rafmacalaba/datause-encoder-data huggingface dataset:
of my medical tests”, and “the doctor should have given me a different or additional treatment or drug”. We aggregate these into three standardized indices following Kling et al. (2007): an overall satisfaction index, which includes measures of satisfaction with tests, medications received, and overall care, as well as two sub-indices that focus on satisfaction with tests and medications separately.<sup>22</sup> Table 5 reports treatment effects. Surprisingly, the extended training had significant negative effects on patient satisfaction: column 1 shows that patients in the extended training arm are 0.092 standard deviation units _less_ satisfied (significant at the 5 percent level), with declines in satisfaction with both tests (column 2, significant at the 1 percent level) and medications (column 3, not significant, but similar in magnitude).<sup>23</sup> Figure 5 graphs CDFs of the satisfaction indices by provider training arm; the distribution of satisfaction in the basic training arm first-order stochastically dominates that in the extended training arm. Figure A1 uses local linear regression to explore how treatment effects on the three patient satisfaction indices vary with predicted malaria risk. The graphs show that patient satisfaction is relatively constant across the risk distribution, with satisfaction for patients visiting extended-training clinics consistently lower than satisfaction among patients visiting basic training clinics.

By contrast, Table 5 shows that the patient information treatment had no effect on patient satisfaction. In other words, the additional information about the proper malaria testing protocols did not change their views about the care they received at the clinic despite the overall high rates of treatment misallocation.

**Mechanisms for the Decline in Patient Satisfaction.** The extended training improved quality of care and increased patients’ ability to predict their home RDT results. Why did patient satisfaction nonetheless decline? One plausible mechanism is that patients themselves are skeptical of malaria RDTs and dislike providers relying on them. Since the extended training increased RDT (only) testing rates, it may be that learning the result of the clinic RDT successfully shifted patients’ beliefs about the outcome of the home RDT, but nonetheless had limited effects on their subjective belief about their own malaria status.

The patient information video explained that RDTs are accurate and that Mali’s official malaria policy is to “only treat confirmed malaria positive cases.” One might therefore expect that the patient information intervention helped ameliorate any negative effects of – the extended training on patient satisfaction at least to the extent patient information

> 22See Appendix C for a complete list of questions included in the patient satisfaction indices and details on how we constructed index components.

> 23Appendix Table A8 reports treatment effects on individual index components. The extended training’s treatment effects on individual components are generally modest and insignificant but consistently point to less satisfaction.

18
education on food nutrient intake and sanitary practices (Cebu Study Team 1991); use and timing of prenatal health services and delivery assistance (Behrman and Wolfe 1987; Wong, Popkin et al. 1987; Schwartz, Akin et al. 1988; Elo 1992; Gertler, Rahman et al. 1993; Panis and Lillard 1994; Sandiford, Cassel et al. 1995; Pebley, Goldman et al. 1996; Guilkey and Riphahn 1998); and child immunizations (Steele, Diamond et al. 1996; Gage, Sommerfelt et al. 1997; Guilkey and Riphahn 1998)). Most studies find that maternal education has a positive impact on health care use and that paternal education is less important.

For the most part, existing work on the impact of schooling on health care use has not adequately controlled for unobserved preferences and capabilities that are correlated with schooling. A few studies have attempted to control for omitted variable bias by using data on the mother’s family to control for family fixed effects (Wolfe and Behrman 1987; Strauss 1990). Wolfe and Behrman’s study in Nicaragua found that once the mother’s fixed effect is removed using data on her siblings, the association between a mother’s schooling and child health disappears. In a separate paper, Berhman and Wolfe (1987) examine the impact of schooling on women’s and children’s nutrition, health care use and outcomes after controlling for unobserved common childhood family background characteristics. They find that female schooling has a positive impact on women’s nutrient and health intakes, even after controlling for unobservables. Using data on extended families living together, Strauss finds that the correlation is reduced once household fixed effects are controlled for.

