rafmacalaba/datause-encoder-data huggingface dataset:
The only missing pieces to compute d are then probabilities of sampling the parameters given the means. Taking into account the left hand and right hand truncations and the variances of the normal distributions these probabilities are given by: where N is the cumulative normal distribution, i indexes the elements of  and is the variance covariance matrix of the prior distribution of the parameters that here is assumed to be a diagonal matrix (i.e., there are no prior correlations between the model parameters).

Table 1: Initial Distributions for the Eight Independent Samples of Model Parameters

|||**Aver**<br>|**age Ear**|**nings**<br>|||
|---|---|---|---|---|---|---|
||**Risk**<br>**Aversion**<br>**(λ)**|**Time**<br>**preference**<br>**(ρ)**|**Alfa 1**<br>**(α1)**|**Prob. Keep**<br>**Formal Job**<br>**(φ1)**|**Alfa 2**<br>**(α2)**|**Prob. Work**<br>**when Retired**<br>**(ηp)**|
|**Mean 1**|1,50|0,04|0,90|0,95|0,30|0,50|
|**Mean 2**|1,30|0,03|0,80|0,90|0,20|0,40|
|**Mean 3**|1,20|0,02|0,70|0,85|0,10|0,30|
|**Mean 4**|1,10|0,01|0,60|0,80|0,05|0,20|
|**Mean 5**|0,80|-0,01|0,70|0,85|0,05|0,20|
|**Mean 6**|0,70|-0,03|0,70|0,85|0,01|0,20|
|**Mean 7**|1,20|-0,02|0,80|0,85|0,10|0,30|
|**Mean 8**|1,20|0,03|0,80|0,85|0,05|0,30|
|**Variance**|0,05|0,01|0,05|0,05|0,05|0,05|
|**Trunc. left**|0|-99|0,5|0|0|0|
|**Trunc. right**|99|99|1|1|1|1|
|||**50% A**|**verage E**|**arnings**|||
||**Risk**<br>**Aversion**|**Time**<br>**preference**|**Alfa 1**|**Prob. Keep**<br>**Formal Job**|**Alfa 2**|**Prob. Work**<br>**when Retired**|
||**(λ)**|**(ρ)**|**(α1)**|**(φ1)**|**(α2)**|**(ηp)**|
|**Mean 1**|1,50|0,04|0,90|0,50|0,30|0,50|
|**Mean 2**|1,30|0,03|0,80|0,90|0,20|0,40|
|**Mean 3**|1,20|0,02|0,70|0,85|0,10|0,30|
|**Mean 4**|1,10|0,01|0,60|0,80|0,05|0,20|
|**Mean 5**|0,80|-0,01|0,70|0,45|0,05|0,20|
|**Mean 6**|0,70|-0,03|0,70|0,85|0,01|0,20|
|**Mean 7**|1,20|-0,02|0,80|0,45|0,10|0,30|
|**Mean 8**|1,20|0,03|0,80|0,85|0,05|0,30|
|**Variance**|0,05|0,01|0,05|0,05|0,05|0,05|
|**Trunc. left**|0|-99|0,5|0|0|0|
|**Trunc. right**|99|99|1|1|1|1|

_Source:_ Range of variation for Risk Aversion and Time preference parameters based Jiménez-Martín and Sánchez-Martín (2006). For the other parameters see main text.

17
# The Impact of Lifting Firing Restrictions on Firms: Evidence from a State-Level Labor Law

# Amendment

Sarur Chaudhary,<sup>_∗_</sup> Siddharth Sharma<sup>_†‡_</sup>

**Keywords–** Labor Laws, Business Regulation, Firms, Firm-level Employment, India JEL **Codes–** O12, K31, L50, J50, D22

- _∗_ University of Cambridge.

> _†_ Corresponding Author, The World Bank. Email: ssharma1@worldbank.org. The views expressed are those of the individual authors and do not necessarily reflect those of the World Bank Group.

