rafmacalaba/datause-encoder-data huggingface dataset:
through the contract with the employees; at worst, they cannot read it in English and employers do not translate the terms into SeSotho, but just tell the women where to sign. Workers do not remember what was in the contract and few, if any, keep their own copy.

Because textile workers tend to have children to support, they do not see going back home as an option. They tend to stay at a specific factory as long as they can, which is usually determined by the current size and number of orders. When retrenched, women stand outside factory gates until they are hired again or work piecemeal jobs to cover costs in the meantime. When given the hypothetical scenario of retrenchment, few talked about moving home, but rather stressed the need to stay away and find other work given their family’s dependence on their remittances: “I would look for another job because my youngest child still needs financial care. He is in a private school... I would go for anything that would get me money”; “Nothing at all, we are hopeless. I would go and look for another factory job”. These intentions are also evident in survey data: 70 percent of textile workers expect to be working away from home in five years, almost 65 percent of whom expect to still be working in the textile industry.

# _The experience of domestic workers_

As noted above, the decision of _when_ women move for work is linked to household finances. The first group of female migrants moves to support their children after some household shock. Domestic workers in Maseru generally belong to a second group, which consists of younger women who drop out of school, often also due to household financial challenges. These women tend to migrate because they see moving for work as one of the few opportunities for them to keep busy and to help their family. They choose domestic service because the work does not have an age restriction and demands less education and experience than other sectors. As such, migrant domestic workers are more likely to be single and not have any children. Among survey respondents, domestic workers were on average 30 years old – slightly younger than textile workers ‐ although their age range was quite large: 15 to 73. Almost half were single and less than one‐third was married. Almost 40 percent had no children.

Overall, domestic workers’ experiences are strongly influenced by the more informal nature of their employment, relative to textile work: they lack contracts, legal representation, and benefits, resulting in little autonomy and greater vulnerability to the personal and professional nature of their specific employer. Like textile workers, this lack of formality is linked to their intentions, the processes of finding and starting a job, and their work trajectories and future plans. The informal nature is also related to their 29
a sharp rise in the early years, stabilization during the late 1990s and a tendency to decline in the last two to three years.<sup>10</sup>

**Table 1.5 Labor Market Indicators**

||**1992**|**1996**|**2002**|**1992**|**1996**|**2002**|
|---|---|---|---|---|---|---|
|||**Population (000)**<br>||**Labor Fo**|**rce Participat**|**ion Rate**|
|Armenia|3600|3774|3212|82|76|61|
|Azerbaijan|7200|7488|8172|75|72|77|
|Moldova|4400|4325|3623|86|69|73|
|Kyrgiz Republic|4500|4543|4965|83|79|72|
|Tajikistan|5600|5904|6441|76|64|56|
|Uzbekistan*|21100|23130|24965|82|78|68|
|CIS-7 (average)||||81|73|68|
|||**Employment Rate**||**Une**|**mployment R**|**ate***|
|Armenia|64|55|45|2.0|9.4|10.8|
|Azerbaijan|62|59|66|0.2|1.1|1.4|
|Moldova|66|53|55|0.1|1.5|6.8|
|Kyrgiz Republic|67|60|57|0.0|7.8|8.6|
|Tajikistan|60|54|48|0.3|2.6|2.5|
|Uzbekistan*|68|64|60|0.1|0.4|0.4|
|CIS-7 (average)|65|58|55|0|4|5|

Source: CIS-Stat (1998 and 2004). (*) The Unemployment Rate (UR) is neither registered nor open unemployment. It is calculated as a difference between labor resources and employment and inactivity (UR=LR-E-NA). Labor resources is a concept heritage of the Soviet Statistical system and still in use among CIS statistical agencies. It includes the labor force (employed and unemployed) and the economically non active in working age The economic decline deeply affected _state finances_ . Public spending shrunk in absolute terms and also relative to GDP and public debts surged qualifying five of these countries for the CIS-5 debt initiative (Armenia, Georgia, Kyrgyzstan, Moldova and Tajikistan). On average, the CIS-7 general government spending declined from 37% of GDP in 1992 to 23% in 2002 with the decline continuing during the more recent growth period. Only Moldova managed to maintain the relative level of spending (with large annual fluctuations) while all other republics saw a net decline. Government deficits have been higher during the 1992-1994 period, in the range of 20-30% of the budget (Unicef TransMONEE database 2002) and these have been financed for the most part with loans from multilateral organizations. In 2000, debt service alone - as % of government revenues - ranged from 15.5% in Armenia to 41.9% in Moldova and total debts averaged for the CIS-5 at 73.5% of GDP.<sup>11</sup> By the time the recession ended in 1996, the CIS-7 looked much more like developing economies than their transitional neighbors. The process of de-industrialization brought many of these economies back to a scenario dominated by subsistence agriculture and transitional issues turned progressively into development issues. The growth period that followed is very encouraging but also expressed a number of inconsistencies that need to be understood in a developmental type of framework.

