rafmacalaba/datause-encoder-data huggingface dataset:
data on ethnicity suggest the vast majority of migrants are from within Africa, and are used to give the major countries of origin.

_Mexico:_ Data for return migration are from the 1997 Encuesta Nacional de la Dinamica Demografica (ENADID), a nationally representative demographic survey with individual records for 175,631 individuals. Data on return migration from the United States is available for 4,917 individuals.

_Oman:_ Customized cross-tabulations were provided by the Minister of National Economy based on the 2003 General Population and Housing Census.

_Pakistan:_ Data on 96 return migrants from international destinations are from the Pakistan Rural Household Survey 2001/02.

_The Philippines:_ Data on return migration are from the 1993 to 2000 waves of the Survey on Overseas Filipinos, providing data on 4548 return migrants.

_South Africa_ : Data used are from the 10% sample of the 2001 Census, supplied by Statistics South Africa. This data contains individual records for 82,593 individuals born outside of South Africa, of whom 13,527 are aged 12 to 24 and are from developing countries. Of these, 2,892 came to South Africa in 1999 or later – which we define as being a recent migrant. Migrants are defined as being from Developing Countries based on place of birth. The occupational classification consists of 170 3-digit occupation categories. Data were available for 2123 South African return migrants who had returned to South Africa in the five years prior to the Census.

_Spain:_ Data used are from the 5% sample of the 2001 Census, obtained from the Instituto Nacional de Estadística (INE), http://www.ine.es/prodyser/micro_censopv.htm. This data contains individual records for 107,394 individuals born outside of Spain, of whom

- 19 -
<!-- Start of picture text --> Demand Projections within 0.5 and 1.0 Standard Deviations<br>120,000,000<br>100,000,000 ee<br>wa oe ral<br>_ .* -<br>Lal] we = -<br>= 80,000,000 _— -<br>o a _ -<br>= oe sseees 1SDHigh<br>c oe = -_ _ — —_<br>= le --" _ | — —0.55D High<br>o 60,000,000 — - —_ — cawenee reese<br>= se = ePere rl Loan ems [Wlodel Projection<br>o = =— 0.55D Low<br>i<br>‘5 40,000,000 sse++» 1SDLow<br>E<br>=<br>z<br>20,000,000<br>0<br>2013 2030<br><!-- End of picture text -->
# **7. Conclusions**

The objective of this paper is to “unpack” both inequality and growth and estimate how different types of inequality may impact subsequent growth at different points of the income distribution. We do this using US micro-census data covering between 1 and 5 percent of the population, at ten-year intervals, over the period 1960-2010. The paper was motivated by two principal concerns: failure to find anything resembling a systematic relationship between inequality and growth in formulations where both inequality and growth were used as single, “homogeneous” variables, and a gradual realization that both variables are much more complex. The very fact that the earlier literature often found that the sign of the effect switched between positive and negative may be interpreted as implying heterogeneity in the variables, with for example one type of inequality being dominant at one time (or place) and producing one type of effect and another type of inequality, with the opposite effect, being more important at a different time or place. Thus, breaking both inequality and growth into inequality/growth among the rich and among the poor provides, we believe, a much more nuanced and realistic picture of the possible relationship.

In the first part of the paper, we keep total inequality as a single variable and look at its effects along US states’ income distribution. We find that it is negatively associated with subsequent growth rates among the poorer income percentiles, and positively among the higher percentiles. In the main part of the paper, we decompose total inequality into inequality among the poor (bottom 40%) and the rich (top 40 %). We find that both top and bottom inequalities are negatively associated with real income growth of the poor, but that the bottom inequality is in addition positively associated with the growth of the rich. As the summary in Table 7 shows, these effects are present in all six formulations even if at times their statistical significance varies and the exact ranges of the top and bottom income distributions for which a given type of inequality is bad or good slightly differ.

27
**Figure 1: GDP level over 2010-2025 (2007 USD trillion)**

<!-- Start of picture text --> 25000<br>20000<br>15000 2010<br>2015<br>2020<br>10000<br>2025<br>5000<br>0<br>Rest of HICs USA Europe China India Brazil Russia Rest of LDCs SSA<br><!-- End of picture text -->

**Figure 2 Per capita GDP growth rates for SSA region over 2010-2025.**

<!-- Start of picture text --> 5.0<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br><!-- End of picture text --> Source: Authors’ simulations and calculations.