> _‡_ We are grateful to Maurizio Bussolo, Ritam Chaurey, Arti Grover, Amit Khandelwal, Jean Nahrae Lee, Aaditya Mattoo, Hans Timmer and Hernan Winkler for helpful comments.
elderly female houseowners and find that those that have taken out RM are more, not less likely than observably similar subjects to sell their house before death. Shan (2011) confirms this picture, showing that RM borrowers leave their houses at a much faster rate than comparable non-borrowers. The author argues that an underlying reason for this phenomenon is that RM are often signed for medical emergencies, which in turn reduce life expectancy. While this may be good news for lenders, it raises serious concerns on the consumer protection side.

Moral hazard is a more serious market failure that may constrain supply. Homeowners who sign an RM have less incentive to keep up their house and pay property taxes. The borrower will never have to repay more than the house value, and thus may seek to avoid expensive house repairs (Miceli & Sirmans, 1994; Shiller & Weiss, 2000; Caplin, 2002). Moreover, since RM debt cannot be passed on to the next generation, heirs who face a house value near or below the amount of funds received have little incentive to take steps to maximize the sales price, raising the risk of corruption in private house sales. Foreclosure by the lender is the most common outcome, but this procedure entails high transaction costs for the lender (Caplin, 2002). The California Reinvestment Coalition (CRC) reported that involuntary RM terminations are expected to double in 2016 compared with the previous year. And loan industry data show that about 22% of foreclosures were triggered by tax and insurance default (Miller, 2018). In order to address this moral hazard issue, since 2015 RM borrowers in the United States are required to undergo a credit assessment and pay a so-called Life Expectancy Set-Aside (LESA) to ensure property taxes and insurance are paid if their credit score is below a certain threshold.

Regulatory responses to moral hazard are available but costly. In the United States, the Federal Housing Agency (FHA) provides mandatory insurance against cross-over risk. Lenders can avoid downside risks by selling RM that are at risk of default to the FHA (overseen by the US Department of Housing and Urban Development) while continuing to benefit from upside gains (if foreclosure proceeds exceed the mortgage balance). Borrowers pay for this insurance at a rather significant premium of 2% of house value at mortgage origination and an annual 1.5% subsequently (Reversemortgages.com, n.d.). This insurance scheme was profitable during the 2000s but the US Department of Housing and Development raised the premium in 2010 and again in 2017 following a rise of foreclosures (Miller, 2018). Nakajima and Telyukova (2017) argue that abandoning this insurance scheme and passing the risk on to lenders and heirs would make RM cheaper and thus increase demand, but the authors dedicate little attention to discussing the clear downsides of this proposal.

18
Pc icy Rv uACti-i WORKIMiŽ PAPER 2075

# Summary findings

Wodon providcs two extensions to Yitzhaki and Lerman's group decomposition of the Gini index.

First, he analyzes stratiflcation (within the group) and inequality (between groups) along several dimensions at once. This makes the determinants of inequality more understandable.

Second, he derives the imnact on the Gin] of marginal changes in income or consumption by group. This can be used to evaluate targeted redistributive policies or to assess the impact of exogenouLs shocks by group.

He applies the analysis to data fromi Bangladesh, with a focus on how inequality affects land ownership, education, and occupation.

Education appears to be a stronger determinant of inequality than occupation, with land ownership ranking third. Marginal targeted transfers and taxes have more effect on redistribution when applied to education (from the well-educated to the illiterate) or occupation groups (fronm officials and managers to tenants and agricultural workers).

