rafmacalaba/datause-encoder-data huggingface dataset:
**Maitra, Pushkar and Subha Mani** . 2014. " _Learning and Earning: Evidence from a Randomized Evaluation in India_ ." GCC Working Paper Series, GCC 14-05.

**Meyer, Bruce D.** 1995. "Natural and Quasi-Experiments in Economics" _Journal of Business and Economic Statistics_ , 13(2): 151–161.

**Ñopo, Hugo, Jaime Saavedra-Chanduví, and Miguel Robles.** 2007. "Occupational Training to Reduce Gender Segregation: The Impacts of ProJoven" , Inter-American Development Bank Working Paper #623.

**O'Brien, Peter C.** 1984. "Procedures for Comparing Samples with Multiple Endpoints" _Biometrics_ : 1079-1087.

**Robalino, D., Friederike Rother, David Newhouse, and Mattias Lundberg.** 2013. "Youth Unemployment: A Development Agenda for the Next Decade" , World Bank Social Protection and Labor Discussion Paper no.1308.

**Rosenbaum, P., and D. Rubin** . 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” Biometrika, 70, 41–50.

**Rubin DB.** 1980. "Randomization Analysis of Experimental Data in the Fisher Randomization Test." _Journal of the American Statistical Association_ , 75: 591–593.

**Smith, Jeffrey and Petra E. Todd.** 2005. "Does Matching Overcome Lalonde’s Critique of Nonexperimental Estimators" _Journal of Econometrics_ (125): 305–353.

**Todd, Petra E.** 2013. "How to Improve Women’s Employability and Quality of Work in Developing and Transition Economies" _<u>http://www.womeneconroadmap.org/sites/default/files/Todd_Improve%20Women%20Employability%20a nd%20Quality%20of%20Work.pdf</u>_ <u>.</u>

25
**Table 8. Results from informal-formal wage gap regressions**

|||**Data: 200**<br>|**4-2016**<br>||<br>|**Data: 2016 on**<br>|**ly**<br>|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Informal in primary job|-0.0483***||-0.00194||-0.177***|||
||(0.00713)||(0.00973)||(0.0279)|||
|Salaried informals||-0.0747***||-0.0255**||-0.152***|-0.183***|
|||(0.00997)||(0.0128)||(0.0356)|(0.0370)|
|Self-employed informals||-0.0296***||0.0180||-0.191***||
|||(0.00905)||(0.0122)||(0.0342)||
|Male|0.269***|0.270***|0|0|0.219***|0.219***|0.248***|
||(0.00523)|(0.00523)|(.)|(.)|(0.0178)|(0.0178)|(0.0181)|
|Age 35-54|0.0242***|0.0236***|0.0134|0.0135|0.0418**|0.0424**|0.0353**|
||(0.00498)|(0.00498)|(0.0116)|(0.0116)|(0.0174)|(0.0175)|(0.0176)|
|Age 55+|-0.165***|-0.165***|-0.0860***|-0.0852***|-0.100***|-0.100***|-0.102***|
|Above general secondary|(0.00668)|(0.00668)|(0.0166)|(0.0166)|(0.0218)|(0.0218)|(0.0222)|
|but less than higher|0.0528***|0.0526***|0.0339***|0.0334***|0.0743***|0.0749***|0.0556***|
||(0.00549)|(0.00549)|(0.0103)|(0.0103)|(0.0196)|(0.0196)|(0.0201)|
|Higher|0.270***|0.270***|0.128***|0.127***|0.275***|0.275***|0.250***|
||(0.00717)|(0.00717)|(0.0185)|(0.0185)|(0.0246)|(0.0246)|(0.0249)|
|married|0.0439***|0.0437***|0.0157|0.0156|0.0501***|0.0500***|0.0222|
||(0.00482)|(0.00482)|(0.0104)|(0.0104)|(0.0165)|(0.0165)|(0.0167)|
|Urban|0.201***|0.202***|-0.137|-0.137|0.210***|0.210***|0.219***|
||(0.00575)|(0.00575)|(0.0950)|(0.0944)|(0.0190)|(0.0190)|(0.0191)|
|Industry|0.335***|0.336***|0.127***|0.127***|0.248***|0.247***|0.211***|
||(0.0131)|(0.0131)|(0.0254)|(0.0254)|(0.0430)|(0.0430)|(0.0421)|
|Market services|0.323***|0.323***|0.118***|0.117***|0.191***|0.191***|0.184***|
||(0.0126)|(0.0126)|(0.0249)|(0.0248)|(0.0415)|(0.0416)|(0.0408)|
|Public services & NGO|0.113***|0.113***|0.0226|0.0229|0.0109|0.00984|-0.0241|