7
**ONLINE   APPENDIX**

# **Online   Appendix   Table   I:   Randomization   Balance**

||**Control**|**Difference**<br>|
|---|---|---|
|||**(Treatment - Control)**|
||(1)|(2)|
|**_Panel A: Village Level Variables_**<br>|||
|_Village Wealth (Median Monthly Expenditure)_|4585.375|87.661|
|||(203.377)|
|_Number of Households in Village_|626.5|9.349|
|||(73.067)<br>|
|_Village Inequality (Gini Index)_|0.533|-0.019<br>|
|||(0.038)|
|<br>_Number of Government Schools in Village_|4.125|0.425<br>(0.372)|
|_Number of Private Schools in Village_|2.643|0.131|
|||(0.441)|
|_Village enrollment % (All)_|70.617|0.400|
|||(2.289)<br>|
|_Village enrollment % (Boys)_|76.464|-0.455<br>|
|||(2.005)|
|_Village enrollment % (Girls)_|64.106|1.389|
|||(2.820)<br>|
|_Level of Competition between Schools in Village (Herfindahl Index)_|0.197|-0.005|
|||(0.014)|
|_No. of Grade 3 Children Tested in Village_|103.321|9.881|
|||(12.815)<br>|
|<br>_Village Adult (>24 yrs) Literacy (%)_|38.472|-2.441<br>(1.910)|
|**_Panel B: School Level Variables_**<br>|||
|_School Average Test Score_|0.028|0.001|
|||(0.062)<br>|
|_School Fees_|510.934|-108.992|
|||(69.986)<br>|
|_Number of Students (Grades 1 to 5) Enrolled at School_|91.613|-5.113|
|||(6.248)|
|**_Panel C: Child Level Variables_**<br>|||
|_Average Test Score_|-0.013|-0.016|
|||(0.061)|
|_Female Child_|0.439|0.001|
|||(0.018)|
|_Child Age_|9.680|0.003|
|||(0.082)|
|_Father's Education_|2.206|-0.081|
|||(0.045)*<br>|
|_Mother's Education_|1.565|-0.002|
|||(0.045)<br>|
|_Wealth (Child Asset Index)_|0.074|-0.173|
|||(0.130)|

# _Notes:_

*   significant   at   10   percent   level   **significant   at   5   percent   level   ***   significant   at   1   percent   level This   table   presents   balance   checks   on   the   village   level   randomization.   We   note   that   our   sample   is   balanced   everywhere   except   for   one   variable (Father's   Education),   which   can   be   expected   by   random   chance.   Column   1   shows   the   raw   mean   of   the   variables   for   the   control   group.   Column   2 tests   the   difference   between   treatment   and   control   villages   and   controls   for   district   stratification.   Panel   A   considers   village   level   variables;   Panel B   considers   school   level   variables;   and   Panel   C   considers   child   level   variables.   Regressions   for   column   2   display   robust   standard   errors   for   Panel A   and   clustered   standard   errors   at   the   village   level   for   Panels   B   and   C.   The   p-­‐value   for   the   joint   test   of   significance   at   the   village   level   is   0.56.
# **Productivity Loss and Misallocation of Resources in Southeast Asia**

Francesca de Nicola, Ha Nguyen, and Norman Loayza<sup>1</sup> <mark>World Bank</mark> JEL classification: D24, L11, O30, O47 Keywords: Productivity, Distortions, Resource Misallocation

> 1 We are grateful for comments and feedback from Lay Lian Chuah, Ergys Islamaj, Andy Mason, Rong Qian, and Sudhir Shetty. The views expressed herein are those of the authors and do not necessarily reflect the views of the World Bank, its Executive Directors, or the countries they represent.