> 10 Verme, P. (2004) _An Update On Growth, Poverty and Pro-poor Policies in the CIS-7_ , World Bank, mimeo

> 11 For more details see IMF and WB (2002) _Poverty Reduction, Growth and Debt Sustainability in Low-Income CIS Countries_ , mimeo,www.cis7.org

12
**Appendix Table 5: Households’ eligibility for transfers at baseline based on survey data.**

|||**Qu**<br>|**intiles of Pr(Tr**<br>|**eat)**<br>|||
|---|---|---|---|---|---|---|
||Q1|Q2|Q3|Q4|Q5|Total|
|**Panel A: HH is eligible for transfers**<br>|||||||
|Treatment|0.003|0.027|0.59|1|1|0.545|
|Control|0|0.001|0.464|1|1|0.517|
|**Panel B: Eligibility criteria**|||||||
|Dependency ratio>=1.5|||||||
|Treatment|0|0.003|0.281|0.758|0.977|0.415|
|Control|0|0|0.217|0.783|0.978|0.424|
|Four or more children household|||||||
|Treatment|0|0|0.262|0.385|0.919|0.321|
|Control|0|0|0.233|0.413|0.899|0.332|
|Single parent household|||||||
|Treatment|0|0.018|0.034|0.07|0.066|0.039|
|Control|0|0|0.026|0.077|0.079|0.039|
|Single female household|||||||
|Treatment|0.003|0.006|0.017|0.002|0|0.006|
|Control|0|0|0.007|0|0|0.001|
|Elderly household head|||||||
|Treatment|0|0|0|0.005|0.011|0.003|
|Control|0|0|0|0|0.029|0.007|
|**Panel C: Number of observations**|||||||
|Treatment|134|237|750|1132|1383|3636|
|Control|1415|1312|799|417|166|4109|

_Notes: Treatment was defined at baseline by program administrators after the initial review of households' applications; some households became eligible(ineligible) for transfers after baseline if they managed to successfully update their registration in the system and their application was reviewed, for the purpose of this evaluation households whose status changed across waves remain in their baseline treatment groups; the propensity score was obtained by regressing treatment status at baseline on a number of baseline covariates shown in Appendix Table 1; an additional criteria, whether the household had at least one member who was at least 40% disabled, could not be included here because data on disability was not collected in the survey, but this criteria was relatively rare in practice; percentages are weighted by population weights and inverse propensity weights._ 46
# **Table 13:  Project impact on enrollment rates for 1 year old cohort**

|||||**Enrollm**|**ent in:**||||
|---|---|---|---|---|---|---|---|---|
||**Projectpl**|**aygroup**|**Non-project**|**playgroup**|**Kindergarte**|**n, MoEC**|**Kindergarte**|**n, MoRA**|
||**Unadjusted**|**Adjusted**|**Unadjusted**|**Adjusted**|**Unadjusted**|**Adjusted**|**Unadjusted**|**Adjusted**|
|Child's village is Batch 1����|0.575***|0.574***|-0.237***|-0.249***|-0.116***|-0.103***|0.005|0.006|
||(0.031)|(0.031)|(0.034)|(0.035)|(0.035)|(0.035)|(0.020)|(0.023)|
|Child's village is Batch 3����|0.539***|0.540***|-0.219***|-0.219***|-0.082**|-0.081**|0.034|0.032|
||(0.030)|(0.030)|(0.034)|(0.037)|(0.036)|(0.036)|(0.022)|(0.023)|
|Constant|0.058|0.019|0.296***|0.339***|0.265***|-0.110|0.010|-0.147|
||(0.041)|(0.132)|(0.039)|(0.116)|(0.044)|(0.147)|(0.016)|(0.091)|
|Number of observations|2,842|2,457|2,842|2,457|2,842|2,457|2,842|2,457|
|Adjusted R2|0.328|0.332|0.172|0.179|0.234|0.277|0.033|0.042|
|Number of clusters|310|309|310|309|310|309|310|309|
|p-value of test of��: ��� ��||0.355||0.259||0.498||0.251|
|**Controls Included**|||||||||
|Child Characteristics|N|Y|N|Y|N|Y|N|Y|
|Household Characteristics|N|Y|N|Y|N|Y|N|Y|
|Height and Weight|N|Y|N|Y|N|Y|N|Y|
|Parenting practices at baseline|N|Y|N|Y|N|Y|N|Y|
|District dummies|Y|Y|Y|Y|Y|Y|Y|Y|

