rafmacalaba/datause-encoder-data huggingface dataset:
|(10)<br>Transport<br>Population<br>0.0107<br>(0.012)<br>0.0107<br>(0.012)|33<br>ion and presents the<br>at the article level, in|
|---|---|
|(9)<br>Transport<br>Trade<br>0.5397<br>(0.447)<br>0.9009***<br>(0.009)|25<br>erator select<br>rs, clustered|
|(8)<br>Transport<br>Labor<br>0.0446<br>(0.048)<br>0.1636***<br>(0.014)|61<br>ASSO for mod<br>standard erro|
|ategories<br>(7)<br>Transport<br>Output Macro<br>0.0474***<br>(0.010)<br>0.0322***<br>(0.005)|208<br>s penalization L<br>enough. Robust|
|Outcome c<br>(6)<br>Transport<br>Output Micro<br>0.0104<br>(0.012)<br>0.0756***<br>(0.014)|34<br>el B uses rigorou<br>ervations is large|
|y Sector-<br>(5)<br>Energy<br>Labor<br>0.0021***<br>(0.000)<br>0.0050<br>(0.004)|55<br>tors and Pan<br>umber of obs|
|Estimates b<br>(4)<br>Energy<br>Output Macro<br>0.0412***<br>(0.003)<br>0.0412***<br>(0.003)|60<br>lude any modera<br>, whenever the n|
|e 6: PEESE<br>(3)<br>Energy<br>Output Micro<br>0.0062<br>(0.005)<br>0.0490***<br>(0.002)|88<br>l A does not inc<br>tcome categories|
|Tabl<br>(2)<br>Digital<br>Output Macro<br>0.0152<br>(0.013)<br>ing RLASSO<br>0.0181***<br>(0.004)|146<br>utcome level. Pane<br>ouped by sector-ou<br>0.1|
|(1)<br>Digital<br>Output Micro<br>oderators<br>0.0583***<br>(0.016)<br>ators selected us<br>0.0543***<br>(0.004)|50<br>es at the sector-o<br>esults. Data is gr<br>1, ** p<0.05, * p<|
|Panel A : No M<br>Main Efect<br>Panel B : Moder<br>Main Efect|Observations<br>Notes: PEESE estimat<br>post-estimation OLS r<br>parentheses: *** p<0.0|

34
village where the facility is located is included in the sample, we ...rst identi...ed the enumeration areas (EA) of the village where the facility is located and second, we selected an additional 2-4 EAs within each CA, with a probability proportional to population size. The number of EAs selected was determined by (6).<sup>45</sup> Finally, within the sampled EAs, we randomly selected the stipulated number of villages in the 1, 3, and 5 kilometer strata in the CA.

The total and the average number of villages sampled according to the sampling strategy and the actual number of villages surveyed are depicted in Table A.4.<sup>46</sup> Summary statistics of the sample of villages surveyed are depicted in Table A.5 and Table A.6. Overall, 293 villages were surveyed (from 242 EAs) with a total population of 29,405. The average village in the sample has 102 households, slightly larger than the average village in the sample frame.

# A.1.2 Part 2: Sampling of Households in Selected Villages

Using the most updated census data, we enumerated all 293 villages included in the ...nal sample and coded them. Two codes were created; one unique code for each household in each village (HHSVC), and one unique code for each household in the whole sample of households in the 293 villages (HHSSC). Then, we determined the number of households that should be surveyed in each village (SHHS). The rule was set as follows:

|SHHS|Condition||
|---|---|---|
|10|if total no.|of households in village 2 [20, 50]|
|0.2*(no. hhs in village)|if total no.|of households in village 2 [50, 100]|
|20|if total no.|of households in village 2 [100, 200]|
|25|if total no.|of households in village > 200|

This resulted in a total sample of 4,978 households to be surveyed in the ...nal sample. The sample design to select the households to be surveyed from the set of eligible households (i.e., the enumerated households) is as follows. First, a random number between 1-10 (between 1-5 in villages with less than 100 households) was drawn. This number is denoted “START”and is the ...rst household selected. Let the last number in the village list of households (HHSVC) be denoted by “LNO”. Then, the remaining (SHHS-1) sampled households are determined by selecting every xth (denoted “EVERY”) household, starting from START up to the point where the total number of sampled households is equal to SHHS. The variable EVERY is de...ned as the maximum integer such that

> 45That is, enough EAs were chosen so that the stipulated number of villages in the 1, 3, and 5 kilometer radius could be surveyed.

