rafmacalaba/datause-encoder-data huggingface dataset:
nonpoor households. Most Haitians lack pensions, social security and savings, and thus children are often the only security for old age. An older Haitian woman expressed the matter this way: “It costs a lot to educate a child in Haiti; you have to work very hard. When I helped them with their education, I considered it like putting money into a savings account. My children are my bank account.”<sup>4</sup> The fertility rate has fallen rapidly in recent decades. During the three decades leading to the 1990s the fertility rate fell from 6.3 children per woman in 1960 to 5.4 in 1990, and then to 4.7 in 2000. (Table 2.7). Women’s increased participation in the labor market is an important factor in the decline in the fertility rate. Moreover, as education attainment increases, the fertility rate drops. Total desired fertility rate is lower than the actual fertility rate. This would indicate that there is still a substantial unmet demand for high quality and reliable family planning services, information, and resources.<sup>5</sup> Table 2.6: Average Number of Household Members Aged Less than 15 Years, 2001

|Artibonite|Center|Grand-<br>Anse|R<br>North|egion<br>North-<br>east|North-<br>west|West|South|South-<br>east|Metro-<br>politan|Are<br>Urban|a<br>Rural|Total<br>Haiti|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||||Ind|igent|||||||
|2.1|2.5|2.3|2.3|2.4|2.1|1.9|2.2|2.6|1.7|2.3|2.2|2.2|
|(1.8)|(1.9)|(1.9)|(1.9)|(1.9)|(1.8)|(1.7)|(1.9)|(1.9)|(1.5)|(1.9)|(1.9)|(1.9)|
||||||P|oor|||||||
|2.0|2.3|2.2|2.1|2.3|2.0|1.7|2.1|2.3|1.9|2.1|2.1|2.0|
|(1.7)|(1.9)|(1.9)|(1.9)|(1.9)|(1.8)|(1.6)|(1.8)|(1.9)|(1.8)|(1.8)|(1.8)|(1.8)|
||||||No|npoor|||||||
|0.6|1.1|1.1|1.3|1.5|0.9|1.0|1.4|1.0|1.2|1.1|0.9|1.0|
|(1.1)|(1.4)|(1.4)|(1.6)|(1.6)|(1.2)|(1.4)|(1.6)|(1.3)|(1.4)|(1.4)|(1.3)|(1.4)|
||||||T|otal|||||||
|1.7|2.1|2.0|2.0|2.3|1.8|1.4|1.9|2.0|1.4|1.9|1.9|1.8|
|(1.7)|(1.9)|(1.9)|(1.9)|(1.9)|(1.7)|(1.6)|(1.8)|(1.8)|(1.5)|(1.8)|(1.8)|(1.8)|
|_Note:_Standar|d deviations|in parenthes|es.||||||||||

_Source:_ Own calculations based on HLCS, 2001 Net migration has been larger than population growth rate since at least 1985 (Table 2.8). Large numbers of Haitians continue to flow over the 275 km border with the Dominican Republic to find work as sugar cane cutters, coffee pickers, and construction laborers. Many Haitians have settled in the Dominican Republic, and today there are an estimated 500,000 Haitians and Dominicans of Haitian descent living in the Dominican Republic.<sup>6</sup> There are also significant Haitian populations in the French Caribbean. Moreover, around one million Haitians live legally in North America.

> 4 Source: http://www.philly.com/mld/inquirer/9102135.htm.

> 5 Unfortunately, fertility rate micro-data are not available, therefore the analysis cannot be taken further

> 6 The Center and Southeast regions are by far the largest suppliers of labor to the Dominican Republic (and seasonal migration is negligible). The other regions appear to be far less affected by this labor pull.

9
# **REFERENCES**

Banerjee, A., Banerji, R., Duflo, E., Glennerster, R., and Khemani, S. (2010). “Pitfalls of Participatory Programs: Evidence from a Randomized Evaluation in Education in India.” _American Economic Journal: Economic Policy,_ 2:1-30.

Berg, G., and Zia, B. (2017). "Harnessing Emotional Connections to Improve Financial Decisions: Evaluating the Impact of Financial Education in Mainstream Media.” _Journal of the European Economic Association_ , Volume 15, Issue 5, October 2017.