_Note:_ Robust standard errors clustered at the village level. Child characteristics include: child’s age and gender. Household characteristics include household size, household wealth and mother’s education. Height and weight include stunting and wasting status at baseline. Parenting practices are self-reported by caregiver at baseline. District dummies are controlled for in both adjusted and unadjusted models.

47
not been used in previous studies. We examine how teachers change their level and type of effort in response to these different incentives.

Third, we use a simultaneous OLS-probit econometric model to jointly estimate how pecuniary and non-pecuniary incentives affect two broad measures of teacher effort—afterschool work hours and after-school tutoring activities—thus obtaining more efficient estimates than a single-equation estimation approach.<sup>5</sup> We incorporate a school random effects component in this model, which can help capture heterogeneity at the school level as well reduce the bias caused by unobservable school characteristics. This type of model appears to be infrequently used in the economic literature, thus we provide a detailed derivation of its likelihood function and first derivatives that can be applied to other similar econometric issues.

Finally, to our knowledge, scanty quantitative evidence currently exists on the education system in Lao PDR, one of the poorest countries in Asia. Thus our analysis using a nationally representative school survey database is relevant to policy making in this country as well as adds new evidence to the current global stock of knowledge.

We find that both pecuniary and non-pecuniary incentives urge teachers to exert more effort in after-school work hours or after-school tutoring activities or both. In particular, one month of salary delay reduces the odds of teachers providing tutoring lesson free of charge by 40 percent and the number of their tutees by 30 percent. The existence of a PTA increases the probability that teachers offer tutoring by 0.12 percent, while the freedom to develop teaching materials induces teachers to work three additional hours per week. Incentives such as teacher autonomy over the teaching method or the principal’s power to dismiss teachers lead teachers to substitute

> 5 Joint models for discrete and continuous outcomes are, nonetheless, widely used in the statistic literature (see, for example, Gueorguieva and Agresti (2001) and Liu, Daniels and Marcus (2009)). The closest version to this model without the random effects in the economic literature is perhaps the instrumental probit model (see, for example, Wooldridge (2010)).

4
there are some uninsured with propensity scores larger than 0.75. After nearest-neighbor matching (NNM), the distributions look markedly similar. The difference can be reduced still further (Table 4) by imposing a 0.001 ‘caliper’ (requiring that the squared difference in predicted odds ratios between the nearest neighbors be smaller than 0.001) and imposition of a common support (dropping insured individuals whose propensity score is higher than the maximum or less than the minimum score of the matched uninsured). But in so doing, 214 cases are lost, and the results—unsurprisingly given how similar the distributions are after NNM—turned out to be not markedly different. The results that follow are based on simple NNM.

_Fig 1: Histograms of propensity scores of uninsured and insured before and after matching_ <!-- Start of picture text --> 0 1<br>0 .5 1 0 .5 1<br>propensity score<br>Graphs by insur_yes<br>0 1<br>0 .5 1 0 .5 1<br>propensity score<br>Graphs by insur_yes<br>6<br>4<br>Density<br>2<br>0<br>4<br>3<br>2<br>Density<br>1<br>0<br><!-- End of picture text --> 10
34

# **Notes**

1. Throughout this paper, CEE will denote Central and Eastern Europe and FSU--the states of the former Soviet Union.

2. The Berkeley-Duke studies of the "second economy" during the Soviet era are an exception. More recently, the qualitative importance of the unofficial economy in Russia is acknowledged in James Leitzel's **Russian Economic Reform** (London: Routledge Press, 1995).