> 46Four villages were dropped due to too few households residing in the village (less than 20 households). We also had to replace a handful of villages where enumeration was not possible. This accounts for the di¤erence between the sample rule and the actual sample.

32
# **4. DATA AND RESULTS**

The analysis is based on the India Annual Survey of Industry (ASI) which was available to us from 1983/84 through 1997/98 disaggregated by state and 3 digit (NIC) industry.<sup>16</sup> This is a survey of registered manufacturing firms, reporting employment in two skill categories (nonmanual and manual workers), which we interpret as representing skilled and unskilled labor, respectively. The data also contain wage-bills for each labor category, output, value added, capital stock and a number of other variables.<sup>17</sup> The public-use ASI data are of uneven quality. We discovered numerous internal and external inconsistencies in these data and believe that we have resolved most of them. In keeping with the Griliches’ tradition of attention to measurement issues (Griliches, 1986) we discuss what we’ve learned here. (A separate appendix on data construction is available upon request.) We examined time-series of employment for sudden changes in each 3 digit industry by state, and then compared these figures with totals available from the Central Statistical Organization with data from other sources when any inconsistencies remained unexplained. We found two types of data problems: 1) Possible coding mistakes - The data indicate a very sharp decline in employment in West Bengal between 1984 and 1985, from 1.1 million manufacturing workers to 800 thousand. 180 thousand of that decline is reported in one 3 digit industry, aluminum manufacturing (NIC 335)<sup>18</sup> . Similar declines are reported for value added, output and other statistics. That sharp reduction in employment is inconsistent with the slight but steady employment _increase_ reported in official statistics by the Bureau of Economics and Statistics in the Government of West Bengal.<sup>19</sup> Compared to national totals for employment, the 1984 figures from our ASI data seem to be about 160,000 too high, indicating that this one industry is probably the source of the entire 15
2007, Pages 670–694.

Hausman, J. A. 1978. "Specification Tests in Econometrics." _Econometrica_ , 46(6):12511271.

- Hirayama, Y. (2007) ‘Reshaping the housing system: home ownership as a catalyst for social transformation’, in Hirayama, Y. and Ronald, R. (eds.), _Housing and Social Transition in Japan,_ London: Routledge, 15-46.

- IDB (2012): Room for Development: Housing Markets in Latin America and the Caribbean, Development in Americas, IDB

- J.C. Davis, J.V. Henderson, Journal of Urban Economics 53 (2003) 98–125 and references therein.

Korea National Housing Corporation (2003) _Year Book of Housing Statics_ , Seoul: KNHC.

- Lakshmanan, T.R, Lata Chatterjee and Peter Kroll. (1978). “Housing Consumption and Level of Development: A Cross-National Comparison”. Economic Geography, 54, 3 pp. 222-233

- Lewis, W.A. (1954), “Economic Development with Unlimited Supplies of Labour”, _The Manchester School_ .

- Manila Bulletin (2012): Projecting Housing Supply and Demand, Sunday Oct 21, 2012. Downloaded from http://ph.news.yahoo.com/projecting-housing-supply-demand095029663--finance.html.

Mankiw, N. G. (2000): Macro Economics, 4<sup>th</sup> Edition, Worth publishers, New York.

- Moulton, B. R. (1986): “Random Group Effects and the Precision of Regression Estimates,” Journal of Econometrics, 32(3), 385–397.

- Office of National Statistics (2009), _Wealth in Great Britain: main Results from the Wealth and Assets Survey_ 2006/08, HMSO London.

