rafmacalaba/datause-encoder-data huggingface dataset:
18 The value of E(2) is always higher for individual income sources than for total income. It also varies a lot across income sources, from “lows” of 2.1 to 3.1 for earnings from employment, to highs of around 50.0 for employer’s incomes and for capital and transfer incomes in 1993! These extremely high values arise mainly from the fact that most households receive zero incomes from the relevant income sources. The E(2) entries in the second row of each panel in Table 4 measure the level of inequality across all households, regardless of whether they actually receive any income from a particular source. But while earnings from employment accrue to 71%-72% of all households in all three years, only 5%-6% of households receive any income as employers. The last two rows of Table 4 present the population share of households receiving positive amounts from each income source, and E(2) for positive incomes only. In this row, the value of E(2) drops precipitously for all income sources – and most pronouncedly for those which accrue only to a minority of households. Even among recipients, however, inequality still remains very high for the “all other incomes” category.<sup>26</sup> For the purpose of the decomposition of total household income inequality by source, all households must be considered in each calculation.

As in most countries, earnings from employment account for the largest share of total household per capita incomes in Brazil – declining from almost 60% to 50% over the period. The share of income from self-employment rises in 1993 and then falls to 15% by 2004. The declining shares of income from employment and self-employment are compensated by rising shares for the labor income of employers and, most importantly, for social security incomes. The relative mean for social security transfers doubles from 10% in 1981 to 20% in 2004, reflecting both the ageing of the population and the expansion and growing generosity of Brazil’s social security system (which is therefore likely to be unsustainable). Unfortunately, this expansion has taken place in a regressive manner, with the correlation between social security incomes and total household incomes rising from 0.36 in 1981 to 0.44 in 2004.

Social assistance transfers – including programs such as the old _Bolsa Escola_ and the new _Bolsa Família_ – are not included with social security incomes. Instead, they are lumped together with

> 26 This is as one might expect from the heterogeneous composition of this residual income category, which includes potentially large rental and capital incomes accruing to rich respondents, as well as small cash transfers to very poor households. With recent improvements in the disaggregation of the PNAD questionnaire, it would already have been possible – albeit still with some assumptions – to separate these disparate income sources for 2004. But this is not possible for earlier years.
correlated with household consumption; fortunately, as with the relevance condition, this additional condition can be easily checked using the cross sections.<sup>10</sup> Cohorts can be constructed from age or some combination of age and other time-invariant characteristics such as gender or ethnicity, as long as the cell size for each cohort is large enough. The assumptions stated in Proposition 1 on large cohort sizes are standard in the traditional pseudo-panel literature (e.g., type 1 asymptotics in Verbeek (2008)) and helps preclude measurement errors with cohort means. While it appears reasonable to assume that N tending to infinity given the usually large number of households interviewed with current household surveys, there seems to be no current consensus in the literature on how large nc should be.<sup>11</sup> Thus Proposition 1 provides an approximation of  _yi_ 1<sup>_y_</sup> _i_ 2 based on asymptotic theory, and how well this approximation turns out to be in practice is an empirical issue. We will see later in our empirical estimates using household surveys that estimation results are rather encouraging. Furthermore Corollary 2.1, to be discussed below, will provide a lower value for  _yi_ 1<sup>_y_</sup> _i_ 2 as a check on our cohort estimate.

Armed with an estimate for  _yi_ 1<sup>_y_</sup> _i_ 2 , we can then proceed to propose an estimate for the partial correlation coefficient  , which in turn helps provide the point estimate for poverty

> 10 Cohorts with a weaker correlation with household consumption would capture less variation in the latter. Clearly, the implicit assumption underlying traditional pseudo-panel analysis is that cohort dummy variables have a strong relationship with household consumption. In the extreme case, consumption (or poverty) mobility can happen entirely within cohorts, but we would expect this phenomenon to be rare in practice. Furthermore, this case would be easily detected since it will result in the synthetic panel cohort-level simple correlation coefficient  _yc_ 1<sup>_y_</sup> _c_ 2 being equal to 0 (i.e., since cohort means remain unchanged over time). We would like to thank David McKenzie for suggesting this point to us.

