rafmacalaba/datause-encoder-data huggingface dataset:
where _C_ ( _p_ ) is simply the Generalised Lorenz curve (see for instance Son (2004)). The estimator of the variance of the quantile _Q_ ( _τ_ ) and the generalised Lorenz curve can be defined as follows: To illustrate these instruments, we use again data from the 1987/88 _Nationwide Food Consumption Survey_ . Assume that the price reform include an increase of 20% in the price of _Meats_ and a reduction of 20% in the price of _Fruits and vegetables_ . Figures 17 and 18 show first and second order dominance of the pro-poor curves constructed with the _LV_ and _CS_ methods, the latter with Cobb-Douglas preferences. As one can observe, the simulated price reform is propoor, and this, even if the change in prices does not practically impact government revenues (the curve averages around zero). It can also be noted that the _LV_ and _CS_ curves largely overlap indicating that there is little difference between measures for this particular price reform. In this case, the use of _LV_ would be appropriate and also simplify computation.

As we have learned by now, the differences across measures diverge as price increase. We therefore repeat the exercise with a large price increase, an increase in the price of meats of 100%. Figures 19 and 20 show that the _LV_ and _CS_ measures provide very different results and the difference is greater for lower quantiles (the poor). The reason for the difference between the _LV_ and _CS_ measures is the fact that the share of expenditure represented by the product affected by price changes varies across the welfare distribution and this share is used by the CS method (alpha in the Cobb-Douglas formula) but not by the _LV_ method. This is one more reason to be very weary of the differences across measures. As prices increase, the differences across measures increase, they are significant and they can be larger for lower parts of the distributions. This makes estimates of the welfare effects on poverty even more complex than estimates for an entire society.

46
2 per-hectare pricing methods, among others. To affect income inequality, a water pricing method should include certain forms of water quantity restrictions.

These results lend some support to the view that income redistribution policies should not be carried out via water prices (see, e.g., Seagraves and Easter, 1983); not because it involves wrong doing but because water prices serve as a poor means to reduce income inequality. However, pricing schemes that involve water quota rules can reduce income inequality. We demonstrate this with a two rate tiered pricing scheme combined with equal quotas of the cheaper water.

# **Acknowledgment**

Comments from K. William Easter, Eitan Hochman, Herve Plusquellec, James Roumasset, and David Steeds are gratefully acknowledged. Data collection on pricing practices by R M Chellappan is acknowledged.
**Table 10: Ratio of All Students to Sanctioned Teaching Positions**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|FE|FE + Controls|Excluding Multan<br>+ Khanewal|
|Stipend District|-3.068***<br>(0.214)||||
|2004|3.438***|3.335***|6.050***|5.688***|
||(0.143)|(0.137)|(0.688)|(0.703)|
|2005|5.797***|5.731***|8.444***|8.065***|
||(0.152)|(0.148)|(0.697)|(0.712)|
|2006|3.246***|3.198***|2.866***|2.681***|
||(0.162)|(0.154)|(0.163)|(0.167)|
|2007|0.858***|0.822***|0.460**|0.229|
||(0.153)|(0.149)|(0.172)|(0.179)|
|2008|-0.367**|-0.430***|-0.825***|-1.007***|
||(0.128)|(0.126)|(0.141)|(0.146)|
|Stipend District * 2004|2.201***|2.220***|2.156***|2.268***|
||(0.212)|(0.205)|(0.204)|(0.215)|
|Stipend District * 2005|2.977***|2.990***|2.921***|2.915***|
||(0.223)|(0.218)|(0.218)|(0.230)|
|Stipend District * 2006|4.250***|4.289***|4.283***|4.480***|
||(0.232)|(0.226)|(0.226)|(0.238)|
|Stipend District * 2007|4.647***|4.678***|4.686***|4.988***|
||(0.200)|(0.194)|(0.196)|(0.206)|
|Stipend District * 2008|4.653***|4.660***|4.659***|4.843***|
||(0.181)|(0.177)|(0.177)|(0.186)|
|Number of Basic Facilities = 1|||1.271|0.952|
||||(0.678)|(0.693)|
|Number of Basic Facilities = 2|||2.432***|2.116**|
||||(0.693)|(0.709)|
|Number of Basic Facilities = 3|||3.260***<br>(0.696)|2.925***<br>(0.712)|
|Constant|24.27***|23.08***|20.40***|20.70***|
||(0.151)|(0.0685)|(0.682)|(0.698)|
|Observations|70205|70205|70205|65601|
|Number of Schools||12170|12170|11381|
|Adjusted R-squared|0.035|0.103|0.105|0.103|
|R-square within||0.103|0.105|0.103|

