rafmacalaba/datause-encoder-data huggingface dataset:
The following section describes the data set we have created for this task. Section 3 provides some key summary statistics, and section 4 investigates the relationship between poverty and overall economic growth. Section 5 studies the poverty impact of urban-rural composition of growth in mean consumption, while Section 6 turns to the sectoral composition of growth in net domestic product. A key focus in sections 3-6 is on changing patterns across the pre-reform and post-reform periods. Section 7 concludes.

# **2. Data**

For the purpose of this study, we have derived a new and consistent time series of poverty measures for rural and urban India over the period 1951 to 2012. This is based on consumption distributions from 51 household surveys conducted by the National Sample Survey Organization (NSSO); beginning with the 3<sup>rd</sup> round for August-November 1951 up to the 68<sup>th</sup> round for July 2011 to June 2012. We use the full period for descriptive purposes. Some of the earliest surveys had smaller sample sizes and covered shorter periods. The shorter periods also make for a more imprecise mapping between NSS rounds and the annual national accounts data. Hence, in our main analysis we dropped some of the early NSS rounds that had survey periods considerably shorter than a year, and our first observation for the poverty regressions in this paper is for the 13<sup>th</sup> round for 1957-58.<sup>24</sup> For the econometric analysis we restrict ourselves to the period 1957-2012 (from NSS round 11 to round 68), giving 41 observations including 18 for the post-1991 period.<sup>25</sup> This series significantly improves upon the most widely-used time series on poverty measures in India to date.

Following now well-established practice for India and elsewhere, a household's standard of living is measured by real consumption expenditure per person. The underlying NSS data do not include incomes, though it can be argued that current consumption is a better indicator of living standards than current income.<sup>26</sup> Nonetheless, there are various “non-income” dimensions

> 24 Data from the earlier rounds are included in the graphs, though poverty measures for rounds 4 and 5, rounds 6 and 7, rounds 9 and 10 and rounds 11 and 12 are aggregated as survey-period-weighted averages.  Thus, for instance, the headcount measure for combined rounds 11 (for August 1956-February 1957) and 12 (for March-August 1957) is 7/13-th of the headcount index for round 11 plus 6/13-th of the headcount index for round 11.

> 25 Most of our regressions for changes in poverty are based on 40 observations; they exclude the 48th round for 1992, the year of the macroeconomic crisis that provided an impetus for economic reforms. Thus, effectively, the change in poverty between the 47<sup>th</sup> round (for July-December 1991) and 48<sup>th</sup> round is not included in either the pre-1991 or the post-1991 period.

> 26 For an overview of these arguments see Ravallion (2016, Chapter 3).

7
-3-

# III. BACKGROUND

The two dominant features of housing in Poland are shortage and subsidies. Housing output in Poland has in relative terms been lower than that of almost all countries with similar incomes. The result is that among the twenty-eight largest Economic Commission for Europe (ECE) countries in 1980, Poland ranked last in terms of the number of housing units relative to households, with about eighteen percent more households than housing units. On-budget housing subsidies represent a major portion of government expenditures (thirteen percent in 1985), ranking behird only food among consumer subsidies. When account is taken of off-budget interest subsidies, the housing sector may rank as the most prominent area of consumer subsidies.

The most striking feature of the Polish housing sector, however, is the shortage of housing units relative to households. In 1984, there were 12.08 million households in Poland but only 10.25 m.llion occupied dwelling units, a shortfall of 1.83 million dwellings. Of this latter number, most (1.27 million) were in urban areas. Such shortfalls find expression in waiting lists for state and cooperative housing where, in 1986, there were roughly 3 million households signed up--not only doubled-up households but also households wishing to improve housing quality, space, or location. Average waiting times for cooperative housing in large cities (Warsaw, Katowice, etc.) are reported to be from fourteen to fifteen years, and in the citi-es with the shortest waiting times (small cities in southern Poland), households could expect to wait five to six years for a cooperative apartment.

The housing deficits that exist now can be best understood in historical and comparative perspective. As a result of the destruction of World War II, Poland emerged with one of the most severe housing deficits in Europe--households exceeded housing units by about 1.5 million units at a time when the stock comprised about 5.5 million units. In the early years after the war, reconstruction proceeded slowly. By 1960, Poland still had one of the highest relative shortages of housing in Europe with 25 percent more households than housing units--a deficit of about 1.2 million units. In relative terms, only the U.S.S.R. (with a 29 percent deficit), the Federal Republic of Germany (28 percent deficit), and the German Democratic Republic (25 percent deficit) were close to the levels of relative shortage exhibited by Poland in 1960.

