rafmacalaba/datause-encoder-data huggingface dataset:
Estimating the E¤ects of Credit Constraints on Productivity of Peruvian Agriculture9 columns D’and E’) and 25.96 (comparing columns F’and G’).<sup>3</sup> Thus, the di¤erences do not completely go away but are substantially reduced.

Next, we estimate that if credit constrained farmers were to become unconstrained then productivity would increase by 10.6%, which is signi...cantly smaller than the estimate of 26% from Guirkinger and Boucher (2008).<sup>4</sup> Moreover, this 10.6% increase is not statistically signi...cant. A full comparison between our estimates and Guirkinger and Boucher’s can be seen in Table 2. The second and third columns (B and B’) are estimates for absolute productivity change by credit constrained type if the credit constraints were removed. The fourth and ...fth columns(C and C’) are relative productivity increases for each group if they were to become unconstrained. The total estimated impact on productivity for removing credit constraints is found in the seventh and eighth columns (E and E’), which includes our overall estimate of 10.6%.

Thus, applying the estimator to a dataset on the productivity in Peruvian agriculture shows that the new estimator changes the conclusions compared to earlier results on the same dataset. In particular, adding a selection equation to a model with ...xed e¤ects causes the coe¢ cients of the credit constrained farmers to be the quite similar to the coe¢ cients of the unconstrained farmers.

# 4. Conclusion

We propose an estimator for the endogenous switching regression models with ...xed effects. The estimator allows for endogenous selection and for conditional heteroscedasticity in the outcome equation. Applying the estimator to a dataset on the productivity in Peruvian agriculture shows that the new, more general estimator substantially changes the conclusions compared to the earlier analysis of the same dataset. In particular, adding a selection equation to a model with ...xed e¤ects causes the coe¢ cients of the credit constrained farmers to be similar to those of the unconstrained farmers, demonstrating the importance of having a selection equation. Relaxing the single index assumption of the selection equation by extending Altonji and Matzkin (2005) is left for future research and

> 3 The p-values of these for test were 0.000, 0.000, 0.0181, and 0.0066.

> 4 We use the same methodology to calculate the change in productivity as Guirkinger and Boucher (2008) but we use the new estimator for the parameter value, using step 1A and step 2.
WPS 3995

# **THE VALUE OF MORTALITY RISK REDUCTIONS IN DELHI, INDIA**

by Soma Bhattacharya, Anna Alberini and Maureen L. Cropper University of Maryland and World Bank

# **Abstract**

We interviewed commuters in Delhi, India, asking them to report their willingness to pay (WTP) to reduce their risk of dying in road traffic accidents in each of three scenarios that mirror the circumstances under which the majority of the road fatalities in Delhi occur. The WTP responses are internally valid, in the sense that WTP increases with the size of the risk reduction, income, and exposure to road traffic risks, as measured by length of commute and whether the respondent drives a two-wheeler. As a result, the “value of a statistical life” (VSL) is individuated, i.e., it varies across groups of beneficiaries. For the most likely beneficiaries of road safety programs—the most highly exposed individuals—the VSL is about 150,000 PPP$.

World Bank Policy Research Working Paper 3995, August 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ We thank the World Bank Research Committee and Transport Anchor, and Fondazione Eni Enrico Mattei for funding.
thus administered to a total of 500 households, out of which 251 were migrant households and 249 were non-migrant households<sup>i</sup> .

Finally, in the analysis presented in the following sections, the 20 villages sampled are classified into three broad regional groups based on the migration corridors identified in the first stage key informants interviews conducted in Dhaka. In particular, our interviews with manpower contracting agencies had indicated that the two principal hubs for recruiting and supplying temporary workers to the Middle East were located in the urban centers of Dhaka and Chittagong. In order to discern variations, if any, in migration-related transactions by these hubs, communities in the districts of Dhaka, Mymensingh, Madaripur, Barisal and Narail were grouped under the “Dhaka corridor” while communities in the districts of Chittagong, Comilla, and Noakhali were grouped under the “Chittagong corridor.” Villages outside Dhaka and Chittagong divisions were part of the third group, which included villages in Sylhet from where most migrants to the UK originate.

