rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6867

# **Abstract**

In the past decade, conditional cash transfer (CCT) programs have become an important component of social policy in developing countries. While the impacts of these programs have been well researched with respect to their effectiveness to achieve intended outcomes, less is known about their impact on private expenditure decisions. This aspect has great policy relevance since changes in private household expenditures can either support or counteract the aim of the programs. This essay investigates the impact of a CCT program on private household expenditure decisions in nutrition, health and education which are seen as principal contributors to child human capital. First, household expenditure behavior under a CCT program is discussed based on Heckman’s model on the technology of skill formation as a conceptual framework. The paper shows how intra-household preferences and perceptions on the substitutability or complementarity of investments can impact household resource allocation decisions. Subsequently, the theoretical implications are tested in the context of the Brazilian CCT program Bolsa Família, using the Brazilian household expenditure survey. Evidence is found that households increase their private expenditure in food and education disproportionally to the amount of cash transfer, that is, more than would be expected when considering the Engel curves of the expenditures under question.

This paper is a product of the Global Modeling and Analytics Team, Development Prospects Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mvargasdacruz@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table 3: Impacts for the subgroup expressing interest in migrating abroad at baseline.**

Sample includes baseline respondents with completed endline surveys who reported being “interested” or “strongly interested” in working abroad at baseline. Stratification-cell fixed effects and baseline covariates described in Table 2 are included. Huber-White standard errors reported in brackets. Coefficients for all treatments reported in Table A8.

* Beam: Department of Economics, National University of Singapore; McKenzie: Development Research Group, The World Bank; Yang: Department of Economics and Ford School of Public Policy, University of Michigan. Correspondence to: deanyang@umich.edu. We gratefully acknowledge funding support from the World Bank’s Gender Action Plan and Research Support Budget. We thank Ditas Ravanilla and Sr. Adelia Oling for their crucial collaboration in this project, as well as PALFSI branch officers and staff for their support and assistance in implementation, Innovations for Poverty Action for overseeing the fieldwork, and in particular, Joma Gonzalez, Jaye Stapleton, Naomi Joseph, Veronica Gonzalez, Cree Jones, Amanda Chang, and the rest of the SWAP team. We obtained human subjects approval for this study from the University of Michigan, Health Sciences and Behavioral Sciences Institutional Review Board, project number HUM00034271, “The Determinants of Temporary Labor Migration in the Philippines.”

> 1 While the Philippines ranks fourth globally in total remittances received annually, just behind Mexico, as a share of its own GDP, it ranks only 18<sup>th</sup> , behind countries including Nepal, Honduras, El Salvador, Serbia and Bangladesh (Ratha, Mohapatra, and Silwal, 2010).

30
observe actual welfare and mobility for households in the former case, but not the latter. Following previous studies (Cruces et al. 2005; Dang et al. 2014; Bourguignon and Moreno 2018), the key time invariance assumption is maintained by considering only those households whose heads are between 25 and 65 years of age in all estimates so that life cycle events are avoided in general.

We follow previous literature that estimates poverty and welfare by including time invariant and deterministic regressors in the models. The harmonized data used in this paper allow estimating poverty transitions and welfare changes with variables frequently associated with wellbeing (e.g., Ferreira et al. 2012; Vakis et al. 2016), specifically harmonized variables commonly found in household surveys that are comparable within and between countries and over time. The models consider the log of per capita household income in 2011 PPP/day as the left-hand side variable in the underlying regressions and the following regressors: [1] the household head’s characteristics (age, age squared,<sup>20</sup> gender, and years of education), household size, and household size squared;<sup>21</sup> [2] regional fixed effects;<sup>22</sup> and [3] the interaction between the first and the second set of covariates. The selected specification follows previous studies that define a core set of timeinvariant variables and correlates of welfare (Cruces et al. 2005; Dang et al. 2014; Bourguignon and Moreno 2018).

