⋅*a−*k * , and the structure of its portfolio as a share of its wealth, as a function of π, σ, and γ. σ We set V = equal to the standard deviation of OECD aggregate per capita GDP η growth during our sample period of 1970-1997, which is 0.02. Since the OECD’s population share of our sample is η=0.2, this implies a benchmark value of σ=0.01,

> 9 This follows from Equations (5), (7) and South’s counterparts.

> 10 To see this, substitute Equation (9) into Equations (3), (5)-(6) and South’s counterparts.

9
Table I.4. Heterogeneous Effects on Indices by Income

||Prosociality<br>Index<br>(1)|Attitudes<br>Index<br>(2)|Beliefs<br>Index<br>(3)|
|---|---|---|---|
|Game [=1]|0.219**|0.323***|0.195**|
||(0.099)|(0.093)|(0.094)|
|Game x High income [=1]|0.158|0.036|-0.112|
||(0.164)|(0.176)|(0.180)|
|Video [=1]|0.192|0.291|-0.025|
||(0.112)|(0.103)|(0.102)|
|Video x High income [=1]|0.020|0.125|0.113|
||(0.183)|(0.182)|(0.189)|
|High income [=1]|0.113|0.007|0.171|
||(0.127)|(0.133)|(0.130)|
|P-value: Game + Game x High income = 0|0.004|0.017|0.593|
|FDR q-value: Game + Game x High income = 0|0.019|0.024|0.274|
|P-value: Video + Video x High income = 0|0.143|0.006|0.580|
|FDR q-value: Video + Video x High income = 0|0.121|0.019|0.274|
|Av. Dep. Var. (Control group)|0.000|0.000|0.000|
|Observations|828|827|827|

_Notes:_ This table presents OLS estimates derived from Equation 1 with interactions between the treatment exposure indicator variables and the indicator variable for high income, denoted as high income [=1]. High Income is an indicator variable equal to 1 if the respondent’s wage is higher than the median of the sample. The dependent variables are constructed following the methodology outlined by (Kling, Liebman and Katz, 2007) for each group of outcome variables: (i) Prosocial Behavior (detailed in Table 2), (ii) Attitudes (detailed in Table 4), and (iii) Beliefs (detailed in Table 5). All columns control for gender and student status. Robust standard errors are reported in parentheses. The reported p-values, such as Game + Game x High income = 0 and Video + Video x Student = 0, represent the p-value of a Wald test for each linear combination. Additionally, the False Discovery Rate (FDR) q-values for Game + Game x High income = 0 and Video + Video x High income = 0 are provided. Significance levels are denoted as<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%.

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Of course, none of the above results controls for other explanatory variables that can influence the school‟s performance measures. The regression analysis presented below will analyze whether these preliminary findings are confirmed once one controls for such confounding factors.

# _b. Are there selection effects?_

Before we can study the determinants of the test scores, we need to discuss a number of sample selection issues as sample selection has the potential to bias our regression estimates.

We have data on 12,075 schools participating in the 2010 EIT<sup>29</sup> . In the MOE database, however, we only have 11,950 schools which have students in the 11<sup>th</sup> and/or 12<sup>th</sup> grade in 2010. For 11683 schools we have information in both databases, implying we have data for at least 94.5 percent of the Ukrainian schools with graduating students (11,683/(11,683+(12,07511,683)+(11,950-11,683))). Another implication is that almost all schools which have students in the last year of secondary education have students who participate in the EIT. Hence, the „selection‟ introduced by the fact that we use EIT results, which is only taken by students aiming to go to the university, rather than some general test which would be taken by all graduating students , is likely to be minor. Of course, there is also the issue of selection among the students, an issue we will discuss next.

Only those students interested in continuing their studies at a university within Ukraine have an interest in taking the Independent External Test, hence the school average we have are based on a selected group of students rather than all students graduating in a given year. Moreover, while the students take the decision to participate or not themselves, schools do have a possibility to try to influence this choice. For example, if a school would like to get a good average score on the EIT, it can try to persuade weaker students not to take the test.