> 11 Monte Carlo simulations by Verbeek and Nijman (1992) suggests that cohort sizes of 100 to 200 are sufficient, (but also see, e.g., Devereux (2007) for a related simulation). Given a typical sample size of 5000 for most household surveys, if the household head’s age is used as the cohort variables, we can have approximately between 25 and 50 such cohorts of this sample size (depending on their age distribution).

11
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Mwangi, Lawrence W. 2007. ―Burkina Faso’s Experience: Small-Scale Water Service Providers.‖ Presentation by chief executive officer of Athi Water Services Board to the PPIAF Annual Meeting, May 23–24. Washington, DC.

Morella, Elvira, Vivien Foster, and Sudeshna Ghosh Banerjee. 2008. ―Climbing the Ladder: The State of Sanitation in Sub-Saharan Africa.‖ AICD Background Paper 13, Africa Region, World Bank, Washington, DC.

WSP (Water and Sanitation Programme). 2010. _Water Supply and Sanitation in Burkina Faso: Turning Finance into Services for 2015 and Beyond. An AMCOW Country Status Overview._ Washington, DC: World Bank.

- WHO (World Health Organization) Joint Monitoring Programme (JMP). 2010. ―Burkina Faso Estimates for the use of Improved Drinking-Water Sources.‖

http://www.wssinfo.org/resources/documents.html.

- ———. 2010b. ―Burkina Faso Estimates for the Use of Improved Sanitation Facilities.‖ http://www.wssinfo.org/resources/documents.html.

49
manager is the principal who designs a contract which induces workers to exert an optimal level of effort, given the limited ability of management to observe labor's exertion of effort on the job. Such a contract may result in wages higher than the marginal productivity of labor (i.e. the competitive wage rate). These 'efficiency wages"40/ reward labor for not shirking on the job by paying a high wage. If a worker is caught shirking, the punishment is losing his/her job and spending time unemployed.

24. The relationship between pay and effort is well-known and has been addressed in other studies of public sector reform.41/ Viewing labor agreements in a principal-agent framework, however, highlights the key elements to the success of increasing wages: the positive probability that a worker will be discovered if he/she shirks on the job and the positive probability of dismissal. Extensive interviews with railway managers and others in this field reveal that inadequate pay, wage compression and difficulty of dismissal are largely responsible for low worker morale, lack of motivation and, in the extreme, absenteeism when workers report to work merely to retain their secure positions.42/ To increase productivity adequate incentives should therefore be paid to labor, combined with the inst:tution of an effective monitoring system that enables managers to de ct shirking on the job. Employees must also recognize that their positions are not secure, but may be lost through dismissal or by closure of the enterprise.

40/ See Janet Yellin, "Efficiency Wage Models of Unemployment," The American Economic Review, Vol. 74, No. 2, May 1984, p. 200-205. and Carl Shapiro and Joseph Stiglitz, 'Equilibrium Unemployment as a Worker Discipline Device," The American Economic Review, Vol. 74, No. 3, June 1984, p. 433-444.

41/ See David L. Lindauer, Government Pay and Employment Policies and Government Performance in Developing Economies, The World Bank, PPR WPS 42, August 1988, p. 9-11.

42/ Op Cit, Eric Beshers, p. 5-10.

18
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Senegal > Nonproductive investments > Macro results**

||||N<br>Reduction in<br>h bli|onproduct|ive invest<br>I|ment<br>Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|1,500,000.00|–0.34|0.29|0.29|0.32|0.43|
|EV|Equivalent variation||–0.33|0.26|0.26|0.31|–2.12|
|s|Wage|1.00|–1.55|1.29|1.27|1.40|1.92|
|yg|Government income|539,000.00|–0.03|5.90|5.90|5.90|5.90|
|g|Total government expenditure|295,000.00|–5.44|5.38|5.38|5.38|5.38|
|ye|Firm income|935,000.00|0.56|–0.45|–0.44|–0.48|–0.67|
|It|Total private investment|860,000.00|0.51|–0.45|–0.65|–1.29|–0.74|
|GDP|GDP|2,100,000.00|–0.01|–0.01|–0.01|–0.01|–0.01|
|e|Nominal exchange rate|1.00|–0.43|0.07|–1.38|–5.44|–0.80|