Blimpo, M., Evans, D., and Lahire, N. (2015). “Parental Human Capital and Effective School Management: Evidence from the Gambia.” _World Bank Policy Research Working Paper 7238,_ Washington DC.

Bruhn, M., Ibarra G., and McKenzie, D. (2014). "The Minimal Impact of a Large-Scale Financial Education Program in Mexico City." _Journal of Development Economics_ , 108: 184-189, 2014.

Bruhn, M., Leao, L., Legovini, A., Marchetti, R., and Zia, B. (2016). "The Impact of High School Financial Education: Evidence from a Large-Scale Evaluation in Brazil," _American Economic Journal: Applied Economics_ , Volume 8, No. 4, October 2016.

Bruns, B., Filmer, D., Patrinos, H. (2011). “Making Schools Work. New Evidence on Accountability Reforms.” The World Bank, Washington DC.

Carneiro, P., Koussihouede, O., Lahire, N., Meghir, C., and Mommaerts, C. (2015). “Decentralizing Education Resources: School Grants in Senegal.” _National Bureau of Economic Research Working Paper 21063,_ Cambridge MA.

Carpena, F., Cole, S., Shapiro, J., and Zia, B. (2017). “The ABCs of Financial Education: Experimental Evidence on Attitudes, Behaviors, and Cognitive Biases.” Forthcoming, _Management Science._ 17
13 of tax collection and administration, not to mention more widespread tax evasion. Because of this lower level of tax revenues, economies with inefficient tax systems tend to have larger (and more monetized) budget deficits as compared to economies with efficient tax systems for any given level of government expenditure.

Because the taxing capacity of a country is technologically constrained by the structure of its economy and its stage of economic development, factors influencing the level of efficiency of the tax system in a country can be grouped into two categories<sup>12</sup> variables that account for the sectoral composition of GDP, and the stage of economic development.

The agricultural sector might be the hardest sector of the economy to tax. Its typically non-corporate structure facilitates tax evasion. Therefore, the larger the relative size of the agricultural sector in an economy, the higher the costs of administration and enforcement of tax collections will be. This implies a less efficient tax system, thus leading to a larger budget deficit for a given level of government expenditures.

On the other hand, the manufacturing sector is generally regarded as one of the easiest to tax. This is because, in sharp contrast to the agricultural sector, the manufacturing industry is largely corporate in structure, making it less capable of tax evasion. Thus, when an economy has a relatively large manufacturing sector, it should face lower tax enforcement costs implying a more efficient tax system, and consequently it should have a smaller budget deficit.

A third sectoral share, imports plus exports as a fraction of GDP, measures the foreign trade sector of the economy. Import and export taxes are commonly regarded as a cheap tax base because they are relatively easy to assess and collect since such foreign traded commodities must pass through a limited number of frontier ports, and are usually handled by a few wholesalers. The ease of collecting such taxes is one reason why countries with extensive foreign trade typically collect a greater proportion of public revenues in the form of import and export duties than countries with limited external trade (Todaro, 1997). An economy with a larger foreign sector, ceteris paribus, should therefore be able to collect more tax revenue thus leading to a smaller budget deficit.

Finally, since tax collection costs are likely to be smaller in urban areas than in rural areas. As such, the higher the urban population share out of the total population should be negatively associated with the budget deficit.

> 12 See Cukierman _et al_ . (1992) and Edwards and Tabellini (1991).
# **Female Labor Participation in the Arab World**

# **Some Evidence from Panel Data in Morocco**

_Paolo Verme, Abdoul Gadiry Barry_<sup>_1_</sup> _, Jamal Guennouni_<sup>_2_</sup>

**JEL** : F66, J01, J08, J16, J21, J42

**Keywords** : Labor market, unemployment, employment, labor force participation, North Africa and the Middle East.

> 1 World Bank. The authors are grateful to Manuel Arellano, Daniela Marotta and David Locke Newhouse for very useful comments. We are also grateful to Mohammed Taamouti (Economic Director, Moroccan Central Bank) for providing essential macro data on Morocco.