This paper-a product of the Poverty Reduction and Economic Management Sector Unit, South Asia Region-was written as part of background work for the Bangladesh poverty assessment. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Jillian Badami, room MC10-336, telephone 202-4580425, fax 202-522-2428, Internet address jbadami(a.worldbank.org. Policy Research Working Papers are also posted on the Web at http:f/www.worldbank.org/html/dec/PublicationsfWorkpapers/home.html. The author may be contacted at qwodond(k.worldbank.org. March 1999. (33 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas alout deveinpolent issues. A7n ohecede of the series is to get the findings osnt qUickly, even if the presentations are less than full polished. sse papers carry the names of the authors and should he cited accordinigly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. 7They do anot necessarily represen_ **_t_** _he vienu of the Wlorld BaeP, its Executive Directors, or the cinntries they represent._ Produced bv the Policy Research Dissemination Center
Figure 5. Socioeconomic Well-being Using Different Bandwidths (RD Estimates) <!-- Start of picture text --> Well-being (Index) Consumption per Capita (log)<br>CERSUM<br>MSERDCERRD CERSUMCERRD<br>MSESUM MSERD<br>MSESUM<br>50 100 150 200 250 300 350 400 450 50 100 150 200 250 300 350 400 450<br> Bandwidth (days around Jun 08-18) Bandwidth (days around Jun 08-18)<br>Labor Income (log) Employed<br>CERSUM<br>CERSUM MSERDCERRD<br>MSERDCERRD MSESUM<br>MSESUM<br>50 100 150 200 250 300 350 400 450 50 100 150 200 250 300 350 400 450<br> Bandwidth (days around Jun 08-18) Bandwidth (days around Jun 08-18)<br>1<br>2 .8<br>1.5 .6<br>1<br>.4<br>.5<br>0 .2<br>-.5 0<br>-1<br>-1.5 -.2<br>-2 -.4<br>1 1<br>.8 .8<br>.6<br>.6<br>.4<br>.4<br>.2<br>.2 0<br>0 -.2<br>-.2 -.4<br>-.4<br><!-- End of picture text --> _Notes:_ Robust bias-corrected point estimators and confidence intervals for different bandwidths measured in days around June 8, 2018. In black, manually inputed ad hoc bandwidths with a frequency of 20 days. In gray, optimal bandwidths according to different methodologies: (i) mean squared error (MSE), (ii) MSE for the sum of regression estimates (MSESUM), (iii) coverage error rate (CER), (iv) CER for the sum of regression estimates (CERSUM). We run the same specification specified in Table 4. Confidence intervals are at the 95% significance level.

42
# **Abstract**

This paper studies the long-term impact of genocide during the period of the Khmer Rouge (1975-79) in Cambodia and contributes to the literature on the economic analysis of conflict. Using mortality data for siblings from the Cambodia Demographic and Health Survey in 2000, it shows that excess mortality was extremely high and heavily concentrated during 1974-80. Adult males have been the most likely to die, indicating that violent death played a major role. Individuals with an urban or educated background were more likely to die. Infant mortality was also at very high levels during the period and disability rates from landmines or other weapons are high for males who, given their birth cohort, were exposed to this risk.

The very high and selective mortality had a major impact on the population structure of Cambodia. Fertility and marriage rates were very low under the Khmer Rouge but rebounded immediately after the regime’s collapse. Because of the shortage of eligible males, the age and education differences between partners tended to decline. The period had a lasting impact on the educational attainment of the population. The education system collapsed during the period, so individuals—especially males—who were of schooling age during this interval have a lower educational attainment than the preceding and subsequent birth cohorts.

# **Contents**

|I.<br>Introduction................................................................................................................ 1|
|---|
|II.<br>Mortality .................................................................................................................... 2|
|II.1<br>Extent and timing of excess mortality ................................................................ 4|
|II. 2<br>Violent mortality: mortality by birth cohort and gender..................................... 5|
|II.3<br>Selective mortality: mortality by socio-economic status.................................... 6|
|II.4<br>Infant and under-five mortality........................................................................... 7|
|III. Fertility and Marriage ................................................................................................ 9|
|IV. Health and Disability ............................................................................................... 10|
|V. Education ................................................................................................................. 11|
|VI. Conclusions and Further Research........................................................................... 12|
|Appendix........................................................................................................................... 14|
|Bibliography ..................................................................................................................... 17|

ii
for outgower costs (in the analysis we have assumed 2% deduction). At the assembly level, because of vertical integration, price was set equal to total accumulated costs (i.e. calculate total profits at processing stage only). Table 22 compares Malawi’s farm-gate indicators to those of other countries.