# **Senegal > Nonproductive investments > Sectoral results**

||||Reduction in<br>other public|Non|productive<br>Import||Income|
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|<br>expenditure|VAT|<br>duties|Foreign aid|<br>tax|
||Crop agriculture|252,000.00|0.04|–0.05|–0.03|–0.01|–0.12|
||Export agriculture|221,000.00|0.73|–0.96|–0.95|–1.07|–0.72|
|Va<br>|Industries|361,000.00|0.41|–1.12|–1.08|–1.26|–0.71|
|Value added or<br>output|Construction|91200.00|2.77|1.86|1.88|2.25|2.07|
||Private services|995,000.00|0.19|–0.32|–0.35|–0.46|–0.57|
||Public services|180,000.00|–4.33|4.18|4.24|5.05|4.44|
||Crop agriculture|1.02|–0.26|0.12|–0.08|–1.07|–1.90|
||Export agriculture|1.02|–0.49|0.49|0.38|–0.66|–0.16|
|pq|Industries|1.14|–0.44|1.67|1.69|–2.32|–0.58|
|Market prices|Construction|1.01|5.75|6.15|6.07|5.24|5.76|
||Private services|1.02|–0.61|0.46|0.28|–0.82|–0.74|
||Public services|1.00|–1.16|1.15|1.09|0.31|0.90|
||Crop agriculture|1.00|0.03|–0.70|–0.13|1.18|–3.14|
|r|Export agriculture|1.00|0.31|–1.18|–1.19|–1.36|0.05|
|Rental rate of<br>|Industries|1.00|0.11|–3.31|–3.17|–3.74|–1.02|
|capital|Construction|1.00|15.47|12.81|12.88|15.45|14.86|
||Private services|1.00|–0.46|–0.52|–0.72|–1.21|–1.35|

36
- Becker, Sascha O., Francesco Cinnirella, and Ludger Woessmann (2010) “The trade-off between fertility and education: Evidence from before the demographic transition,” _Journal of Economic Growth_ , 15 (3), 177–204.

- Black, Dan A., Natalia Kolesnikova, Seth G. Sanders, and Lowell J. Taylor (2013) “Are Children “Normal”?,” _The Review of Economics and Statistics_ , 95 (1), 21–33.

- Bleakley, Hoyt and Fabian Lange (2009) “Chronic disease burden and the interaction of education, fertility, and growth,” _The Review of Economics and Statistics_ , 91 (1), 52–65.

- Chassambouli, Andri and Giovanni Peri (2015) “The Labor Market Effects of Reducing the Number of Illegal Immigrants,” _Review of Economic Dynamics_ , 18 (4), 792–821.

- (1995) “Immigration Reform: The Effects of Employer Sanctions and Legalization on Wages,” _Journal of Labor Economics_ , 13 (3), 472–498.

- Cohen, Alma, Rajeev Dehejia, and Dmitri Romanov (2013) “Financial incentives and fertility,” _Review of Economics and Statistics_ , 95 (1), 1–20.

- DeFronzo (1980) “Female Labour Force Participation and Fertility in 48 States: Crosssectional and Change Analysis for the 1960 - 1970 Decade,” _Sociology and Social Research_ , 64 (2), 263–278.

- Devillanova, Fasani F. Frattini T., C. (2017) “Employment of Undocumented Immigrants and the Prospect of Legal Status: Evidence from an Amnesty Program,” _ILR Review_ , 71.

- Falasco, D. and D.M. Heer (1985) “Economic and Fertility Differences Between Legal and Undocumented Migrant Mexican Families: Possible Effects of Immigration Policy Changes,” _Social Science Quarterly_ (65), 495–504.

- Fallah, Belal, Caroline Krafft, and Jackline Wahba (2019) “The impact of refugees on employment and wages in Jordan,” _Journal of Development Economics_ , 139 (C), 203–216.

18
In this framework, several factors contribute to a lower demand for the schooling of girls in Turkey. First, due to the distinctly lower labor-market participation rates for women in Turkey (25 percent for women vs. 70 percent for men in 2008; TÜİK 2012), the higher earnings capacity resulting from schooling would be less important for girls.<sup>13</sup> It is not obvious whether the opportunity cost of schooling would be higher for boys or girls because while boys are more likely to work in the market, the value of girls’ home production would be higher. On the other hand, the psychic costs of schooling would be especially high for girls due to the social norms in Turkey. For instance, the cost of traveling away from home to go to school as well as the cost of attending schools would be much higher for girls than for boys. There is no reason to expect the cost of not complying with the policy to differ by gender. Finally, the value of future earnings would be discounted more for girls as daughters are more likely to move away from their parents after marriage.