> 29 We have 12267 schools in the EIT 2009 data and 13678 in the EIT 2008 data. The 2008 EIT database includes vocational schools, which is not the case in 2009 and 2010. In this section, we focus on the descriptive data for the 2010 EIT. The descriptive statistics for 2008 and 2009 give a qualitatively similar picture

23
For instance, our work suggests that organized sector is moving to rural regions and that most organized plants located in rural areas utilize self-generation of electricity at least in part. Governments worldwide are focusing on the development of clean cities and efficient urban use.<sup>5</sup> For India, our work suggests that effective policy may need to extend beyond urban regions and also consider the incentives and regulations that govern self-production of electricity.

It is important to also clarify from the outset a boundary of this project. We are attempting to trace out usage patterns, not establish efficiency levels. Usage is an important component of efficiency, and, all else being equal, using less electricity to achieve a given level of output is more efficient and positive. But, for many reasons, we should not label every increase in electricity cost per unit of output as a bad thing. In a context where electricity supply is often incomplete and uncertain, efficiency can involve increased usage levels as it unlocks better production techniques (e.g., uses less labor and greater mechanization). Throughout this paper, we discuss points about efficiency as we are able, and we hope to continue clarifying these two dimensions in future work.

While our paper is principally about the measurement of energy use within Indian manufacturing, we document some comparable figures for land and building usage. This work draws heavily on the Duranton et al. (2014) study of misallocation of land in India. India’s policy makers are frequently framing their thoughts in terms of which input to industrialization—e.g., land, energy, labor—is the biggest bottleneck for India’s advancement. While it is beyond the scope of the paper to conclusively answer this question, our comparison of land and energy indicates that land and building access is likely the more pressing constraint for India’s manufacturing than energy. This is most evident in plots of simple costs shares over time, and it holds in more rigorous frameworks too. This is not to downplay the importance of further progress in India’s energy sector, where we describe below ample room for improvements to world levels. But it does provide greater context for our work and encouragement in the energy trends evident, echoing the extensive review conducted by Pargal and Banerjee (2014).<sup>6</sup>

> 5 For example, http://www.worldbank.org/en/news/feature/2014/07/03/urban-energy-efficiency-key-to-mexicosambitious-goals-for-energy-and-low-carbon-growth.

> 6 It is also important to emphasize the multi-faceted nature of energy challenges. Roughly 20% of the world’s population lack energy access, many of which are in India, and this in turn can have severe health consequences due to indoor pollution when using poor cooking materials. This widespread and worrisome energy poverty (e.g.,

7
political resistance to revising the poverty line.<sup>24</sup> The fact that actual poverty lines in practice are sticky is not a compelling reason against allowing them to vary for the purposes of measuring global relative poverty.

There are, of course, random differences in national lines at given mean consumption or income that one would not want to attach any normative significance to in measuring global poverty. The fact that there is political resistance to revising real poverty lines upwards, and that they are set at different times in different countries, will create random differences in the poverty lines found at given current mean consumption. There are also differences in methodologies used to set poverty lines in practice (as discussed in Ravallion et al., 2009). The issue here is whether there is a systematic pattern in the conditional mean national poverty line (conditional on mean consumption), such that it has a very low gradient amongst poor countries but then rises with mean consumption. Such a pattern was first found in national poverty lines by Ravallion et al. (1991) and we will confirm below that it is also evident in new data on national lines.

# **4. Data for measuring global relative poverty**

In measuring relative poverty in the developing world we shall draw on three new data sources. The first is a new compilation of national poverty lines documented in Ravallion, Chen and Sangraula (RCS) (2009). This exploits the new analytic work on poverty at country level that has been done since 1990, when Ravallion et al. (1991) collected the data on national poverty lines used for setting the “$1-a-day” line (and by AB for setting their encompassing line). Much of the new work has been done under the World Bank’s program of country _Poverty Assessments_ and the program of _Poverty Reduction Strategy Papers_ by national governments, often with assistance from the Bank or other governments or international agencies. There were very few of these studies available in 1990, but they have now been done for some 100 developing countries. Almost all include estimates of national poverty lines.

