rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Abstract**

While there have been numerous impact evaluations of unemployed individuals participating in retraining programs or in programs to foster self-employment, impact evaluations of enterprises benefiting from training programs for small and medium enterprises (SMes) are rare. The authors reevaluate the impact of the largest SME program in Mexico, the Comprehensive Quality and Modernization Program (CIMO). They show that compared to the control group, CIMO firms increased investments in worker training, had higher rates of capacity utilization, and were more likely to adopt quality practices. The evidence also suggests that these improved intermediate outcomes were associated with increased productivity growth among CIMO participants, impacts that were especially strong throughout the 1991-93 period. However, the productivity impacts of CIMO are not apparent in the 1993-95 period.

This paper – a collaborative product of the World Bank Institute and the Poverty Sector Unit, Latin America and Caribbean Region – was prepared as part of the World Bank Report (2004) on “How Well Do SME Programs Work? Evaluating Mexico’s SME Programs Using Panel Firm Data” and is also part of larger effort in the region to study small and medium enterprise (SME) development and evaluate policy interventions to promote SME productivity and competitiveness. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Policy Research Working Papers are also posted on the Web at <u>http://econ.worldbank.org. The authors may be contacted at htan@worldbank.org</u> or <u>gacevedo@worldbank.org. (18 pages)</u>
by Leslie (1945), which has been used widely in mathematical demography and population dynamics studies. In such a model, China's population is taken as a whole, all factors that influence population growth, such as fertility and motility rate, are defined in terms of statistical means. We assume that changes of time, birth, and death are the three major determinants of population growth. The influence of other social and economic factors is implicitly included in those three determinants. Economic growth is assumed to affect fertility rate and motility rate through exogenously specified total fertility rate and life expectancy changes. We do not consider migration as a determinant because the number of immigrants is very small compared with China's huge population.

Newborn population equals the number of women at their fertility age (age 15 to 49) multiplied by the corresponding fertility rate. The population in each age group equals previous year's population at the next younger age group multiplied by the corresponding motility rate. There will no people live longer than 100 years. Fertility and motility rates, proportion of each gender in the newborn population, and base year population demographic distribution can be obtained from population survey data.

Labor supply at each age (15-70) for a given year equals the population in that age group for that year multiplied by a labor participation rate, which is gender and age specific. These specified labor forces are then aggregated into the 22 groups of labor inputs specified earlier in the cost and labor demand functions. We assume that workers start to retire at age 40. The total new retirees in a given year is the sum of all surviving workers age 40 and over multiplied by a retirement rate that is gender and age-specific. The total retiree in a year is the sum of the surviving retirees from all previous 18
maps assumes that malnourished individuals exclusively live in impoverished households. This may not always be accurate, since typical poverty measures do not measure differences in welfare levels among members of the same household. On the other hand, DHS or MICS surveys may miss households with average poor caloric intake as these surveys do not collect detailed food consumption data. To overcome this challenge and enhance precision in targeting, we recommend an alternative approach that combines small area estimates of monetary poverty and stunting.

This study aims to estimate the prevalence of hidden hunger in Madagascar by employing small area estimation techniques. Our approach involves identifying areas where poverty rates are low but stunting, a common indicator of malnutrition, is high; and vice versa. Poverty estimates are derived from the 2021/2022 household survey, which provides detailed food consumption at the household level. Stunting estimates, on the other hand, are derived from the 2021 Demographic and Health Survey (DHS), a nationally representative survey capturing anthropometric data and nutritional indicators. We develop a small area estimation (SAE) statistical model that “borrowed strength” from the 2018 census to allow the two nationally representative sources of survey data to produce precise and accurate estimates at the commune level and investigate the relationship between monetary poverty and nutrition. This approach enables us to estimate the prevalence of hidden hunger at the commune level within Madagascar and create a taxonomy of different types of hidden hunger, providing valuable insights for nutrition policy prioritization.

2 | P a g e
/sup> While our paper focuses on the application of growth regressions for country analysis, it seems

> 1 For example, McMillan et al. (2014) suggest that undervaluation and labor market flexibility promote productivity-enhancing structural change. But devaluation may make capital accumulation more expensive, and deregulation may deter labor market participation. This factor-reducing effect and associated policy tradeoffs would not be accounted for.

> 2 We plan to regularly update and expand the data set and invite scholars and analysts to comment on the code and suggest improvements; either by contacting the corresponding author or by push requests on GitHub.