> 2 Chief of the Labor Force Survey Unit, Department of Statistics, High Commission for the Plan of Morocco.
taken over by the government during the colonial period and designated as crown land (Peebles (2006), De Silva (1981)). The crown land was later distributed through settlements, and restrictions on sales, mortgage and rental were imposed. The historical malaria thus is significantly correlated with the extent of land restrictions in an area through the availability of crown land. We exploit this correlation between historical malaria and the incidence of land restrictions in a sub-district to identify the causal effects of land restrictions. To be more precise, we rely on the interaction of historical malaria and average rainfall across different sub-districts for identification in an empirical model with district fixed effects. This approach uses subdistrict level rainfall as weights to uncover variations in malaria across subdistricts from the district level average estimates available from Newman (1965) (see the discussion on empirical strategy in section 4 below). This strategy is motivated by two considerations. First, the variation in land restrictions in the data is at the subdistrict level and the interaction of district level malaria with the subdistrict level rainfall provides an instrument that varies across subdistricts. Second, a large literature shows that rainfall is one of the most important determinants of spatial variations in malaria in Sri Lanka; the malaria incidence is lower in a subdistrict within a district if it has higher rainfall ((Clemesha, 1934; Rustomjee, 1944; Briet et al, 2008). As we discuss in detail later, we control for rainfall in a subdistrict in the regressions to ensure that the exclusion restriction imposed is credible. In addition, the interpretation that the interaction of historical malaria with subdistrict rainfall provides an estimate of historical malaria variations across subdistricts implies testable sign restriction in the first stage regression, which is borne out by the empirical results reported later. The strength of our identification strategy derives from the following observations: (i) the timing of the malaria eradication program was determined by the technological breakthrough abroad for tackling malaria (DDT), and thus can plausibly be treated as exogenous,<sup>4</sup> (ii) a successful nationwide malaria eradication program was implemented in Sri Lanka in 1947; malaria endemicity (as measured by enlarged spleen rates) fell close to zero by 1950-51.<sup>5</sup> We thus rely

> 4Although DDT was first synthesized in 1874, its insecticidal properties were discovered in 1939 by Swiss scientist Paul H Muller. It was widely used during second World War to control malaria and typhus, and after the war DDT was made available as an agricultural pesticide and for malaria eradication programs.

> 5Reported malaria cases in Sri Lanka were reduced from about 3 million per year during pre-eradication era to only 29 in 1964 (Harrison, 1978). The number of malaria death cases were 30 in 2002 among a population of 21

3
# _The NAIS portfolio_

Following James and Nair (2009) and Nair (2010), the NAIS portfolio takes the following form. The NAIS program covered about 19 million farmers during the Kharif season 2008 (June to September) and the Rabi season 2008-9 (October to December), as shown in Figure 1. On the basis of there being approximately 110 million farmer households in 2008, the annual crop insurance penetration was approximately 17 percent. However, for borrowing farmers, approximately two thirds of the insured farmers, NAIS purchase is compulsory. The penetration for non-borrowing farmers, for whom purchase is not compulsory, is therefore around 6 percent. Small and marginal farmers account for two thirds of the farmers covered under NAIS.

The NAIS farmer premium volume reached almost Rs.800 crores (US$178 million) in 2008 having steadily increased since 2003 (see Figure 1).<sup>5</sup> Food crops represent about 75 percent of the total NAIS premium volume and small and marginal farmers contribute to about half.

The average premium per farmer insured slightly exceeded Rs.400 (US$9) in 2008, ranging from Rs.250 (US$5.5) for non-borrowing farmers to about Rs.500 (US$11) for borrowing farmers. The average area insured per farmer has slightly decreased from 1.56 ha in 2004 to 1.34 ha in 2008.

Figure 1: NAIS Premium Volume and Farmers Covered, 2000-2008 <!-- Start of picture text --> 200 25<br>180<br>160 20<br>140 Total Total<br>120 15<br>100<br>80 Borrowing 10<br>(compulsory Small and<br>60<br>40 purchase) 5 marginal<br>farmers farmers<br>20<br>- -<br>Year Year<br>millions)<br>Number of farmers (million)<br>Farmer premium income (USD<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> Source: Data from AICI The demand for crop insurance is concentrated in the states where crops grow under rain-fed conditions and natural risks are greater, including Andhra Pradesh, Gujarat, Karnataka, Orissa, Uttar Pradesh and Rajasthan (Figure 2). Some states, such as Bihar, Karnataka and Gujarat, have historically collected more claims in proportion to their premium contribution than other states.

