rafmacalaba/datause-encoder-data huggingface dataset:
# Table 1: Summary Statistics

|Variable|Full sample<br>mean|Migrant<br>subsample<br>mean|Difference:<br>migrant -<br>fullsample|
|---|---|---|---|
|Categorical Variables||||
|Knows Pradhan|0.8279|0.8382|0.0102|
|Knows Community or religious leader|0.5237|0.5332|0.0095|
|Knows Bureaucrat|0.0973|0.0934|-0.0039|
|Knows Police|0.1434|0.1349|-0.0085|
|Knows and interacts with NGO Staff|0.0798|0.0892|0.0094|
|Knows Elected politician (MLA, MP, Munic. Council)|0.2993|0.3216|0.0223|
|Init. contact: close relative|0.1210|0.1371|0.0161|
|Init. contact: villager|0.5187|0.5851|**0.0664****|
|Init. contact: other|0.1646|0.1846|0.0201|
|Female|0.0623|0.0705|0.0082|
|(Other) Backward Caste|0.1782|0.1846|0.0065|
|Scheduled Caste or Tribe|0.5307|0.5207|-0.0100|
|Muslim|0.1047|0.1203|0.0156|
|Pradhan-shared identity|0.2606|0.2448|-0.0158|
|Pradhan-politicalaffil.|0.9339|0.9544|0.0204|
|ContinuousVariables||||
|Household assets net of housing values|3156.35|3131.49|-24.86|
||(8162.08)|(8660.51)|(488.55)|
|Age (In Years)|38.0087|39.3817|**1.3730****|
||(11.0357)|(10.4392)|**(0.6148)**|
|Years in Delhi|19.5412|19.7905|0.2493|
||(10.8554)|(6.3580)|(0.4804)|
|Education (Years)|4.4501|4.1452|-0.3049|
||(4.5781)|(4.4846)|(0.2605)|
|Household size|4.9950|5.2863|**0.2913****|
||(1.9327)|(1.9250)|**(0.1111)**|
|Pradhan's education|6.2743|6.4979|0.2236|
||(4.8157)|(4.8298)|(0.2781)|
|Pradhan's community years|17.4825|17.1017|-0.3809|
||(7.0037)|(6.6633)|(0.3915)|
|Age of basti settlement|20.4489|19.9108|-0.5381|
||(7.0669)|(6.4739)|(0.3863)|
|Number of households in the basti|2308.33|2568.58|260.25|
||(3445.52)|(3706.86)|(208.11)|

Notes: 1) standard deviations, and standard errors for the means and respectively the difference, in parentheses, for the continuous variables

- 2) * difference significant at 10%, ** difference significant at 5%, using a t-test for the continuous variables, and a chi-squared test for the categorical variables

- 3)Pradhan-shared identity is an indicator = 1 if the respondent has the same caste or religion as the pradhan

- 4)Pradhan political affiliation is an indicator = 1 if the pradhan reported any political affiliation

27
- 11 -

Table 3. West and Central African Countries:<sup>Productionand Net Exports of Cotton, 1980/81-2001/02</sup> (In thousands of metric tons)