Second, we use the PPP of individual consumption by households from the latest (2005) round of the _International Comparison Program_ (ICP) (World Bank, 2008). This is the most

> 24 See, for example, the discussion in Blank (2008) on why the official poverty line in the US has not been updated, despite considerable dissatisfaction with the old line, proposed in 1963 and only updated for inflation since. Similarly, the poverty line in China has not been revised upwards in real terms for over 20 years, despite a four-fold increase in mean income. This has led many observers to question the relevance of their poverty lines to current conditions; see, for example, Osberg and Xu (2008). The government of China is in the process of revising upwards the country’s official poverty lines. The Planning Commission of the Government of India is also revisiting the country’s official poverty line.

11
household desiring to “keep up with the neighbors” by making additional investments in housing may also decide to deploy a member overseas or to another state to earn the money to finance those investments. To the extent that the omitted variables (e.g., risk-taking, illness, business loss, feeling of deprivation) are time-invariant, a first-difference regression at the household level will purge the estimates of such factors.

A limitation of the current empirical specification is the inability to control for _unobserved_ household shocks that vary over time—which could simultaneously affect migration decisions and household outcomes. An instrumental variable approach to address such concerns would require finding appropriate instruments that would help to explain migration, but would not directly influence a household’s asset choices. I resorted to using three candidate instrumental variables. The first instrument was motivated by the observation drawn from the KMS data that Muslim households were more likely to send a member to work abroad in the Arabian Gulf, and the least likely to have a member move within India for employment. The former is likely due to shared religious norms and a preference among Gulf recruiters for Muslim employees during this period. The second and third instruments relied on the fact that it was predominantly men who left Kerala to work elsewhere. Young male adults were more suited for the types of low-skilled jobs available in the Gulf nations, and traditional norms in Kerala during the period in question discouraged women from migrating for employment. A household with more young adult male members for a given family size was therefore in a better position to send members away for work, all else being equal. To capture presence of prime-age males over the period between 1998 and 2003, I used a count of all adult male members in a household between the prime ages of 19 and 40, as of 1998—which formed the bulk of all migrants—along with a measure of the change in the number of prime-age adult males between the 1998 and 2003 survey rounds.

The validity of these exclusion restrictions depends on the condition that they do not directly affect asset choices. For example, if Muslim households and households with more prime-age males 14
income per capita growth and productivity (La Porta et al, 2002; Barth et al. 2001), as well as poorer financial access by small firms (e.g. Beck, Demirguc-Kunt and Maksimovic, 2005). Our findings are also consistent with those of Boyreu-Debray (2003) and Boyreau-Debray and Wei (2005) who show that the depth of the financial sector does not promote growth of provinces in China, another country where the banking system is largely state owned and controlled. Hence, our findings on India provide additional evidence that state-ownership in banking hampers the impact of finance on growth.

The finding that differences in financial depth in a state-owned financial system do not affect the life-cycles of firms does not imply that there are no qualitative differences in financial access within those systems and that they do not benefit individual firms. For example, Cole (2009)’s Indian study uses a regression discontinuity design and finds that government ownership of banks adversely affects the development of small enterprises in villages in which these banks have branches. In another study of Chinese banks and enterprises, Ayyagari, Demirguc-Kunt and Makimovic (2010) find that controlling for local financial development, firms that receive bank financing outperform firms that do not. However, Ayyagari et. al. do not address the question of whether differences in financial depth at the province level materially affect the aggregate province level growth or the life-cycles of the firms comprising the whole of the manufacturing sector.

Overall, our analyses of firm lifecycle in India shows that the average 40 year old firm in the formal sector in India is 2 to 4 times the size of firms less than five years of age. Our results hold true when we take sampling weights into account and look at the entire population of firms. Thus, while firms in India may not be growing at the same rate as in developed countries such as 6
The treatment assignment is by construction independent of the error term and potentially a valid exclusion restriction. We therefore estimate eq. (2) by instrumental variables (IV). The identifying assumption is that the treatment only affects the outcome by changing the hours of usage of the platform. In our context this assumption is likely to hold. First, unlike other settings (Muralidharan et al., 2019) students do not use the application in computer lab, and it is therefore unlikely that the intervention could affect the peer composition. Since the intervention was conducted during the school closing where most of the teaching was already being conducted online, it is unlikely that the intervention could differentially affect teaching practices in the treatment and control groups.