Several African countries, such as Ethiopia and Mozambique, are expected to grow relatively fast, averaging 7% annual growth over 2010-2025. Even though productivity growth has not been very impressive in those two countries and is expected to continue that way, factor accumulation is projected to be very rapid. The average annual growth of the labor force is expected to be around 3% over 20102025, while capital accumulation is projected to be above 8%, by far the fastest among all countries. Meanwhile, some countries with relatively fast productivity growth such as Malawi or Zambia are expected to also achieve significant growth of about 6%. Despite faster growth rates in smaller countries, Nigeria and South Africa will still be the two largest economies in 2025 (Figure 3).

5
The seasonality of underemployment among farm workers is only part of the story. Non-farm workers represent more than half of the workforce in the informal sector, and the pattern of labor demand they face differs from that of farm workers. The data in Table 8 show that during the second quarter, when underemployment among farm workers is highest, non-farm workers are experiencing their lowest rates of underemployment.9 This phenomenon explains why the aggregate SAKERNAS data display no seasonality; while both farm and non-farm sectors experience seasonality, these peaks and troughs occur during different quarters. As a result, although aggregate demand in the informal sector appears relatively stable across quarters, vulnerability at the household level is probably higher than these aggregate statistics suggests.

**Table 8. Percent of Non-Farm Workers Underemployed by Quarter and Gender**

||**1st**|**2nd**|**3rd**|**4th**|**Total**|
|---|---|---|---|---|---|
|Male|11.7|10.1|10.8|10.8|10.9|
|Female|11.4|11.5|12.5|12.7|12.0|
|Total|11.6|10.7|11.5|11.6|11.4|

The figures provided in Tables 7 and 8 have two important implications. First, aggregated underemployment statistics almost certainly understate the magnitude of the problem of underemployment as experienced at the household level. While approximately 12 percent of the informal sector may be underemployed at any given time, a far higher percentage is probably underemployed at some time during the year. If the average duration of underemployment for any single worker were three months (and experienced only once a year), for example, the 12 percent underemployment rate would imply that nearly half of the workers in the informal sector experience underemployment at some time during the year. Even such short periods of underemployment (indeed, perhaps even the possibility of underemployment) may be a sufficient source of uncertainty to lower household welfare levels significantly, especially if these periods are unpredictable occurrences.

Second, although different sub-groups of the population experience patterns of seasonality, with higher vulnerability during certain times of the year, in aggregate, the informal sector (both in urban and rural areas) has a fairly stable degree of need. In thinking about the parameters of a policy response to income insecurity, the stability of need is probably more important than the shifting description of those who are vulnerable at any given time.

9 sNon-farm workers in the informal sector experience lower levels of underemploymen **t** han their farmbased counterparts, but they experience the same (for men) or higher (for women) seasonal fluctuation in employment demand.

12
31 Psacharopolous, George, and A. M. Arriagada, 1992, "The educational composition of the labor force: An international update," World Bank PHREE Backgound Parer Series $ PFREE/92/49.

- Pritchett, Lair, 1994, "Population, factor accumulation and productivity, mimeo (World Bank).

- Ram, Rati, 1986, "Government Size and Economic Growth: A New Framework and Some Evidence from Cross-Section and Time Series Data," _American Economic Review, 76,_ 191-203.

- Skinner, Jonahn, 1987, "Taxation and Output Growth: Evidence From African Countries," NBER Working Paper #2335 (Aulgst).

- Solow, Robert M., 1956, "A Contribution to the Theory of Economic _Growth," Quarterly Journal of Economics,_ 70, pp 65-94.

- Summers, Robert, and Alan Heston, 1991, "Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950-1988," _Quarterly Jounal of Econonmcs,_ 106:2, pp. 329-68.

UTNFPA, 1992, State of the World'g Population, United Nations: New YorkWinfrey, Wlliam, 1992, "Population growth and economic development What are the statistical analyses crying to tell us about the period since 1950?," mimeo.
10 retroActively. In the absence of retroactive enforcement, the rate of market loss of copiers will depend in part on the rate of introduction of new patented drugs.

# Table 1: ESTIMATES OF INCREASED INCOME OF PHARMACEUTICAL COMPANIES HOLDING PATENTS (US$ M)

|Countries<br>Vdriables<br>Argentina|Brazil|India|Mexico|Total|
|---|---|---|---|---|
|1. Pharmaceutical market<br>size<br>1,200.0|2,000.0|4,200.0|1,000.0|<br>8,400.0|
|2. Patented pharmaceutical<br>market<br>771.0|1,750.4|2,546.0|852.5|5,919.9|
|2.1 Sales by domestic<br>firms of copied<br>drugs<br>231.S|93.8|920.0|136.5|<br>1,381.3|
|2.2 Sales of drugs by<br>firms who are patent<br>owners<br>540.0|1,656.6|1,626.0|716.0|4,538.6|
|3. Percentage share of U.S.<br>companiesa/<br>45.0|50.0|45.0|50.0|<br>n.a.|

a/ Refers to share of sales of U.S. companies in total sales by multinationals.

