rafmacalaba/datause-encoder-data huggingface dataset:
# **sae: A Stata Package for Unit Level Small Area Estimation**

Minh Cong Nguyen,<sup>_∗_</sup> Paul Corral, João Pedro Azevedo, and Qinghua Zhao<sup>_†‡_</sup> Octber, 2018

**Key words:** Small area estimation, ELL, Poverty mapping, Poverty map, Big Data, Geospatial **JEL classification:** C55, C87, C15

> _∗_ Minh Cong Nguyen, Paul Corral, and João Pedro Azevedo: The World Bank Group - Poverty and Inequality Global Practice

> _†_ Qinghua Zhao: The World Bank Group - Development Economics Research Group

> _‡_ The authors acknowledge financial support from the World Bank. We also thank Ken Simler, and Roy Van der Weide for comments on an earlier draft and several discussions. We thank Alexandru Cojocaru, William Seitz, Kristen Himelein, Fernando Morales, Pallavi Vyas, and David Newhouse for beta testing earlier versions of the command. We also thank Carolina Sanchez, Francisco Ferreira, and Luis Felipe Lopez-Calva for providing support and space to work on this. Finally we thank the Global Solutions Group on Welfare Measurement and Statistical Capacity, as well as all attendants of the ECAPOV Seminar Series and Summer University courses on Small Area Estimation. Any error or omission is the authors’ responsiblility alone.
# **International migration and gender differentials in the home labor market: Evidence from Albania**<sup>*</sup>

Mariapia Mendola University of Milan Bicocca and Ld’A Gero Carletto the World Bank

*Authors wish to thank Carlo Azzarri, Massimiliano Bratti, Francesco Fasani, Juan M. Gallego, Talip Kilic, Francesca Mazzolari, Barbara Petrongolo and participants to the IMISCOE Conference in Durres and the II TOM CEPR Meeting on Transnationality of Migrants in Louvain-la-Noeve for useful comments and inputs. Usual disclaimers apply. _Correspondence to:_ Mariapia Mendola, Department of Economics, University of Milan Bicocca, P.za dell’Ateneo Nuovo 1, 20126 Milan, Italy. E-mail: <u>mariapia.mendola@unimib.it. Gero Carletto, Development Research Group, The World Bank,</u> 1818 H Street NW, Washington, DC 20433. E-mail: <u>gcarletto@worldbank.org.</u>
We now turn to a parametric test exploiting cross-importer variation in our base relationship (although the time dimension of the panel is still collapsed by the construction of our volatility variables). In Table 7, the normalized standard errors of Theil and unit values are computed using both the whole sample (column 1) and the sub-sample of (importer × product) pairs with at least 30 non-missing observations over 1963-2006 (column 2). Results presented in Table 7 confirm the positive correlation between volatility in concentration indices and variability in product quality. As shown in columns 3 and 4, our results are also robust to the use of standard deviations in the numbers of partners as the dependent variable instead of standard deviations in Theil indices.

|Table 7|
|---|
|Regression results, volatility of concentration on product quality heterogeneity|

|Dep. Var|σ_Theilik|σ_Theilik|σ_Nberik|σ_Nberik|
|---|---|---|---|---|
|Regressors|(1)|(2)|(3)|(4)|
|σ_UVik|1.63E-03 ***|1.02E-03 ***|1.75E-02 ***|1.01E-02 ***|
||_(2.28E-04)_|_(2.27E-04)_|_(1.10E-03)_|_(1.02E-03)_|
|Observations_(ik)_|36,209|26,820|36,209|26,820|
|Nber of products_(k)_|1,299|1,299|1,299|1,299|
|Nber of importers_(i)_|29|29|29|29|
|Period|1963-2006|1963-2006|1963-2006|1963-2006|
|Importer Fixed Effects|Yes|Yes|Yes|Yes|
|Product fixed effects|Yes|Yes|Yes|Yes|

Notes: estimation with OLS; standard errors in parentheses: heteroscedasticity consistent and adjusted for product-level clustering; * : p=0.1, **: p=0.05, ***: p=0.01.