* p<0.05 ** p<0.01 *** p<0.001. Standard errors in parentheses are robust to heteroskedasticity and clustered at the level of the school. The dependent variable is the ratio of students in a given school to sanctioned teaching posts. The average school in a treatment district school at baseline had a ratio of 29. Sample excludes mosque and primary schools. Column 1 reports estimates from an OLS specification. Column 2 reports estimates from a fixed effects specification at the school level. Column 3 adds a series of indicators for how many of the following three facilities a school has: toilets, drinking water and electricity. The excluded group is no facilities. The excluded year is the baseline year 2003. Column 4 removes two districts whose treatment status was interchanged: Khanewal and Multan.

48
**Table 13: Restricting Sample to Parliamentary Countries, DIVIDED model**

||(1)|(2)|(3)|
|---|---|---|---|
||Total<br>Expenditure|Total<br>Tax<br>Revenues|Deficit|
|Divided|-0.064<br>(0.023)***|-0.041<br>(0.018)**|-0.0001<br>(0.012)|
|Ethno-linguistic<br>Fragmentation|-0.172<br>(0.081)**|-0.10<br>(0.067)|0.08<br>(0.024)***|
|Turnout|0.23<br>(0.081)***|0.18<br>(0.063)***|0.059<br>(0.031)***|
|Majoritarian System<br>(=1, 0 if Proportional)|0.006<br>(0.026)|-0.004<br>(0.016)|-0.003<br>(0.01)|
|Regular|-0.021|-0.004|-0.038|
|Elections<br>(=1, 0 otherwise)|(0.027)|(0.02)|(0.017)**|
|Observations|79|79|93|
|R-squared|0.65|0.68|0.44|

Robust standard errors in parentheses, clustered by country.

* significant at 10%; ** significant at 5%; *** significant at 1%. Controls as in Table 1. Countries included in sample: Austria, Belgium, Belize, Botswana, Canada, Denmark, Finland, France, Germany, Greece, Guyana, Hungary, Iceland, India, Ireland, Italy, Japan, Malaysia, Nepal, Netherlands, New Zealand, Norway, Pakistan, Portugal, South Africa, Spain, Sri Lanka, Sweden, Switzerland, Thailand, United Kingdom, Zimbabwe.

39
Policy Research Working Paper 10826

# **Abstract**

This paper examines the effect of compensating a private sector provider of employment services for successful placement of jobseekers with employers. Within this program, active job matching, relative to only job counseling, contributes to a significantly higher rate of employment, and these effects are more pronounced for subgroups that are less likely to find employment without assistance, including youth and low-skilled workers. Among the jobseekers randomly allocated to matching, those who were judged by counselors to be “employable” were far more likely to be matched to employers. To understand the decisions made to rate jobseekers as employable, the paper makes use of both information directly observable to job counselors and characteristics and personality traits from a separate survey of the jobseekers. Evidence suggests that under incentivized matching, counselor assessments of employability and designation for more intensive effort are consistent with cream-skimming.

This paper is a product of the Development Research Group, Development Economics and the Social Protection and Jobs Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at merima.balavac@efsa.unsa.ba, jgiles@worldbank.org, shari@worldbank.org, and movadiya@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
# **Appendix Table 6.3: Pooled Impacts on Firm Growth**