Housing shortage ratios (measured in terms of households relative to dwellings) in these four countries with the most serious war-related deficits, which are compared to those of other ECE countries in Table 1, were well above the median ratios for all countries in 1960--1.03 for western countries and 1.16 for socialist countries. After 1960, housing production increases decreased housing deficits in both western and socialist countries. By 1970, parity between households and dwellings had been achieved in Western Europe and North
|**Item**|**IEA**|**IMF pre-tax**|
|---|---|---|
|End-user price<br>data frequency|Monthly in most cases but less frequent<br>in some cases|Varying from monthly to quarterly in<br>MENA and to once a year (typically Dec)<br>in some countries|
|FOB hub<br>locations|Rotterdam, Singapore, U.S. Gulf, IEA data|U.S. Gulf prices from U.S. EIA for non-<br>OECD countries|
|Quality<br>adjustment|Adjustment made|No adjustments made (IEA adjustments<br>flow through where IEA values are used)|
|International<br>shipping|Shipping costs are from Argus, which<br>covers major routes, such as from the<br>Middle East to East Africa and Caribbean<br>to the United States, and takes into<br>account different vessel classes and sizes.<br>For large economies, country-specific<br>costs are available.|US$0.10/liter for non-OECD net-<br>importing countries|
|Domestic|Assumed to be USUS$0.08/liter for|US$0.10/liter for non-OECD countries.|
|transportation|gasoline, diesel, and kerosene in all<br>countries based on data from the U.S.<br>Department of Energy|Canceled by US$0.10/liter that would<br>have paid to transport from consumption<br>center to exporting port for net<br>exporters.|
|LPG bottling|Costs differ by end-use sector. A study<br>was conducted taking data from the<br>United States|<br>Not applicable (LPG not estimated)|
|Consumption<br>quantities|Consumption by end-users using specific<br>subsidized fuels. Fuels used in power<br>generation are captured under the<br>electricity end-use subsidy calculation.|Economy-wide consumption<br>Diesel fuel used in power generation<br>captured here also|
|Treatment of<br>tax|Country-specific VAT by fuel and<br>consumer category included in the<br>reference price.|Exclusion of end-user taxes|
|Net trade<br>status|Country-wide net imports calculated for<br>each fuel|Country-wide net imports calculated for<br>the sum of crude oil and all petroleum<br>products|
|Time lag for<br>pass through|None. Comparison of annual averages of<br>reference and actual prices|<br>None. Dec reference prices are compared<br>to Dec end user prices in countries where<br>only one set of prices are obtained per<br>year.|
|**Natural gas**|||
|Method|Price gap|Price gap. For 37 countries, IEA subsidy<br>estimates are used after subtracting VAT|
|End-user prices|Data provided by IEA’s network of<br>country partners and commercial<br>databases|IEA data where IEA calculations are used,<br>prices obtained by IMF staff otherwise|
|End-user price<br>data frequency|Monthly or yearly|Same as IEA where IEA calculations are<br>used, quarterly or yearly otherwise|
|<br>Reference price<br>data source|Prices from a wide range of commercial<br>sources|<br>Where IEA calculations are not used, U.S.<br>EIA|
|Hub locations|All key locations. For example, for LNG in<br>Asia, prices for Asia average, China, India,<br>and Rep. of Korea are available.|For 4 countries in MENA not captured by<br>IEA, average U.S. export prices|
|International|<br>Landed costs obtained for many markets,|Same as IEA where IEA calculations are|
|shippingcosts|otherwise adjust for distance.|used,not considered otherwise|

33
Policy ReseaRch WoRking PaPeR 4350

# **Abstract**

This paper uses firm level data from a cross-section of 57 countries to study how financial development affects innovation in small firms. The analysis finds that relative to large firms in the same industry, spending on research and development by small firms is more likely and sizable in countries at higher levels of financial development. The estimates imply that among firms doing research and development in a country like Romania, which is at the 20th percentile of financial development, a 1 standard deviation decrease in firm size is associated with a decrease of 0.7 standard deviations in research and development spending. In contrast, this decrease is only 0.2 standard deviations in a country like South Africa, which is at the 80th percentile of the distribution of financial development. Small firms also report producing more innovations per unit of research and development spending than large firms, and this gap is narrower in countries at higher levels of financial development. As a robustness check, the author shows that these patterns are stronger in industries inherently more reliant on external finance.

This paper—a product of theEnterprise Analysis Unit, Financial and Private Sector Vice Presidency—is part of a larger effort in the Bank to study the effects of financial development on firm performance. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at SSharma1@ifc.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
in Armenia. The data has been collected by the National Statistical Service and the Ministry of Health of the Republic of Armenia and is co-funded by the United States Agency for International Development (USAID). The main goal of the survey is to collect demographic and health indicators, particularly from women of reproductive age, and in some questions from men as well. In total, 2015-2016 survey has collected data on 6,116 women and 2,755 men of the age range of 15 to 49. The data is representative on the national and rural-urban areas. The 2015-2016 survey has a total of 313 clusters, of which 121 are rural.