||1980/81<br>|1985/86<br> <br>|1990/91<br> <br>|1993/94<br>Pr<br>|1997/98<br> <br>oduction<br>|1998/99<br>|1999/00<br>|2000/01<br>|2001/02<br>Proi.<br>|
|---|---|---|---|---|---|---|---|---|---|
|Benin|4|32|59|116|152|142|150|131|136<br>|
|Burkina Faso|23|46|77|51|138|120|110|114|164|
|Cameroon|32|46|44|51|76|78|75|94|95|
|CentralAfricanRepublic|8|13|12|7|20|16|11|9<br>|11<br>|
|Chad|31|39|60|37|109|65|76|65|76|
|Coted'lvoire|56|82|116|114|147|157|164|125|164|
|Mali|43|68|115|101|216|218|196|105|240|
|Senegal|7|11|12|15|17|5|9|9|14|
|Togo|9|27|41|33|65|76|57|49|60|
|||||Ne|tExports|||||
|Benin|6|27|50|105|141|131|142|136|125|
|Burkina Faso|21|44|69|44|120|114|113<br>|113<br>|147<br>|
|Cameroon|27|38|34|37|63|65|65|81|82|
|CentralAfricanRepublic|5|11|9|3|17|15|10|8|9|
|Chad|33|44|57|33|101|63|72<br>|65<br>|65<br>|
|C6ted'lvoire|43|84|81|80|98|120|160|109|131|
|Mali|38|60|98|87|174|202|196|125|196|
|Senegal|4|7|7|11|11|2|3|4|8<br>|
|Togo|7|22|36|35|57|65|55|47|55|

Source: International Cotton Advisory Committee.

increasing globalization and regional integration.<sup>Thefactthatproducerorganizationsare</sup> getting stronger reduces the room for taxation<sup>inthefuture.Similarly,increasedpressureon</sup> budgetary resources will reduce the ability of<sup>governments torescuenationalparastatals</sup> during future crises.

The current pricing system provides some stability<sup>tofarmers,butat ahigh costin termsof</sup> income foregone and lower long-term growth<sup>potential. Table4illustratestheimpactof the</sup> current system on pricing in individual countries<sup>inWestandCentralAfrica.8Beforethe</sup> devaluation of the CFA franc in 1994, producer<sup>pricesin theWCA countrieswereabout half</sup> of the international levels. In the period immediately<sup>following thedevaluationof theCFA</sup> franc (1994-95 to 1996-97), producer<sup>pricesintheCFAfranccountries declined</sup> significantly as a ratio to international prices;<sup>this was duetodeliberatedomesticpoliciesto</sup> consolidate fiscal positions and lower inflationary<sup>pressures.Duringtheperiod1997-98to</sup> 2000-01, producers' shares increased to slightly<sup>abovetheirpre-devaluationlevelson</sup> average. Nonetheless, this ratio was higher<sup>incountriessuchasBeninandCoted'Ivoire,</sup> which were relatively more advanced in<sup>theimplementationofstructuralreformsin the</sup> sector.

8 Part of the difference in this ratio across countries<sup>is explained by the higher transportcosts</sup> from ginneries to seaports in landlocked economies.
# **1. Introduction**

This paper investigates the efficiency and equity of the present price structure in Mexico. It estimates the behavioral responses of Mexican households to prices and income changes using a series of household surveys collected between 1989 and 2000, and then computes the efficiency and equity of the price structure, therefore providing information and policy guidance on the welfare effects of tax rate movements.

In estimating changes in social welfare, the knowledge of households’ responses to prices is important for at least two reasons. First, there is a direct relationship between changes in prices and households’ welfare (McCulloch, Winters and Cirera, 2002). Second, is the link between the prices of taxed goods and government revenues is especially critical in developing countries where taxes are collected through an indirect system that taxes consumer goods (Ahmad and Stern, 1991).

While behavioral responses are usually estimated through the analysis of time-series data, it is often the case that the scarce availability of data series for developing countries prevents the use of this standard methodology. Therefore, in order to calculate effectively how prices and income are reflected in the patterns of consumption, this paper adopts a methodology developed by Deaton (1987, 1988, 1990) and Deaton and Grimard (1992), which introduces a technique based on the variation of prices across space to identify behavioral responses. This paper extends Deaton’s methodology in two directions. First, it estimates the elasticities differentiated by income quintile and by rural and urban areas.<sup>1</sup> Second, it applies Deaton’s methodology to be used with panel data assuming each time-spatial location as a separate cluster, and thereby uses a series of six household surveys collected over a period of 12 years from 1989 to 2000.