Informal Employment and Worker’s Wellbeing in Russia / June 2019 52
# **2 Economic reforms in China and the development of non-farm activity**

The labor market in China was fragmented with a significant rural-urban divide. Following the model of the former-USSR, the government of China heavily taxed agriculture to support the development of heavy industries in the planned-economy era. Before the economic reforms in 1978, although labor productivity in rural areas was very low, farmers were not allowed to leave the land. The rural collectivization and the civil registration system were the two major barriers that made rural-urban migration impossible or impractical.<sup>6</sup> The income of a farmer was mainly determined by the time he or she spent on the collective land (rural collectivization, _gongfenzhi_ ) _._<sup>7</sup> Under the civil registration system, one with rural _hukou_ was not eligible for most of the social services in urban areas, such as health care services and education for children.

The economic reforms in the late 1970s brought big changes in China. First, the collapse of the system of “People's Commune” and the implementation of the HRS in rural areas restored greater liberty to farmers in choosing their careers and their modes of production.<sup>8</sup> To some extent, rural households can allocate their labor to maximize their expected returns between farm activity, local rural non-farm activity, migration, etc. Second, agricultural reforms greatly increased the supply of grains, which gradually led to the abolishment of the rationing system. Food became available in free markets in urban areas in the 1980s. Housing, health services, and education for children became more accessible for rural-to-urban migrants without urban civil status ( _hukou_ ) in the 1990s. Third, the development of the non state-owned economies generated large demand for informal workers. Most of the job openings in informal sectors were low-paid and unattractive to urban citizens, which offered opportunities for rural migrants.<sup>9</sup> All these factors induced a vast movement of agricultural labor from rural areas to cities.

Although the fragmentation of the rural-urban labor market has been much improved after the economic reforms, the misallocation of labor resources still leads to a significant economic welfare loss. A recent study by the World Bank estimates large potential gains from greater labor market integration – using 2001 as a baseline, with a mere 1% labor relocation from rural areas to urban areas, the overall economy will gain by 0.5%. If the share of labor outflow reaches 5% and 10%, GDP will grow by 2.5% and 5%, respectively (The World Bank, 2005).

> 6 See Davin (1999); Zhu (2002).

> 7 See McMillan et al. (1989).

> 8 See de Beer and Rocca (1997); Zhu and Jiang (1993).

> 9 Because of the wide gap in earning opportunities between rural and urban areas, such low-paid jobs were still attractive to rural migrants.

3
5

# 1.  Data

Data for this study come from the 2005/06 Ghana Living Standards Survey (GLSS 5), a new, nationally-representative survey of 8,000 households carried out by the Ghana Statistical Service (GSS). This survey, administered from September 2005 to September 2006, contains detailed information on all aspects of living conditions in Ghana, including income, expenditure, health, education, savings, and credit. As part of this survey, a supplemental migration and remittances module was administered to a nationally representative sub-sample of 4,000 households.<sup>6</sup> This paper uses the data from the migration and remittances sub-sample of 4,000 households. In carrying out the analysis we dropped 59 households because of missing data, which resulted in a sample of 3,941 households.

Since the focus here is on remittances, it is important to clarify how these income transfers are measured and defined. Data on remittances includes transfers received in three forms: (1) money (cash); (2) food; and (3) non-food goods.<sup>7</sup> While most remittances (about 75 percent) come in the form of money (cash), including food and non-food goods is important because it leads to a more accurate measure of the total flow of remittances to households in Ghana. In this study each household that is classified as receiving remittances – either internal (from Ghana) or international (from African or other countries) -- is assumed to receive exactly the amount reported in the survey. Households which report having migrants but do not report receiving remittances are classified as nonremittance receiving households. Using this definition distinguishes our work from much of the previous literature on migration and remittances by focusing on the origin of income flows rather than presence or absence of a migrant in the household. This approach seems
move among sectors of work. Individuals are matched by position in an identified household, sex, level of education, and age to ensure against generating spurious transitions. The analysis restricts itself to men aged 16-65 with a high school education or less. It also focuses on formal salaried workers and the "informal" self-employed, including owners of firms under 16 employees who do not have social security or medical benefits and are therefore not protected.<sup>4</sup> Only those who begin in formal salaried employment and move only once over five quarters into self-employment are retained, yielding a sample of 1087 workers. In the estimations, we employ predicted earnings in each sector as a measure of the "own" and "alternate" earnings, given the standard human capital variables, experience, experience squared, education, education squared. The return to accumulated capital (the opportunity cost of using savings to open a business) is the real 30-60 day deposit rate as calculated from the International Financial Statistics of the IMF deflated by growth of the consumer price index. We also test state dependence through introducing the lag of the independent variables in the regressions.