Results are reported in **Table 5** . One standard deviation increase in the number of hours of use of ALEKS increases test scores by 0.20sd and reduces the probability of having failed any subjects by 6 percentage points. In line with the ITT results, the impact on the probability of enrolling in the third semester is null.

## _6.4. Potential Mechanisms_

There are different ways through which ALEKS might improve learning outcomes. First, by improving student preparedness, it might make learning more efficient. Unfortunately, our data do not allow us to test this hypothesis. Besides increasing the quality of learning, ALEKS might have increased the number of hours devoted to it (quantity). Information collected through the online survey applied contextually to the math test supports this hypothesis. Results presented in columns 1 to 4 in **Table 6** suggest that students are, on average, studying mathematics autonomously for more hours without sacrificing other subjects (column 5). Students in the treatment group displayed a higher perceived ability of their math skills with ALEKS (column 6). Altogether, these results are suggestive that, either because they are better equipped to understand more complex concepts or because of the gamification of the learning method, students who are eligible to use ALEKS enjoy studying mathematics more, and they spend more hours doing it, without sacrificing other subjects.

# **7. Conclusions**

Providing remedial education is the primary way higher education institutions cope with students who do not have the academic preparation needed to succeed in tertiary education. Expenses for remedial education programs represent a significant share of the university and non16
# **Table 4. Egypt: Sales Growth Trends by Industry**

||1996|1997|1998|1999|2000|
|---|---|---|---|---|---|
|Agriculture||||||
||13.38|1.45|12.52|8.26|4.34|
|Construction|21.76|13.89|13.00|4.60|-9.85|
|Building Construction|34.37|-4.17|7.98|1.84|-22.50|
|Manufacturing|6.17|6.56|3.89|0.13|-3.11|
|Food and kindered products|6.25|12.31|2.84|3.31|-1.96|
|Chemicals and Allied Products|11.94|7.62|4.80|3.20|1.53|
|Stone, Clay, Glass and Concrete|11.06|7.88|1.82|-1.51|-8.28|
|Utilities|3.76|6.80|-3.71|3.88|-3.17|
|Whole trade and retail trade|3.76|7.65|0.53|-10.47|-14.95|
|Finance, Insurance and Real estate|5.26|9.32|0.01|-1.70|-10.88|
|Real Estate|1.59|6.72|2.90|-7.96|-24.53|
|Services|6.21|16.94|3.78|13.54|6.34|
|Hotels|9.38|0.46|-0.54|8.74|7.94|

Notes: Source CMA (own calculations). Only the main subsectors are reported separately. Number of firms and total sales for main sectors include the numbers reported in subcategories.

19
Table 7 Correlation Analysis for the Economies in South Asia 1978-1990

> Pearson Correlation Coefficients / Prob > I R I under HE,: Rh0=0 / Number of Observations

|India<br>MCY|Indonesia<br>MCY|Malaysia<br>MCY|Pakistan<br>MCY|Thailand<br>MCY|
|---|---|---|---|---|
|M1Y<br>0.238|0.629|0.708|0.641|-0.244|
|0.4563|0.0213|0.0068|0.0101|0.4005|
|12|13|13|15|14|
|QMY<br>0.696|0.739|0.599|-0.617|0.683|
|0.0119|0.0039|0.0306|0.0142|0.0069|
|12|13|13|15|14|
|DCPY<br>0.657|0.813|0.731|0.351|0.767|
|0.0202|0.0007|0.0045|0.1997|0.0006|
|12|13|13|15|14|
|GNPCAP 0.800|0.212|0.930|0.528|0.887|
|0.0018|0.4860|0.0001|0.0433|0.0001|
|12|13|13|15|14|

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# **Table A.11. The effect of sample restriction, RLMS 1994-2017**

|_Main variables_|**Pooled Ordered Logit**|**BUC**|
|---|---|---|
|Lo of household income|0.640***|0.396***|
|g|_(0.011)_|_(0.079)_|
|L f hhld i|-0.369***|-0.121***|
|og o ouseo sze|_(0.022)_|_(0.033)_|
|Children*Lo of hosehold size|0.013*|-0.005|
|g u|_(0.007)_|_(0.009)_|
|_Number of observations_|245,777|746,710|
|_Log pseudolikelihood_|-418,712|-276,200|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

46