rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6487

# **Abstract**

During Vietnam’s two decades of rapid economic growth, its fertility rate has fallen sharply at the same time that its educational attainment has risen rapidly— macro trends that are consistent with the hypothesis of a quantity-quality tradeoff in child-rearing. This paper investigates whether the micro-level evidence supports the hypothesis that Vietnamese parents are in fact making a tradeoff between quantity and quality of children. The paper presents data on private tutoring—a widespread education phenomenon in Vietnam—as a new measure of household investment in children’s quality combining it with traditional measures of household education investments. To assess the quantity-quality tradeoff, the paper instruments for family size using the distance to the nearest family planning center. IV estimation results based on data from the Vietnam Household Living Standards Surveys (VHLSSs) and other sources show that families do indeed invest less in the education of schoolage children who have larger numbers of siblings. This effect holds for several different indicators of educational investment and is robust to different definitions of family size, identification strategies, and model specifications that control for community characteristics as well as the distance to the city center. Finally, estimation results suggest that tutoring may be a better measure of qualityoriented household investments in education than traditional measures like enrolment, which are arguably less nuanced and less household-driven.

This paper is a joint product of the Poverty and Inequality Team, Development Research Group; and the Education Unit, Human Development Network. I. 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 hdang@worldbank.org or hrogers@ 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
beneficiaries of assistance programs, more attention must be paid to the often idiosyncratic and city-specific policies which control the type, location, and standards of housing.

**From Supply Conditions to Greater Community  Participation.** Recent research has revealed that very often highly variable local supply conditions matter greatly.  These conditions have implications for the structure of the projects themselves.  For example, the implications of the finding of strong regularities with respect to housing demand lead to an emphasis on affordable design standards, and an understanding of beneficiaries’ likely willingness to pay.  In the early years of Bank lending, little attention was given in this perspective to involving the beneficiaries in the projects in any way other than as direct consumers.<sup>19</sup> The result was that the Bank’s sites and services, projects, in effect, often became low-cost public housing programs rather than an exercise that captured the initiative and imagination of the beneficiaries.  In many ways, as shown by Baross (1990) in the early Bank shelter projects, Turner’s (Turner and Fichter 1972) aspirations for enabling and empowering the poor to house themselves remained unfulfilled.  Bank projects had helped scale back the costs of the units produced, but the public sector often remained he producer.

In recent years, the nature of the involvement of beneficiaries has changed. The importance of community involvement and the importance of using social capital to leverage Bank resources has become a major thrust of Bank policy. Efforts to stimulate community driven development have grown rapidly as Bank studies, such as Gibbs, Fumo and Kuby (1999), have shown the strong positive effects community-based organizations (CBOs) and non-governmental organizations (NGOs) can have on Banksupported projects. As a consequence, rather than the engineering, technocratic approach

> 19 This is not to say that community participation was completely ignored in the early urban projects.  Many projects did indeed have community components to them but these were constrained by absence of techniques available today as well as highly centralized decision making structures in client countries.

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Figure 4: Primary deficit as percent of GSDP<sup>30</sup> <!-- Start of picture text --> .<br>Mizoram<br>Nagaland<br>Sikkim Himachal Jharkhand<br>Himachal Pradesh<br>Pradesh<br>Uttarakhand<br>75 percentile<br>M edia n<br>25 percentile<br>1991-1998 1999-2004 2005-2010<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br><!-- End of picture text --> GSDP = gross state domestic product.

As expected, most of the states that reflected weak fiscal performance are also plagued with high debt and repayment burdens across the three periods. As can be seen from the box plot in figure 5, a large number of states have both debt and interest burden above the 75th percentile across the three periods under study. A case in point is West Bengal, which has persistently had high debt (an average of 45 percent during 2005–10) and a large interest burden (39.3 percent) continuing relentlessly, even during the current period (figure 6).

> 30 The box plot is created by Stata. The line in the middle of the box indicates median (50 percentile), top of the box 75 percentile and bottom 25 percentile.  The lines above and beneath the box represent the adjacent values, which are within 1.5 iqr of the nearer quartile (75 percentile for the line above and 25 percentile for the line below). The iqr is calculated as value of 75 percentile minus that of 25 percentile. So the largest value (upper end of the upper line) is identified by 75 percentile plus 1.5 times iqr and the smallest value (lower end of the lower line) is 25 percentile minus 1.5 times iqr.