The resulting estimates of income, price and cross price elasticities are utilized in a simulation exercise to investigate the efficiency and equity of the price structure in

> 1 Especially for developing countries with high inequality, households’ responses to changes in prices likely vary according to the income level of the household. Therefore, the use of aggregate price elasticities may be misleading.
# **Figure 4.4 Share of employment, by workers’ characteristics based on text analysis applying green narrow dictionary to ISCO-08 taxonomy**

<!-- Start of picture text --> a. Green employment, by gender b. Green employment, by education achievement<br>13 11<br>4 3<br>59 33<br>51<br>91<br>19<br>19<br>41 21<br>13<br>9 10 4<br>Zero GTI GTI Zero GTI GTI<br>Less than elementary Elementary school<br>Female Male Middle school High school+vocational<br>Diploma University or higher<br><!-- End of picture text --> _Source_ : GTI and Sakernas 2017

# Application #4: Green jobs wage premium

Green jobs are better paid jobs. This result that is found for the U.S. and other countries is corroborated for Indonesia. Comparing jobs within broad occupational groups which can be thought to require similar levels of effort and training and considering individual workers’ and jobs’ characteristics,<sup>63</sup> green jobs pay on average 6 percentage points more than a job that does not involve any green task (Table 4.2 column A). The higher the GTI, the better on average the job pays: when the GTI increases by 1 percentage point, the wage increases by 0.2 percentage points (column C). However, wage gains depend on the major occupational group: professionals; skilled agricultural, forestry, and fishery workers; and craft and related trades workers, who all represent 72 percent of green jobs, are paid better to those in the same occupational group but at non-green jobs. While managers, Technicians and associate professionals, Service and sales workers, and Plant and machine operators, and assemblers are paid less that those at the same non-green major occupational group.

**Table 4.2 Green jobs wage premium based on text analysis applying green narrow dictionary to ISCO08 taxonomy**

||GTI (binary, g<br>gre|reen vs non-<br>en)|GTI Index(|continuous)|
|---|---|---|---|---|
||A<br>ln(wage)|B<br>ln(wage)|C<br>ln(wage)|D<br>ln(wage)|
|**GTI**|0.062***|0.100***|0.002***|0.007***|
||(0.000)|(0.003)|(0.000)|(0.000)|
|**Major occupational group (1-digit ISCO) *GTI**|||||

63 Based on a wage regression controlling for gender, education level, broad occupational groups (1-digit KBJI), province of residence, working hours, and sector of employment.

32
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**Table 3. Change in the informality rate after a US$100 increase in imports per worker**

|Type of worker||2009-2010|2009-2012|2009-2015|2009-2017|
|---|---|---|---|---|---|
|||(1)|(2)|(4)|(5)|
|All|Coefficient|-0.003|0.000***|-0.006|-0.000|
||t-statistic|(-0.18)|(2.74)|(-0.17)|(-0.60)|
||N|249|250|248|246|
|Manufacturing|Coefficient|-0.000|0.001|-0.001|-0.006|
||t-statistic|(-0.21)|(1.41)|(-1.10)|(-0.26)|
||N|151|150|155|138|
|Services|Coefficient|-0.000|0.000|-0.034|-0.000|
||t-statistic|(-0.02)|(0.53)|(-0.02)|(-0.54)|
||N|249|250|248|246|
|Males|Coefficient|0.000|0.000|-0.002|-0.001|
||t-statistic|(0.20)|(0.27)|(-0.49)|(-0.55)|
||N|249|250|248|246|
|Females|Coefficient|0.001|0.000|0.001|0.000|
||t-statistic|(1.21)|(1.39)|(1.44)|(0.11)|
||N|229|226|226|224|
|Low Skill|Coefficient|-0.001|0.000|-0.002|-0.000|
||t-statistic|(-0.22)|(0.44)|(-0.40)|(-0.36)|
||N|245|247|245|242|
|High Skill|Coefficient|-0.001|-0.000|-0.000|-0.000|
||t-statistic|(-0.52)|(-0.53)|(-0.89)|(-1.34)|
||N|239|241|239|238|
|Young|Coefficient|-0.606|0.001**|0.070|-0.000|
||t-statistic|(-0.00)|(2.02)|(0.03)|(-0.20)|
||N|249|250|248|246|
|Old|Coefficient|-0.001|0.000|-0.002|-0.001|
||t-statistic|(-0.19)|(0.87)|(-0.27)|(-0.59)|
||N|249|250|248|246|

