rafmacalaba/data-use-mentions-extended huggingface dataset:
..<br>.|..........<br>.............<br>.CPRS<br>discussed atthe**9T**Party Congress|

Reducing the PPA fieldwork to two boxes is not to diminish the effort and resources that went into the PPA fieldwork. Within the shaded boxes in Table 1 lies a whole range of subtasks. These were central to producing good quality research which could be credible to local and national policy makers and which could be useful in informing both the poverty assessment and the development of the partner agencies' own work.

Diagram 1 overleaf presents the detailed activities that lie within the implementation stage, This is taken from the Lao Cai PPA, but the other PPA studies went through 11
Thus, the results presented in the empirical section do not correct for errors-in-variables in the elasticity data.

# 4 Results

Table A0 shows the summary statistics for the variables used in our empirical analysis. The sample mean of the U.S. MFN tariffs is 6.7%. The sample mean of the U.S. preferential tariffs applied to Latin American exports is 3.8%. In the case of the elasticities of export supply, the sample mean is 12.248. The main empirical results of the paper are presented in tables 1-6. The results shown in these tables use data organized at the HS 8-digits level and under the assumption that the errors are spherical. We leave the discussion of the presence of non-spherical errors to the next section. The total number of observations at the HS 8-digit level is 14,372.

Tables 1 and 2 present the results of Tobit regressions for equations (27) and (28), respectively, using the fi rst quartile of the product of elasticities of export supply and tariff preferences to divide the sample. As explained in detail, the observations for which the product of the elasticity of export supply and tariff preference is greater than the value of the fi rst quartile threshold should have tariff preferences predicted by equation (27). Otherwise, they should have tariff preferences predicted by equation (28). Similarly, tables 3 and 4 and tables 5 and 6 show the results of Tobit regressions for equations (27) and (28) using the second and third quartiles of the product of elasticities of export supply and tariff preferences to divide the sample, respectively. The results shown in tables 3-6 are robustness checks to the results presented in tables 1 and 2.

The coefficients’ estimates shown in table 1 con fi rm the basic predictions of the theoretical model since coefficient φ0 is positive and coefficients φ1 and φ2 are negative. All estimates are signi fi cant at the 1% level and they are robust to the choice of thresholds which determines the value
One additional simulation is executed with the population of _N_ = 100 _,_ 000, but the sample size is kept as _nc_ = 10 households per area, which makes the total sample size _n_ = 800 . Each area is made up of _Nc_ = 1 _,_ 250 households, which means that sampling fractions are now 0.8 percent. The purpose of the simulation is to assess the performance of the estimators under a scenario more aligned to ELL. ELL (2002) proposed the location effect be at the cluster level, and in some applications this is a cluster of 10 observations. However, in this case the total survey sample size may not be so realistic, since in household surveys from many countries, the overall sample size is of several thousands. As expected, under this scenario, the differences in bias of the different methods is less stark. Note that with zero area sample size ( _nc_ = 0), Census EB and traditional ELL become approximately the same, so, as we approach this scenario, estimators come together (Figure 15). Because of the limited sample, all methods now show considerably more bias and a higher MSE. Concurrently, H3-CensusEB now appears to be more aligned to the ELL and H3-CBEB. However, H3-Census EB still outperforms the older methods in terms of bias and MSE. Note also, that usually ELL is done by setting the location effect at the cluster level but results are presented at higher levels.<sup>32</sup> However, aggregation to higher levels may underestimate the true MSE as noted by Das and Chambers (2017). The inclusion of contextual level variables may help ( _ibid_ ) although this requires being able to link clusters in the survey sample to the census which in practice is not always feasible.