18
Because the analysis assumes prices do not change, the Ricardian model overestimates the welfare benefits/losses of increases/decreases in global output. Further, it is important to remember that welfare reflects what happens to consumers as well as farmers. Reductions in prices would clearly benefit consumers whereas increases in prices would benefit farmers. However, although it is likely that climate change will have large impacts on local production, it is not as clear what impact climate change will have on global production because some regions will become more productive as others become less so (McCarthy et al. 2001). Consequently, it is not evident how prices will change.

# **3. Data**

We collected 2500 surveys from seven countries in Latin America. Argentine, Uruguay, Chile, and Brazil were chosen from the ‘Southern cone’ region, and Venezuela, Ecuador, and Colombia were chosen from the Andean region. Countries were chosen to cover a wide range of climate zones and given the availability of researchers. The distribution of surveys used by country is displayed in Table 1.

The districts were sampled to obtain representation of each climate zone within each country. A wide range of climates are therefore present in the sample. Of course, climates that could not support any agriculture were not surveyed. In each country, 1530 districts were selected and 20-30 households were interviewed in each district. Within each district, cluster sampling was done to control the cost of the survey. We treat this sample as though it were a representative sample of farms in Latin America although it is not a truly random sample.

The surveys collected data about farming activities and crop and livestock 8
# **REFERENCES**

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- <u>Gordon, C., Senior, C., Banks, H., Gregory, I., Johns, J., Mitchell, J., and Wood, R.: 2000. “The simulation of SST, sea ice extents, and ocean heat transports in a</u> version of the Hadley Centre coupled model without flux adjustments”, _Climate Dynamics_ **16** , 147-168.

- <u>International Research Institute for Climate Prediction (IRI): 2003. “IRI Net Assessment</u> Predictions for Sri Lanka”, <u>http://iri.columbia.edu/%7Emahaweli/clim/forecast.html.</u>

- Intergovernmental Panel on Climate Change (IPCC): 1997. _The regional impact of climate change: an assessment of vulnerability._ S _pecial report of IPCC Working Group II:  Summary for policy makers,_ New York.

- Intergovernmental Panel on Climate Change (IPCC): 2001. “ _Climate Change 2001: Impacts, Adaptation and Vulnerability._ Cambridge University Press, Cambridge, UK _._

- Kurukulasuriya, P. and Rosenthal,: 2003. Climate Change and Agriculture:A Review of Impacts and Adaptations, _Climate Change Series, 91,_ Agriculture and Rural Development Department and Environment Department Joint Publication, The World Bank, Washington DC.

- <u>Maddison, D.: 2000. “A Hedonic Analysis of Agricultural Land Prices in England and</u> Wales.” _European Review of Agricultural Economics_ **27** : 519-532.

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# IXIV SPECIAL Topic 1: PROJECTING AIDS MORTALnY

Table S3. Regressions for HIV prevalence **In** future years, female and male adults