||I.H.S.<br>Monthly<br>Sales|I.H.S.<br>Yearly<br>Sales|I.H.S.<br>Monthly<br>Profits|I.H.S.<br>Yearly<br>Profits|Sales &<br>Profits<br>Index|Total<br>Employment|I.H.S.<br>Employment|I.H.S.<br>Profits<br>Recall Pan|I.H.S.<br>Sales<br>el Round 2|
|---|---|---|---|---|---|---|---|---|---|
|Assigned to Insourcing|0.599|0.635|0.431|0.548|0.105|-0.019|0.087|0.374|0.562|
||(0.374)|(0.419)|(0.425)|(0.463)|(0.074)|(0.689)|(0.094)|(0.583)|(0.604)|
|Assigned to Outsourcing|0.719*|1.050***|0.567|0.951**|0.162**|-0.017|0.152|0.835|1.229**|
||(0.368)|(0.376)|(0.420)|(0.438)|(0.069)|(0.721)|(0.093)|(0.545)|(0.554)|
|Assigned to Training|-0.039|0.260|-0.094|0.435|0.018|0.106|0.068|0.244|0.358|
||(0.426)|(0.439)|(0.453)|(0.461)|(0.077)|(0.742)|(0.099)|(0.579)|(0.613)|
|Assigned to Consulting|0.474|0.426|0.470|0.301|0.070|0.496|0.161*|0.738|0.898|
||(0.380)|(0.425)|(0.426)|(0.489)|(0.077)|(0.703)|(0.093)|(0.561)|(0.573)|
|Mean of Control Group|12.388|14.626|10.571|12.766|0.000|8.117|2.317|9.720|11.223|
|Sample Size|1348|1348|1348|1348|1348|1368|1368|2712|4746|
|P-value: all treatments zero|0.176|0.051|0.443|0.271|0.124|0.923|0.395|0.508|0.181|
|P-value: all treatments equal|0.295|0.150|0.465|0.459|0.214|0.838|0.627|0.622|0.341|
|Notes:||||||||||

Regressions control for stratification batch fixed effects, baseline value of outcome, and survey round. Robust standard errors in parentheses, clustered at the firm level. *, **, *** denote significance at the 10, 5, and 1 percent levels respectively. Recall Panel Round 2 uses monthly recall of three additional months of profits, and six additional months of sales, asked in round 2 survey. I.H.S. denotes the inverse hyperbolic sine transformation. Firms that are closed are coded as having zero profits and sales. **Monthly sales** is real (January 2018) sales in the past month; **Yearly Sales** are real sales in the past year; **Monthly profits** and **Yearly profits** are real profits in the past month and past year respectively. **Sales and Profits Index** is the average of standardized z-scores of these first four sales and profits measures. **Total Employment** is total number of workers.

75
estimating the _ex-post_ standard errors of poverty and inequality measures from HBSs, that is, _complex measures_ from _complex surveys_ . In this way, it allows the evaluation of the impact of the so-called _implicit stratification_ (or _hidden_ ; Wolter, 1985), which is present when PSUs are selected with systematic sampling from one or more lists that are ordered according to some variables correlated with the phenomenon under investigation.

The present paper is composed of five sections. Following this introduction, Section 2 presents the theory behind the estimation of standard errors in complex surveys. Attention is paid to the two-stage stratified sampling design, where, in the first stage, selection of PSUs is performed with systematic sampling, i.e., where implicit stratification is present. Since systematic sampling can be seen as a random selection of a single PSU from each implicit stratum, it is not possible to derive a consistent estimator of the sampling variance (Wolter, 1985). Therefore, recent replication methods have been developed and implemented for estimating the variance of complex measures from complex surveys. Among the most adopted methodologies are bootstrap, Taylor Linearisation, Jack-knife Repeated Replications (JRR) and Balanced Repeated Replications (BRR). On this basis, the second part of Section 2 briefly presents the recent methodology of Verma and Betti (2011) for the implementation of a variant of JRR in the case of systematic selection of PSUs and the possibility of decomposition of the design effect in several components (Kish, 1965; Verma, 1991; Liu _et al._ , 2002).

The definition of these components is useful for highlighting the proposed implicit stratification. As previously mentioned, we aim to introduce a further implicit stratification by selecting the PSUs with a systematic sampling within each explicit stratum. PSUs are ordered by their relative ranking in terms of the per capita consumption expenditure obtained from the poverty mapping approach. Therefore, Section 3 is devoted to a brief presentation of the World Bank methodology.

Since, in general, we believe that any new methodological proposal is useful if it can be put into practice, the second part of Section 3 presents the implementation of poverty mapping in Tunisia, based on the 2014 Census and the 2015 Household 4
first option. We do not find a statistically significant difference in the frequency of violence across the two orderings (Table 1). We interpret this as evidence that the respondents understood the mapping of images to answer the choices accurately.