> All authors are affiliated with the World Bank Group. Francesca de Nicola and Ha Nguyen are with the Offices of the Chief Economist for the East Asia and Pacific Region and the Middle East and North Africa Region, respectively; Norman Loayza is with the Development Research Group and the EAPCE Research Center. Email addresses: <u>fdenicola@worldbank.org; hanguyen@worldbank.org; nloayza@worldbank.org.</u>
|subsamples<br>at least one child 6–14|(10)<br>(11)<br>(12)<br>foster<br>foster<br>foster<br>child<br>girl<br>boy<br>0.013**<br>0.010**<br>0.003<br>(0.005)<br>(0.005)<br>(0.003)<br>0.005<br>0.004<br>-0.000<br>(0.005)<br>(0.005)<br>(0.003)<br>12477<br>12477<br>12477<br>0.037<br>0.038<br>0.044<br>s. _∗∗∗_, _∗∗_, and _∗_indicate signifcance at<br> foster child is defned as being a resident<br>old) religion and ethnicity, years of<br> dummy for polygynous household head and|
|---|---|
||(9)<br>foster<br>boy<br>-0.005*<br>(0.003)<br>-0.008***<br>(0.002)<br>20578<br>0.039<br> survey weight<br> boy, where a <br>eaded househ<br> agriculture, a <br>male).|
|full sample|(8)<br>foster<br>girl<br>-0.013***<br>(0.003)<br>-0.019***<br>(0.003)<br>20578<br>0.046<br>heses. Using <br> child, girl or<br>d if female h<br> working in <br> (male and fe|
||(7)<br>foster<br>child<br>-0.017***<br>(0.004)<br>-0.025***<br>(0.003)<br>20578<br>0.044<br>evel in parent<br>ast one foster <br>or woman hea<br>e and husband<br>older than 15|
|6–14|(6)<br>foster<br>boy<br>-0.001<br>(0.001)<br>-0.004***<br>(0.001)<br>12477<br>0.046<br> the village l<br>d hosts at le<br>mmy, wife (<br>nership, wif<br>d members|
|subsamples<br>ast one child|(5)<br>foster<br>girl<br>-0.005***<br>(0.002)<br>-0.007***<br>(0.002)<br>12477<br>0.042<br> clustering at <br> the househol<br>ects, urban du<br>ehold land ow<br> # of househol|
|at le|(4)<br>foster<br>child<br>-0.007***<br>(0.002)<br>-0.011***<br>(0.002)<br>12477<br>0.042<br> adjusted for <br> for whether<br>ion fxed ef<br> index, hous<br>ive age 0–5,|
||(3)<br>foster<br>boy<br>-0.004***<br>(0.001)<br>-0.006***<br>(0.001)<br>20578<br>0.043<br>ndard errors <br> are dummies<br>ls include reg<br>ead), a wealth<br>rs and sons al|
|full sample|(2)<br>foster<br>girl<br>-0.011***<br>(0.002)<br>-0.014***<br>(0.002)<br>20578<br>0.056<br>ed. Robust sta<br>dent variables<br> Other contro<br> (or woman h<br> # of daughte|
|Households with:|(1)<br>dep. var:<br>foster<br>child<br># biol. sons<br>-0.015***<br>(0.002)<br># biol. daughters<br>-0.020***<br>(0.002)<br>more biol. sons<br>more biol. daughters<br>Observations<br>20578<br>Pseudo R-squared<br>0.054<br>_Notes._ Probit estimates, marginal efects report<br>1%, 5% and 10% levels, respectively. The depen<br>child whose parents are alive and live elsewhere.<br>education and age of the male head and his wife<br>female headed household, # children ever born,|

31
than in other sectors, although the coefficient on the interaction between our measure of political connections and being in the manufacturing sector is negative. Thus, the manufacturing sector is by no means an outlier; political connections are neither significantly less prevalent nor significantly less valuable in this sector.

As an additional robustness check, Table B4 examines whether connections to Suharto impacted how share price responded to regime turnover and elections. To start with, we examine the impact of Suharto’s departure. He resigned on May 21. However, the response of the stock market, which re-opened on May 22, was muted and, moreover, connected firms did not appear to lose more value than nonconnected firms. Fisman (2001) warned about the difficulty of interpreting this event, omitting it from his own paper; _“it is difficult to utilize this event for a number of reasons. Most importantly, there are many confounding events that took place simultaneously (...). Moreover, by the end of 1997, shares on the Jakarta Stock Exchange were very thinly traded, making it relatively easy for prices to be manipulated. There is also serious difficulties in defining an appropriate event window; expectations of regime shift had begun to form long before Suharto was replaced, so it is difficult to allow for a reasonably short event window...”_ The results are generally consistent with the value of the measured political connections being Suharto specific; they do not significantly predict how share prices respond to (the announcement of the results of) elections held in the post-Suharto era. We cannot reject the null that they do not predict share price responses to the first democratic legislative and presidential elections held in 1999, nor to the legislative and first round of presidential elections held in 2004. We do find some evidence that the valuation of connected firms grew less than that of non-connected firms when the 2<sup>nd</sup> round of the presidential elections in 2004 were announced, but the effect is only significant at the 10% level. Similarly, we find some evidence that connected firms’ share prices responded less positively to the announcement of the legislative elections in 2009, but again the effect is only significant at the 10% level. Moreover, in either instance, these results are not robust to widening the event window over which returns are calculated (results are omitted to conserve space but available upon request).