# **3.0. Characteristics of migrant households**

The census in the 20 selected communities suggests that 11% of households had members who had migrated abroad in the last 10 years. Since the communities within the _upazilas_ were purposively sampled on the basis of higher-than-average rates of migration, computed migration prevalence rates cannot be generalized at the district or even the _upazila_ level.

Table 2 provides basic demographic characteristics of migrants from the census of the 20 villages. While the paper focuses on international migrants, the census data provides an opportunity to compare domestic with international migrants as well. Our data shows that the 6
across surveys in how people with disabilities are identified and cross-country comparisons in prevalence can only be made with caution, if at all.<sup>9</sup> Despite the lack of cross-country comparability in the definitions and measurement of disability, these surveys are still potentially useful in describing the association of disability with other characteristics. That is, conditional on a particular definition, the analysis is valid for a given survey (the definition is common to all individuals in the survey). Moreover, it is less likely that cross-country comparisons of the association between disability and other characteristics would suffer from these problems.

Nevertheless, if some types of disabilities are more associated with a correlate than others, then surveys that include that type of disability will show a higher association with the correlate than those that do not. For example, say loss of a limb was typically more associated with poverty than other types of impairments, then a survey that included loss of a limb in its definition of disability would yield a higher correlation between disability and poverty. Therefore even the cross-country comparisons of the relationship between disability and correlates needs to be treated with caution.

The analysis of the relationship between disability and economic status should be interpreted as an association and not necessarily a cause or consequence. Disability is both a determinant of poverty as it lowers earning power and consumption expenditures (Gertler and Gruber 2002) and a consequence of poverty as the cumulative depravations of poverty can manifest themselves in disability (e.g. infant and child development, exposure to dangerous working conditions). Moreover, the presence of a person with a disability entails direct costs which result in lower standards of living (Jones and O'Donnell 1995, Zaidi and Burchardt 2005). Indeed, Hoogeveen (2005) estimates that in Uganda, households headed by a person with a disability have substantially lower consumption— and are significantly more likely to be poor. Children in those households are also more likely to have lower education attainment for their age.<sup>10</sup> Table 4 reports the percent of youth ages 6 to 17 characterized as having a disability in each economic status quintile: it is lower in the richest than in the poorest quintile in all surveys except Burundi, Cambodia 1999, Mongolia and Mozambique. But the relationship is not neatly ordered with lower prevalence in each higher quintile. A useful way of summarizing the entire distribution of a characteristic (such as disability) across the economic status distribution is through the use of concentration curves. These plot percentiles of a population ranked by economic status on the horizontal axis, against

> 9 Developing good data on disability is complex, United Nations (2001) contains a guide to doing so. 10 Disability among household heads is defined differently in the survey used in Hoogevenn (2005). A head of household is considered disabled if this “prevents him or her from being actively engaged in labour activities during the past week”.

7
On the whole, the results for Chile strongly suggest that the widening of the band, together with the adoption of multiple instead of simple pegs, make verification of the announced regime more difficult using simple econometric estimates. **26**

# **III.b Israel**

Israel presents another interesting experience of basket band with weight changes and progressive widening of the band. During the periods on which we will focus, the band included the same five currencies **[US** dollar **(US$),** Deutsche mark (DM), British pound (BP), French franc (FF), and Japanese yen **(JY)]** and the bandwidth rose from **3%** to **_15%._** The Bank of Israel had already introduced an exchange rate band around a basket in **1976.27** It lasted for a year before being replaced with a floating exchange rate, followed in turn **by** a dollar peg in **1985** and a basket peg in **1986,** with basket weights determined **by** trade shares and subject to relatively frequent revisions.<sup>28</sup> At the beginning of **1989,** the Bank of Israel reintroduced a band system **by** allowing the exchange rate to fluctuate within a region of **±3%** around the currency basket defined **by** the five currencies already mentioned. The band was later widened to

> trajectory of the peso, clearly visible from the scatter plot in Figure **3,** is behind the poorer performance of Tests 1 and 2 in the third period that is apparent _from_ Table **lb.**

> **26** One could object that the contrast between the results we obtain for the earlier and later periods of Chile's band regime might be due instead to some underlying change in the behavior of the strong currencies or in the way the peso moved within the band. However, the intuition that verifiability is more difficult with wider bands and baskets with more currencies is confirmed **by** the Monte Carlo experiments in Appendix **1,** which are not subject to those objections.