One of the advantages of the DHS survey is that the program collects the GPS location data of surveyed clusters. In order to protect the confidentiality of the respondents, the locations have been displaced. Each urban cluster has been displaced from the actual location up to 2 kilometers, and each rural cluster up to 10 kilometers.<sup>10</sup> The survey also provides a wide range of geospatial covariates, very useful for the purpose of the study.

|Occupation|Men|Women|Total|
|---|---|---|---|
|Not working|27%|62%|50%|
|Professional/technical|9%|25%|17%|
|Clerical|1%|2%|1%|
|Sales|6%|9%|8%|
|Agricultural|26%|45%|36%|
|Services|16%|6%|11%|
|Skilled manual|30%|6%|18%|
|Unskilled manual|9%|7%|8%|
|Other|4%|0%|2%|
|Total|1,233|2,571|3,804|
|Working|904|981|1,885|

Table 1: Rural employment by gender (DHS) Rural respondents report lower labor activity. 43% of DHS respondents report living in rural areas, of which half indicate that they have not worked in last 12 months. The difference is very high between men and women. More than double the women report not working than men - 62% and 27% respectively.<sup>11</sup> Table 1 reports rural employment indicators. From those working, women are overwhelmingly employed in agriculture, 45%

> 10For more information please see Mayala et al. (2018).

> 11Unfortunately, limited employment variables do not fully allow the calculation of the proportion of unemployed people versus the ones who do not work for other reasons.

11
- 35 -

Additional support for the effect of blood lead on diastolic blood pressure is provided by another major population-based study conducted in Britain by Pocock et al.,"K" data from the Canada Health Survey,"<sup>3</sup> and a study of San Francisco bus drivers by Sharp.<sup>7</sup> ' Taken together, these studies indicate a significant effect of lead on blood pressure and hypertension. In addition, investigators have estimated the subsequent impact of a increase in blood pressure (due to increases in blood lead) on other significant cardiovascular events. For example, largescale epidemiologic studies including the Pooling Project Research GroupP and the Framingham study73 have shown that elevated blood pressure increases the risk of cardiovascular disease. For example, the Pooling Project reported that smoking, serum cholesterol, and diastolic blood pressure were major risk factors in the incidence of coronary heart disease (CHD). The Framingham stud? was one of the studies included in the Pooling Project. Besides estimating the incidence of CHD, this study of white middle-aged men considered the incidence of deaths from all causes. Diastolic blood pressure was a3ain identified as a significant predictor of all-cause mortality. These studies were used by Pirkle et al.63 to estimate the quantitative effects of blood lead on diastolic blood pressure and subsequent CHD and mortality.

The methodology used here to estimate the effects of lead on blood pressure is similar to that used by the U.S. EPA in its analyses of the effects of reducing lead in gasoline?' and the effects of reducing lead in drinking water."5 Dose-response functions are provided to estimate the effect of a clange in air lead on the likelihood of hypertension, and the effects of more serious health outcomes including myocardial infarction (heart attacks) and mortality.

Hvpertension. To estimate the dcange in hypertension related to air lead we use dose-response information provided by Schwartz et al.' Additional documentation for this estimation process is found in Pirkle et al. and Brennan et al-" The estimates are based on a logistic regressiona of the probability of hypertension versus blood lead. The original estimaLes were conducted for the subs? of the population of adult males age 40 to 59. However, sensitivity analysis conducted by Schwartz a al." and analysis
Policy Research Working Paper 7430

# **Abstract**

Previous research has shown that people with higher cognitive skills (mental abilities) and socioemotional skills (behaviors and personality) get better labor market outcomes. It is unclear, however, if this conclusion applies to low- and middle-income countries, given that existing literature builds on studies that are dominantly about highincome countries. In this paper, we explore how cognitive and socioemotional skills of adults, ages 15–64, relate to their labor market outcomes in the context of Colombia. Controlling for a range of confounding factors in a crosssectional survey, we do find that adults with higher skills also have better outcomes, while cognitive and socioemotional skills correlate with different ones and seemingly through different channels. Adults with higher cognitive skills have better jobs (with higher earnings, more formal, and highskilled) and are more likely to complete tertiary education. Socioemotional skills correlate more modestly with having a better job but more strongly with labor market participation and tertiary-education completion. Results suggest that adults with both cognitive and socioemotional skills tend to do better in the labor market and that policies boosting the development of both types may be beneficial in Colombia.