Aggregate results across areas are presented in Table 1 for the simulations discussed.<sup>33</sup> Results from column 1 are those that replicate the simulation experiment of Molina and Rao (2010).
***|15.6***|
|NFE owner|46.5**|50.1**|52.2|52.8|41.6***|48.2***|
|NFE supporting work|27.0|29.0|33.6|29.8|22.7|28.4|
|Main wage employment|46.9|46.7|48.0|48.1|46.5|46.2|
|<br>Across all activities (all)|45.3***|38.7***|50.5**|46.3**|43.4***|36.1***|
|Across all activities (just those in multiple activities)|56.2|56.9|64.7|66.5|54.3|55.0|
|Among those in off-farm (NFE and wage) work:<br>annual earnings|||||||
|<br>Share of individual earnings/total HH earned income|0.51***|0.44***|0.48***|0.38***|0.53***|0.47***|
|Total Observations|1,845|2,093|573|655|1,272|1,438|
|Notes:|||||||

(1) Sample includes men and women reporting in the LSMS+ labor module, aged 18 and older. All estimates are weighted using the household sampling weight. Statistically significant differences between men and women are indicated by asterisks (***p<0.01, ***p<0.05, * p<0.10).

(2) 442 men respondents and
**Figure 7.The share of minimum wage earners across birth cohorts**

<!-- Start of picture text --> 50.00%<br>40.00%<br>30.00%<br>20.00%<br>10.00%<br>0.00%<br><!-- End of picture text --> Source: _Own estimates based on administrative tax data_

**The distribution of minimum-wage individuals showcases significant disparity throughout the country. In the region exhibiting the highest prevalence of minimum-wage earners (Suceava county— 38%), this percentage is nearly twice as high as in the region with the lowest frequency of minimumwage earners (Sibiu—21%). Figure 8 shows the proportion of minimum-wage** earners at the county level (NUTS 3 regions).

**Figure 8. Share of minimum wage earners by NUTS 3 regions**

<!-- Start of picture text --> 45.00%<br>40.00%<br>35.00%<br>30.00%<br>25.00%<br>20.00%<br>15.00%<br>10.00%<br>5.00%<br>0.00%<br>Suceava Harghita Vaslui Maramures Giurgiu Bistrita-Nasaud Neamt Botosani Brailia Constanca Olt Calarasi Vrancea Salaj Tulcea Bihor Buzau Teleorman Galati Valcea Ialomita Dolj Mehdinti Satu Mare Mures Caras-Severin Gorj Dambovita Bacau Hunedoara Arges Iasi Prahova Ilfov Covasna Alba Arad Cluj Bucharest Timis Brasov Sibiu<br><!-- End of picture text --> _Source: Own estimates based on administrative tax data (January 2020)._

# **IV. Ex-ante Evaluation of Policy Reform**

**In this section, we evaluate the potential ex-ante distributional impacts of the alternative minimum wage policy on the labor market outcomes, relying on micro-simulation methodologies and administrative tax data.** More
Policy Research Working Paper 7773

# **Abstract**

A good understanding of the constraints on agricultural growth in Africa relies on the accurate measurement of smallholder labor. Yet, serious weaknesses in these statistics persist. The extent of bias in smallholder labor data is examined by conducting a randomized survey experiment among farming households in rural Tanzania. Agricultural labor estimates obtained through weekly surveys are compared with the results of reporting in a single end-ofseason recall survey. The findings show strong evidence of recall bias: people in traditional recall-style modules report working up to four times as many hours per person-plot relative to those reporting labor on a weekly basis. If hours are aggregated to the household level, however, this discrepancy disappears, a factor driven by the underreporting by recall households of people and plots active in agricultural work. The evidence suggests that these competing forms of recall bias are driven not only by failures in memory, but also by the mental burdens of reporting on highly variable agricultural work patterns to provide a typical estimate. All things equal, studies suffering from this bias would understate agricultural labor productivity.