> **27** For a detailed account of the exchange rate policy in Israel, see http://www.bankisrael.gov.il and Appendix Table **A.2.**

**28** The number of units of each currency in the new basket was originally determined according to its share in trade during the previous calendar year and to international cross rates. Since then, the trade shares were revised annually and when significant changes produced, the weights and units in the basket were recalculated. The number of units of each currency in the basket is kept constant, but its weight **-**

**26**
> - **1.51** -

# Figure I.C.8 FARM INCOME UNDER POPULATION GROWTH

(Billions of 1987 Csdis) <!-- Start of picture text --> 700<br>500<br>300 e<br>so~~~~~~~~~~~~ /<br>200-<br>1992 2002 2012 2022 2032<br>PERIOD WLDPOINTS<br>0 120 + 140 o 160 180 = 200<br><!-- End of picture text -->
**Table 2**

**Literacy Ratio (Population age 10 and over)**

||**Districts**|**Census 1998**|
|---|---|---|
|1|Rawalpindi|71|
|2|Lahore|65|
|3|Jhelum|64|
|4|Gujrat|62|
|5|Sialkot|59|
|6|Chakwal|57|
|7|Gujranwala|57|
|8|Narowal|53|
|**9**|**Faisalabad**|**52**|
|10|Toba Tek Singh|51|
|**11**|**Attock**|**49**|
|12|Mandhi Bahauddin|47|
|13|Sargodha|46|
|14|Sahiwal|44|
|15|Sheikhupura|44|
|16|Mianwali|43|
|17|Multan|43|
|18|Hafizabad|41|
|19|Khushab|41|
|20|Khanewal|40|
|21|Layyah|39|
|22|Okara|38|
|23|Jhang|37|
|24|Vehari|37|
|25|Kasur|36|
|26|Bahawalnagar|35|
|27|Pakpattan|35|
|28|Bahawalpur|35|
|29|Bhakkar|34|
|**30**|**Rahim Yar Khan**|**33**|
|31|Dera Ghazi Khan|31|
|32|Lodhran|30|
|33|Muzaffar Garh|29|
|34|Rajanpur|21|

The dotted line represents the literacy cutoff used by the government in determining program eligibility at the district level. The one highlighted district below the line is the treatment district in this analysis. Districts above the dotted line were deemed ineligible. The two districts in bold are control group districts in the present analysis.

25
# **Figure A10 – Whether people would encourage a family member to get vaccinated disaggregated by their willingness to get vaccinated**

<!-- Start of picture text --> Would you encourage a family member to get vaccinated?<br>Planning on being vaccinated Not planning on being vaccinated Unsure<br>Yes 57.4 Yes 3.1 Yes 6.0<br>No 12.2 No 72.5 No 30.4<br>Not Sure 30.4 Not Sure 24.4 Not Sure 63.6<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>Share of respondents that selected this answer<br><!-- End of picture text --> Note: This chart is based on answers to question 3 and 12 in Appendix B.

**Figure A11 – Respondents’ level of concern about them or someone in their household getting COVID19, disaggregated by willingness to get the COVID-19 vaccine**

<!-- Start of picture text --> Concern about getting COVID-19<br>Planning on being vaccinated Not planning on being vaccinated Unsure<br>Very worried 40.0 Very worried 46.9 Very worried 54.9<br>Somewhat worried 24.3 Somewhat worried 26.5 Somewhat worried 21.2<br>Not too worried 29.3 Not too worried 23.2 Not too worried 19.6<br>Not worried at all 6.3 Not worried at all 3.4 Not worried at all 4.3<br>0 20 40 60 0 20 40 60 0 20 40 60<br>Share of respondents that selected this answer<br><!-- End of picture text --> Note: This chart is based on answers to question 3 and 13 in Appendix B.