- Pesaran, M. H. 2004. General diagnostic tests for cross section dependence in panels. University of Cambridge, Faculty of Economics, Cambridge Working Papers in Economics No. 0435.

- Poterba, James M. (1984). ``Tax Subsidies to Owner Occupied Housing: An Asset Market Approach,'' Quarterly Journal of Economics 99(4), 729±52.

- Qianwei Ying, Danglun Luo and Jie Chen (2013): The Determinants of Homeownership Affordability among the 'Sandwich Class': Empirical Findings from Guangzhou, China, _Urban Stud_ 2013 50: 1870 originally published online 10 January 2013

- Regmi, A, M.S. Deepak., J. L. Seale Jr., and J. Bernstein. (2001). “Cross-Country Analysis of Food Consumption Patterns” in _Changing Structure of Global Food Consumption and Trade_ , ed.  Economic Research Service, USDA, 14-22. Washington, D.C.: United States Department of Agriculture.

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# **8. Conclusions**

This paper analyzes the relationship between intergenerational educational mobility and income levels using panel data covering 68 countries from across the world over the period 2000-2020. The dataset relies on data from the Life in Transition Survey and the Global Database on Intergenerational Mobility. It introduces and applies a new oriented distance-based measure of educational mobility - the upward mobility gap - which improves over the standard absolute mobility measures and provides unambiguous mobility rankings. It compares the patterns of educational mobility over generations and across regions observed by this measure with those observed by other absolute and relative mobility measures. Countries with high upward mobility gaps may have low mobility as measured by relative mobility indicators.

The empirical analysis shows that the relationship between intergenerational educational mobility and country income is region-specific. For instance, the indicators of relative mobility in Europe and Central Asia are uncorrelated with a country’s income levels over time, and only a specific dimension of absolute mobility—upward mobility in higher education—shows a positive and statistically robust association with country income across different specifications. In Latin America and the Caribbean countries, higher relative mobility is associated with _lower_ income, while higher absolute mobility is associated with _higher_ income. These findings suggest that the relationship between social mobility and long-term development may be context-specific, with some aspects of mobility more relevant for economic growth in some contexts but not others.

The reasons behind the heterogeneous relationship between intergenerational mobility and country income over time are potentially manifold. In contexts where growth is driven by investments in capital -be it physical or human- higher absolute mobility may be associated with a greater success in increasing the human capital stock across generations. When growth is driven by increases in total factor productivity, a higher human capital stock across generations will only be associated with higher income levels if such capital leads to the adoption of new technologies or to generate innovations (Akcigit, Pearce, and Prato, 2024; World Bank, 2024). This could be the case in countries with higher upward mobility in higher education. Further research will be needed to understand the mechanisms behind the complex relationship between mobility and development.

20
demand. Our point estimates for the framing effects considered are generally close to zero, and the standard error bounds are tight enough to imply smaller effects than those found in Marianne Bertrand et al. (2010), to the extent we can compare them.

In sum, insurance demand is price sensitive, and reducing prices (e.g., through greater efficiency or competition, or government subsidies) would significantly increase take-up. But this would still not be enough to induce most households to buy. Liquidity constraints, trust and salience are important non-price influences on demand. At the end of the paper, we suggest potential improvements in contract design that can help mitigate these frictions.