-2-
_Table 6: Spearman rank correlation alternative targeting mechanisms relative to independent PMT estimates_

||Spearman rank<br>correlation|Area under the curve|
|---|---|---|
|100 Poorest Cantons plus machine learning<br>withphone data|0.45|0.73|
|Feasible alternatives|||
|Uniform targeting within poorest<br>prefectures|0.34|0.66|
|Uniform targetingwithinpoorest cantons|0.38|0.68|
|Infeasible alternatives|||
|Asset index|0.51|0.75|
|Progress out of PovertyIndex|0.63|0.81|
|ProxyMeans Test|0.72|0.85|

Source: Aiken et al. (2022)

# 5. Conclusion

Thirty-five years after the publication of Battese, Harter, and Fuller (1988) and seven years after the publication of Jean et al. (2016), the literature on combining survey and geospatial data to predict wealth and poverty is maturing rapidly. It is clear that indicators derived from geospatial data are strongly predictive of wealth and poverty across space in several contexts, although the extent of this correlation depends on many factors. The accuracy of predictions, however, is particularly sensitive to the nature of the household data on welfare or wealth used to train the model. It is also clear that the coverage of the training sample also matters.

Estimates for out-of-sample areas are almost always less accurate than for in-sample areas, because informative sampling introduces bias, and because Bayesian and empirical Bayesian methods do not benefit from sample-based priors. In addition, the evaluation benchmark in out-of-sample areas, if it is a model-based estimate, will also be biased due to informative sampling. Finally, the few studies that have considered the prediction of changes over time have found that this is much harder than prediction across space, even for medium to long-run changes. Seeing short-run changes in welfare from space may prove challenging but is a challenge
standardized indexes for each of these outcomes. All scores are standardized using the means and standard deviations of the control group.

_Parental Engagement._ We also collected information on parental participation in school activities, such as meeting the principal of the primary school or the teacher of the target child, and being part of the school community. These outcomes are measured as dummy indicators that take the value 1 if the parent or other household members reported meeting the principal or teacher or being part of the school committee. Moreover, we also collected information on the intensive margin of parental involvement. Specifically, we also asked parents to report the number of meetings they or any other member of the family attended with the principal or teacher.

_Sibling Enrollment_ . To study spillovers of the program on siblings, we use data on the preschool and primary school enrollment collected through the household roster. We include in the estimation sample all children aged 3 to 12 years that were either the child, stepchild, or adopted child of the household head. The preschool and primary school enrollment outcomes are measured as dummy indicators that take the value 1 if the household head reported that each non-target child aged 3 to 12 living in their same household was enrolled in a preschool center or in primary school at the time of the survey.

# **Other Measures**

_Quality of Program Key Components._ We used an adapted Measure of Early Learning Environments (MELE) instrument. It was adapted by specialists to the Mozambique context and uses two approaches to measure the quality of early learning environments: classroom observation tool<sup>12</sup> and oral interviews to caregivers, teachers (facilitators), and school administrators (CCC member).<sup>13</sup> Using these two approaches, we collected infor-

> 12The MELE Classroom Observation Tool collects information about the activities and interactions that occur during a typical class session, including learning activities; classroom interactions and approaches to learning; classroom space and materials; and facilities and safety
>SSSSSESSSSSEGBSSBSSGSSSSESESae ee ee ee ee ee ne ee ee ee ee ee ee | SSRRSRSRSRSERRRSRARSRARSA<br>. ; Note: GOP in 2010 amounted<br>—— Central projection — — -Upper estimate GOP per capita in 2010 amoun t oedUSD to USD60 billion 2,857, (IMF based WEO on Octoberpopulation 2018).of<br>21.018 million (United Nations).<br><!-- End of picture text -->
model. In practice, using probit or logit to determine the weights makes very little difference, as leakage and undercoverage rates change by less than one percentage point.

The final exercise conducts a simple out‐of‐sample test to examine the sensitivity of the targeting performance to the choice of the sample used to estimate the PMT model. We implement this as a two‐ fold cross‐validation exercise in which we split the sample into two parts (say, A and B). We first estimate the PMT model using sample A, predict consumption for sample B, and compute performance measures for sample B. We then conduct the same exercise with sample B. We finally compare the performance of these two scenarios to the one with the full sample. We assume that the bottom 25% of the population is targeted and the 25<sup>th</sup> percentile PMT cutoff score is used. Table A9 presents the results of this exercise. Although we only see small differences in the performance measures across the samples, these differences are likely to be aggravated in smaller samples. This exercise is difficult to evaluate since we do not estimate the standard errors of the performance measures. But if Table A9 gives any indication, out‐of‐sample validation is not a concern if a sample as large as HIES 2016 is used to estimate the PMT model.