# **V. Results:**

Table 1 presents the results of the estimation of the model set out in section II.

**Table 1: Results from Rotating Panel Logit Regression**

||COEF.|S.E|COEF|S.E.|
|---|---|---|---|---|
|Wage (Salaried)|-.347|1.34|-.357|1.28|
|Wage (-1)|.132|1.41|X|l|
|Earnings (Self)|4.02|.249|4.03|.235|
|Earnings (-1)|_-.115_|.354||l|
|Interest Rate|-3.39 e-3|2.45 e-5|-2.71 e-3|1.92 e-5|
|Interest Rate(-1)|-2.68 e-3|1.93 e-5|-3.83 e-3|1.65 e-5|

Nobs=1078, Sample includes 24 complete panels of 5 quarters each spanning 1987-1993 4 It is often the case that the informal sector is defined as firms with five or less workers. As we are ocusing on informality defined as being unprotected by social security or other legislation, we loosen the size limit to the next category tabulated. In practice, the vast majority of fimns are under 3 workers.

11
> IRS, market size (location of demand), the costs of trading across regions and countries, and/ or backward and forward linkages among producers. The dispersion (centrifugal) forces differ across **NEG** models, in some cases they arise from the costs that firms face to reach an

> exogenously dispersed demand of immobile consumers (working in the agricultural sector) while in other cases they arise from congestion costs (rent and commuting costs) associated with large industrial **in. .tii**

Several **NEG** models generate the prediction that trade liberalization favors the geographic concentration of manufacturing activities within the liberalizing country. Paluzie (2001) and Monfort and Nicolini (2000) derive this prediction **by** extending the models **by** Krugman **(1991)** and Krugman and Venables **(1995)** to the set-up of, respectively, a country with two symmetric regions liberalizing its trade with the rest of the world and a country with two symmetric regions liberalizing trade against a foreign country (also with two symmetric regions). As international trade is liberalized, (i) access to foreign demand (exports) lowers the incentives for domestic firms to locate near domestic consumers that now represent a smaller share of sales and (ii) the presence of foreign supply (imports) lowers the incentives for domestic firms to locate near other domestic firms for input-output **(10)** linkages since foreign firms now represent a larger share of supply to domestic consumers. The incentive for a firm to locate away from domestic competitors (in the periphery) is provided **by** the possibility of being sheltered when serving the local market. With trade liberalization, competition in the periphery comes also from foreign firms and makes the dispersion of manufacturing activities less attractive. Briilhart et al. (2004) and Crozet and Koenig (2004) consider regions that differ in their distance to the rest of the world: a border region and an interior region. The main prediction from their **NEG** models is that trade liberalization fosters spatial concentration of manufacturing in the border region with better access to international markets. The novel forces in these models are that domestic firms may be attracted to the border region to reap the full benefits from improved access to foreign demand and domestic

> (IRS) internal to the firm (e.g., due to indivisible fixed costs) and monopolistic competition generally of the Dixit and Stiglitz **(1977)** type in the manufacturing sector; (2) costs of trading outputs produced and inputs used **by** firms across distances; **(3)** endogenous firm location decisions **;** (4) endogenous location of demand. Despite a vast **NEG** literature following Krugman **(1991),** due to technical difficulties in characterizing the equilibrium distribution of economic activity, only recent models that assume many regions within a country and distinguish across regions and countries allow researchers to characterize the response of agglomeration to international trade liberalization.