All these covariates are natural candidates as time invariant and deterministic regressors. However, it is important to bear in mind that all variables considered vary to some extent over time. The age, gender, and education of the head of the household can change each time the head

> 20 Age is a deterministic function of time. Therefore, we subtract the number of years betwen rounds of data from the household head’s age when predicting first round incomes for those surveyed in the second round. On the other hand, we add the number of years betwen rounds to the household head’s age when changing the forecasting direction. This procedure is commonly applied in the Synthetic Panel’s literature. See Dang and Lanjouw (2013) and Dang et al. (2014) for specific details and a more comprehensive explanation.

> 21 In the validation, we also include area of residence (i.e., urban vs. rural) in Chile, since it is the only country for which this variable is available.

> 22 We consider all regions for which surveys are representative in each country. In the application, we use area of residence (i.e., urban vs. rural) when regional fixed effects are not available.

19
_Figure 5. Education distribution: Working-age population of Syrians who became -_ _<u>refugees in Lebanon, Jordan, and KRI (2010) and all working age Syrians (2011)</u>_ <!-- Start of picture text --> 80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Secondary<br>Never<br>Primary or Intermediat (Highschoo Graduate or<br>attended,<br>less e l or more<br>illiterate<br>Vocational)<br>All Syrians, 2010 5.2 45.8 15.8 23.0 10.2<br>Lebanon, attended 13.8 31.2 37.7 14.5 2.8<br>Kurdistan, completed 15.0 49.6 17.1 9.8 8.5<br>Jordan, completed 8.1 70.5 8.3 8.4 4.8<br>SHARE (%)<br><!-- End of picture text --> _<u>Notes</u>_ <u>: Authors’ own tabulation using data from SRHCS.</u> _Figure 6. Distribution of the pre-crisis, working-age, employed population of Syrian_ _<u>refugees (2010) and all Syrians (2011)</u>_ <!-- Start of picture text --> INDUSTRY CATEGORY<br>Syrian refugees, Lebanon Syrian refugees, Kurdistan<br>Syrian refugees, Jordan All Syrians (2011)<br>35.6 34.2<br>25.5 25.0<br>23.0<br>SHARE 15.1 12.0 17.2 13.3 13.6 11.4 9.9 14.2 18.3 15.4 18.9 10.2 16.6 6.5 7.6 12.6 11.2<br>5.0 4.3 4.3 4.8 4.5<br>1.3 0.4 1.5 1.5 2.9 2.1 0.1 0.0 0.2<br>OTHE R<br>L<br>SER VIC ES PUB LIC<br>AGR IC ULTUR E C ONSTR UC TION PR OFE SSIONAL HE ALTH, AND E DUC ATION<br>M ANUFAC TUR IN G<br>WHOLESALE / R E TAI FOOD & B E VE R AGE ADM INIS TR AT IO N, HOUSE HOLD WOR K<br><!-- End of picture text --> _<u>Notes</u>_ <u>: Authors’ own tabulation using data from SRHCS.</u> 14
# **<mark>Table 11: Household Enterprise</mark>**

<mark>Coefficients, standard errors in parentheses</mark>

||Pooled|Mean|Impact|byRound|(OLS)|Pooled|Impac|t byRound|(SAR)|
|---|---|---|---|---|---|---|---|---|---|
||N|R3|R1|R2|R3|Impact|R1|R2|R3|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**_Panel A: Reported by Women_**<br>Involved in any self-employed, non-farm IGA|2,313|.561|.106***|.122***|.084**|.104***|.083**|.097***|.059*|
|<br>in the past year [yes=1]|||(.035)|(.035)|(.034)|(.020)|(.035)|(.034)|(.034)|
|Total average monthly profit for female|1,210|75.2|-52.7|97.8|203***|98.3**|-58.9|82.7|191***|
|<br>controlled non-farm enterprises [in Cedis]<br>**_Panel B: Reported by Men_**|||(74.0)|(66.0)|(61.4)|(39.1)|(73.4)|(65.3)|(60.6)|
|Involved in any self-employed, non-farm IGA|1,779|.303|.039|.041|-.008|.024|.025|0.016|-0.027|
|<br>in the past year [yes=1]|||(.036)|(.035)|(.035)|(.021)|(.035)|(.035)|(.034)|
|Total average monthly profit for male|433|555|-498|123|564**|145|-484|81.7|544**|
|<br>controlled non-farm enterprises [in Cedis]|||(315)|(258)|(247)|(159)|(310)|(252)|(244)|

