rafmacalaba/datause-encoder-data huggingface dataset:
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weekly income of parents in our sample, _y_ ̄). These estimates for the perceived and actual returns to an additional year of school are taken from Jensen (2010) where he estimates that in the Dominican Republic the return to an additional year of secondary school translates into an 8% increase in monthly income, while the perceived increase in monthly income is only 2% (note, few papers have investigated the difference between actual and perceived returns to schooling, hence, why we use estimates from the Dominican Republic as opposed to Mexico).<sup>37</sup> Finally, we assume that annual utility of consumption is CRRA with parameter _γ_ = 1 _._ 05. Thus, we get the following expression for _w_ ( ̃ _μ,_ 1) _− v_ ( ̃ _μ,_ 1): We then proceed to evaluate the discretized version of Equation (15) under the average Progresa grants (for our sample of treated, eligible children in years 1998 and 1999): We find that under the average Progresa grants, the targeting benefit is 38% as large as the enrollment benefit. Alternatively, if the actual return to an additional year of schooling is only 4% (and parents still perceive it as 2%) then _EBTB_<sup>=1</sup><sup>_._12,whereasif</sup> the actual return to an additional year of schooling is 12%, then _EB_<sup>_<u>TB</u>_=0</sup><sup>_._23.Thus,the</sup> targeting benefit is comparable in magnitude to the enrollment benefit, and therefore should be taken into account when trading-off the costs and benefits of CCTs vs. UCTs.

# VII Conclusion

In this paper, we argue that there exists an unexplored targeting benefit of imposing conditions on cash transfers to send children to school: by imposing conditions, a planner can direct more money to those households who forgo a discrete loss of child income. We argue that this benefit mitigates the adverse targeting effects that arise from imposing conditions on a normal investment. Ultimately, we argue that there exists a targeting trade-off when considering how to allocate a budget between a CCT and a UCT, and that it can be optimal to allocate some of a budget to a CCT based on targeting arguments alone.

We formalize this intuition by developing a theoretical framework that captures the

> 37. Note, Attanasio and Kaufmann (2014) investigate perceived returns (of both mothers and children) of education in Mexico. However, they are unable to compare these perceived returns to actual returns given differences between samples for which perceived and actual returns are calculated.

43
# Empirical Econometric Evaluation of Alternative Methods of Dealing with Missing Values in Investment Climate Surveys<sup>*</sup>

by Alvaro Escribano<sup>†</sup> , Jorge Pena<sup>‡</sup> and J. Luis Guasch*

**Keywords:** Investment Climate surveys, missing observations, incomplete data, random sampling, sample selection, EM-algorithm and bootstrap.

**JEL classification:** C15, C24, C63, C81, C83.

> * We have benefited from suggestions from  Daniel Peña, Ariel Pakes, Rodolfo Stucchi and Eric Verhoogen. † Telefonica-UC3M Chair of Economics of Telecommunications, Department of Economics, Universidad Carlos III de Madrid; alvaroe@eco.uc3m.es.

> ‡ Department of Economics, Universidad Carlos III de Madrid; <u>jpizquie@eco.uc3m.es.</u>

> *Senior Adviser, World Bank, Head of the World Bank Global Experts Group on Private-Public-Partnerships, and Professor of Economics, Univeristy of California, San Diego; jguasch@worldbank.org
companies in the same industries found no significant differences in efficiency and profitability (Feng _et al._ , 2004).<sup>19</sup> Although the empirical studies are generally favorable, the performance of mixed enterprises probably varies substantially and in ways we only partially understand. The characteristics of the shareholders probably matter considerably. The studies described above count as a mixed enterprise any company which has both public and private shareholders, but performance may depend on the percentage that is privately owned and how widely distributed share ownership is. The agency exercising the government’s ownership responsibilities is presumably also important. An inexperienced technical ministry may interfere too much, for example, or conceivably be taken advantage of by the private co-investors. By the same token, a special government holding company may be particularly advantageous if it is more sensitive to the importance of respecting minority shareholder rights. Singapore manages most of its mixed enterprises through a special holding company, for example, while in Canada Vining and Boardman (1992, p. 227) found that the mixed enterprises supervised by a holding company performed better than those that were not.