**13**
||**Ta**|**ble 3.** Opt|imal|Motor|Fuel Tax|es: Bench|mark Case||
|---|---|---|---|---|---|---|---|---|
|Benc|hmark <br>(_εLL_|Values for <br>= 0_._20, _ε_<sup>_c_</sup><br>_LL_|Labor <br> <sup>= 0</sup><sup>_._3</sup>|Elasti<br><sup>5, and</sup>|cities an<br> <sup>_EPF_ = </sup>|d Climate-<br><sup>$25 per to</sup>|Change Exte<br><sup>n of carbon.)</sup>|rnality|
|||Optimal Fue<br>(U.S. cents/|l Tax<br>gal)||Actual||Excess|% World GHG|
||Pigou|Ramsey<br>C|ong.|Total<br>_tF_|Tax<br>_t_<sup>0</sup><br>_F_|Diference<br>(_tF −t_<sup>0</sup><br>_F_ <sup>)</sup>|Burden<br>(1 +_MEBL_)|Motor Fuel<br>Emissions 1/|
|Countries tha|t undert|ax motor fue|l (_tF > _|_t_<sup>0</sup><br>_F_ <sup>); ac</sup>|<sup>count for </sup>|<sup>41% of mot</sup>|<sup>or fuel GHG wo</sup>|<sup>rld emissions</sup>|
|China|147|16|1|165|44|121|1.05|8.03|
|Russia|122|27|2|151|70|81|1.10|2.98|
|US|75|23|1|99|40|59|1.10|22.39|
|Brazil|80|73|2|156|100|56|1.19|2.28|
|Mexico|82|22|1|105|61|44|1.06|2.39|
|Canada|58|59|1|118|92|26|1.14|2.45|
|Countries th|at overta|x motor fuel|(_tF < _|_t_<sup>0</sup><br>_F_ <sup>); acc</sup>|<sup>ount for </sup>|<sup>28% of moto</sup>|<sup>r fuel GHG wor</sup>|<sup>ld emissions</sup>|
|Hungary|71|151|2|<br>224|227|-3|1.24|0.17|
|Greece|96|117|2|216|241|-25|1.17|0.49|
|France|98|251|5|353|379|-26|1.28|2.16|
|Luxembourg|124|114|2|241|280|-39|1.15|0.07|
|Italy|104|204|4|312|362|-50|1.23|2.06|
|Australia|62|42|1|105|156|-51|1.12|1.04|
|India|81|28|1|110|184|-74|1.08|3.15|
|Czech Republic|72|126|1|199|276|-77|1.18|0.23|
|Belgium|78|203|1|282|374|-92|1.24|0.51|
|Germany|129|175|5|309|402|-93|1.19|2.76|
|Austria|53|159|0|213|308|-95|1.23|0.35|
|Spain|64|94|1|158|254|-96|1.14|1.77|
|Denmark|48|227|0|275|385|-110|1.26|0.20|
|Sweden|34|175|0|209|321|-112|1.25|0.32|
|Japan|67|42|1|110|233|-123|1.11|5.45|
|UK|109|43|3|155|280|-125|1.15|1.76|
|Poland|72|123|1|196|327|-131|1.17|0.56|
|Portugal|62|172|1|236|371|-135|1.21|0.32|
|Slovak Republic|73|100|1|174|323|-149|1.14|0.08|
|Finland|73|145|2|219|390|-171|1.18|0.25|
|Iceland|74|77|1|152|327|-175|1.11|0.02|
|Netherlands|71|184|1|256|432|-176|1.20|0.63|
|Switzerland|51|74|0|126|311|-185|1.13|0.31|
|Ireland|52|83|0|135|327|-192|1.12|0.24|
|Korea|107|22|1|131|327|-196|1.06|1.82|
|South Africa|53|13|1|67|266|-199|1.08|0.57|
|Norway|53|126|1|180|399|-219|1.14|0.21|
|New Zealand|48|28|0|76|385|-309|1.10|0.17|
|Turkey|61|34|1|96|491|-395|1.11|0.72|

1/ Source: Authors’ calculations and IEA (2006) CO2 Emissions by Sector.

12
|**Table 1. Affordability Indi**<br>**Indicator (per GDP/capita)**|**cators**<br>**Formulas**|
|---|---|
|<br>Education costs|Education Costs|
|Total costs|(Education Costs) + Living Costs|
|Net costs|<br>(Education Costs + Living Costs–Grants)–Tax Breaks|
|Out-of-pocket costs|<br>(Education Costs + Living Costs–Grants–Tax Breaks)–Loans|

<u>Education costs. Education cost includes not only tuition, but also any additional</u> mandatory fees, payments for textbooks and other study materials, such as reading sets. It excludes one time fees such as academic record fee or registration fee.

The education cost per student is calculated separately for the public and the nongovernmental sector. These two cost estimates are weighted according to the share of student population represented by the public and the non-governmental sectors.