Thus by and large, results are as suggested by the model. Re-concentration phases occur on goods of better quality and the volatility of concentration indices is higher for products with high quality heterogeneity. This indeed suggests alternating periods of diversification and concentration in search for quality.

# **6.   Concluding remarks**

Looking at the evolution of OECD imports at a high degree of disaggregation (over 1,000 product lines) 0ver the 40-year period where data are available, we find striking evidence of geographical diversification at the product level. That is, OECD 27
stituting bilateral trade with bilateral GVC trade flows. For this purpose, I use a novel comprehensive method to measure GVC participation developed by Borin et al. (2021). They decompose GVC-related trade participation in three components: a backward component corresponding to the activities at the beginning of the chain, a forward component corresponding to the activities at the end of the chain, and an intermediate component (two-sided) for all activities encompassing both sourcing and selling intermediates (activities neither at the beginning nor at the end of the chain). The results show that, in general, RTAs do not have a positive effect on GVC-related trade participation, except for the small effect in agriculture. RTAs in South Asia have an additional effect of 2 to 3 percent in manufacturing and negative in agriculture. RTAs within South Asia on the other hand have a strong positive effect on manufacturing and even more on agriculture. These results show an important snapshot of the RTAs’ effects at the world level. An average world RTA has little effect on GVC-related trade participation but there are some RTAs with a significant effect, with RTAs in South Asia among them.

||M|anufactur|ing|A|gricultur|e||Services||
|---|---|---|---|---|---|---|---|---|---|
||(1)<br>gvcbp|(2)<br>gvcfp|(3)<br>gvcmix|(4)<br>gvcbp|(5)<br>gvcfp|(6)<br>gvcmix|(7)<br>gvcbp|(8)<br>gvcfp|(9)<br>gvcmix|
|RTA|0.00|0.01|-0.00|0.03<sup>_∗∗∗_</sup>|0.07<sup>_∗∗∗_</sup>|0.04<sup>_∗∗∗_</sup>|0.01<sup>_∗∗_</sup>|0.00|-0.00|
||(0.56)|(1.30)|(-0.71)|(7.12)|(13.50)|(8.01)|(2.57)|(0.50)|(-0.17)|
|RTA_×_ SAR|0.02<sup>_∗_</sup>|0.03<sup>_∗∗∗_</sup>|0.03<sup>_∗∗_</sup>|-0.15<sup>_∗∗∗_</sup>|0.03<sup>_∗∗_</sup>|-0.07<sup>_∗∗∗_</sup>|0.00|0.01|0.00|
||(1.87)|(3.28)|(2.56)|(-3.58)|(2.14)|(-2.78)|(0.67)|(0.90)|(0.80)|
|RTA_×_ SAR-intra|0.12<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.08<sup>_∗∗∗_</sup>|0.44<sup>_∗∗∗_</sup>|0.06|0.23<sup>_∗∗∗_</sup>|0.02<sup>_∗∗∗_</sup>|0.00|0.02<sup>_∗∗∗_</sup>|
||(5.41)|(6.41)|(5.09)|(7.20)|(1.51)|(4.58)|(3.68)|(0.38)|(3.71)|
|_N_|922766|911368|921975|923708|913334|922928|923717|916146|923058|

**Note:** the dependable variables are GVC participation measures with gvcbp standing for pure backward, gvcfp for pure forward, and gvcmix for two-sided. The variable RTA measures the world average effect of trade agreements. The SAR effect is measured in addition to the average, and SAR-intra is in addition to the SAR effect. _t_ statistics in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