This paper is a product of the Development Data Group, Development Economics. 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 apalacioslopez@worldbank.org or kbeegle@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
The theoretical framework motivates our estimation strategy. Our empirical model estimates the relationship between responses to climate change questions C i and age of the respondent controlling for respondent’s characteristics: C i i i i , (4) ii where, i is a set of individual characteristics such as the respondent’s age, gender, highest level = ββXX + γγGG + θθ + εε of education, marital status, religious affiliation, indicators of mental health, risk aversion, trust in XX government, main sources of information, household composition, and a proxy for household income. i is a vector of regional characteristics such as type of location (urban or rural). is time-invariant country _c_ fixed effect, and i is an innovation term. ii GG θθ εε

# **4. The data**

We use the latest 2023 round of the Life in Transition Survey (LiTS), implemented by the European Bank of Reconstruction and Development (EBRD) and the World Bank (EBRD 2023). It covers the transition economies of Europe and Central Asia and several comparator countries in Western Europe, the Middle East, and North Africa, including a total of 38 countries. The survey included a nationally representative sample of around 1,000 households per country.

The analysis focuses on climate beliefs and climate actions. Beliefs about climate change are captured by the following questions: “ _How convinced are you personally that climate change is real?_ ” and “ _How convinced are you personally that climate change is manmade?_ ” Responses are recorded on a 5-step Likert scale ranging from “entirely unconvinced” to “entirely convinced.” We distinguish between impacts on one’s self and impacts affecting the next generation. The former is based on responses to the yes/no question “ _Do you think climate change seriously affects or will seriously affect you during your lifetime?_ ”, while the latter is based on responses to the question “ _Do you think climate change seriously affects or will seriously affect the children of
4 in 2013. Since then, shortages of food and basic necessities became common, and looting began to occur systematically throughout the country (Revista Semana, 2017). Moreover, insecurity became endemic, repression of the opposition became common, and systematic human rights violations by public authorities were repeatedly reported by the international media (see El Nuevo Herald, 2014; BBC News, 2017; BBC, 2016). External factors such as plummeting oil prices since 2014 and international sanctions on Venezuela, have exacerbated the crisis.<sup>5</sup> This situation triggered large waves of out-migration by Venezuelans, who most often moved to neighboring Colombia. According to the official statistics, since that start of the humanitarian crisis caused by Chavez’ and Maduro’s regimes, about 7.7 million Venezuelans have left their country. Of these, about 2.5 million have settled in Colombia.

Initially, Venezuelan migrants consisted mainly of wealthy Venezuelans and entrepreneurs who came to invest in Colombia and fled to save their capital from expropriations and from high inflation (Revista Semana, 2017; Crasto and Alvarez<sup> ́</sup> , 2017). As the crisis intensified, however, the core of Venezuelan migration shifted to the less educated population, who report fleeing to escape violent crime, political repression, and to look for basic necessities for survival (NPR, 2018). Indeed, according to recent characterizations of Venezuelan migrants based on the Colombian household surveys of 2015 and 2016, over 80% of registered migrants have not completed a high school education, at least half are 25 years old or less, and they are balanced in terms of gender (see OLR, 2017).

> 5Following the repression of protests by the Maduro administration, several countries led by the U.S. and the European Union established in 2017 sanctions to individuals associated with Maduro, including politicians, military personnel and private citizens. The sanctions were soon extended to include private companies and, in 2019, entire industries associated
Policy Research Working Paper 7951

# **Abstract**

On October 15, 2015, World Bank Group President Jim Yong Kim announced the World Bank Group’s commitment to support the 78 poorest countries to implement a multi-topic household survey every three years between 2016 and 2030, for monitoring progress toward ending extreme poverty and boosting shared prosperity. This paper estimates the resource requirements to achieve the objectives of implementing 390 surveys across 78 International Development Association countries from 2016 to 2030, and providing direct technical assistance to the national statistical offices on all facets of survey design, implementation, and dissemination toward timely production of quality household survey data. The approach to the costing exercise is unique, as it makes use of detailed data on actual survey implementation and technical assistance costs from a group of countries, unlike previous attempts at costing household survey data gaps. The required total budget, in accordance with the survey design features recommended by the World Bank Household Survey Strategy, is estimated at US$945 million for the period of 2016-2030. Of this, US$692 million is projected to cover the survey implementation costs across 78 countries, and US$253 million is projected to cover the costs of direct technical assistance to be provided to the national statistical offices.