23
Log of refugees (50km) predicted by Log of Distance IV (square-root of mean border distance)

|Distance IV (square-root of mean border<br>distance)|||||||||0.02838<br>(0.04699)|0.02706<br>(0.04663)|0.01731<br>(0.05162)|0.04242<br>(0.05057)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Observations|2,015|2,014|1,489|1,488|2,015|2,014|1,489|1,488|2,015|2,014|1,489|1,488|
|Kleibergen-Paap rk Wald F|14.45|14.45|12.47|12.24|13.78|13.76|11.84|11.61|12.69|12.68|11.35|11.16|
|Root MSE|0.546|0.540|0.476|0.340|0.546|0.540|0.476|0.340|0.546|0.540|0.476|0.341|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status,<br>education, occupation, other income sources,<br>land ownership)|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

34
bility for program implementation was delegated to state governments. Funding came from a combination of taxes on diesel fuel (0.75 INR per liter), central government support, and loans from the Asian Development Bank and World Bank. By 2015, over 400,000 km of roads had been constructed, benefiting 185,000 villages – 107,000 previously lacking an all-weather – road at a cost of almost $40 billion.<sup>6</sup>

# **IV Data**

# **IV.A Dataset construction**

To take advantage of village-level variation in road construction, we combine village-level administrative data from the PMGSY program with multiple external datasets, including data covering every firm and household in rural India. This section describes the data sources and collection process; additional details are provided in Appendix A.

Identities of connected villages and completion dates come from the official PMGSY website ( `http://omms.nic.in` ), which we scraped in January 2015. Household microdata comes from the Socioeconomic and Caste Census (SECC) of 2012, which describes every household and individual in India. This dataset was collected by the Government of India to determine eligibility for social programs. It was made publicly available on the internet in a combination of formats; we scraped and processed over two million files covering 825 million rural individuals. After extracting text from the PDF tables, we translated fields from various languages into English, classified occupations into standardized categories and matched locations to the 2011 Population Census based on names. This process yielded a range of variables covering both household characteristics (assets and income) and individual characteristics (age, gender, occupation, caste, etc). Anonymized microdata from the 2002 Below Poverty Line (BPL) Census, an earlier national asset census, was used to construct village level controls.

> 6Source: PMGSY administrative data. This figure describes the total amount disbursed by the end of 2015. The cost per village connected is approximately $150,000.

9
> <sup>- 16 -</sup>

Average investment returns on total assets over the decade 1995-2004 amounted to 7.4 percent (Table 8). With inflation averaging 2 percent over the past decade, investment returns were substantially positive in real terms. All types of institutions achieved higher returns in the first half of the period. Returns in the second half were affected by the global collapse of equity prices and the large fall in interest rates.

Pension institutions in Denmark suffer when interest rates fall because the vast majority of mortgage bonds that represent a substantial part of their assets are callable and borrowers refinance their mortgages when rates are falling. Falling interest rates and the embedded call option of mortgage bonds also affect the financial solvency of pension institutions because the increase in the value of their long-term liabilities is not matched by a corresponding increase in the market value of their holdings of long-term bonds.