Notes: Standard errors are reported in parenthesis and are clustered at the individual level. *** denotes significance at 1%, ** at 5% and * at 10%. Standard errors are clustered by respondent and are shown in brackets. All outcome variables in Panel A (B) are measured conditional on a female (male) respondent interviewed. Control variables include household structure, household size, number of females in the household, number of household members under five years of age, dummy variable for household head being male, age of household head, dummy for whether household head has ever been to school, three dummies for the predominant chieftaincy institution in the community, number of sampled households situated around respondents’ house within a 200m radius (household density), distance between the house of the respondent and the main road demarcation.

33
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||With|individual co|ntrols|With individ|ual + househo|ld controls|With individ|ual + househo<br>controls|ld + regional|
||Earner|LFP in last<br>30 days|Worked in<br>last 30 days|Earner|LFP in last<br>30 days|Worked in<br>last 30 days|Earner|LFP in last<br>30 days|Worked in<br>last 30 days|
|Log of HH annual expenditure per capita||||-0.013|0.025|-0.037|-0.008|0.030|-0.034|
|||||(0.055)|(0.049)|(0.055)|(0.057)|(0.049)|(0.056)|
|HH living standard index||||0.031*|0.055***|0.043**|0.031*|0.057***|0.047**|
|||||(0.018)|(0.018)|(0.018)|(0.018)|(0.018)|(0.018)|
|Number of children aged 6 to 14 years||||0.049*|0.028|0.037|0.059**|0.039|0.048|
|||||(0.029)|(0.028)|(0.029)|(0.030)|(0.029)|(0.030)|
|Number of HH members aged 15–64 years –||||0.044|0.060|0.053|0.053|0.070*|0.061|
|women||||(0.039)|(0.039)|(0.039)|(0.038)|(0.038)|(0.038)|
|Number of HH members aged 15–64 years –||||-0.132***|-0.127***|-0.125***|-0.131***|-0.126***|-0.123***|
|men||||(0.036)|(0.035)|(0.035)|(0.035)|(0.035)|(0.035)|
|Number of HH members aged 65+||||<br>0.000|<br>-0.012|<br>-0.001|<br>-0.004|<br>-0.016|<br>-0.000|
|||||(0.080)|(0.080)|(0.080)|(0.079)|(0.077)|(0.078)|
|Dhaka City Corporation (=1)|||||||0.057|0.040|0.051|
||||||||(0.043)|(0.043)|(0.043)|
|Slum (=1)|||||||-0.031|-0.037|-0.056|
||||||||(0.049)|(0.049)|(0.049)|
|_Ref: Birthplace: Central_||||||||||
|Northern|||||||0.161*|0.155*|0.163*|
||||||||(0.084)|(0.086)|(0.083)|
|Eastern|||||||-0.001|-0.019|-0.032|
||||||||(0.070)|(0.071)|(0.071)|
|Southern|||||||0.101*|0.116**|0.087|
|Observations|1,076|1,076|1,076|1,076|1,076|1,076|1,076|1,076|1,076|

Note:<sup>a.</sup> *** 1%, ** 5% and * 10% level of significance.

b. Standard errors are in parentheses.

b. Survey weight applied.