This paper is a product of the Development Data Group and the Poverty and Equity Global Practice Group. 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 tkilic@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
- Ratha, D., and W. Shaw. 2007. ―South-South Migration and Remittances.‖ World Bank Working Paper 102, World Bank, Washington, DC.

- United Nations Statistics Division. 1998. Recommendations on Statistics of International Migration Revision 1. New York: United Nations.

- United Nations, Department of Economic and Social Affairs, Population Division. [2008]. United Nations Global Migration Database. New York: United Nations. http://esa.un.org/unmigration

- ———. 2006. Trends in Total Migrant Stock 1960–2000, 2005 Revision. Database. POP/DB/MIG/Rev.2005/Doc. New York: United Nations.

- ———. 2009. Trends in International Migrant Stock: The 2008 Revision. Database. POP/DB/MIG/Stock/Rev.2008. New York: United Nations. <u>http://www.un.org/esa/population/.</u>

- ———. 2010. ―World Population Prospects: The 2009 Revision, Highlights‖, Working Paper No. ESA/P/WP.210, New York, New York

33
As earlier results suggest that spillover effects are almost exclusively limited to the 0-25 km range, table 9 reports results from estimating equation (3) at the kebele level for this range with total or crop-specific area as an independent variable in the top and bottom panel, respectively. Coefficients on rainfall deviations imply that having rainfall that is up to or more than one standard deviation below the long-run mean would be predicted to reduce yields for maize and teff by 4% and 11% and 4% and 10%, respectively, compared to the base category of the z-score between 0 and 1 but not significantly different from 0 for sorghum or wheat. Maize is the only crop that benefits from higher than normal levels of rainfall over the growing season.

Coefficients on interactions between rainfall deviations and commercial farm presence suggest that having more area cultivated with the same crop by large farms in the vicinity helps to reduce, though not fully offset, negative effects of drought on yields.<sup>20</sup> While technologies for moisture conservation in general might play a role for teff, the fact that estimated effects are most pronounced for same crop area rather than area by commercial farms in general points towards a more technology-driven mechanism such as learning from commercial farmers’ crop-specific practices rather than construction of moisture conserving structures.

# **5. Conclusion and policy implications**

Exploring the magnitude of spillovers from large farm establishment allows us to contribute to the debate from a methodological and substantive perspective. Methodologically, we show that it is possible to assess spillover effects from large land-based investment by using data on location to link information on intertemporal changes in large farm presence over time to household or farm survey data. As the latter are routinely available in many countries where large farms are a policy issue –and modest investments to adjust data collection instruments and sample frames can further enhance their relevance– this provides an opportunity to regularly monitor and assess the impacts of large land-based investment in a way that can complement and go beyond potentially non-representative case study
3 examine this possibility by testing for equality in baseline characteristics for the sample interviewed in the follow-up survey. In Table 2 we see the means for the follow-up sample look similar in magnitude to those of the full sample answering the baseline, and we only reject equality of coefficients in 5 out of 48 cases at the 10 percent level, which is consistent with random chance. Table 3 looks at balance on the listing variables and the small number of follow-up survey variables which are likely to be time invariant. We find only 3 out of 22 coefficients significant at the 10 percent level, and 1 at the 5 percent level, which is again consistent with random chance.