# VI. Conclusion

The PMT method has been successfully used to distribute social safety net benefits in many countries. A PMT formula is preferable to a verified means test (e.g. an income criterion) in the Sri Lankan context, because reliable data on income are not available for many workers. The PMT is also attractive because it provides a single index of welfare that can be used for harmonized targeting of several different social welfare programs. Finally, an advantage of the PMT over other targeting methods is its transparency and verifiability. It provides a clear, unbiased determination of a household’s eligibility whereas other approaches, such as simple means
# **Entry Barriers, Idiosyncratic Distortions, and the Firm Size Distribution**

Roberto N. Fattal-Jaef<sup>_∗_</sup> _JEL classification code: D21, D24, D61, O41_ _Keywords: entry barriers, misallocation, average size, total factor productivity_

> _∗_ Development Research Group, Macroeconomics and Growth, The World Bank. 1818 H st NW, Washington DC 20433. Email: rfattaljaef@gmail.com. We are grateful to Francisco Buera and seminar participants at the Federal Reserve Bank’s of St. Louis’ Research Department, the Universidad Torcuato Di Tella, and the International Monetary Fund for helpful comments and suggestions. We also thank Norman Loayza and the World Bank Research Hub in Malaysia for performing computations with Malaysia’s Census of Manufacturing Sector for 2015. The views expressed here do not necessarily represent the views of the World Bank Group, its affiliated organizations, or any of its member countries.
--|---|---|---|---|---|---|---|
|Model||||Household-<br>level|Area-<br>level|Household-<br>level|Area-<br>level|
|Burkina<br>Faso|Headcount<br>poverty|Single<br>survey|Census-based<br>EBP<br>estimates|0.76|0.56|0.21|0.26|
|Sri<br>Lanka|Non-<br>monetary<br>poverty|Single<br>survey|Census|0.77|0.71|N/A|N/A|
|Tanzania|Non-<br>monetary<br>poverty|Single<br>survey|Census|0.77|0.78|N/A|N/A|
|Mexico|Headcount<br>poverty|Single<br>sample|Census-based<br>EBP<br>estimates (in-<br>sample)|0.74|0.63|0.49|0.44|
|Mexico|Labor income<br>poverty|Design-<br>based<br>simulation|<br>Intracensus|0.89|0.88|0.64|0.56|

Source: Edochie et al. (forthcoming), Masaki et al. (2022), Newhouse et al. (2022). The results for design-based simulation for Mexico reported in the bottom row are based on area-level predictors while all other rows are based on sub-area level predictors.

In general, the household model predicts more accurately than the area-level model in contexts where they have been directly compared. The one notable exception is out-of-sample areas in Burkina Faso.

17
+ππ uuS SS) + uu took 0 sessions, 0 otherwise; 1to4S S i takes the value 1 if student _i_ , who was originally assigned to any of the treatment groups, took between 1 and 4 sessions, 0 otherwise; 5p S i takes the value 1 if student _i_ , who was originally assigned to any of the treatment groups, took 5 sessions or more, 0 otherwise. The omitted category in eq. 2 is represented by individuals in the control group. uuS SS The parameters 0, 1 and 2 measure the ATE of taking 0, 1 to 4 sessions and more than 5 sessions. When looking at the effects of CBT sessions, we focus on categorical variables, since for policy purposes it is more interesting to identify whether there is a threshold effect rather than the ππ ππ ππ effect of an additional session. To increase the probability of matching student _i_ with a student who is observationally equivalent, we do not control for school fixed effects, thus allowing for potential pairs to be assigned in different schools.

# **4.2 Baseline Data**

The main sources of data at baseline are the administrative records from the COMIPEMS assignment process, which allows us to observe the full ranked list of schooling options requested; the score in the admission exam (measuring math and Spanish language); the GPA in lower secondary; plus the information contained in the socio-demographic survey filled out at registration, which provides us with variables such as gender, age, parental education and occupation, assets, and more.

Table 2 presents the baseline characteristics of students selected to participate in the pilot, separately for those in the two treatments and the control arms. On average the three groups display similar characteristics, with only 3 tests (out of 66) failing to reject the equality between one of the 13
poor.<sup>5</sup> Diamond et al (1990) estimate a multinomial logit model on U.S. data to predict the probability of belonging to an income quintile, conditional upon certain personal and household characteristics. Diamond et al justify their approach, relative to a continuous welfare regression, by arguing that the restrictions imposed by the functional form of a levels regression (often linear or log-linear) may cause it to fit poorly on the actual distribution, and demonstrate that this is the case for their U.S. data set. The multinomial logit model allows for discontinuities in the underlying welfare model and thus also solves the concern of imposing equal parameters over the entire distribution discussed earlier. In the case of two groups (poor and non-poor) the approach collapses to a binary logit or probit model, although then the underlying welfare model is again continuous (Ravallion, 1996). There has been a recent debate in the literature on the merits of welfare regressions versus binary poverty models. Ravallion (1996) argues that the binary response model is redundant, since the parameters measuring the effect of household characteristics on the probability to be poor can be derived from the levels regression, which is consistently estimable under weaker assumptions about the distribution of the error. As argued in Grootaert (1997b), this argument applies if there is only random measurement error and if a case can be made for imposing constant parameters over the entire distribution.