|Predictor|1995|<br>2000|<br>2005|<br>2010|HIV Pr<br> <br>2015|evalenc<br>(<br> <br>2020|e<br>tn)<br>in year<br> <br>2025|
|---|---|---|---|---|---|---|---|
|FEMALES||||||||
|Constant<br>1990 HIV (In)<br>1990<br>HIV squared<br>(In)<br>Year<br>of start|-158.50<br>(3.06)<br>1.11<br>(0.01)<br> <br>-0.06<br>(0.01)<br>0.08<br>(0.00)|-250.39<br>(6.45)<br>1.18<br>(0.02)<br>-0.09<br>(0.01)<br> <br>0.13<br>(0.00)|-320.94<br>(9.22)<br>1.27<br>(0.03<br>-0.11<br>(0.02)<br> <br>0.16<br>(0.00|-352.79<br>(10.61)<br>1.30<br>) (0.04)<br> <br>-0.11<br>(0.02)<br> <br>0.18<br>) (0.01)|-380.33<br>(11.82)<br>1.33<br>(0.04<br> -0.11<br>(0.03)<br> <br>0.19<br>(0.01)|-404.87<br>(13.20)<br>1.36<br>) (0.05<br>-0.12<br>(0.03)<br> <br>0.20<br>(0.01|-556.77<br>(31.57)<br>1.20<br>) (0.07)<br>0.00<br>(0.05)<br> <br>0.28<br>) (0.02)|
|R2<br>N-243|0.99|0.97|0.95|0.94|0.93|0.92|0.86|
|MALES||||||||
|Constant<br>1990HIV(In)<br>1990 HIV squared<br>(In)<br>Year<br>of start|-158.36<br>(3.18)<br>1.08<br>(0.01)<br> <br>-0.05<br>(0.01)<br>0.08<br>(0.00)|*251.55<br>(6.56)<br> <br>1.14<br>(0.02)<br> <br>-0.08<br>(0.01)<br> <br>0.13<br>(0.00)|-323.41<br>(9.33)<br> <br>1.22<br>(0.03)<br> <br>-0.10<br>(0.02)<br> <br>0.16<br>(0.00)|-356.36<br>(10.73)<br> <br>1.24<br>(0.03)<br> <br>0.00<br>(0.02)<br> <br>0.18<br>(0.01)|.385.34<br>(11.98)<br> <br>1.28<br>(0.04)<br> <br>-0.11<br>(0.03)<br> <br>0.20<br>(0.01)|-409.76<br>(13.23)<br> <br>1.30<br>(0.04)<br> <br>-0.12<br>(0.03)<br> <br>0.21<br>(0.01)|-550.86<br>(31.26)<br> <br>1.12<br>(0.07)<br> <br>0.00<br>(0.04)<br> <br>0.28<br>(0.02)|
|RK<br>U-243|0.99|0.97|0.9f|<br>0.94|<br>0.93|<br>0.92|0.85|

Table S4. Regressions for life expectancy at **age** 10 and under-live mortality rate

|di||Lif<br>|e<br>expectan<br>|cy at age 1<br>|0 In perio<br>|d<br>||
|---|---|---|---|---|---|---|---|
|Prector|1990-9|1995-0|2000-0|2005-1|2010-1|2015-2|2020-25|
|FEMALES||||||||
|Constant<br>HIV (In)<br>HIV squered (in)|55.96<br>(0.02)<br>-0.87<br>(0.01)<br>0.01<br>(0.00)|<br>58.10<br>(0.03)<br> <br>-0.88<br>(0.01)<br>0.01<br> (0.00)|<br>59.06<br>(0.06)<br> <br>-0.97<br>(0.01)<br>0.01<br>(M.OO)|<br>59.95<br>(0.07)<br> <br>-1.01<br>(0.01)<br>0.01<br>(0.00)|<br>61.33<br>(0.03)<br> <br>-1.39<br>(0.01)<br>0.01<br>(0.00)|<br>62.73<br>(0.01)<br> <br>-1.74<br>(0.00)<br>0.02<br>(0.00)|<br>64.02<br>(0.06)<br> <br>-1.45<br>(0.08)<br>-0.06<br>(0.02)|
|R2|0.99|0.99|0.99|0.99|0.99|0.99|0.95|
|MALES||||||||
|Constant<br>HIV (in)<br>HIV squared<br>(In)|54.59<br>(0.02)<br>-0.70<br>(0.01)<br>0.00<br>(0.00)|55.73<br>(0.03)<br> -0.88<br>(0.01)<br> <br>0.02<br>(0.00)|56.31<br>(0.14)<br>-0.87<br>(0.02)<br> <br>0.01<br>(0.00)|56.77<br>(0.14)<br>-0.84<br>(0.02)<br> <br>0.01<br>(0.00)|<br>58.03<br>(0.11)<br>-1.19<br>(0.03)<br> <br>0.02<br>(0.00)|<br>59.23<br>(0.08)<br>-1.49<br>(0.04)<br> <br>0.03<br>(0.00)|60.30<br>(0.05)<br>-1.34<br>(0.07<br> <br>0.03<br>(0.02)|
|R2|0.99|0.95|0.96|0.97|0.97|0.98|0.94|
**Table 6** Impact of Treatment on Partner Churn and <u>Quality</u> of Sex Partners