3. Daniel Kaufmann, "Diminishing Returns to Administrative Controls and the Emergence of the Unofficial Economy," **Economic Policy,** 19, December 1994; idem, "Market Liberalization by Stealth: Curse or Blessing in Disguise?" in **Trade in the New Independent States,** Constantine Michalopoulos and David G. Tarr, eds., Washington, DC, World Bank/UNDP (United Nations Development Programme), 1994; Simon Johnson, Daniel Kaufmann, and Oleg Ustenko, "Winners, Losers and Survival Strategies in Ukraine," mimeo, The World Bank and Duke University, October 1995.

4. Istvan Dobozi and Gerhard Pohl, "Real Output Decline in Transition Economies--Forget GDP, Try Power Consumption Data," **Transition Newsletter,** World Bank, vol. 6, JanuaryFebruary 1995.

_5._ It is well known that many enterprises keep three sets of books: one for the enterprise's own management, one for potentially interested investors, and one for tax officials.

6. A rigorous formalization of this conceptual framework will be presented in a forthcoming paper.

7. This factor (type of activity) needs to be interpreted flexibly. In the FSU and CEE states almost any type of activity can operate unofficially. The difference is one of probability and the extent of doing so along a continuum.

8. Except for the fact that in the latter group there has been significant political liberalization. The relative sophistication and efficacy of this group's market and legal institutions could be expected, however, to neutralize potential bureaucratic discretion in applying regulations (and negotiating "rules of the game" with entrepreneurs).

9. Let us consider two countries suddenly facing a democratic transition to market, and hence a reduced cost structure for carrying out unofficial activities but without a concomitant reduction in the costs of doing official business. The country starting from a very low share of unofficial activities would be expected to exhibit a larger increase in its unofficial economy than would the country that starts from a much higher unofficial economy share, all things being equal.

10. See Kaufmann, "Diminishing Returns to Administrative Controls"; and Johnson, Kaufmann, and Ustenko, "Winner, Losers and Survival Strategies".
labor income. As is common in many such surveys, the survey tracks a dwelling unit, so that individuals which move houses attrit from the panel.

We examine income dynamics at the household level and test whether poverty traps exist for households. The ENEU only collects data on labor earnings for each household member in their principal occupation. We add this over household members and deflate by the Consumer Price Index for the relevant quarter from the Bank of Mexico to obtain real household labor earnings. To focus only on households for whom labor earnings are likely to be a main source of income, we restrict our sample to households with heads aged 25 to 49 years old. On average two percent of the observations have household labor income of zero. Using data from the ENIGH income and expenditure survey, which does include non-labor sources of income, we calculate that labor income represents 95 percent of total monetary income for urban households with heads in the 25-49 year old age range. In Section 5 we examine the robustness of our results to using full income and expenditure from the ENIGH.

After imposing these restrictions on the age of the household head and dropping missing values, we have 54 five-quarter panels, beginning with the panel of 3,930 households which were sampled from the first quarter of 1987 through to the first quarter of 1988, and ending with the panel of 11,158 households sampled from the second quarter of 2000 through to the second quarter of 2001. This gives a total sample of 402,052 households. Ten percent of households are observed for only one quarter, while approximately 65 percent can be followed for all five quarters. For our panel data analysis we use unbalanced panels, resulting in 1,671,530 household-quarter observations in all.

Pseudo-panels are constructed on the basis of five year birth intervals of the household head interacted with three education levels (primary schooling or less, 7 to 12 years education, and more than 12 years education). For example, all household heads born between 1960 and 1964 with primary schooling or less would form one cohort. The household head is defined as the person recognized as the head by the other household members and is generally male. A potential concern with the panel data is that households who fall into or escape out of 15
# BUILDING RESILIENT HEALTH SYSTEMS:

EXPERIMENTAL EVIDENCE FROM SIERRA LEONE AND THE 2014 EBOLA OUTBREAK<sup>_∗_</sup> Darin Christensen<sup>_†_</sup> Oeindrila Dube<sup>_‡_</sup> Johannes Haushofer<sup>§</sup> Bilal Siddiqi<sup>_¶_</sup> Maarten Voors<sup>_∥_</sup>