As we discussed earlier, the latter issue has been dealt with in this analysis through Tobit estimation of the expenditure of the "poor" segment of the distribution. The possibility of systematic measurement error has led us to undertake also probit estimation

> 5 To our Inowledge, **the** first use of such model in the empirical poverty literature is by Bardhan (1984) in **a study** of **poverty** in **rural** West Bengal.

12
while the crowding externality would marginally reduce it but the di¤erences are on the order of a small fraction of a percentage point.

# 5.4 Accounting for employment rates

While the model is based on employment the empirical analysis is based on working-age population data. As reduced labor market participation of immigrants and/or natives may alter the wage e¤ect of immigration (see Angrist and Kugler 2003, or D’Amuri et al 2010) it is important to check whether excluding from the analysis non-employed natives and immigrants a¤ects our predictions.

Using the European Labor Force Survey, we correct the size of each skill group (highly and less educated natives and immigrants) for their employment/population ratio as of year 2000. If these employment rates are identical across the four groups, the results would not change at all. If they are very di¤erent between natives and immigrants, they may imply di¤erences in relative employment e¤ects for given population sizes. While employment/population ratios may di¤er across countries what matters for our wage e¤ects is their within country di¤erence across the four skill groups described above. Figure 9 shows the simulated wage

> 14Notice that we simply model the density e¤ect as depending on average density: total population divided by area. The fact that immigrants move mainly into cities that are already dense should increase even further the e¤ect, if one uses the exact Ciccone and Hall (1996) index that counts density in cities (where most population lives) as more relevant.

19
coverage of rural firms was better in 2014. We ameliorate this issue in two ways. Firstly, we limit our analysis to firms with 5 or more persons engaged, as discussed above, since we think that the enumeration of larger firms was more comparable between 2003 and 2014 in both urban and rural areas. Secondly, in our regression analysis of the determinants of employment growth we include a control for the proportion of firms in each district in 2003 in rural areas.

Both NIC and IBES lacked information that could be used to classify firm locations as urban or rural. This classification is useful for discussions about economic development, as discussed above; it was therefore necessary to obtain a measure of whether a firm was based in an urban or rural area. This information is not typically obtained in a firm census or survey, despite being common in household surveys and population censuses. The solution undertaken in Kerr and McDougall (2019) was to use an urban/rural classification at the enumerator area level from the Ghanaian 2010 population census, data given to us by the Ghana Statistical Service. We then matched enumerator areas in this data to the firms in NIC/IBES based on the detailed location data collected in the firm censuses, which we have access to, such as the region, district, town and suburb. In this way firms were given a geotype depending on their location. The matching process resulted in the successful matching and classification of 94% of firms in NIC and 98.5% of firms in IBES as either urban or rural.<sup>4</sup> We use this variable in our analysis below.

# 3.2 Other sources of data

We also use two other sources of data which we merge into the industry‐district panel created from the firm censuses. The first is administrative data on imports and exports in each of the 13 industries. This is obtained from the Atlas for Economic Complexity.<sup>5</sup> The Atlas data were collected 4 Note
It is common in the literature to classify skilled and unskilled workers either by educational attainment or by occupation (white- vs. blue-collar). There is some difficulty in differentiating Vietnamese individuals into a skilled and unskilled worker dichotomy by educational attainment, however, since it is actually the intermediately educated who are most likely to be engaged in export-oriented production.<sup>29</sup> I therefore use the occupation codes available in the VHLSS which are similar to the International Standard Classification of Occupations (ISCO) by the International Labour Organization (ILO) to categorize individuals into skilled and unskilled workers.<sup>30</sup> The summary statistics of demographic and economic characteristics for skilled and unskilled workers are reported in Appendix Table A.7 along with all the other variables used in my regressions. For my sample of the panel individuals for the period 2002-2004, the average wage growth rate of 27 percent for skilled workers was larger than that for unskilled workers (18 percent), suggesting that the skill premium increased for Vietnam’s economy as a whole. (ii) Export index I first develop an index to represent the province-specific labor demand induced by exports as a mechanism through which trade affects wages. The increase in production for exports may increase wage levels in the location where export firms operate by raising the demand for labor in that location. In addition, it may influence wage levels indirectly, for instance, by increasing 29 Winters (2002) points out that it is not clear that the least skilled workers are the most intensively used factor in the production of tradable goods in developing countries. Thus, it might be the case that the wages of workers with primary education increase with trade liberalization, those of the least educated workers may be left behind (Winters, 2002). The information on whether or not individuals work for exports is not available in the VHLSS. Using production workers who worked for formal firms in export-oriented sectors (namely apparel and textiles, footwear and leather, furniture and miscellaneous