35
# **The Analysis of Emerging Policy Issues in Development Finance** A SURVEY OF THE L1IERATURE'

_A solid analyticalfoundation is the key to formulating sound economic policies, the challenge is to ensure that there exists an appropriate analyticalframework that can be applied to developing countries and that can be used to address the policy question at hand today rather than tomorrow. It is a challenge because the world of development finance, that one must endeavor to keep up with and explain on the basis of sound fundamentals, is a rapidly changing one. But this is also what makes this task all the more interesting._

## **Introduction**

In any endeavor to undertake analytical research on developing countries, one is often faced with incomplete information about key economic variables that are required to make effective policy decisions. This is due to the fact that factor markets may be imperfect, there may be no institutional structure in the country for monitoring these key variables (such as private portfolio investment flows), and in several countries one faces a lack of institutional capacity (in terms of highly skilled staff) in the Government to efficiently manage the information and effectively use it make policy decisions on a day-today basis. Often the lack of timely, reliable and consistent data series on key variables mitigate any econometric testing of sophisticated economic models in developing countries. For example, rigorous analytical work on the policy implications of the use of derivatives in developing countries has become possible only until very recently, although these markets may have been in existence for quite a while. The market for Brazilian derivative instruments is one of the largest markets in the world. Also, Mexico's Telmex is the second most actively traded listed equity option in the United States.

> I am grateful for the comments and suggestions on an earlier draft of this paper from Sarwar Lateef, Leonardo Hernandez, and seminar participants at the University of Illinois, Urbana-Champaign.

2
24 Regression 10 Dependent variable: Standard Deviation of Unweighted Terms of Trade Shocks 1960-95 Number of observations: 114 Mean of dep. var. = .126255 LM het. test = 3.77342 [.052] Std. dev. of dep. var. = .072712 Durbin-Watson = 2.36931 [<.9971 Sum of squared residuals = .314207 Jarque-Bera test = 3.85662 (.145] Variance of residuals = .296422E-02 Ramsey's RESET2 = .084551 [.772] Std. error of regression = .054445 F (zero slopes) = 13.6498 (.000] R-squared = .474073 Schwarz B.I.C. = -5.56154 Adjusted R-squared = .439342 Log likelihood = 174.193 Estimated Standard Variable Coefficient Error t-statistic P-value SSA .146951 .011390 12.9012 t.0003 ASIA .094062 .011595 8.11254 [.000] ECA .046957 .553333E-02 8.48622 [.000) MENA .138424 .029647 4.66910 (.000] LAC .140449 .014181 9.90428 [.000] OIL .074365 .023821 3.12183 [.002] COMMOD .023068 .012845 1.79589 [.075] MICROSTATE -.011439 .011375 -1.00558 [.317] Standard Errors are heteroskedastic-consistent (HCTYPE=2).

Dependent variable: Standard Deviation of Unweighted Terms of Trade Shocks 1960-95 Number of observations: 114
**Figure 10: Product Space for Penang Where Penang Does Not Have Revealed Comparative Advantage, 2000**

<!-- Start of picture text --> 5 10 15 20<br>invdensity<br>RCA>=1 RCA<1<br>20000<br>10000<br>0<br>prodyminusexpy<br>-10000<br><!-- End of picture text --> Source: authors’ calculation

**Figure 11: Product Space for Penang Where Penang Does Not Have Revealed Comparative Advantage, 2007**

<!-- Start of picture text --> 5 10 15 20 25<br>invdensity<br>RCA>=1 RCA<1<br>20000<br>10000<br>0<br>prodyminusexpy<br>-10000<br>-20000<br><!-- End of picture text --> Source: authors’ calculation 17
42