> _∗_ This study utilizes a field experiment implemented in collaboration with Sierra Leone’s Decentralization Secretariat, Ministry of Health and Sanitation, World Bank, International Rescue Committee, Concern Worldwide, and Plan International. We thank the Njala University Museum and Archive for sharing the de-identified data on Ebola cases. We also thank Innovations for Poverty Action for collecting the original survey data, and the respondents for donating their time. Gieltje Adriaans, Ali Ahmed, Carolina Bernal, Alix Bonargent, Fatu Conteh, Afke de Jager, Sarah Dykstra, Caroline Fry, Kevin Grieco, Anne Karing, Anthony Mansaray, Josh McCann, Niccolo Meriggi, Nick Otis, Moritz Poll, Mirella Schrijvers, and Samantha Zaldivar Chimal who provided excellent research assistance. For comments, we thank Rachel Glennerster, Dan Posner, Manisha Shah, and workshop participants at Berkeley, Columbia, LSE, UC San Diego, Zurich, Yale, Northwestern, Norwich, Amsterdam, Rotterdam, EGAP Nairobi, UCLA IDSS, the World Bank’s ABCA, and APSA. We gratefully acknowledge funding from USAID-DIV, the International Growth Centre, AFOSR grant #FA9550-09-1-0314, NWO grant #451-14-001, ESRC grant #ES/J017620/1, the Royal Netherlands Embassy in Ghana, and UCLA’s California Center for Population Research. All errors are our own. The analysis of the survey based outcomes was pre-registered on the AEA registry: `https://www.socialscienceregistry.org/trials/2085`

> _†_ UCLA, Luskin School of Public Affairs ( `darinc@luskin.ucla.edu` )

> _‡_ University of Chicago, Harris School of Public Policy and NBER ( `odube@uchicago.edu` )

> §Princeton University, NBER, Busara Center for Behavioral Economics, and Max Planck Institute for Collective Goods ( `haushofer@princeton.edu` )

- UC Berkeley, Center for Effective Global Action ( `bilal.siddiqi@berkeley.edu` )

> _∥_ Wageningen University ( `maarten.voors@wur.nl` )
**10**

hit with a fourth quarter GDP (2008) plummeting by 13 per cent. Demand for durable goods has been particularly hard hit. With the downturn there has been a sharp upturn in savings by households (and commensurate reduction in consumption), driven by a reappraisal of risk by households and a loss of net worth with falling house prices and equity prices. So shocks need to be devised to account for three things;

- The bursting of the housing bubble and loss in asset prices and household wealth with consumers cutting back on spending and lifting savings.

- A sharp reappraisal of risk with a spike in bond spreads on corporate loans and interbank lending rates with the cost of credit, including trade credit, rising with a commensurate collapse of stock markets around the world.

- A massive policy response including a monetary policy easing, bailouts of financial institutions and fiscal stimulus. Also, signs of emerging trade and financial protectionism appear.

These three outcomes can be represented by six shocks — three for the crisis itself and three for the policy response.

**_Three main shocks capture the onset of the global financial crisis:_**

1. The bursting of the housing bubble causing a reallocation of capital and a loss of household wealth and drop in consumption.

2. A sharp rise in the equity risk premium (the risk premium of equities over bonds) causing the cost of capital to rise, private investment to fall and demand for durable goods to collapse.

3. A reappraisal of risk by households causing them to discount their future labor income and increase savings and decrease consumption.

# _Shock 1: The bursting of the housing bubble_

Falling house prices had a major effect on household wealth, spending and defaults on loans held by financial institutions. Events in the United States typify a global phenomenon. From 2000 to 2006, house prices in some areas doubled to subsequently collapse (chart 3.1). These changes in some areas have generated dramatic news headlines but, overall the United States index of house prices has fallen by 6.2 percent in real terms from the 1<sup>st</sup> quarter 2008 to the same quarter in 2009<sup>8</sup> .

While house prices were rising so strongly, credit was supplied liberally to meet the demand as perceptions of risk fell. The rising wealth boosted confidence and spending. The housing bubble was a global phenomenon centered mainly on the Anglo-Saxon world.

8 Federal Housing Finance Agency May 2009, http://www.fhfa.gov/webfiles/2406/1q09hpi.pdf
**Figure 1: Cumulative density of the number agricultural leases issued and covered after independence**

<!-- Start of picture text --> Year<br>Number of agricultural estates<br>Area under agricultural estates (1,000 ha)<br>30000 1000<br>800<br>20000<br>600<br>400<br>10000<br>200<br>Number of agricultural estates<br>0 0<br>Area under agricultural estates (1,000 ha)<br>1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> _Source:_ Own computation from the National Geographical Estates Database.

23