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|A. Partner Churn|||||
|With same partner as baseline<br>treatment|-0.020<br>(0.021)|-0.039*<br>(0.021)|0.117|2,591|
|treatment x Goal|0.057*<br>(0.031)||||
|B. Partner Quality|||||
|Quality index|||||
|treatment x post|-0.089<br>(0.080)|0.218**<br>(0.089)|-0.000|1,711|
|treatment x post x Goal|-0.105<br>(0.132)||||
|His age|||||
|treatment x post|0.114<br>(0.537)|-0.488<br>(0.410)|24.998|1,711|
|treatment x post x Goal|0.318<br>(0.580)||||
|Dropout/never enroll|||||
|treatment x post|-0.007<br>(0.023)|-0.047**<br>(0.023)|0.041|1,711|
|treatment x post x Goal|0.042<br>(0.036)||||
|Use contraceptive|||||
|treatment x post|-0.174**<br>(0.078)|0.057<br>(0.071)|0.620|1,711|
|treatment x post x Goal|0.078<br>(0.105)||||

_Notes._ In Panel A, the reported coefficients are _β_ 1, _β_ 2, and _γ_ 1 from an adapted version of equation 1 where the outcome variable “With same partner as baseline” is the change in partnership status from baseline to endline. Panel B presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 1. Age is the average age in years of all sexual partners listed; Dropout/Never Enrolled is the share of sexual partners listed whose enrollment status is dropped out or never enrolled in school; and Use contraceptive is the share of sexual partners that the female reports use contraceptives. The Quality index is generated by taking the unweighted mean of the indicators after they have each been recoded so that positive coefficients indicate improved outcomes and standardized at baseline and endline separately to the mean and standard deviation among females in ELA only communities who were not assigned to the _Goal_ treatment. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *.

_Source._ Panel A: Female data, outcome from endline, controls from baseline, balanced panel. Panel B: Female baseline and endline data, balanced panel. Sample is restricted to females in the balanced panel who list at least one sexual partner at baseline or endline.

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**Figure A.1. NIRP announcements: changes in selected financial variables up to one month**

# **A. 2-year government bond yields**

# **B. 10-year government bond yields**

<!-- Start of picture text --> Basis points Basis points<br>20 Median 20 Median<br>10<br>0<br>0<br>-20<br>-10<br>-40<br>-20<br>-60<br>-30<br>-80 -40<br>1 2 3 4 1 2 3 4<br>Weeks after Weeks after<br>announcement announcement<br>C. Nominal effective exchange rates D. Equities<br>Percent Percent<br>6 10 Median<br>Median<br>8<br>4 6<br>4<br>2<br>2<br>0<br>0<br>-2<br>-2 -4<br>-6<br>-4 -8<br>1 2 3 4 1 2 3 4<br>Weeks after Weeks after<br>announcement announcement<br><!-- End of picture text -->

# **C. Nominal effective exchange rates**

Sources: J.P. Morgan, Bloomberg, Haver Analytics, European Central Bank, World Bank. Notes: Basis points or percent change from closing values on the day before the NIRP announcement. Line indicates median across 17 NIRP announcements. Grey area indicates 10<sup>th</sup> and 90<sup>th</sup> percentile range. Each event is extended out for 30 calendar days. Variable definitions and events are the same as in Figure 9. C. A decrease in the nominal effective exchange rate indicates depreciation.

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and physical capital accumulation, and population growth. We capture these growth determinants through the ratio of real investment to GDP, the stock of years of education and demographic variables such as life expectancy, the ratio of labor force to total population and population growth. In addition to those four determinants, Durlauf, Johnson, and Temple’s (2005) survey of the empirical growth literature identi...es 43 distinct growth theories and 145 proposed regressors as proxies; each of these theories is found to be statistically signi...cant in at least one study. Due to data availability, our set of growth determinants is a subset of that identi...ed by Durlauf, Johnson and Temple (2005). We consider the three broad variable categories below.