Source: Mac Laughlin, et. al. (1988).

The US$1.4 billion is a fraction of the worldwide sales of R&D intensive pharmaceutical companies; it is likely that this fraction has increased. This is certainly one of the reasons wny the pharmaceutical drug
28

# Regression 14

|Dependent<br>variable:<br>Averag<br>1960-95|e<br>Capital<br>Inflo|ws<br>Plus<br>Capital<br>Outflows<br>as<br>a Fraction<br>of GDP,|
|---|---|---|
|Number<br>of observations:|132||
|Mean of dep. var.|= .062080|LM het. test=12.0349 [.001]|
|Std. dev. of dep. var.|=.058247|Durbin-Watson=2.03507 [<.866]|
|Sum of squared residuals|=.329045|Jarque-Bera test= 111.565<br>[.000]|
|Variance of residuals|=.269709E-02|<br>Ramsey's RESET2=.463370 (.497]|
|Std. error of regression|= .051933|F (zero slopes)=4.75418 [.000]|
|R-squared|=.259653|Schwarz B.I.C.=-5.62445|
|Adjusted R-squared|=.205037|Log likelihood=208.328|
|Estimated|Standard||
|Variable<br>Coefficient|Error|t-statiozic<br>P-value|
|SSA<br>-. 109303|.089036|-1.22763<br>[.222]|
|ASIA<br>-. 123786|.091137|-1.35824<br>[.177]|
|ECA<br>-. 126912|.102485|-1.23834<br>[.218]|
|MENA<br>-. 116162|.103124|-1.12643<br>(.262]|
|LAC<br>-.153697|.104193|-1.47512<br>[.143]|
|OECD<br>.016410|.028921|.567432<br>[.571]|
|COMMOD<br>-.013906|.011405|-1.21930<br>[.225]|
|OIL<br>-.027007|.015827|-1.70637<br>[.090]|
|LOGQAV6095 .023934|.013285|1.80152<br>(.074]|
|MICROSTATE<br>.027312|.013028|2.09646<br>[.038]|
|Standard<br>Errors<br>are<br>heteros|kedaBtic-consistent|(HCTYPE=2).|
# **References**

Ashraf, N., Field, E. And Lee, J. (2010) Household bargaining and excess fertility: an experimental study in Zambia. http://www.econ.ubc.ca/seminars/ashraf.pdf Barber, S. and Paul J Gertler (2009) Empowering women to obtain high quality care: evidence from an evaluation of Mexico's conditional cash transfer programme _Health Policy Plan vol._ 24(1): p. 18-25 Barber, S. L. and Gertler, P. J. (2008) The impact of Mexico’s conditional cash transfer programme, Oportunidades, on birthweight. _Tropical Medicine & International Health_ , vol. 13: p. 1405–1414 Bashir, Hamid, Sarfaraz Kazmi, Rena Eichler, Alix Beith, Ellie Brown (2009) Pay for Performance: Improving Maternal Health Services in Pakistan. Bethesda, Maryland: Health Systems 20/20 project, Abt Associates Inc.

<mark>Bellows NM, Bellows BW, Warren C. (2010) The use of vouchers for reproductive health services in developing countries: systematic review.</mark> _<mark>Trop Med Int Health</mark>_ <mark>. Nov 2. p. 1365-3156</mark> Bellows, Ben, Matthew Hamilton, and Francis Kundu (2009) Vouchers for Health: Increasing Utilization of Facility-Based STI and Safe Motherhood Services in Uganda. Maternal and Child Health P4P Case Study. Bethesda, Maryland: Health Systems 20/20 project, Abt Associates Inc.

Boler T and Harris, L. (2010) Reproductive Health Vouchers: from Promise to Practice. London: Marie Stopes International.

Borghi, J., Anna Gorter, Peter Sandiford, and Zoyla Segura (2005) The cost-effectiveness of a competitive voucher scheme to reduce sexually transmitted infections in high-risk groups in Nicaragua _Health Policy & Planning_ vol 20(4): pp. 222-231 Cechini, Simone (2009) Do CCT Programmes work in low-income countries? New York, NY: International Policy Centre for Inclusive Growth, United Nations Development Programme (One Pager No. 90, Poverty Practice Bureau for Development Policy).