**Table 5: Regression results for neonatal mortality in 2003 and 2008 in Kenya**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Intermediate malaria risk zone (IMRZ)|0.0086|0.0089|0.0131|0.0120|0.0124|0.0114|0.0081|
|High malaria risk zone (HMRZ)|0.0058|0.0086|0.0169|0.0156|0.0164|0.0141|0.0093|
|Interaction LMRZ and ITN ownership|0.0142|0.0139|0.0088|0.0098|0.0072|0.0077|0.0103|
|Interaction IMRZ and ITN ownership|-0.0143|-0.0141|-0.0179|-0.0175|-0.0160|-0.0160|-0.0154|
|Interaction HMRZ and ITN ownership|-0.0054|-0.0056|-0.0104|-0.0088|-0.0105|-0.0091|-0.0082|
|2008 survey|0.0050|0.0011|0.0062|0.0056|0.0066|0.0061|0.0038|
|Full immunization (district level)||0.0250|0.0197|0.0265|0.0290|0.0273|0.0364|
|Born in a facility|||0.0296***|0.0325***|0.0302***|0.0316***|0.0322***|
|Antenatal tetanus protection|||0.0008|0.0005|0.0008|0.0008|0.0011|
|Antenatal antimalarial drugs|||-0.0099|-0.0097|-0.0103|-0.0110|-0.0097|
|Antenatal iron supplement|||-0.0026|-0.0028|-0.0037|-0.0033|-0.0044|
|Unimproved water source (not surface)||||0.0089|0.0084|0.0099|0.0111|
|Improved water source||||0.0024|0.0004|0.0041|0.0043|
|Unimproved sanitation||||-0.0099|-0.0091|-0.0042|-0.0014|
|Improved sanitation||||-0.0236|-0.0347*|-0.0296|-0.0292|
|Mother has primary education|||||-0.0021|-0.0003|0.0010|
|Mother has secondary education|||||-0.0078|-0.0053|-0.0042|
|Mother has tertiary education|||||0.0669*|0.0704*|0.0704*|
|2nd wealth quintile||||||-0.0141|-0.0136|
|3rd wealth quintile||||||-0.0036|-0.0017|
|4th wealth quintile||||||-0.0168|-0.0126|
|5th wealth quintile (richest)||||||-0.0149|-0.0092|
|Rural area|||||||0.0205*|
|Central province|||||||-0.0353|
|Coast province|||||||-0.0124|
|Eastern province|||||||-0.0343|
|Nyanza province|||||||-0.0166|
|Rift Valley province|||||||-0.0206|
|Western province|||||||-0.0258|
|North Eastern province|||||||-0.0137|
|Constant|0.0302***|0.0161|0.0063|0.0078|0.0085|0.0117|0.0088|
|Number of observations|4668|4668|4668|4668|4668|4668|4668|
|R-squared|0.0009|0.0014|0.0074|0.0085|0.0138|0.0148|0.0166|
|F-statistics|0.3099|0.5431|1.6654|1.3013|1.2799|1.1153|0.9228|

Source: Authors ̳ calculations using Demographic and Health Surveys microdata. Notes: Linear probability model, pooled sample. Dependent variable is a dummy variable on child's survival status. It takes value 1 if the child died in postneonatal or infant period. For categorical variables with more than two categories, the left-out categories are: Low malaria risk zone (LMRZ), Surface water, No sanitation, Mother has no education, 1st wealth quintile (poorest), Nairobi province. Full immunization variable measures fraction of children
Table 12 presents the basic needs poverty line per month as well as the poverty headcount rates for the base year surveys HBS 2011/12 and NPS 2010/11. The first column presents the official basic poverty line from the HBS 2011/12 and the second column the NPS 2010/12 poverty line and headcount estimates. The first line shows the original NPS 2010/11 poverty line of TZS 23,933 and the basic need poverty headcount of 17.9 percent.<sup>14</sup> The second line of the table presents the NPS 2010/11 basic needs line recalculated using the HBS 2011/12 method. The re‐estimated poverty line uses the adjusted food line in Table 10 (TZS 23,371) and the HBS 2011/12 methodology for calculating poverty. The basic needs line is measured by the ratio of the food line and the food share of adults whose total expenditures fall between the food line and 1.2 times above the food line. These adjustments in the food line and the reference group yield a poverty line of TZS 30,052 and a poverty headcount of 24.4 percent for the NPS 2010/11. Despite the remaining differences in the survey designs, using a comparable method for estimating the poverty line leads to a relatively closer poverty line and headcount estimates.

**Table 12: Basic needs poverty line in Tanzania Mainland**

||HBS|NPS|
|---|---|---|
||2011/12|2010/11|
|Povertyline|||
|(a)Original values|36,482|23,933|
|% individuals below thepovertyline|28.2|17.9|
|(b)Newpovertyline(harmonized)||29,113|
|% individuals below thepovertyline||24.4|

Note: in line (a) the NPS estimates are using the reference period of 28 days and in line (b) the reference period is 30.4 days.

# **iv. Comparison of the basic needs poverty lines across the survey rounds**

Table 13 compares the basic needs poverty line values across the different rounds for the HBS and the NPS and shows their respective poverty headcount rates. Naturally, time comparisons require adjusting real values for cost of living differences over time. This table uses various price deflators to convert statistics from a given survey into values comparable to a different survey.

> 14 As the difference between the estimates incorporating Zanzibar and considering Mainland only is marginal, we included Zanzibar in the NPS calculations.

38