The presence of administrative data (detailed below) allows us to test further whether there is differential attrition in the follow-up survey by the key outcome of interest: whether or not a firm has formalized during our intervention window. We estimate the following regression for firms in the baseline survey assigned to the control, communication, or free cost groups: InterviewedatFollowupi,s = α+ λ1Formalizedi,s + λ2Formalizedi,s ∗Communicationi,s + λ3Formalizedi,s ∗FreeCosti,s + βCommunicationi,s + γFreeCosti,s + ∑δsds + εi,s (4) Where _InterviewedatFollowup_ is an indicator of whether the firm was interviewed in the follow-up survey, and _Formalized_ is an indicator of whether they probably formalized during the intervention period according to our administrative data. Then λ1 measures whether firms in the control group which formalize are more or less likely to answer the follow-up survey than firms which did not formalize, and λ2 and λ3 measure whether any differential survey response varies with treatment status. We estimate the analogous equation similarly for the inspector versus control block groups.

Table 5 reports the results. For the baseline sample (who had already been interviewed once before), we cannot reject that whether or not the control group answered the follow-up survey is unrelated to whether or not they formalized during their intervention window. Moreover, likewise we cannot reject that there is
2. Using wage elasticity values and the expected wage increases estimated in (1), estimate the probability of losing the job by age, education, occupation, gender and skill level.<sup>27</sup> 3. To select “job losers”, for each individual generate a random variable that takes the value of “1” with the probability computed in (1); in other words, assign unemployment status randomly.

4. Compute simulated earnings and unemployment.

5. Repeat this procedure 1,000 times and compute average earnings and unemployment across simulations and 95 percent confidence intervals.

**Table 2. Values of wage elasticity used for baseline simulation**

||Base|line|Low|Hi|gh|
|---|---|---|---|---|---|
||Short-term|Long-term|Short-term<br>Long-term|Short-term|Long-term|
|Skilled|-0.5|-0.8|-0.1<br>-0.4|-0.7|-1.0|
|Unskilled|-0.7|-1.0|-0.3<br>-0.6|-0.9|-1.2|

_Source: Lichter, Peichl and Siegloch (2013). Note: Because this is a meta-analysis, the definition of skilled workers is not provided._

# Results

**Our results show that disemployment effects are relatively modest. The overall unemployment rate is simulated to increase less than 1 percentage point, and the overall effect of the proposed minimum wage policy on earnings is small and positive.** Table 3 reports the simulated labor market indicators under the minimum wage policy.<sup>28</sup> The overall unemployment rate is simulated to increase by 0.4 percentage point in the short run and by 0.6 percentage point in the long run and this
As elaborated in Modebaze and Eroyants, our estimates of the ad valorem equivalents of the barriers against foreign direct investment in Armenian services sectors is based on a survey of the regulatory barriers in services in Armenia. As listed in table 4, our estimates are as follows: banking, 5.6 percent; insurance, 15.8 percent; pipeline transportation, 0 percent; rail transportation, 0 percent; air transportation, 106.8 percent; and telecommunications, 0.3 percent. The CASE team did not conduct a survey of the barriers in Armenia. Rather they assumed that the barriers in Armenia are 25 percent lower than in Ukraine. The estimates for Ukraine were done by the Institute for Economic Research with guidance from Jesper Jensen, and were done using the same methodology as ours in Armenia. The resulting estimates by CASE of the ad valorem equivalents of the barriers against foreign direct investment in Armenian services sectors were considerably higher than the present study, with the exception of air transportation. The CASE study estimates are as follows: banking and insurance, 18 percent; transportation, 12 percent; and telecommunications, 4.5 percent.

Regarding the second issue, the CASE study assumed that commitments to foreign investors in services as part of a DCFTA with the EU will be extended to all services suppliers, i.e., that the preferential commitments will become multilateral. The assumption that the commitments will be multilateral implies substantially larger estimated gains for two reasons. First, the cost reduction from foreign suppliers is for all FDI, not just the EU share. Second, in our framework, since commitments are preferential, the gains from liberalizing with the EU come at a cost of some lost varieties of services from foreign suppliers. We have shown that there is an imperfect competition analogy in preferential liberalization of services to the trade diversion issue in goods. By assuming all commitments are multilateral, there is no trade diversion.

# (ii) **Border costs** .

The CASE study assumes that border costs