The benefits of schooling, in particular the sheepskin effects, are likely to be larger in urban areas than in rural areas due to the higher prevalence of wage employment in the former— the sheepskin effects matter more in wage employment as compared to self-employment and agricultural work (Wolpin 1977; Glewwe 2002).<sup>14</sup> The opportunity cost of schooling would surely be higher in rural areas as field work is readily available. In addition, the monetary as well as psychic costs of schooling would also be higher due to the longer distances to schools.<sup>15</sup> The discount rate would also be higher in rural areas as residents of rural areas are on average 13 Another reason for the lower schooling demand of girls would be lower returns for them; however, using the same quasi-experiment, Aydemir and Kırdar (2013) estimate higher returns for girls than for boys.

14 Using household data from 46 developing countries, Orazem and King (2008) find higher returns on schooling for urban residents than for rural residents.

15 Glewwe and Jacoby (1994), Alderman et al. (1996), Lavy (1996), and Glick (2008) report negative association between distance to school and educational outcomes in various developing countries.

13
The literature also advances the testable hypothesis that increased access to infrastructure services should help raise the income and the value of the assets of the poor. However, the availability of information on access to infrastructure services varies dramatically across countries and infrastructure sectors. For telecommunications, water and sanitation, existing data provide fairly good coverage across countries and over time. For power and transport, availability is sparse, especially in the time dimension. Subject to these constraints, Calderón and Servén (2010b) find a negative correlation across countries between measures of access to multiple infrastructure services and standard indicators of inequality – although it is not obvious to what extent this result may reflect a causal relation.

At the microeconomic level, another strand of literature examines the poverty effects of infrastructure interventions using matching techniques that combine samples of beneficiaries with samples drawn from regular household surveys (taken as control group). These studies usually evaluate the impact on income of a particular intervention affecting a given group of households or a specific geographic area. The implicit rationale is that, if such an impact is found, similar interventions targeted to poor households and/or low-income areas can reduce poverty and inequality.

Some studies of this type find that physical infrastructure in roads and communications facilitates spatial access and information flows, raising labor mobility, boosting rural non-farm economies, and reducing the incidence of poverty in some geographic areas (Jalan and Ravallion 2003, Zhu and Luo 2006, Reardon et al. 2007). They also show that public infrastructure provides a boost for local community and market development. For instance, rehabilitating rural roads in Bangladesh raised non-agricultural wage employment in targeted households and fostered markets that have become increasingly diversified across sectors —with the largest impact on households in the second-lowest quartile of the income distribution, the most mobile in changing activity from agriculture to non-farm work (Khandker and Koolwal, 2007, 2010). This type of intervention has also proved successful in Vietnam by increasing workers’ wages and developing local markets in poor communities (Mu and van de Walle 2007).

Granting access to new and improved roads in rural areas has also expanded the set of opportunities in non-agricultural activities in Peru (Escobal and Ponce 2008) and in non-farm activities among women in Georgia (Lokshin and Yemtsov 2005). There is also evidence from 14
|Adjusting or converting their<br>production or services|0.141|0.336**|-0.101|-0.552**|0.657**|1.047**|0.344|0.432|
|---|---|---|---|---|---|---|---|---|
||(0.115)|(0.136)|(0.287)|(0.260)|(0.255)|(0.418)|(0.311)|(0.417)|
|Started or increased online<br>business activity|-0.353*|-0.550**|0.144|0.362|-0.686|-0.568|-0.783*|-0.812|
||(0.212)|(0.255)|(0.531)|(0.479)|(0.431)|(0.436)|(0.415)|(0.537)|
|Started or increased delivery of<br>goods, services or carryout|0.264*|0.451*|-0.415|-0.408|0.594|0.173|1.405**|1.364**|
||(0.145)|(0.271)|(0.341)|(0.434)|(0.474)|(0.490)|(0.584)|(0.629)|
|Started or increased remote work|0.135**|0.045|0.276|0.259|-0.958***|-0.961***|-0.265|-0.234|
||(0.065)|(0.145)|(0.256)|(0.325)|(0.326)|(0.357)|(0.329)|(0.359)|
|Exporting directly (at least 10%<br>of sales)|0.156|0.244|0.073|0.387|0.284|0.153|0.808|1.067|
||(0.103)|(0.173)|(0.447)|(0.522)|(0.598)|(0.678)|(0.717)|(0.748)|
|Constant|0.329***|0.352|-1.959***|-2.623***|-2.144***|-1.605***|-3.269***|-3.266***|
||(0.112)|(0.227)|(0.551)|(0.475)|(0.339)|(0.358)|(0.490)|(0.513)|
|Number of observations|618|468|629|472|678|521|553|426|
|r2_w|0.238|0.352|||||||
|r2_b|0.104|0.079|||||||
|r2_o|0.096|0.079|||||||
|rho|0.640|0.681|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the Georgia COV-ES data. Size information used as the interaction terms is taken from the total number of full-time equivalent workers in the baseline ES. Columns 3 through 8 report marginal effects from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