Table 1: Impact of the Measurement Experiment I on ACASI IPV Reporting

||(1)<br>Push, Shake,<br>Throw|(2)<br>Slap|(3)<br>Twist arm,<br>Pull hair|(4)<br>Punch|(5)<br>Choke,<br>Burn|(6)<br>Threaten to attack<br>with weapon|
|---|---|---|---|---|---|---|
|Ascending|-0.0156|0.0100|-0.00549|-0.00407|-0.00385|-0.000552|
||(0.0135)|(0.0139)|(0.0131)|(0.0130)|(0.0115)|(0.00921)|
|Observations|6133|6134|6135|6134|6135|6135|

Notes: The table reports results from an experiment where we randomised the order of the frequency answer options. Ascending is an indicator of the order options displayed to the respondent in ascending order for questions related to the experience of violence asked using ACASI. The dependent variables take on the value 0 if the respondent did not experience the type of violence in the last 6 months, 1.5 if she experienced it once or twice and 3 if she experienced it three or more times. Standard errors are in parentheses. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

Second, we asked generic (non-IPV) questions on food consumption to women using both F2F and ACASI methods. We find a very high agreement between ACASI and F2F: 94% agreement for the question “How many times did your husband eat meat in last 7 days?” 96% for the question “How many times did your husband eat fruit in last 7 days?” and 95% for the question “How many times did your husband eat eggs in last 7 days?” This provides additional evidence that respondents understood how to use ACASI.

# **4 Experiment 2: Does answering sensitive questions using ACASI change disclosure face-to-face?**

We study whether asking the same respondent questions related to their experience of violence F2F versus via ACASI affects their rate of IPV reporting. We chose two questions for the experiment: one on physical violence “During the last 6 months, how often did your husband slap you?” and one on injuries sustained as a result of violence “During the last 6 months, did you ever have cuts 6
# **1. Introduction**

The frequency and intensity of extreme weather events and weather-induced food crises have increased across Africa and are expected to increase in the future with continued climate change ( <mark>Venäläinen</mark> et al., 2016; Chinsinga, 2012). As described in our literature review below, empirical evidence across the continent reveals that weather shocks reduce agricultural production and income among smallholder households, and, to the extent that farm households are limited in their ability to shield consumption losses from production losses, can lead to reduced food consumption, less diverse diets, lower expenditures on health and education, and lower school enrollment rates among school aged children.

Between late 2015 and 2016, an unprecedented drought ravaged Southern Africa. Malawi was the worst-hit country in the region and faced the largest humanitarian emergency in its history, and given the 60 percent poverty rate prevailing at the time, many households were extremely vulnerable to impacts of the drought (National Statistical Office of Malawi and World Bank, 2018). It was forecasted at the time that nearly 7 million people (about 40 percent) would be unable to meet their food requirements between April 2016 and March 2017 - the 12-month period following the main harvest (MVAC, 2016). The drought came on the heels of devastating floods that had occurred the previous January, which had been catalyzed by the highest rainfall on record (a 1 in 500-year event) and that had been projected to affect the ability of over 2.8 million people to meet their food requirements in the post-harvest period (MVAC, 2015). Many farm households suffered severe crop losses in both seasons, particularly those located in southern Malawi (World Bank Group, United Nations, European Union, 2016).

A large body of literature establishes that when covariate shocks hit, some degree of risk-sharing takes place, but consumption is not entirely smoothed out (Hill, 2019; de la Fuente, Gine and Hill, 2013). Del Ninno et al. (2001) review the large-scale 1998 flooding in Bangladesh and find negative impacts on food security, with food consumption and calorie intake falling. A study of Ethiopian rural households 2
45 Republics using district level data. For the Czech Republic, their variables capturing regional ALMP activity are potential stocks of PUJ and SPJ or total district expenditures on ALMP. The available data on ALMP participants are in the form of "agreements" concluded with municipalities or private employers and are, therefore, clearly an upper bound of the actual stocks.