This paper is a product of the Social Protection and Labor Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at pacosta@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
340 per month more on food than the control group a year after _Shombhob_ started (see column 2 in Table 8). This significant increase occurred despite controlling for the fact that food expenses went up by BDT 250 for both the treatment and control group between the baseline and the endline surveys. Given that the total food consumption prior to the program was on average of BDT 3,168 for families who were about to become beneficiaries, BDT 340 represents an increase of 11 percent.

Possibly a more significant result from a nutrition point of view that we find is the impact of the intervention on the food consumption pattern of beneficiary households compared to their control group. Upon disaggregation of the expenditure date by item, we find that food expenses on proteins – meat, eggs, dairy, fish and pulses – increased significantly for _Shombhob_ recipients. This impact was larger for beneficiary households receiving the nutrition package compared to those who received only the education benefit. On average, a Shombhob recipient who received the nutrition benefit spent BDT 174 more on protein consumption relative to a non-recipient. While recipients of the education benefit also had a significantly greater expense on protein consumption than non-recipient households, it was less than the additional expense made by beneficiary households who received the nutrition benefit. This result is consistent with the fact that 70.5 percent of the sample in Jaldhaka, irrespective of the type of transfer they received, said that the money they received under the project was used for purchasing food for their children.

The above results are consistent with distributions illustrated in Figure 4. When we plot per capita consumption for each of the three groups at endline - (i) _Shombhob_ beneficiaries; (ii) those who applied and were rejected; and (iii) those who did not apply, we see a shift in the consumption distribution of _Shombhob_ beneficiaries to the right, indicating that the gap between the two distributions found at baseline has diminished over the period of the panel survey.

31
# **Do Stronger Intellectual Property Rights Increase International Technology Transfer? Empirical Evidence from U.S. Firm-Level Panel Data**<sup>**1**</sup>

Lee G. Branstetter Columbia Business School and NBER Raymond Fisman Columbia Business School and NBER C. Fritz Foley University of Michigan

> 1   The statistical analysis of firm-level data on U.S. multinational companies was conducted at the International Investment Division, Bureau of Economic Analysis, U.S. Department of Commerce under arrangements that maintain legal confidentiality requirements.  The views expressed are those of the authors and do not reflect official positions of the U.S. Department of Commerce.  The authors thank Vincent Leung, Changxiu Li, Grace Lin, and Yoshiaki Ogura for excellent research assistance, and we are grateful to the Columbia Business School Finance and Economics Division, the Chazen Institute, the NBER, the National Science Foundation, and the World Bank Research Group for financial support.  We also thank (without implicating) Adam Jaffe, Daniel Johnson, Tarun Khanna, Joshua Lerner, Jim Levinsohn, Jim Markusen, Keith Maskus, Joanne Oxley, Krishna Palepu, Michael Riordan, Kamal Saggi, Manuel Trajtenberg, and workshop participants at Columbia, the University of Michigan, Yale, Harvard Business School, and the NBER for helpful comments.
Figure 1 – Italy, by survey <!-- Start of picture text --> Italy<br>Relative inequality of opportunity Return to education<br>1995 2000 2005 2010 2015<br>year of survey<br>st.deviation logs mean log dev. 1995 2000 year of survey2005 2010 2015<br>Parental networking Intergenerational persistence in education<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br>year of survey year of survey<br>Regressors include gender, age, age2, born in South Italy and foreign citizenship<br>.5 .09<br>.4<br>.49<br>.38 .08<br>.48<br>.36<br>st.deviation logs .47 .34 mean log dev. .07<br>.46 .32<br>.06<br>.45 .3<br>.05<br>.04<br>.5<br>.03<br>.45<br>.02<br>.01<br>.4<br><!-- End of picture text --> Figure 2 – Italy, age-cohort decomposition <!-- Start of picture text --> Italy<br>Inequality of opportunity - Standard deviation of logs personal incomes Return to education<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Age Age<br>. .<br>1920 1940 1960 1980 1920 1940 1960 1980<br>Born in... Born in...<br>. .<br>Parental networking Intergenerational persistence in education<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Age Age<br>. .<br>1920 1940 1960 1980 1920 1940 1960 1980<br>Born in... Born in...<br>. .<br>Regressors include gender, age, age2, born in South Italy and foreign citizenship - Age and cohort effects obtained from Deaton's decomposition<br>.1 .08<br>.06<br>.08<br>beta1 beta1 .04<br>.06 .02<br>.04 0<br>.15 .05<br>.04<br>beta1 .1.05 beta1 .03.02<br>.01<br>0 0<br>0 -.02<br>-.04<br>-.01 -.06<br>beta1 beta1<br>-.08<br>-.02<br>-.1<br>-.03 -.12<br>0 .3<br>-.02 .2<br>beta1 beta1 .1<br>-.04<br>0<br>-.06<br>-.1<br><!-- End of picture text --> 14