<u>Living Costs. Living costs encompass the cost of a living space and food.</u> Although the form of living varies from one country to another, students tend to live in dormitories, apartments, houses and, above all, parents’ house. Usher and Cervenan, 2005 assumes all students live independently. However, this poorly reflects reality in Latin America. A study on tertiary education in Argentina revealed 80 percent of tertiary education students live with at least one parent (Rozada and Menendez, 2002). We therefore estimate a reasonable cost of housing in the following way. The household surveys include data on household expenditure at the national level including information on the monthly rent. If students live at their parents’ house, they do not actually pay for the rent. However, it is a cost for parents, which is considered a living cost. We estimate the living cost by computing the family’s monthly rent divided by the number of occupants. We calculate the rent for two semesters (approximately nine months), because we are only interested in the cost during the academic year.<sup>7</sup> We do not include the families who own a house or who do not pay a rent. This method is applied for the cases of Brazil, Colombia and Mexico. For Peru, the household survey explicitly collects information regarding the costs of accommodation.

To estimate the price of food consumption, we rely on the information available in the expenditure part of the national household survey. This was only available for Brazil, and not for Colombia, Mexico and Peru. For these countries, we approximate the cost of food with the cost estimate for Brazil, which is $1 per day for nine months –two semesters.

This method generates a conservative estimate because the cost of living would be considerably higher for students living outside of parents’ house. Further, transportation costs are not considered in the methodology of Usher and Cervenan 2005, although it is often an important cost for the household.

> 7 For example, in a case of a student living in a household of four people and a monthly rent of $100; the rent per person per month is $25. Then, the rent per student a month is multiplied by the number of months the student attends university. The total rent per student for two semesters becomes $225 per year.

5
-3-

How large is the alleged movement in the net barter terms of trade? Spraos (1980) calculated many varieties, and these can be used as one reference point. For periods going back to the 1870s and ending before World War 1I the annual deterioration f-r primary product terms of trade reported ranged from a quarter of one per cent to one per cent per year, according to his calculations, clustering roughly around one half of one per cent. For periods after 1950, to 1975 or so, and excluding petroleum from primary products, the rates of deterioration range up to over 2 per cent per year. For the totality of primary products, Spraos thought the evidence for the long run, extending through the 1970s, did not show any statistically significant trends. Extending the data through the 1980s would probably restore that negative coefficient for relative primary product prices but would not bring it outside the range mentioned above. A paper by Cuddington (1990) examining terms of trade for 26 individual commodities from 1900 to 1983 lists time coefficients ranging fr6m +2 per cent per year to -1.2 per cent per year in trend-stationary models, with 5 out of 6 negative coefficients smaller than 1 per cent in absolute value. Of 10 negative time coefficients for commodities for which difference - stationary models are used, only 2 are above 1 per cent per year.

Grilli and Yang (1988) calculated that the trend rate of decline in primary commodity prices relative to manufactures prices from 1900 through 1986 was .5 per cent per year, or .6 per cent per year if fuels were excluded from primary products.

# A. Measures of Prices of Manufactured Goods<sup>1</sup>

Measures of manufactured goods prices have long baza-.x a weak link in all 1This section is taken mainly from Lipsey, Molinari, and Kravis (1991).
# **Figure 2: Sectoral distribution of Employment**

<!-- Start of picture text --> 100%<br>90% 100%<br>Agriculture<br>80%<br>80%<br>70% Manufacturing (Formal)<br>60%<br>60%<br>50%<br>40%<br>40%<br>30% Services<br>20% 20% Manufacturing (Informal)<br>10% Manufacturing<br>0% 0%<br>2003 2004 2006 2009 2010 2011 2012 2006 2009 2010 2011 2012<br><!-- End of picture text --> Source: UBAEUS, CSA 5
- 13 -

# C. Distinctive Characteristics of LSMS Surveys

Aside from collecting the data needed to get a comprehension measure of consumption, LSMS surveys are different from other household surveys in several other respects. First, while other surveys are primarily designed to measure different aspects of living standards, LSMS surveys collect information which allows one to analyze the determinants of the various outcomes that one observes. For example, school attendance of children may depend on the distance to the nearest school. The need for data on determinant factors explains why LSMS surveys collect data on prices, local schools and health services (including distance from the households and fees charged), conditions of local infrastructure (roads, sources of fuel and water, availability of electricity, communications, etc.), local agricultural conditions and practices, and historical information on household members themselves (characteristics of parents, employment history, migration history, etc.). Further, information at the household level is gathered on special government poverty alleviation efforts such as feeding programs, food subsidy schemes, employment generation efforts, food stamp programs, etc. In fact, many parts of the questionnaire have been designed by those who will use the data: economists, demographers, nutritionists and other researchers. This insures that the data collected are of the sort that can be used effectively to analyze the impact of government programs on household welfare.

Second, since information is collected rather intensively at the household level, LSMS surveys tend to have a smaller sample sizes in order to focus on data quality, rather than quantity. The amount of information collected from households is quite large, so attempts are made to minimize the