According to their results, in the Czech regressions ALMP participation stocks are not statistically significant predictors of outflows from unemployment into employment while total district spending on ALMP are statistically significant in all specifications. The imputed long-run elasticity of spending with respect to outflows, 0.068 means that if in the Czech Republic spending had not fallen from 1992 to 1993 by 56%, outflows from unemployment would have been raised by 12.4% implying a decrease in the steady state rate of unemployment from 3.3% to 3%. Their econometric evidence confirms the previous supposition about the marginal influence of ALMPs on the Czech labour market as it points to a discernible but small effect of ALMPs on the Czech unemployment rate.

Regional labour mobility might also contribute to outflows from unemployment. High regional labour mobility can offset regional differences in unemployment and vacancies, as job applicants move (migrate or commute) from regions with high unemployment to vacancy ratios (low wages) to regions with less tight labour market conditions (high wage). As a result regional matching according to skill demand and supply is improved, even if firms and capital are slow to move. Better factor allocation leads to lower regional unemployment dispersion, but also to lower overall unemployment due to more efficient use of available resources. According to Erbenova (1995) regional disparities in unemployment, vacancies and wage have increased in the last four years. The eastern half of the CR has almost entirely unemployment rates above 3 and often above 5%, whereas the west of the country, especially that part which borders at Germany has distinctly lower regional unemployment rates. Vacancies follow a reversed pattern. Regional wage variations have increased since the beginning of transformation, while regional net migration flows are small.

Prague has a net inflow of people, which is conform with the fact that unemployment is extremely low there and the private sector is booming. Commuting, which was considerable even before transformation, has increased further and in 1991 about 30% of the labour force commuted to work, mainly short distances. Because of the limits of the housing market, commuting seems to be preferred to migration. PDeople
# **Figure 5**

Occupational Wage Premiums in 1983 and 2002 among Routine and Non-Routine Occupations <!-- Start of picture text --> Routine Non-Routine<br>-1 -.5 0 .5 1 -1 -.5 0 .5 1<br>Occupational Wage Premium - 1983 Occupational Wage Premium - 1983<br>1 1<br>.5 .5<br>0 0<br>-.5 -.5<br>Occupational Wage Premium - 2002 Occupational Wage Premium - 2002<br>-1 -1<br><!-- End of picture text --> _Notes_ : Wage premium are calculated by a standard Mincerian regression of the log wage on education, experience, age, sex, race, year fixed efffects and state fixed effects among all workers in the CPS MORG between 1983 and 2002. Each point in the plot is a separate occupation identified in the CPS (N=474). The occupations are considered routine if the share of tasks that is routine is greater than the median occupation. Occupations with higher wage premiums in 2002 than in 1983 are shaded in.
students make choices within the of this environment.

Real-world education systems are diverse, and in particular the dynamics of the public and private sector — and the type of private sector offerings — are different across countries. In some contexts, private schools target affluent households, and jobs in private schools are often seen as more attractive than public sector jobs, typically providing some form of performance-based compensation. In other contexts, there has been a growth of ‘low-cost private schools’ that deliberately cater for the lower end of the income distribution and, in these settings, jobs in the public sector typically confer significant rents relative to the private sector.

In view of this diversity, we focus our model and empirical test on one particular regional system: Latin America. We choose Latin America because its education systems are reasonably homogeneous across countries in terms of the character of public and private schools. Specifically, the private school system caters to the middle (and upper) classes and accounts for about one-fifth of high school students. Private schools tend to be better funded (via costly school fees) and in turn pay higher teacher salaries and offer better facilities. Public schools, on the other hand, are often poorly funded and operate in highly centralized environments. Teacher pay is set on a rigid scale dictated by strong unions. Focusing on a region that has such systems makes the applied theory exercise substantially less complex, and the prevalence of large-scale national surveys in the region opens many possibilities for future empirical work.

Table 2 reports the correlation exercise in Columns (1) to (3) of Table 1 for Latin American countries only, and confirms that the relationship between management and student performance is strong in the region. Further, the coefficient on private schools indicates that students in private schools achieve higher scores, by about 55 points, than students in public schools. This affords a suitable empirical environment to study the channels that we are interested in this section.

# **3.1 Overview of the theoretical framework**

The analysis is built around a student-level education production function. A common, general formulation is _y_ = _A_ ( _L, K_ ) + _ε_ where _y_ is a measure of student learning, _L_ and _K_ are respectively labour and physical capital inputs into the student’s education, _A_ is a (school-specific) productivity 12