The level of competition in the industry probably matters as well since competition should reinforce the pressures from private investors to pursue efficiency and profits. Competition also makes it possible to avoid the problem of regulating the firm’s tariffs, which adds another layer of complexity to the relationship between the government and the mixed enterprise. Unfortunately none of the studies of mixed enterprise focus on infrastructure or other industries that have elements of monopoly.<sup>20</sup> Finally, the laws concerning corporate governance, the quality of securities regulation and the integrity of the legal system presumably matter as well. The concern is whether minority and small shareholders have the information and the opportunity they need to influence important decisions of the firm. The favorable studies described above all involve countries—Canada, India, Singapore—with well-run stock markets and wellregarded legal systems.

> 19 Ramirez and Tan (2003) find that mixed enterprises trade at a market premium of 20 percent, which they attribute to the investors’ belief—correct or not—that the Singapore government would not allow a mixed enterprise to fail.

> 20 Broadman and Vining deliberately exclude utilities, for example, while the sample of 26 Singaporean SOEs used by Feng and his colleagues includes only one utility (Singtel).

41
- 143 -

incorporating future commodity prices either directly or indirectly into its planning. Even allocating money to basic agricultural research involves some judgment about future relative scarcities. The accumulating evidence suggests that this dilemma can be resolved if the government - simply does not try to outguess the market that is, anticipate the direction of future prices independently of current market prices. Whether these guesses are based on complicated econometric models of individual commodity systems or on attempts to discern underlying trends in historical price data, the record suggest they will be wrong in a very important way. Price forecasts based on either of these two techniques do not provide as good an estimate of future prices, even when the time horizon is as long as a decade, as the current Drice at the time the forecast price must be used in determining goveranent investment and program priorities.49 5.44 The implication for the role of government in price stabilization schemes is stark. Unless a strong case can be made for reasons of efficiency and income distribution to attempt short-run price stabilization, the government should probably find other instruments to reach its policy objectives. Stabilizing rice prices clearly makes sense for these reasons if the levels do not deviate too much from border prices for too long a time, where 'too much' and "too long' can be determined only relative to budgetary flexibility and the seriousness of any spillovers from rice price stabilization to other commodities or other countries.

5.45 The dilemma hero has been stressed before. Policy that stabilizes rice prices is important for political and economic reasons, but distortions caused by too much stabilization create rigidities in the whole agricultural economy. Long-run economic policy must promote efficient allocation of resources; otherwise there will be too little output to worry about distribution or food security. But granting this long-run goal, how much premium can a poor country pay in the short run to achieve food 49/ See Robert J. Schwartz, "Optimal Trends for Forecasting Prices: An Empirical Assessment of Three Grains," Ph.D. Dissertation, Economics Department and Harvard Business School, Harvard University, Cambridge, Mass., 1987.
28

# **Table A1-2: Results** **<u>of the probit regressions of being interviewed</u>**

||Interviewed<br>in round 2|Interviewed<br>in round 3|
|---|---|---|
|Belong to Pare group|0.238|0.232|
||(0.312)|(0.227)|
|Christian|0.085|0.085|
||(0.231)|(0.168)|
|Distance to town (km)|-0.005|-0.009***|
||(0.003)|(0.002)|
|Market place in village|0.042|-0.193|
||(0.220)|(0.133)|
|Cell phone coverage in village|0.353|-0.304*|
||(0.235)|(0.175)|
|Agricultural extension agent in village|0.194|0.419***|
||(0.211)|(0.122)|
|Dispensary, health services in village|-0.480**|-0.128|
||(0.186)|(0.114)|
|Constant|0.358|-0.304|
||(0.627)|(0.417)|
|R-squared|0.14|0.12|
|N.ofObs.|949|949|
To vary the detail of the questionnaire, we developed a short and a detailed labor module focusing on differences in screening questions used to determine economic activity and labor force participation. The detailed questionnaire reflects the approach that is generally considered to be best practice and is typically used in multipurpose household surveys, such as the Living Standard Measurement Surveys (LSMS). Increased demands from policy makers to evaluate changes over time, requires frequent data collection that is also simple to implement. Many countries therefore collect data on an annual basis using short questionnaires. The short module used in our survey experiment reflects the approach followed by more concise surveys used in many low‐income countries, such as the Core Welfare Indicator Questionnaire (CWIQ) and the Welfare Monitoring Surveys (WMS), as well as other surveys listed in Table 1.