Our evidence contributes to a large literature on financial contracting and incomplete risk-sharing (Stefano Athanasoulis and Robert Shiller, 2000, 2001; Townsend, 1994; Franklin Allen and Douglas Gale, 1994; Andreas Fuster and Paul Willen, 2010) and points to specific frictions that limit risk pooling. We focus on a risk where the welfare benefits of diversification are likely to be especially large. Previous research shows that farmers use a range of mechanisms to mitigate rainfall risk, such as borrowing and saving, remittances, and asset sales (e.g. Christina Paxson, 1992; Dean Yang and HwaJung Choi, 2007). However, other evidence suggests that these channels only partially insulate consumption and welfare from rainfall risk (e.g. Sharon Maccini and Dean Yang, 2009; Stefan Dercon and Pramila Krishnan, 2000; Esther Duflo and Chris Udry, 2004), and also that farmers engage in costly ex-ante “income smoothing,” shifting towards safer but less profitable production activities to reduce risk exposure (Mark Rosenzweig and Hans Binswanger, 1993; Morduch, 1995). One factor limiting consumption insurance is that rainfall shocks affect all farmers in a close geographic area, reducing the benefits of risk-sharing between neighbors or through local credit and asset markets.<sup>2</sup> Our findings also contribute to a growing literature on household finance and risk management (Annamaria Lusardi and Olivia Mitchell, 2007; Cole and Guari Shastry, 2009). Amongst our contributions, we provide what we believe is the first experimental evidence of how trust influences financial market participation, extending previous research by Luigi Guiso, Paola Sapienza and Luigi Zingales (2008) and others.

Finally, our results relate closely to the literature on adoption of new technologies and financial products in agriculture. Duflo, Michael Kremer and Jonathan Robinson (2010) focus on

> 2 Indeed, Townsend (1994) finds that within-village risk-sharing in India is relatively close to the full insurance benchmark, even though _aggregate_ village incomes and consumption vary significantly over time.

4
- 41 -

# GRAPH la: ACTUAL COMMODITY PRICE CHANGES LICP33 FORECASTS AND ERROR BARS

<!-- Start of picture text -->
*a _O &LICP33  _  FORECAST=,,.__<br>.498<br>.199~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~4<br>*age~~~~  -<br>-.190<br>-. 390<br>i971  198'4L981  l982  1993  1994  1985  198t  1987  1988  1989<br><!-- End of picture text -->

Forecasted Sample Period: 1979-1988
occurred because replacing dirt floors with cement floors interrupted the transmission of parasitic infestations. Finally, we report the impact on maternal happiness of offering the benefits afforded by the _Piso Firme_ program.

# **6.1. Treatment and Control Group Balance**

We now explore the comparability of treatment and control groups in terms of a large set of observable pre-treatment and current variables. If the two groups turn out to be different in terms of these observable variables, then it is likely that they will also have displayed differences in terms of child health and maternal happiness in the absence of treatment, thereby invalidating our identification strategy. We first compare the preintervention balance in terms of characteristics measured in the 2000 census, and then consider characteristics from our 2005 survey.

Table II compares the means for 22 variables at the block level gathered from the 2000 census for treatment and control areas, prior to the implementation of the _Piso Firme_ program in the State of Coahuila. These variables measure household demographic structure, housing characteristics, assets, poverty and education of household members. For each of these variables, we present the means and standard errors for both treatment and control groups, as well as their mean differences and respective standard errors. All standard errors in this paper are clustered at the census-block level.

The first four variables in Table II were used to match the sampling of treated and control census blocks, and they are therefore balanced in our sample. One of these variables is the proportion of households that report having dirt floors in 2000, which is particularly important since reducing the presence of dirt floors is the main objective of the intervention. In addition, there are no significant differences in the other eighteen pretreatment variables presented in Table II. Thus, we conclude that the treatment and control areas from which we drew our samples were well balanced prior to the intervention and this increases our confidence that the treatment and control groups were comparable prior to the implementation of the program.<sup>7</sup>

> 7 Computing these mean differences at the household level by assigning each household the value of the variables corresponding to its census block does not change the results. All mean differences remain insignificant.

14
with the statement ‘Governments should play an active role in controlling and managing prices’?”<sup>19</sup> Table 2 reports the percentage of respondents who agreed/disagreed with this statement, showing that the overwhelming majority (93 percent) agreed that governments should control prices.