15
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE monthly subsistence consumption of 10 cubic meters, the associated utility bill would come to $11 per month. Based on the distribution of household budgets in Côte d’Ivoire, these monthly utility bills for power and water would be affordable by 99 and 85 percent of the population, respectively (figure 22). In fact, the share of the population that could afford the service is much higher than the share of the population that already has the service, suggesting that Côte d’Ivoire has scope to increase coverage at cost-reflective tariffs before affordability would become a serious impediment. Even then, consumption of 25 kilowatt-hours per month for power and 4 cubic meters per month for water, which meets the most basic needs, would cost $4.5 and $4.7 per month, respectively, and would be affordable to close to 100 percent of the population. By comparison, household incomes are lower elsewhere in Sub-Saharan Africa, and affordability of cost recovery tariffs is therefore a much more serious problem (figure 22).

**Figure 20 Affordability in Côte d’Ivoire much better than in other low income countries**

<!-- Start of picture text --> Cote d'Ivoire monthly utility bill (US$) low-income countries<br>budget<br>% of urban households whose<br>utility bills would be less than 5<br>percent of the monthly household<br><!-- End of picture text --> Source: Banerjee and others 2009.

Operational inefficiencies of power and water utilities cost Côte d’Ivoire a further $194 million a year, equivalent to 0.83 percent of GDP. The annual value of inefficiencies in the power sector ($189 million) is substantially higher than for the water sector ($5 million). The burden of utility inefficiencies in Côte d’Ivoire is lower than for the benchmark countries, particularly in the case of water (figure 23). Under-collection of revenues and distribution losses are the main sources of inefficiencies in the power sector. CIE collects only 88 percent of its billings and has distribution losses of 16 percent, compared with a best practice benchmark of around 10 percent. In the water sector, SODECI has distribution losses of only 22.5 percent, compared with a best practice benchmark of 20 percent, and manages to collect close to 100 percent of billings.

39
# **1. Introduction**

Banks have become increasingly international through the ownership of foreign subsidiary networks and the provision of cross-border loans. The Bank for International Settlements (BIS) reports that international banks’ foreign claims, which include claims through foreign subsidiaries as well as cross-border loans, stood at 15.9 trillion dollars in 2012.<sup>2</sup> While banks have become more international, bank regulation and supervision remain mostly national. This implies that international bank flows to some extent could be driven by international regulatory differences as banks seek to avoid burdensome regulation in their home countries.

Using aggregate data on international bank flows from the BIS, Houston, Lin, and Ma (2012) find that banks tend to have more claims on countries with fewer regulations, which they interpret as evidence of regulatory arbitrage by international banks. The provision of loans to countries with less stringent regulations is indeed consistent with regulatory arbitrage, if these loans are originated by subsidiaries located in these countries, as then the more lenient regulatory regime applies. However, the greater claims on countries with fewer regulations could reflect more lending by international banks’ establishments located in other countries with relatively stringent regulation, in which case there is no regulatory arbitrage. The aggregate BIS data do not give details on where international loans have been originated, and hence cannot provide conclusive evidence of whether international banks engage in regulatory arbitrage.

In this paper, we examine regulatory arbitrage using micro data on cross-border syndicated loans.<sup>3</sup> For each loan, we know where a bank has originated the loan. In

> 2 <mark>Based on BIS Locational Banking Statistics data.</mark>

> 3 <mark>Cross-border syndicated loans are an important component of international banking flows with total new commitments of $1.8 trillion in 2012 (BIS Quarterly Review, September 2013, Table 10). 2012 is the last full year for which the BIS reports aggregate cross-border syndicated loan volumes.</mark>