Specifically, the detailed module contains three questions at the start to determine employment status, namely, (i) whether the person has worked for someone outside the household (as an employee), (ii) whether s/he has worked on the household farm, and (iii) whether s/he has worked in a non‐farm household enterprise. In each case the response is either yes or no.<sup>17</sup> In the short module there was only one question to determine employment status, namely whether s/he did any type of work, which also invited a response of yes or no. In both cases the questions were asked with respect to the last 7 days (the reference period for identifying those who are “employed” and the set of detailed questions on that employment) and, if not reported to work in the last 7 days, then asked for the last 12 months. Those identified as working in the last 7 days in either module were then asked identically the same questions to gather information on their occupation, sector, employer, hours, and wage payments in their main job. The short and detailed employment modules are reported in Annex. The analysis focuses on the response with respect to the last 7 days, which is generally considered the most reliable for labor market analysis.

To create variation in the second dimension, we randomly varied asking the questions directly to the respondent or a proxy respondent. The proxy respondent was randomly chosen among household members at least 15 years old to be able to compare across different proxy

> 17 The short and detailed module in our experiment differed in two ways: in dropping the set of screening questions to determine employment status and in not asking about second and third jobs. Since we only consider labor outcomes in the first job, the analysis focusses on the effect of the additional screening questions.

9
**Table 9: Snowball Summary Table**

||Names on<br>seed list|Interviews<br>Seed list|1st<br>reference|2nd<br>reference|3rd<br>reference|Total|
|---|---|---|---|---|---|---|
|Seed list 1|67|42|19|8|7|76|
|Seed list 2|69|18|5|1||24|
|**Total**|**136**|**60**|**24**|**9**|**7**|**100**|

The seed households are drawn from names provided by Nikkei associations, and hence one would expect these households to be more closely connected to Japan than a randomly chosen Nikkei household. The hope with snowball sampling is that the process of chain-referral will lead to coverage of other individuals, not as closely connected to Japan. However, as Table 10 shows, the snowball seed and referral households have very similar characteristics. In fact, the only variable where the means are significantly different is for watching Japanese/Nikkei TV programs, which more of the referral households do than the seed households. Thus the snowballing does not seem to have succeeded in giving households which are that different from the initial seeds.

**Table 10: Differences between Seed and Referral households**

||Snowball<br>Seeds|Snowball<br>Referrals|
|---|---|---|
|Household Size|3.48|3.95|
|_Percentage of households with member who:_|||
|Reads Japanese/Nikkei newspapers|48|41|
|Listens to Japanese/Nikkei radio programs|16|18|
|Watches Japanese/Nikkei TV programs|46|67**|
|Reads Japanese/Nikkei books/magazines|52|51|
|Reads newspapers from Nikkei associations|59|69|
|Checks Japanese/Nikkei websites on the internet|21|32|
|_% of households which:_|||
|**Migration**|||
|Have a Member currently in Japan|28|33|
|Have a Member who has returned|||
|from work/study in Japan|49|54|
|**Remittances**|||
|Receive remittances from Japan|8|13|
|Refuse to say if they receive remittances|8|5|
|**Sample Size**|61|39|

*, **, *** indicates referral mean differs from seed mean at the

10%, 5% and 1% significance levels respectively.