55
24 Table 6 lists the growth, redistribution and residual components of poverty changes for the subperiods 1981-1993 and 1993-2004, as well as for the whole period 1981-2004. This is done both for Rocha (1993)’s region-specific poverty lines, and for the “administrative poverty line” of R$100 per capita per month. In the discussion, we refer predominantly to the administrative poverty line numbers.

**Table 6: Brazil 1981-2004: Decomposition of Changes in Poverty into Growth and Redistribution Components**

||**Regi**|<br>**onal Poverty Li**|**ne**<sup>**1**</sup>|**Adm**|<br>**inistrative Povert**|**y Line**<sup>**2**</sup>|
|---|---|---|---|---|---|---|
||Headcount|Poverty Gap|FGT(2)|Headcount|Poverty Gap|FGT(2)|
|**1981**|0.399|0.163|0.090|0.296|0.124|0.070|
|**1993**|0.466|0.215|0.130|0.326|0.151|0.093|
|**Observed Change**|**0.067**|**0.052**|**0.040**|**0.031**|**0.027**|**0.023**|
|**Growth**|0.023|0.012|0.007|0.017|0.009|0.006|
|**Redistribution**|0.048|0.039|0.032|0.018|0.019|0.018|
|**Residual**|-0.003|0.000|0.001|-0.004|0.000|0.000|
|**1993**|0.466|0.215|0.130|0.326|0.151|0.093|
|**2004**|0.345|0.145|0.083|0.222|0.093|0.054|
|**Observed Change**|**-0.121**|**-0.070**|**-0.047**|**-0.104**|**-0.059**|**-0.039**|
|**Growth**|-0.089|-0.049|-0.032|-0.062|-0.034|-0.022|
|**Redistribution**|-0.035|-0.023|-0.018|-0.035|-0.027|-0.020|
|**Residual**|0.003|0.002|0.002|-0.007|0.002|0.003|
|**1981**|0.399|0.163|0.090|0.296|0.124|0.070|
|**2004**|0.345|0.145|0.083|0.222|0.093|0.054|
|**Observed Change**|**-0.054**|**-0.018**|**-0.007**|**-0.073**|**-0.031**|**-0.016**|
|**Growth**|-0.070|-0.035|-0.021|-0.052|-0.026|-0.016|
|**Redistribution**|0.013|0.017|0.015|-0.019|-0.007|-0.002|
|**Residual**|0.004|0.000|-0.001|-0.002|0.002|0.002|

Source: Author’s calculations from PNAD's. Note: 1 - From Rocha (1993). See disaggregated line values in Appendix 2 <u>2 - Poverty line set as R$100 in September 2004 values.</u> By all three FGT measures, and for both lines, poverty rose during 1981-1993 and then fell during 1993-2004. The decline in the second sub-period was sufficient to imply a decline over the entire period. In terms of poverty incidence, the rise in 1981-1993 was of 3 percentage points, followed by a decline of ten percentage points (or almost a third) to 0.22 in 2004. During both sub-periods, the growth and inequality components moved together. Rising poverty during the 1980s was driven and the observed pattern of redistribution.
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