> 5 Indian rupees (INR) have been converted to US Dollars (USD) using an exchange rate of 45.23 INR to 1 USD.

5
in conducting rapid HIV tests through the Ministry of Health HIV Unit HCT Counselor Certification Program. In addition they tested for hemoglobin and measured the height and weight of all children aged 59 months or younger.

Early childhood development (ECD) tests were administered to all 36-59 month-old children of the study participants. These tests consisted of the Malawi Development Assessment Tool (MDAT) for fine motor skills, language, and hearing, which were administered directly to the child (Gladstone et al. 2008) and the Strengths and Difficulties Test (SDQ), administered to the mother or the guardian responsible for the child (Goodman 2001; Woerner et al. 2004).

Prior to the analysis of data from Round 4, a pre-analysis plan was registered at the AEA RCT Registry (AEARCTR-0000036; <u>https://www.socialscienceregistry.org/trials/36; please see</u> Appendix A). Our outcomes cover six domains for the core respondent – education and competencies, marriage and fertility, health and sexual behavior, empowerment and aspirations, employment and wages, and consumption – and outcomes in relevant domains for their husbands and children.<sup>13</sup> A detailed description of all outcomes in this paper is provided in Appendix B.

# **3. ESTIMATION STRATEGY**

In this section, we discuss the experimental estimation strategy used to examine program impacts on core respondents. The causal identification of program impacts on husband characteristics and children’s outcomes is more challenging and the estimation strategies used to analyze those outcomes are discussed in Sections 4.6, 4.7, and Appendix C.

> 13 Many of our outcomes are in the form of indexes that are constructed using the following rubric: First, we ensured that all sub-questions are aligned so that higher scores always have a consistent meaning (good or bad). We then calculated the mean and standard deviation of the responses to each sub-question in the control group – separately for baseline schoolgirls and baseline dropouts. We then normalized each sub-question by subtracting the mean and dividing by the standard deviation. Finally we constructed (and then normalized) the raw mean of the normalized variables for all sub-questions within a family of variables to create the final index.

13
# Cleaning RDB’s business registration database

A number of companies appear to have – for a variety of reasons ranging from tax accessing tax discounts to newly introduced regulations, and not fully investigated in this Paper – re‐ registered with RDB after the introduction of the one‐stop shop in 2008. As these are not new companies, they need to be excluded from the analysis.

Any company registered with the Revenue Authority prior to registering with the RDB, is excluded from the analysis. Similarly, companies that registered with the RDB after 2008 and that had already filed taxes prior to registration, are also excluded from the analysis.

Upon closer inspection of the data — particularly concerning large taxpayers — a further group of companies are excluded from the analysis. This includes a manufacturing company operating in Rwanda since the mid‐1980s, which had re‐registered after 2008, as well as two large insurance companies with a split in their life and non‐life businesses as a result of sector regulation introduced in 2010.

# Defining economic activity

Formal economic activity is defined as any sort of interaction with the RRA. In defining whether an RDB‐registered business is active, this study follows a three‐step process.

**First** , using individual level tax declarations for the 2008‐2012 period for corporate and personal income taxes, employees tax withholdings (PAYE), value‐added tax, and import duties, a company is deemed “active” in a given year if in that year, it has registered any of the following activities:

- Recorded any turnover or operating expenses;

- Filed taxes for any of its employees;

- Made any input or output VAT declaration or requested a VAT refund for 0‐rated goods;

- Paid import duty, VAT, or withholding tax on imports.

A company that fails to register any of these activities in a given year is deemed “inactive” for the year. This category also includes those companies that are present in the RDB’s registration records but that do not show tax activity.

These steps result in an indicator of company activity for every year during the 2008‐2012 period. It is important to note that being classified as an active taxpayer does not equate to making a positive tax contribution. Rather, it can be interpreted as having interacted with the RRA.

**Second** , a company maintains its “active” status if it registers consistent activity up to the year 2012. If a company has ceased activity at any point up to 2012, it is deemed to have “closed.” Companies that do not register activity in the 2008‐2012 period are maintained as “inactive.” However, it is worth noting that this is the case for none of the approximately 97,000 taxpayers captured by the RRA. All the inactive companies in the study are RDB‐ registered companies that have never filed taxes.