29

-  -
**Table 2: UYEP Streams**

|**1. Basic YJC Stream**||**Additional YJC Activities**|
|---|---|---|
|Basic Life Skills Training|+ YJC Work Experience|+<br>Community Based Project<br>**Or**<br>+<br>2<sup>nd</sup>YJC work<br>experience/additional semi‐skilled<br>work<br>**Or**<br>+<br>Pre‐Employment Trainingand OJT|
|**2. Basic OJT Stream**||**Additional OJT Activities**|
|Basic Life Skills Training|+ Pre–<br>Employment<br>training<br>+<br>OJT Work<br>Placement|+<br>Mentoring during OJT|

Source: Compiled by authors.

# 4. **Impact Evaluation, Data, and Sample Balance**

# _4.1 Objectives and Methodology_

The main objective of the impact evaluation of the UYEP was to assess the impacts of the program participation on the participants’ social behavior and criminal activity. To do so, we utilize a Difference‐in‐ Differences (DD) evaluation strategy to estimate the effect of program participation on a wide variety of indicators (Abadie, 2005; Imbens et al., 2009) This strategy compares the average change over time in the outcome variable for the treatment group to the average change over time for the control group. The key identifying assumption is that trends, in the absence of the program, would be identical for treatment and control households. A wide range of outcomes are considered, including social (or anti‐social) behavior and social environment, (self‐reported) engagement in a bad/criminal activity, and the reported incidence of being a victim of such an activity. We also explore, to the extent possible given data limitations, potential channels underlying the results.

# _4.2 Data_

As part of the impact evaluation design of the UYEP, the Eligibility Baseline Survey (EBS) was administered during the 4<sup>th</sup> and 5<sup>th</sup> intakes to both treatment group and control groups. The treatment group consisted of youth who were selected for the 4<sup>th</sup> and 5<sup>th</sup> intake of the project, while the control group consisted of eligible youth from neighboring districts who had not participated in program at the time of the follow‐ up survey (FUS). This survey was conducted in the summer of 2015, about a year after participants’ completion of the program, to evaluate the changes in the variables of interest over time. The primary purpose of the FUS was to collect information on post‐intervention outcomes of treatments and controls to estimate various impacts of the program. A more detailed description of the ESS, EBS and FUS is provided in Annex 1.

9
Indeed, it turns out that for all of the population groupings considered in the first two rounds of data collected (1957-8 and 1962-3) there is no dramatic difference between the conventional between-group contributions and the ELMO partitioning statistic. In particular, although in some cases the ELMO statistic is much higher in proportionate terms to the conventional between-group contribution, it remains low in absolute terms and there is no clear insight offered by this statistic.<sup>xvii</sup> However, when one turns to the two latter survey years – 1974-5 and 1983-4 – the two statistics start to provide quite a different impression of how group differences matter in inequality. For 1974/5, the conventional between-group contribution remains fairly low (below 20% in all cases of less than 4 groups and only as high as 31% in the case of 8 groups), but the ELMO partitioning index is now considerably greater. In particular, when the village is divided into two groups comprising Jatabs and Muslims on the one hand and all other castes on the other, one is nearly 40% of the way of having divided the income distribution into two non-overlapping partitions of the same size as these two population groups.

The ELMO partitioning index becomes even more clearly differentiated from the conventional between-group statistic in the 1983/84 survey year, when the population groups are defined in terms of Jatabs versus all others. In this case the statistic suggests that one is nearly 50% of the way to a non-overlapping partitioning of the income distribution. A threeway breakdown of the population into Jatabs, Muslims, and all others also yields an Elbers et al statistic that is quite high – nearly 40% - even though the conventional between group contribution is only 26%.

[Tables 4-7 about here] 19
The population totals for 2018 and geographic aggregations are mapped in Figure A2.