Chapman, K. (2006) _Using social transfers to scale up equitable access to education and health services: background paper_ , DFID, London.

CREHPA (2008) _Rapid assessment of Safe Delivery Incentive Programme (SDIP) (Round II)_ , Centre for Research on Environment Health and Population Activities, Kathmandu.

Dagur, Vikas, Katherine Senauer, and Kimberly Switlick-Prose (2010) Paying for Performance: The Janani Suraksha Yojana Program in India. Bethesda, Maryland: Health Systems 20/20 project, Abt Associates Devadasan, N., Elias, M., John, D., Grahacharya, S., & Ralte, L. (2008) "A process evaluation of the Janani Suraksha Yojana in India," in _Financing obstetric care_ , F. Richard, Witter S., & V. De Brouwere, eds., ITM, Antwerp 18
“floating supply” of the market, or the tradable fraction of the total market capitalization, India’s stock market is only half of its banking sector.<sup>13</sup> “Structure activity” and “Structure size” measure whether a financial system is dominated by the market or banks. India’s activity (size) figure is below (above) even the average of English origin countries, suggesting that India has a marketdominated system; but this is mainly due to the small amount of bank private credit (relative to GDP) rather than the size of the stock market. In terms of relative efficiency (“Structure efficiency”) of the market vs. banks, India’s banks are much more efficient than the market (due to the low overhead cost), and this dominance of banks over market is stronger in India than for the average level of LLSV countries. Finally, in terms of the development of the financial system, including both banks and markets, we find that India’s overall financial market size (“Finance activity” and “Finance size”) is much smaller than the LLSV-sample average level. Overall, based on the above evidence, we can conclude that both India’s stock market and banking sector are small relative to the size of its economy, and the financial system is dominated by an efficient (low overhead cost) but significantly under-utilized (in terms of lending to non-state sectors) banking sector.

# **_Financial Markets_**

The number of India’s stock exchanges has grown from four at Independence (1947) to 23 today. India also boasts the largest number of listed companies in the world – well over 10,000. However, the equity markets were not important as a source of funding for the non-state sector until as recently as the early 1980s. Before then, the Indian capital markets were a closed club of brokers with few primary market offerings and little trade and marginal participation of the common public, a state of affairs caused mostly by the lack of transparency in Indian businesses and little protection of minority shareholders. The ratio of India’s market capitalization to GDP rose from about 3.5% in

> 13 We estimate that 45% of the total market capitalization of listed firms is actively traded in India, and hence a value traded/GDP ratio of 0.16.  The float supply figure of 45% is based on our own calculation of free float adjustment factor of about 1,000 large firms listed on the BSE (small firms are less frequently traded than large firms).

13
data set being previously used for the comparative analysis of inequality of opportunity anywhere in the world.

The paper presents parametric and non-parametric estimates of our pathindependent measure of inequality of opportunity for three distinct indicators of economic advantage – labor earnings, household per capita income, and household per capita consumption – and discusses the significant differences among them. A number of interesting cross-country patterns appear, both with respect to overall levels of inequality of opportunity, and to the relative importance of individual circumstance variables. Brazil, Guatemala and Panama are found to be systematically more opportunity-unequal than Colombia, Ecuador and Peru. Ethnic inequalities are also stronger in Brazil and in the two Central American countries, whereas geographic inequalities are greater in the two Central American countries and in Peru.

At the lower bound, inequality of opportunity is found to account for a substantial share of observed economic inequality in Latin America. For inequality in household consumption expenditures per capita, for instance, the (parametrically estimated) opportunity share ranges from 24% to 50%, depending on the country. The results are different for earnings and for household incomes, reflecting differences both in the economic mechanisms through which circumstances affect outcomes, and in questionnaire design and likely measurement error. The opportunity profiles also differ substantially among countries, with ethnicity being fundamental in Brazil but much less important in Colombia, for instance. Opportunity profiles also differ from poverty profiles, reflecting the fact that circumstances matter, but are not destiny: effort and luck enable some of those born in opportunity-disadvantaged groups to escape poverty, while others - born to more advantaged groups - fall into it.