**Table 22: Values at farm gate (un-ginned seed cotton)**

||**%**<br>**Foreign**|**Tax as %**<br>**DVA**|**Total SV**<br>**(USD/MT)**|**Net Profit**<br>**(USD/ha)**|
|---|---|---|---|---|
|**Malawi (2007/08)**|||||
|FAM-low|25%|11%|232.69|173.80|
|FAM-high|46%|15%|262.68|209.55|
|**Cameroon (2007)**|||||
|FAM-low<br>|21%|10%|477.78|85.28|
|FAM-high|28%|13%|384.56|93.61|
|**Mozambique (2006/07)**|||||
|FAM|17%|1.6%|120.44|76.67|
|ECF|27%|2.4%|83.60|121.44|
|**Nigeria (2006)**|||||
|FAM|n/a|n/a|255.00|63.33|
|**Zambia (2005/06)**|||||
|FAM|25%|6%|181.75|148.00|
|ECF|27%|4%|234.17|152.33|
|**Zambia (2006/07)**|||||
|FAM-low|24%|9%|277.24|56.41|
|FAM-high|36%|10%|286.28|37.84|
|Yield program|21%|9%|183.62|163.66|
|**Brazil (2006)**<br>|||||
|LCF|20%|27%|447.23|145.55|

91. The cost composition of cotton at the farm, assembly and processing levels are as shown in Figure 23. The farm-level cost elements for cotton include labor, fertilizer (for high management), chemicals, marketing costs and depreciation of the capital equipment such as sprayers. At the high input level, it is assumed that the ginner makes an additional investment in improved extension and other out-grower services beyond the very basic types of support offered now. Specifically, the ginner is assumed to spend USD 2.67 per hectare or USD 3.36 total per hectare (equal to MWK 574 or USD 4.10 per MT seed cotton) in out-grower services. As shown, this table also includes data from an analysis of the so-called “yield program” operated by Dunavant Cotton in the Eastern Province of Zambia just across the border from Malawi. Unlike the high-input model in Malawi, which relies on fertilizer to achieve high yield results, the Zambia “yield program” is based primarily on farmer extension to help growers understand the importance of panting date and to carry out pest scouting to achieve the maximum benefit from expensive insecticides.

43
and thus, the relative importance of credit constraints and risk aversion may be confounded (Chaudhuri and Osborne, 2002).

This last point is illustrated by the well-known positive correlation between wealth and adoption of new technology.<sup>4</sup> Some have argued that this correlation provides evidence for credit constraints, because wealthier farmers have better access to credit and thus face lower financial constraints to adopt. However, even if we ignore that unobserved heterogeneity (correlated with wealth) may be ultimately responsible for the observed correlation, under plausible assumptions wealthier households will also be more tolerant of risk. Therefore, it is not clear whether the correlation is driven by credit constraints and thus imperfections in the credit market, or by risk aversion and therefore lack of insurance instruments to hedge risk. Disentangling the two explanations is crucial because they call for very different government interventions.

This paper describes the findings of a randomized field experiment we implemented testing whether bundling insurance with credit increased farmers’ willingness to adopt a new technology. The specific context of the study was the adoption of high-yield hybrid maize and improved groundnut varieties among small landholders in Malawi. Nearly all Malawian households (97% in 2004-2005) are engaged in maize production, but only 58% use hybrid maize varieties (World Bank 2006). Smale and Jayne (2003) note that hybrid maize adoption in Malawi has lagged behind adoption in Kenya, Zambia, and Zimbabwe.