**<u>Table 8  Investment Returns</u>**

||**1995-99**|**2000-04**|**1995-2004**|
|---|---|---|---|
|ATP|9.9%|5.5%|7.7%|
|SP||2.9%||
|LD|11.0%|6.0%|8.5%|
|Total Public|10.1%|5.4%|7.8%|
|LifeInsurance Companies|10.4%|4.5%|7.4%|
|Multi-Employer Pension Funds|9.8%|4.1%|6.9%|
|CorporatePension Funds|11.5%|2.8%|7.0%|
|Total Private|10.3%|4.3%|7.2%|
|Grand Total|10.2%|4.6%|7.4%|

Source: The Danish Financial Supervisory Authority, LD Pensions and ATP The operating expenses of pension institutions, at least for those institutions for which detailed data are available, are comparable to those of similar institutions in other continental European countries and significantly lower than those prevailing in Latin American private pillars. The operating costs of the pension accounts held with banks are not published but they are probably close to those of retail mutual funds and thus somewhat higher than those of life insurance companies and pension funds.

Benefiting from economies of scale because of its large size and also from its compulsory nature, ATP reports very low operating costs, again replicating the experience of similar institutions in other countries.
<mark>Bardhan, P (2007). ―Poverty and Inequality in China and India: Elusive Link with Globalization‖,</mark> _<mark>Economic and Political Weekly</mark>_ <mark>, 42 (38).</mark>

- <mark>Basu, K (2008), ―The Enigma of India‘s Arrival: A review of Arvind Virmani‘s Propelling India: From Socialist Stagnation to Global Power‖,</mark> _<mark>Journal of Economic Literature</mark>_ <mark>, 46 (2).</mark>

- Behrman, Jere R. and Paul Taubman (1986). ―Birth Order, Schooling and Earnings‖, _Journal of Labor Economics_ , 4(3).

- Behrman, Jere, R. (1997). ―Mothers‘ Schooling and Child Education: A Survey‖. PIER Working Paper 97–025.

- Behrman, J., E. Hannum, M. Wang, and J. Liu (2008): Education in the Reform Era, in Brandt and T. Rawski Ed: China's Great Economic Transformation, Cambridge university Press.

- Birdsall, N and C. Graham (eds) (1999): New Markets, New Opportunities? Economic and Social Mobility in a Changing World, Brookings Institution.

- Bjorklund A and K. Salvanes. (2011). ―Education and Family Background: Mechanisms and Policies,‖ _Handbook in the Economics of Education_ vol 3, EA Hanushek, S Machin and L Woessmann (es.), The Netherlands: North Holland, 2011, pp. 201-247.

- Bjorklund A, Lindahl L, Lindquist M. (2010). ―What More Than Parental Income, Education and Occupation? An Exploration of What Swedish Siblings Get from Their Parents.‖ _B.E. Journal of Economic Analysis & Policy_ , 10 (1) (Contributions), Article 102.

- Bjorklund A, Jantti M, Lindquist M. (2009). ―Family background and income during the rise of the welfare state: trends in brother correlations for Swedish men born 1932-1968.‖ _Journal of Public Economics_ , 93 (5-6), 671-680

- Black, S.  E. and P. Devereux (2010). ―Recent Developments in Intergenerational Mobility‖, Forthcoming _Handbook of Labor Economics_ .

- Black, S. E., P. Devereux and K. Salvanes (2005). ―The More the Merrier? The Effect of Family Composition on Children‘s Outcomes‖. _The Quarterly Journal of Economics_ , 120(2), 669– 700 _._

- Blanden, J, P. Gregg, S. Machin (2005). Intergenrational Mobility in Europe and North America, Center For Economic Performance Report, April, 2005.

- Blanden J.,  A. Goodman,  P.  Gregg and  S.  Machin (2004). Changes in Intergenerational Mobility in Britain, in M. Corak (ed.) Generational Income Mobility, Cambridge University Press.

- Blanden, J. P. Gregg and L. Macmillan (2008). Accounting for Intergenerational Persistence: NonCognitive Skills, Ability and Education, Economic Journal, 117, C43-C60.