Table B5 presents regressions showing that firms’ share prices responses to these events were in no case significantly correlated with the measure of Suharto-era political connections. Even when pooling all episodes (as is done in column 6) we cannot reject the null hypothesis that political connections to Suharto did not predict firms stock prices responses to news about Wahid’s impeachment. These results are consistent with our assumption that the value of political connections to Suharto diminished after his 45
Policy Research Working Paper 8307

# **Abstract**

Migration, internal and external, continues to be a dominant livelihood strategy for households in Lesotho, with almost half (43 percent) of households reporting at least one member living away. The past decade has seen a sharp increase in female migration, due to a halt in new hires of Basotho men in South African mines and a concomitant expansion of jobs primarily for women in the export garment sector in Maseru and Maputsoe. This study analyzes female migration using three waves of the Demographic and Health Survey (2004, 2009, and 2014) as well as primary data collected by the research team in March-April 2015. The findings indicate that female migration in Lesotho is primarily driven by economic “push” (rather than “pull”) factors, often due to shocks to the household, such as job loss, death, or bad crops. Migrants are often seen as “strugglers” and their households of origin are just as poor as rural households with no migrants. Moreover, the study finds conclusive evidence that women’s employment in sectors dominated by migrants is strongly correlated with HIV/AIDS: 55 percent of women working in garment factories and 38 percent of domestic workers are HIV positive, as opposed to the national average of 30 percent. These findings point to three policy recommendations to support female migrants and their families: (i) lower the barriers to secondary education in rural areas, (ii) diversify and expand employment opportunities for men and women, and (iii) provide HIV/ AIDS prevention and treatment services to garment factory workers as well as migrants working in the informal sector.

This paper is a product of the Office of the Chief Economist, Africa Region and the Gender Global Theme. 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 schakravarty@worldbank.org and ibotea@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 7: Impacts on toilet uptake and subsidy uptake by September 2016

||(1)<br>Toilet (survey)|(2)<br>Toilet (survey)|(3)<br>Toilet (SBM)|(4)<br>Toilet (SBM)|(5)<br>Subsidy|(6)<br>Subsidy|
|---|---|---|---|---|---|---|
|SL|0.104<sup>_∗∗∗_</sup>||0.116<sup>_∗∗_</sup>||0.0814<sup>_∗∗_</sup>||
||(0.0329)||(0.0542)||(0.0381)||
|SL - Subsidy eligible||0.0677||0.0781||0.0866|
|||(0.0446)||(0.0483)||(0.0546)|
|SL - Subsidy ineligible||0.168<sup>_∗∗∗_</sup>||0.241<sup>_∗∗∗_</sup>||0.0426|
|||(0.0458)||(0.0694)||(0.0477)|
|Subsidy eligible||0.0197||-0.354<sup>_∗∗∗_</sup>||0.185<sup>_∗∗∗_</sup>|
|||(0.0442)||(0.0540)||(0.0537)|
|Strata FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Household covariates|Yes|Yes|Yes|Yes|Yes|Yes|
|F<br>~~i~~nteraction||0.135||0.0434||0.563|
|controlmean|0.185||0.341||0.113||
|controlmean<br>~~n~~otel||0.175||0.585||0.0150|
|controlmean<br>~~e~~l||0.190||0.193||0.172|
|N|935|935|935|935|935|935|

_Notes_ : Sample restricted to households not having a toilet at survey baseline. We drop 24 observations for which we do not have toilet ownership data available in the SBM dataset; The dependent variable in (1)-(2) is an indicator equal to one if the household owned a toilet in September 2016 as per the age of toilet constructed reported at endline survey; The dependent variable in (3)-(4) is an indicator equal to one if the household owned a toilet as per the SBM September 2016 snapshot; The dependent variable in Column (5)-(6) is subsidy uptake as recorded in the September 2016 SBM snapshot; SL stands for sanitation loan treatment arm; Robust standard errors clustered at the village level are shown in parentheses; *, **, *** indicates significance at the 10, 5 and 1 percent level; F-interaction stats show p-value of F-test of equality of subsidy eligible/non-eligible interactions with SL; Covariates: Indicator for presence of a child aged 0-2 at baseline, ratio between number of sampled clients and village size, indicator for whether there is any uncertainty around the matching quality; Strata and interviewer fixed effects included.

Table 8: Baseline toilet ownership of VG households

|No toilet at SBM baseline|Toilet at SBM baseli|ne<br>N|
|---|---|---|
|No toilet at survey baseline<br>460 (58.5%)|115 (14.6%)|575|
|Toilet at survey baseline<br>129 (16.4%)|83 (10.5%)|212|
|N<br>589|198|787|

Notes: Sample restricted to matched households for which there was almost zero uncertainty about the quality of the match and for which SBM data was not missing.