Existing studies document that hybrid seed use in Malawi is correlated with wealth and other indicators of household socioeconomic status. Data from the country’s nationally representative Integrated Household Survey conducted in 2004-2005 documents higher adoption of hybrid maize among households in the highest quintile of land ownership (66 percent) than in the lowest quintile (53 percent) (World Bank 2006). Among maize farmers in southern Malawi, Chirwa (2005) finds that close to 60 percent do not use hybrid maize varieties, and that adoption rises in income, education, and plot size. Simtowe and Zeller (2006) find higher maize adoption among households with access to credit. Due to the potential correlation between access to credit and ability (or willingness) to cope with risk, it is unclear in these studies whether credit constraints or 4 See Feder, Just, and Zilberman (1985), Just and Zilberman (1983), Besley and Case (1993).
|Father‘s schooling|(0.0619)<br>0.0367|(0.100)<br>-0.249**|(0.0797)<br>0.110|(0.0994)<br>0.0925|(0.0727)<br>0.00213|
|---|---|---|---|---|---|
||(0.0702)|(0.124)|(0.0813)|(0.121)|(0.0818)|
|Mother‘s schooling|0.265***|0.205|0.371***|0.182|0.303**|
||(0.0933)|(0.184)|(0.113)|(0.144)|(0.125)|
|Father rural|0.0958|0.102|0.0347|0.130|0.0718|
||(0.0725)|(0.114)|(0.0937)|(0.138)|(0.0830)|
|Mother rural|-0.129*|-0.108|-0.119|-0.104|-0.143*|
||(0.0699)|(0.107)|(0.0945)|(0.134)|(0.0808)|
|Log hh size|-0.154***|-0.0743|-0.245***|-0.129**|-0.167***|
||(0.0430)|(0.0646)|(0.0563)|(0.0561)|(0.0518)|
|Log cell size|-0.246***|-0.417***|-0.164***|-0.234***|-0.212***|
||(0.0398)|(0.0620)|(0.0523)|(0.0688)|(0.0590)|
|Share of cell aged<5|-0.563***|-0.579***|-0.440**|-0.784***|-0.561***|
||(0.141)|(0.196)|(0.205)|(0.301)|(0.166)|
|Share of cell adults|0.696***|0.371**|0.824***|0.627***|0.671***|
||(0.116)|(0.168)|(0.162)|(0.214)|(0.139)|
|Has formal schooling|0.400***|0.336***|0.396***|0.315***|0.468***|
||(0.0497)|(0.0843)|(0.0620)|(0.0761)|(0.0662)|
|Fostered|0.187**|0.123|0.199*|0.305***|0.0413|
||(0.0751)|(0.109)|(0.103)|(0.110)|(0.101)|
|Fostered young|-0.0334|0.00367|-0.0545|-0.298**|0.177|
||(0.0966)|(0.142)|(0.130)|(0.150)|(0.130)|
|Age at first marriage|0.00422|0.0118**|-0.00149|-0.00420|0.0173***|
||(0.00395)|(0.00590)|(0.00523)|(0.00534)|(0.00537)|
|Inherited land|-0.0527|0.0410|-0.0959|-0.0825|0.00956|
||(0.0609)|(0.0858)|(0.0896)|(0.0910)|(0.0762)|
|Inherited house|0.0164|-0.104|0.0962|0.00775|0.0160|
||(0.0519)|(0.0836)|(0.0666)|(0.0765)|(0.0650)|
|Other inheritance|0.126**|0.137**|0.0914|0.237***|0.0189|
||(0.0547)|(0.0674)|(0.0860)|(0.0758)|(0.0685)|
|Rural|-0.670***|||-0.669***|-0.722***|
||(0.0896)|||(0.125)|(0.100)|
|Constant|11.68***|10.55***|10.63***|11.26***|12.15***|
||(0.379)|(0.727)|(0.539)|(0.627)|(0.461)|
|Observations<br>|3,165|1,637|1,528|1,419|1,746|
|R<sup>2</sup>|0.504|0.301|0.394|0.479|0.529|

Note: Robust standard errors in parentheses, and are clustered at the household level. *** p<0.01, ** p<0.05, * p<0.1. The variables ̳First same gender‘, ̳First of siblings‘ and ̳First born is male‘ all refer to children of the same father, same mother. Regressions also contain regional (department) dummies. The reference variables are Wolof ethnicity, all other religions, occupation ̳inactive‘, share of cell members 5- 15, no and non-formal schooling.

31
# **Table  13c:       Piecemeal Sensitivity Analysis: Impact on Uganda of EACU Deep Integration and  Multilateral Liberalization**