32
**Table 5.3: Marginal Effects for School Attendance in Southeast of Brazil**

|Probitestima|tes||||Numberof <br>LRchi2(9)<br>Prob>chi2|obs=257<br>=874.88<br>=0.0000|63|
|---|---|---|---|---|---|---|---|
|Loglikelihoo|d=-8489.9|813|||PseudoR2|=0.0490||
||dF/dx|Std.Error|Z|P>Izl|x-bar|[95%|C.I.]|
|female*|0.013|0.004|3.570|0.000|0.490|0.006|0.021|
|Famsize<br>|0.056|0.004|14.130|0.000|4.738|0.049|0.064|
|faM<sup>2</sup>|-0.004|0.000|-13.870|0.000|25.125|-0.005|-0.004|
|PO*|-0.047|0.005|-9.380|0.000|0.218|-0.057|-0.036|
|primlH*|0.085|0.004|18.640|0.000|0.336|0.077|0.093|
|prim2H*|0.028|0.005|5.770|0.000|0.241|0.019|0.037|
|secH*|-0.002|0.006|-0.440|0.662|0.161|-0.014|0.009|
|femHH*|-0.025|0.005|-5.210|0.000|0.220|-0.035|-0.015|
|M5Ysene*|-0.080|0.020|-4.840|0.000|0.015|-0.119|-0.041|

obs. P: 0.889997

> pred. P: 0.900897 (at x bar)

> (*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>Izl are the test of the underlying coefficient being 0

_Note:_ Variable uniH was dropped during probit estimation.

_Source:_ Author's own calculations based on PNAD 1999.

The findings with respect to school performance (i.e. is the child in an appropriate grade given his or her age?) (Tables 5.4 and 5.6) are similar to those for attendance. Girls in both regions are less likely to repeat than their male peers. Younger students and students from poor households are more likely to repeat in both regions. The education of the household head is an important determinant of the school performance of a child. Children whose parents have secondary or higher education are 39 percent (24 percent) more likely to be in the appropriate grade given their age in the NE (SE) compared to children whose parents have no education, which is the reference group. There is a positive correlation between school performance and the education of the household head if the household head has completed primary II or secondary education. As in the school attendance equations, we observe a regional difference for children from female-headed households. Children from female-headed households in the Northeast do better than children of those from male-headed households, but in the SE, those in female-headed households are nor better nor worse off.

The migration dummy, _mySsene,_ is again significant for NE-SE migrants. Children of migrants from the NE to SE are nine percent more likely to fall behind in school compared to the rest of the SE population.

33
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responses of issuances to supply-side factors. Section VI reports the use-of-funds analysis. Section VII concludes.

# **II. Data**

We collect data on capital inflows using balance of payments information from the International Monetary Fund (IMF). The IMF provides data on annual private gross capital inflows and outflows by category: foreign direct investment (FDI), portfolio equity, portfolio debt, bank credit, and others. We focus on portfolio equity inflows, defined as the difference between foreign purchases of domestic shares and foreign sales of domestic shares. Equity inflows are positive (negative) when foreign investors purchase more (less) domestic securities than what they sell. Foreign retail investors and foreign institutional investors (such as mutual funds, pension funds, hedge funds, and sovereign wealth funds) are often behind the foreign purchases and sales of domestic shares. Those investors purchase both existing and newly issued shares. To verify that the relation we observe between equity capital inflows and equity issuances reflects influences that are specific to equity markets, in additional analysis reported in the Appendix, we also use data on non-equity capital inflows, including other categories of capital inflows to the private sector (FDI, portfolio debt, and other debt investments in banks and other sectors, excluding general government and central banks), which are in fact positively correlated with equity inflows.

Our sample consists of the 25 emerging market countries included in the MSCI Emerging Markets index (explained below) during the 26-year period 1991-2016. The countries are: the Arab Republic of Egypt, Argentina, Brazil, Chile, China, Colombia, Czech Republic, Hungary, India, Indonesia, Israel, Jordan, Malaysia, Mexico, Morocco, Pakistan, 9