The remainder of the paper is structured as follows. Section 2 provides a conceptual framework by deriving a simple class of measures of inequality of opportunity from Roemer’s definition of the concept. Section 3 describes four alternative members of that class that can be estimated in practice, and discusses their properties. Section 4 provides some information on the six household survey data sets used in the analysis. Section 5 reports the results of the alternative estimation procedures for labor earnings. Section 6 presents the results for household welfare, based on per capita income and 5
# **3 Results**

## _Fact 1. Firms facing higher competition have increased more their use of ICT._

First of all, we analyzed whether firms that faced more competition, measured as a value above the median in our competition variable, experienced higher increases in terms of ICT use. Figure 1 shows that for the period 2008-2012 this is indeed the case. Firms producing goods that experienced higher import competition from China invested more in ICT; the difference is statistically significant at the 10% level for computers-per-worker and the share of labor with Internet.<sup>5</sup> A possible mechanism behind the relation between ICT use and competition is trade-induced technical change. Under this hypothesis, competition generates incentives for innovation and the adoption of new technologies, speeding up the creative destruction process (Bloom et al., 2015). In this sense, considering all the side investments in terms of changes in processes and organization that entail increasing ICT use, firms that face higher competition pressures have more incentives to pursue these investments.

Figure 1: Mean change in ICT use 2008-2012 <!-- Start of picture text --> Computers−per−worker *<br>Share of labor with computer<br>Share of labor with internet *<br>0 .02 .04 .06 .08<br>Low Chinese competition High Chinese competition<br><!-- End of picture text --> Source: Authors’ calculations with data from ENTIC 2009 and 2013, INEGI. Differences in means significant at the: * 10% level, ** 5% level, *** 1% level Note: High Chinese competition is defined as a value above the median in the change of the share of China in total Mexican imports between 2000 and 2008.

_Fact 2. Firms that become more intensive in their use of ICT exhibit higher increases in productivity._ In order to test whether the predictions from previous studies regarding the relation between ICT and firm performance hold for developing countries, we analyze if firms that showed higher increases in their

> 5In Iacovone et al. (2015) this is tested using regression analysis and the results indicate that this relation is significant for the three ICT use variables considered, even after controlling for firm characteristics.

5
|**Appendix table 1: Discrep**|**ancy in agricultura**<br>|**l estate sizes betw**<br>**% of l**<br>|**een lease records a**<br>**eases with discrepa**<br>|**nd calculation from**<br>**ncy between**<br>|**mapped boundaries**<br>|
|---|---|---|---|---|---|
||**-1% to 1%**|**-5% to 5%**|**-10% to 10%**|**-20% to 20%**|**-50% to 50%**|
|**Total Malawi**||||||
|Valid leases|32.44|60.63|74.70|85.23|93.01|
|Expired leases|23.22|68.18|81.76|90.80|97.58|
|Indeterminate leases|16.08|47.31|62.35|74.71|86.60|
|**North**||||||
|Valid leases|33.05|57.63|71.19|80.93|91.95|
|Expired leases|16.77|52.80|68.11|83.03|96.37|
|Indeterminate leases|9.27|31.14|46.90|67.93|90.27|
|**Center**||||||
|Valid leases|31.70|59.52|73.82|84.15|92.16|
|Expired leases|24.02|70.54|84.13|92.27|97.94|
|Indeterminate leases|16.92|50.84|66.10|76.09|85.31|
|**South**||||||
|Valid leases|33.61|63.92|77.76|88.96|95.06|
|Expired leases|29.86|78.78|87.84|92.81|96.26|
|Indeterminate leases|23.10|56.78|69.60|79.14|86.74|

_Source:_ Own computation from the National Geographical Estates Database.

27
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constraints, such as books and material supplies. There is some indication that the quality of education may suffer as a result.

One would expect that at least Ivorians get something more for the money, but output is not better than in other countries: the enrollment ratios are lower at the primary and the secondary levels as shown in Table below.

|Country|Level|of Education||
|---|---|---|---|
||Primary|Secondary|Higher|
|Gote d'Ivoire|60|15|1.9|
|Francophones|59|4|2.4|
|Anglophon,s|77|17|1.2|

Source: Mingat and Psacharapoulos [19851.

The high level of wages in the formal sector inflates the measurements of the private rate of return to education. Those have been found to be high in Cote d'Ivoire (van der Gaag and Vijverberg [1987]. This is not surprising in a system where institutional constraints affect wages at all levels. In this situation of rationing on expects that signalling is an important allocation mechanism. indeed van de Gaag and Vijverberg find that holding a diploma is a very important determinant of th. wage rate. The measurement of the rate of return is obviously overstated since many graduates have some difficulties in finding adequate jobs.