**Table 2: Citizen responses to “How strongly do you agree/disagree with the statement ‘Governments should play an active role in controlling and managing prices’?**

||**Frequency**|**Percent of total**<br>**(3000 responses)**|
|---|---|---|
|Don’t know|16|0.53|
|Strongly Disagree|50|1.67|
|Disagree|62|2.07|
|Neither agree nor<br>disagree|68|2.27|
|Agree|1232|41.07|
|Strongly Agree|1572|52.40|

Some evidence from the United States on citizen attitudes to combat climate change with energy policies suggests that cognitive constraints and ideological beliefs, that resist evidence, are likely to be significant even among educated citizens in an advanced economy. Using laboratory experiments, Kahan et al. (2012, 2013) show that those who score highest on cognitive abilities are also the most likely to adopt ideological beliefs in defiance of technical evidence. The authors explain this as “motivated reasoning” adopted by people in order to conform to the values of the cultural groups to which they belong. Their findings suggest that public divisions over climate change are not a reflection of the public's lack of knowledge or misunderstanding of science. Rather divisions stem from conflicting interests – between an individual’s interest in adopting common beliefs held by one’s community versus a collective interest in making decisions to promote common welfare. Holding a viewpoint that goes against one’s surrounding cultural community can bear a cost. When such a cost is high, it is rational for individuals to assimilate their reasoning to that prevailing in the group.

Sunstein (2007) and Dal Bo et al. (2013) provide evidence also from laboratory experiments in the United States that citizens are cognitively constrained to demand policies that confer short-term benefits at the expense of long-term or “equilibrium” costs. That is, citizens fail to internalize the full impact of the demand for a particular policy. They value immediately available private benefits from policies that 19 This survey was undertaken as part of the impact evaluation of the World Bank’s Tanzania Urban Local Government Support Program. The principal investigators of this study, who designed the survey, are: Vincenzo Di Maro, David Evans and Stuti Khemani. For this particular question, we drew inspiration from a pattern in Afrobarometer data of citizens expressing especially high rates of dissatisfaction with government performance in controlling prices.

30
major manufacturing economies: from relatively high-productivity Vietnam to relatively low-productivity Bangladesh.

I focus the analysis on manufacturing firms (two-digit ISIC codes 15-37). I also report data specific to the garment sector (ISIC codes 17-19). The key variables I extract from the data are number of workers (defined as the sum of employees on permanent and temporary contracts), total sales, total labor costs (including wages, payroll taxes, benefit payments, etc..), total cost of materials (the cost of physical inputs in production), and total value of physical capital. I measure productivity as sales per worker. Further, I calculate a measure of values added that takes into account rental payments for capital and land. All figures are converted to 2010 USD by using exchanges rate from the relevant year and US CPI data.<sup>2</sup> I use sampling weights to compute averages representative of the underlying population.<sup>3</sup> Missing data is not common, with a few exceptions – in particular for material costs. All variables are truncated at the 95th percentile of the country-specific distribution. I report all key descriptives in Tables A.1-A.5.

I also use data from two other sources to complement the analysis. First, I use original survey data collected for manufacturing firms in Addis Ababa by Abebe et al. (2017) to check the robustness of the sales figures reported for Ethiopia. Second, I use data from the _World Management Survey_ to compare the quality of management across the countries in my sample (with the exception of Bangladesh, which is not included in the World Management Survey). This survey studies management practices in manufacturing firms in 34 countries.<sup>4</sup> Firm managers are asked to describe several management practices in the firms over a phone interview and are then scored by a trained enumerator. The instrument produces an overall management score, as well as specific scores for the quality of operations management, monitoring of performance, target setting, and HR practices (including recruitment and retention strategies).

> 2I used exchange rate data from the World Bank, which is available here `https://data.worldbank. org/indicator/PA.NUS.FCRF?locations=ET` . For each country, I use the exchange rate for the year prior to the date when the survey was released to the public (e.g. for Ethiopia, I use the 2014 exchange rate). The CPI data is available here `https://www.measuringworth.com/datasets/uscpi/` .

> 3The World Bank Enterprise Survey offers three types of weights. I use the recommended median weights.

> 4I use the 2004-14 combined manufacturing survey data available here: `https:// worldmanagementsurvey.org`

4