A number of companies found to be fluctuating in and out of activity have been classified as “data issues” and kept distinct from the rest in order not to influence the results. This category remains comfortably small: just 2.8 percent of the total number of taxpayers and 0.4 percent of RDB‐registered companies are classified as having “data issues”.

36
# Did Higher Inequality Impede Growth in Rural China?<sup>†</sup>

Dwayne Benjamin University of Toronto Loren Brandt University of Toronto John Giles The World Bank JEL Classification: O12, O15, P20 Keywords: Inequality, growth, rural China †This is a completely rewritten version of the previous working papers, “Inequality and Growth in Rural China: Does Higher Inequality Impede Growth,” (University of Toronto Working Paper #237, June 2006; and Institute for the Study of Labor (IZA) Discussion Paper 2344, September 2006). We thank Gustavo Bobonis for helpful comments, and the editor and referees for constructive suggestions for this revision. Benjamin and Brandt gratefully acknowledge the Social Sciences and Humanities Research Council of Canada for financial support, and Giles acknowledges support from the National Science Foundation (SES-0214702).
Growth and Transformative Effects of ICT Adoption: A Survey<sup>1</sup> Estefania Vergara Cobos<sup>2</sup> and Eduardo A. Malásquez<sup>3</sup>

**JEL codes:** O0, O1, O4, I30

**Keywords:** information and communications technology, digital technology adoption, internet, economic growth, productivity, labor market, welfare.

> 1 The authors wish to thank Vivien Foster and Tania Begazo for their support and valuable comments, and Iago Bueno Bojczuk for their help editing this paper. The findings, interpretations, and conclusions in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent.

> 2 Economist, Infrastructure Chief Economist Office, World Bank (evergaracobos@worldbank.org).

> 3 Economist, Poverty and Equity GP, World Bank (emalasquez@worldbank.org).
Wahba (2002), using data from Egypt, shows a phenomenon called "dynastic poverty traps," which means that the probability of children being sent to work increases 10% when their parents had worked during their childhood. Emerson & Souza (2003) reach the same conclusion and explain such event as "social norms", parents that worked during their childhood years face child labor more naturally. As mentioned earlier, this paper aims to provide an additional element for that discussion and test the relation between land titling and child labor force participation using the case of the City of Osasco.

# **2. Microeconomic Framework – The Basics**

Cockburn (1998) points out that one of the principal gains of strong property institutions is to shift the burden of property protection and enforcement away from individual households and informal communities to the state.

There is little microeconomic evidence documenting the cost of informality to individual households. Carter & Zegarra (2000), World Bank (2000) and Field (2007) note that, in many settings, informal institutions arise to compensate for the absence of formal property protection. In such context, there is one important mechanism by which it is assumed that tenure security removes individuals from the labor force and incremental income. Untitled households are constrained by the need to provide informal policing, both to deter prospective invaders from invading private properties and to actively participate in community enforcement efforts to protect neighborhood boundaries.

Hence, an important outcome of titling efforts that effectively increase household tenure security should allow households and communities to reallocate time, resources and human talent away from this role.

The acquisition of a property title has a direct value in terms of freeing up hours of work (and income generation) previously devoted to maintaining tenure security through informal means.

_w_ = market wage _H_<sup>_m_</sup> = work in the outside market Assumptions: a) There is no outside labor market for provision of home/tenure security. Assuming a missing labor market for the provision of home security is reasonably justified by incomplete contracts (there some risk involved in employing non-members to guard property - especially in those poor communities in Brazil).

b) Leisure and home production hours are assumed to be perfect substitutes for the hours individual spend on property protection.

c) All households face a common wage _wi_ .

d) The household is assumed to maximize per capita leisure ( _li_ ) and not the leisure of individual members.

e) Household talent ( _Φ_ ) and endowment ( _E_ ) are assumed to be fixed.

Assuming, Z = time spent at home = H<sup>h</sup> +L H<sup>h</sup> = work at home and L = leisure And L = ∑ = li,H<sup>h</sup> = ∑ = H<sup>h</sup> ,H<sup>m</sup> = ∑ = H<sup>m</sup> ,X = ∑ = X N is the number of household members, NN NN li is leisure, NN xi consumption, NN H<sup>hi</sup> work hours in ii=1 ii=1 ii=1 ii=1 home production, and H<sup>m</sup> outside market work hours of household member i, and xi = X/N, li = L/N.

7