2
Policy Research Working Paper 6244

# **Abstract**

Using an extraordinarily rich panel dataset from Ghana, this paper explores the nature of self-employment and informality in developing countries through the analysis of self-reported happiness with work and life. Subjective job satisfaction measures allow assessment of the relative desirability of different jobs in ways that, conditional wage comparisons cannot. By exploiting recent advances in mixed (random parameter) ordered probit models, the distribution of subjective well-being across sectors of employment is quantified. There is little evidence for the overall inferiority of the small firm informal sector: there is not a robust average satisfaction premium for formal work vs. self-employment or informal salaried work, and owners of informal firms that employ others are on average significantly happier than workers in the formal private sector. Moreover, the estimated distribution of parameters predicting satisfaction reveal substantial heterogeneity in subjective well-being within sectors that conventional fixed parameter models, such as standard ordered probit models, cannot detect: Whatever the average satisfaction premium in a sector, all job categories contain both relatively happy and disgruntled workers. Specifically, roughly 67, 50, 40 and 59 percent prefer being a small-firm employer, sole proprietor, informal salaried, civic worker respectively, than formal work. Hence, there is a high degree of overlap in the distribution of satisfaction across sectors. The results are robust to the inclusion of fixed effects and alternate measures of satisfaction. Job characteristics, self-perceived autonomy and experimentally elicited measures of attitudes toward risk do not appear to explain these distributional patterns.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group; and the Office of the Chief Economist, Middle East and Northern Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at wmaloney@ worldbank.org and brijkers@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Comparison across different amounts of disaster management investment

<!-- Start of picture text --> GDP compared to baseline Potential GDP compared to baseline<br>4 —— 0%60% ———— 80%20% ———— 40%100% 4 ——— 60%0% ———— 80%20% ———— 40%100%<br>ae a 0<br>4<br>4<br>-8<br>-8<br>-12<br>co -16<br>-16 -20<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Consumption compared to baseline Debt/GDP<br>—_——_0% =— 20% —— 40% — 0% —— 20% —— 40%<br>2 — 60% —— 80% —— 100% 160 — 60% —— 80% —— 100%<br>0 140<br>2 120<br>4 AS DBA<br>YY 4 | f| py fet 100<br>6 SALT}S eeJ aananw 80<br>Wagan ee anv<br>-10 Vi V 40<br>-12 20<br>2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100<br><!-- End of picture text -->
47 in constructing new systems of social insurance. The social consequences – poor income security and deficient human services – are consistent with the traditional inference in microeconomic textbooks that a market economy does not automatically generate adequate arrangements in these fields. Naturally, I refer to various limitations of markets for voluntary income insurance, myopia among some individuals, and difficulties to borrow for investment in human capital; poverty (and liquidity constraints) is, of course, another reason why individuals to not acquire voluntary insurance policies or invest in human capital.

It is not clear why the Chinese authorities were so late in building up new systems of income insurance and the provision of human services, when the old systems broke down in connection with the economic reforms. We may speculate that the overriding concern for economic growth meant that other objectives were put aside, possibly since such objectives were regarded as potentially harming the growth strategy. Alternatively, the authorities did not realize that markets do not automatically solve problems in the social field.

However, the shift to a new economic system is not the only background factor for the need, and demands, to reform the social arrangements in China. Two other factors should also be emphasized, namely changes in demography and the urbanization process. Behind the recent _demographic_ development in China, there are dramatic changes in both fertility and longevity. Broadly speaking, the fertility rate has dropped from about 6.0 immediately after World War II to about 1.5 today, and life expectancy (at birth) has increased from about 35 years to about 72 years.<sup>96</sup> To begin with, these developments “improved” the age composition of the population in the sense that the share of individuals of working age (15-59) increased from about 53 % in 1975 to about 67 % today – a development often characterized by demographers as a “demographic dividend”. However, as a result of the rise in longevity, this dividend is bound to be transformed into a “demographic deficit” when the number of elderly increases substantially.<sup>97</sup>

> 96 While the US Population Reference Bureau (PRB, 2005) reports today’s fertility rate at 1.6, The China 2000 County Population Census (All China Marketing Research Co., 2003, reports the figure at 1.3; see, for instance, the discussion in Liu (2006).

> 97 For instance, the UNPD (2000) predicts that the population share of individuals of working age will start to fall after 2010, and return to about 53 % by 2050 – the lowest predicted share at that time among