|in Equivalent Variation (E|V) as a percentage of consum|ption||**Equiv**|**alent V**|**ariatio**|**n**||
|---|---|---|---|---|---|---|---|---|
||**Parameter Valu**|**e**|**EACU D**|**eep Integ**|**ration**|**EACU**|**Liberal**|**ization**|
|Parameter|Lower<br>Central|Upper|Lower|Central|Upper|Lower|Central|Upper|
|σ(qi, qj) – services sectors|2<br>3|4|1.25|1.24|1.24|2.75|2.79|2.82|
|σ(qi, qj) – goods sectors|see below||1.09|1.24|1.35|2.90|2.79|2.79|
|σ(va, bs)|0.625<br>1.25|1.875|1.24|1.24|1.24|2.74|2.79|2.83|
|σ(D, M)|see below||1.22|1.24|1.27|2.77|2.79|2.81|
|σ(M, M)|GTAP values<br>30||1.24|1.24|NA|2.76|2.79|NA|
|σ(L, K)|0.5<br>1|1.5|1.27|1.24|1.23|2.81|2.79|2.78|
|σ(A1,...An)|0<br>0|0.25|NA|1.24|1.24|NA|2.79|2.79|
|εEACU,εCOMESA,εSADC|Lower (upper) values are 0.5 (1.|5) central|125|124|124|258|279|291|
|εEU ,εROW,εUSA,εCHINA|values. See table 7 for central va|lues.|.|.|.|.|.|.|
|θr|0<br>0|1|NA|1.24|1.19|NA|2.79|1.38|
|θm|0.025<br>0.05|0.075|1.24|1.24|1.24|2.79|2.79|2.79|
|**σ(qi, qj) – IRTS goods**|**Parameter Valu**|**e**|**σ(D, M)-**|**-CRTS s**<br>|**ectors**<br>|**Par**|**ameter V**|**alue**|
|chemicals and metals|3.4<br>6.8|10.2||||Lower|Central|Upper|
|energy and minerals|5.8<br>11.6|17.4|agricultur|e and for|estry|1.3|2.5|3.8|
|food products|2.6<br>5.1|7.7|other serv|ices||0.8|1.5|2.3|
|petroleum and coal prod.|2.1<br>4.2|6.3|trade|||0.8|1.5|2.3|
|other manufacturing|3.9<br>7.7|11.6|utilities|||1.4|2.8|4.2|
|textiles, apparel and leather|3.8<br>7.6|11.4|||||||
|wood andpaperproducts|3.2<br>6.3|9.5|||||||

## **Key:**

σ(qi, qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors σ(va, bs): Elasticity of substitution between value-added and business services σ(D, M): Elasticity of substitution between domestic goods and imports in CRTS sectors σ(M, M): Elasticity of substitution between imports from different regions in CRTS sectors σ(L, K): Elasticity of substitution between primary factors of production in value added σ(A1,...An): Elasticity of substitution in intermediate production between composite Armington aggregate goods εROW, εEU, εCHINA, εUSA εEACU, εCOMESA, εSADC: Vectors of elasticities of imperfectly competitive firms' supply in the Rest World, EU, China. USA, EACU, COMESA and SADC with respect to the price of their outputs.

θr: Share of rents in services sectors captured by domestic agents θm: Shares of value added in multinational firms due to specialized primary factor imports Source: Authors’ estimates.

81
15 of the inclusion of two indexes that satisfy (some of) these requirements on a regression that explains the economic performance of a group of developing countries.

# II.3 A Digression on Liberalization. Effective Protection and Do1 estic Resource Costs

Mos: of the indicators commonly employed to characterize the: trade regime prevailing in a particular country -- such as the bias index B -- reflect, at best, the avejage level of protection given to two broadly defined sectors of the economy. However, almost every country 1?rotects different sectors at different rates. In particular, final goods usually have different tariffs and QRs than capital goods and intermediate inputs. A number of authors have indeed recognized that having information on both the average level and the complete structure of protection are important. As a result, most studies on trade liberalization -- including the BhagwatiKrueger (1978), the Balassa (1982) and the Krneger (1983) projects -- have tried to compute Effective Rates of Protection (ERPs) and Domestic Resource Costs indexes (DRCs) to measure more accurately the way in which inputoutput relations affect the extent of "true" protection granted to a particular sector of the economy.

The concept of effective protection was pioneered by Corden (1966), Balassa (1965) and Johnson (1965) and tries to capture in a single indicator the rate of protection granted to value added in a given industry.8 During the 1960s and early 1970s the literature on measurement and implications of

> 8The rate of effective protection to industry j is defined as r - (VAj-VA*)/VA*, where VA is domestic value added, and VA* is "world value added taken to be a proxy of the most efficient way of producing J. Assuming a linear relation between inputs and outputs -- where a denotes an input-output coefficient -- the ERP for industry J can be rewitten as: J - (tJ-Eaijti)/(l-Eaij), where ti is the tariff on input i.