35
_Infrastructure spending needs amount to around 16 percent of GDP._ This is somewhat lower than for other countries in East and southern Africa (figure 25). Investment would absorb around 14 percent of GDP, about the same level of effort China made on infrastructure during the mid-2000s.

**Figure 25. Uganda Republic’s infrastructure spending needs in the regional context, share of GDP, 2006-2015**

Estimated infrastructure spending needed to meet targets, as percentage of GDP <!-- Start of picture text --> SSA<br>LIC‐Fragile<br>LIC‐NoFragile<br>SADC<br>Resource‐Rich<br>MIC<br>Congo, Dem. Rep.<br>Zimbabwe<br>Ethiopia<br>Central African Republic<br>Kenya<br>Uganda Capex O&M<br>South Africa<br>Botswana<br>% of GDP 0 10 20 30 40 50 60 70<br><!-- End of picture text --> _Source_ : Mayer and others 2009; Rosnes and Vennemo 2009; Carruthers, Krishnamani, and Murray 2009; You and others, AICD 2009. Derived from models that are available online at www.infrastructureafrica.org/aicd/tools/models. _Note:_ WSS = water supply and sanitation; ICT = information and communication technology.

_Uganda already spends a sizable $1 billion per year to meet its infrastructure needs with a mix of public and private funds_ (table 17). About 65 percent of the total spending is allocated to capital expenditure and 35 percent to operating expenditures. About half of existing spending is channeled to the power sector. Operating expenditure is entirely covered from budgetary resources and payments by infrastructure users. Unusually, Uganda relies heavily on the private sector to fund as much as 40 percent of total capital spending on infrastructure. As in most countries, ICT attracts the largest share of private investment in infrastructure. But in contrast to many other countries, the private sector also invests significantly in Uganda’s power, transport and even water infrastructure. The second most important source of funding for infrastructure investment in Uganda is overseas development assistance (ODA), which supplies 33 percent of total funding and is directed mostly to power, transport and water. As is rarely the case in Africa, ODA and private participation in infrastructure (PPI) flows to the water and sanitation sectors in Uganda are broadly comparable. Third, about 26 percent of capital expenditure is funded by federal and district governments and state-owned enterprises (SOEs). Non–Organisation of Economic Co-operation and Development (non-OECD) financiers account for only around 5 percent of capital spending; their presence is most pronounced in the water and transport sectors.

43
Table 4 Determinants of Wage Increases in CMte d'Ivoire

||<br>|Change in the l<br>|D<br>og of average<br>|ependent varia<br>labor costs per<br>|ble:<br>worker in for<br>|mal sector firm<br>|s<br>|
|---|---|---|---|---|---|---|---|
|Explanatory variables|(A)|(B)|(C)|(D)|(E)|(F)|(G)|
|Change in thelogof|0.7235**|||0.7221**|- 0.0921||-0.0862|
|consumer prices|(5.338)|||(5.321)|(0.393)||(-0.367)|
|Change in the log of||-0.0623||-0.0378||0.2713|0.2653|
|minimum wages||(-0.480)||(-0.296)||(0.654)|(0.6392)|
|Change in the log of|||0.4748**||0.5166**|0.4737 **|0.5128 **|
|average government wages|||(4.397)||(3.405)|(4.385)|(3.376)|
|Change in the log of|0.0388|-0.0207|0.0178|0.0346|0.0209|0.0481|0.0504|
|terms of trade (lagged)|(0.760)|(-0.392)|(0.207)|(0.653)|(0.243)|(0.494)|(0.516)|
|Change in the log of|0.0718|0.6618 **|-0.2826|0.1064|-0.3075|-0.4325|-0.4525|
|real output (lagged)|(0.381)|(3.334)|(-0.816)|(0.480)|(-0.873)|(-1.041)|(-1.079)|
|Independent|0.0143|0.0674 **|0.0485 **|0.0100|0.0511 **|0.0441 **|0.0488 **|
|term|(1.149)|(7.903)|(5.844)|(0.842)|(3.306)|(4.768)|(3.074)|
|Adjusted R<sup>2</sup>|0.045|0.017|0.025|0.044|0.024|0.024|0.023|
|F test|16.12|6.51|7.15|12.10|5.39|5.46|4.39|
|Number of<br>observations|969|969|724|969|724|724|724|

Note: Data on average labor costs per worker are from the _Banque de Donnees Financieres,_ which is composed of about two thousand medium and large firms. There is one observation per industry per year, over period 1976-1994. Values in parentheses are "t" statistics. Significant coefficients at the _5_ and 1 percent level are indicated by one and two asterisks respectively.

21