# **4.1.2 Gallup World Polls**

Our primary outcome, attitudes towards immigrants, comes from the Gallup World Polls (GWP). We use the GWP because it offers significantly more coverage across locations and time than other opinion polls. The GWP aims to conduct annual, nationally representative surveys of approximately 1,000 individuals in each country on a wide range of topics.<sup>16</sup> We use data from 2005 to 2018. This covers 168 countries with at least one survey, 1,732 survey-years, and 2,017,774 observations in total to select sub-samples for analysis.

Since refugees are often geographically concentrated within a host country, we use the lowest sub-national level that is reported in the GWP data. There are multiple considerations when using the data at this level. First, sub-national locations are not reported for all country-years and the geographic divisions vary by country. For instance, respondents’ locations in Kenya are reported at the province level, which divides the country into 47 sub-national units. Locations in Uganda, in contrast, are reported as one of four sub-national regions.<sup>17</sup> Second, the data is not representative of specific sub-national regions, since the multi-stage stratification at the country level may select only a few sampling units within the region. However, analyzing the aggregatate of enough regions (according to their refugee presence and policy) mitigates most concerns about representativeness. Third, some subnational regions were not included in the GWP. Some were excluded randomly during sampling, while others’ exclusion was an intentional decision by Gallup due to security or sparse populations.<sup>18</sup>

> 16We exclude those who were not born in the country of interview from the sample.

> 17The number of reported geographic divisions could affect the statistical power, precision, and accuracy of our estimates. Countries with fewer geographic units likely capture respondents who are further away from the refugee presence. If so, and if effects decline with distance, then the effects we measure are attenuated towards zero by including more respondents in the affected regions who are largely unaffected. We are currently exploring geographic spillovers and levels of aggregation.

> 18The country coverage, sampling strategy, and more details can be found here: `https://www. gallup.com/file/services/177797/World_Poll_Dataset_Details_052920.pdf`

10
15 bimodality to unimodality and the coefficient of variation fell from 0.233 to 0.194 (World Bank, 2005). This heartening trend was partly reversed, however, during the 1990s, when LEB fell precipitously in some of the world’s poorest countries, due largely to the spread of HIV/AIDS.<sup>21</sup> Educational inequality, measured for the distribution of years of schooling, has also fallen substantially over the last four decades or so. As mean years of schooling in the world rose from 3.4 in 1960 to 6.3 in 2000, the coefficient of variation fell from 0.739 to 0.461. (Note that inequality measures for variables like life expectancy or years of education have to be interpreted with care, as both variables are effectively bounded from above.) This pattern of rising means and falling inequality in attainment was common to all regions of the world and, in addition, all regions also saw a reduction in gender disparities, as measured by the male to female schooling ratio (World Bank, 2005).<sup>22</sup> Unfortunately, this reduction in _attainment_ inequality has not always meant a reduction in the disparities in true educational _achievement_ . Indeed, internationally comparable test score data suggests that these disparities remain strikingly large with, for example, the reading competence of the average Indonesian student in 2001 being equivalent to that of a student in the 7<sup>th</sup> percentile of the French distribution.

These changes in the distribution of health and education should be taken into account when assessing global inequality in a broad sense. While this paper provides only a very brief summary of the existing evidence along each dimension, a number of scholars have attempted to explore the correlations among the different dimensions. Because increases in longevity have been greater in poorer countries, for instance, Becker, Philipson and Soares (2005) argue that inequality in measures of wellbeing that account for the quantity, as well as quality, of life have been declining throughout the post-war period.

# **3. The growth-poverty-inequality triangle**

Given the negative correlation between mean incomes and inequality levels across countries that is illustrated in Figure 1, it is not surprising that there is an even stronger correlation between mean incomes and poverty rates. Given the mathematical relationship that must always hold between mean income, poverty and inequality, the first correlation more or less automatically implies the second. To see why, we can assume (without loss of generality) that

> 21 See Deaton (2003) on the relationship between health outcomes and inequality more broadly.

> 22 See also Castello and Domenech (2002) on international inequality in education.