- Macroeconomic and external environment: A stable macroeconomic environment characterized by low and predictable in‡ation, sustainable budget de...cits, and limited departure of the real exchange rate from its equilibrium level sends important signals to the private sector about the commitment and credibility of a country’s authorities to e¢ ciently manage their economy and increase the opportunity set of pro...table investments. In this paper, the impact of macroeconomic stability is captured by the government consumption relative to GDP. Since the seminal work of Barro (1991), many authors have considered this ratio (g<sup>c</sup> /GDP) as a measure of stability and distortions in the economy. The argument is that government consumption has no direct e¤ect on private productivity but lowers saving and growth through the distorting e¤ects from taxation or government-expenditure programs. Moreover, following Easterly (1993) among others, we also consider the investment price level (i.e., the PPP investment de‡ator) as a proxy for the level of distortions that exists in the economy. Finally, the trade regime/external environment is captured by the degree of trade openness, measured by imports plus exports as a share of GDP. Many authors such as Levine and Renelt (1992) and Frankel and Romer (1999) have considered this ratio. However, since this measure is sometimes criticized because it only captures the volume of trade and not the degree of openness as a proxy for distortions in trade policies, we also consider an alternative indicator, the SW openness index constructed by Sachs and Warner (1995). The objective is to conclude which measure of openness is a better (in the sense of more robust) proxy.

- Institutions and governance: The role of democracy and institutions in the process of economic growth has been the source of considerable research e¤ort. In this paper we examine the hypothesis that political freedom and institutional quality are signi...cant determinants of economic growth using political rights and civil liberties indices to measure the quality of institutions and capture the occurrence of free and fair elections and decentralized political power. Kormendi and Meguire (1985), Barro (1991), Barro and Lee (1994) and Sala-i-Martin (1997) among others considered these two indices as proxies of the quality of institutions and governance.

14
microfinance institutions such as _Banco Compartamos_ in Mexico which offers individual loans at quite high interest rates to both men and women) give strong reasons to question this archetypical model of microfinance as necessarily the best way to expand access to finance to the poor and to improve the small business sector going forward.<sup>9</sup> Many microfinance organizations focus almost exclusively or largely on female borrowers. For example, 97 percent of Grameen Bank’s seven million borrowers are 10 11 women , as are 70 percent of FINCAs borrowers , and 65 percent of ACCIÓN’s five million clients.<sup>12</sup> While part of this reflects a social mission, many of the justifications are economic in nature. Women are argued to be poorer than men on average (e.g Burjorjee et al., 2002; FINCA, 2007), have less collateral, and hence be more credit-constrained (e.g. Khandker, 1998, Armendáriz and Morduch, 2005). But if this is the case, when women do receive access to credit, it should generate higher returns than when men receive access. The experimental evidence from Sri Lanka (and supporting nonexperimental evidence from Mexico and Brazil) in de Mel et al. (2008b) provides a reason to question this extensive focus on women, and a suggestion that more products need to be developed to fit the needs of urban male clients.

Group liability is often hailed as one of the central innovations of the microfinance movement, mitigating both the adverse selection and moral hazard problems which can give rise to credit market failures. The idea is that borrowers who know they will be liable for the debts of others in their group will have an incentive to screen others so that only reliable people will join their group, and then to monitor their group members to ensure they invest their funds wisely and exert enough effort. However, as Giné and Karlan (2008) note, group liability has several pitfalls which may cause it to be disliked by many borrowers. It may be particularly troublesome for small business owners, who might be discouraged from undertaking somewhat risky but high return projects by other group members, may need different size loans or different loan periods from other group members, and find frequent group meetings costly in terms of

> 9 See Cull et al. (2009) for a description of the heterogeneity in the microfinance sector, and the debate generated by the successful stock offering of Banco Compartamos. Karlan and Morduch (2009) provide an excellent overview of recent research on access to finance.

> 10 - <u>http://www.grameen info.org/bank/index.html</u> [Numbers as of May 2007], accessed August 15, 2007. 11http://www.villagebanking.org/site/c.erKPI2PCIoE/b.2604299/k.FFD9/What_is_Microfinance_What_is_ <u>Village_Banking.htm, accessed August 15, 2007.</u>

> 12 <u>http://www.accion.org/about_key_stats.asp [all clients 1976-2006], accessed August 15, 2007.</u>

- 8 -