Table 6: South Asia results by type of good This paper, in line with recent contributions in the literature, emphasizes the importance of including domestic trade flows in the estimation of the RTA effects. So far, the trade flows used in the estimations have included domestic transactions. Now I study the implications of omitting the domestic part of the economy in this type of analysis. It is clear that all trade flows need to be included for having a complete model, but this exercise uncovers interesting details like the trade diversion effects on RTAs. Table (7) 13
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offered. One of those spent 1200 less than we offered, one about 2500 less and the other two about 100 less than we offered. More commonly, the entrepreneurs contributed funds of their own to purchase a larger item. This occurred in 65 of the 116 equipment treatments. However, in most cases, the amount contributed by the entrepreneurs was trivial. In two-thirds of these cases (44 out of 65), the extra spending was less than 500 rupees, or $5. In 11 percent of the equipment treatments (13), the entrepreneurs contributed 2000 or more rupees from other sources. In three of those cases, their contribution was more than 10,000 rupees. We use the amount offered rather than the amount spent in our analysis of the effects of treatment. We have both receipts and pictures of all of the goods purchased with the equipment grants. Approximately 57 percent of the goods purchased were inventories or raw materials, 39 percent machinery or equipment, and 4 percent construction materials for buildings.

Cash treatments were given without restrictions. Those receiving cash were told that they could purchase anything they wanted, whether for their business or for other purposes. In reality, the grant was destined to be unrestricted because we lacked the ability to monitor what they did with the funds, and because cash is fungible. We felt that being explicit about the lack of restriction was likely to lead to more honest reporting regarding use of the funds. In the survey subsequent to the treatment, we asked what they had done with the treatment.<sup>5</sup> Approximately 58 percent of the cash treatments were invested in the business between the time of the treatment and the subsequent survey. An additional 12 percent was saved, 6 percent was used to repay loans, 5 percent on expenditures for the household, 4 percent on repairs to the house, 3 percent on equipment or inventories for another business, and the remaining 12 percent on “other items.” Of the amount invested in the enterprise, about two-thirds was invested in inventories and onethird in equipment.

# _Verifying Randomization_

Table 1 compares the baseline characteristics of firms ever assigned to treatment with those firms always in the control group. In general the randomization appears to

> 5 Our question noted that some entrepreneurs had told us they had spent the money on furniture or other items for the household, some had spent it on food and clothing, and some had invested in their business. In fact, they had told us this during piloting of the round 2 survey.

8
66

# Table 11

# **Estimate of Augmented Matching Function in Poland Males and Females Pooled Cross Section Time Series Quarterly Data: 1.1993 - 11.1994**

|Dependent variable is In of hirings||
|---|---|
|Regressor|Coefficient|
|Constant|-1.7865**<br>(.39693)|
|In of Vacancies|0.07204**<br>(.01837)|
|In of Short-term<br>Unemployment|0.75627**<br>(.07655)|
|In of Long-term<br>Unemployment|0.1 6356**<br>(.06175)|
|Training|-0.00142<br>(.00372)|

Standard errors in parentheses.

Diagnostics: Rbar<sup>2</sup> : .82; S.E. of Regression: .221.

*(**) significant at the 5%(1%) level of significance.

Note: Higher-order term, regional and time dummies were included in the regression, but are not shown. No. of observations: 294.
price of 25Rs, or 0.59 $/lt (at an exchange rate as of 2007 of 42.4 Rs/$). Kukrika (2008) indicated that few producers are selling to these distribution centers, as the prices are below the costs of production. Peters and Thielman (2008) also caution that their projected estimates could be rather optimistic.

**Table 16: Cost of Jatropha based Biodiesel Production (inflation adjusted to 2008)**

|Item|Costs ($/lt)|Country setting and comments|
|---|---|---|
|Gonsalvez (2006)|0.44|India|
|Francis et al (2005)|0.54|India (feedstock at 441.8$/ton<sup>a</sup>)|
|Peters and Thielman (2008)<sup>b</sup>|1.44-2.87|India-current|
|Peters and Thielman (2008)|0.42-1.30|India-projected|
|Peters and Thielman (2008)|2.29-2.45|Tanzania-current|
|Peters and Thielman (2008)|0.72-0.82|Tanzania-projected|
|Kukrika(2008)|0.72-1.67|India-see table 11|

Source:<sup>a</sup> Assuming a seed cost of 0.12 US$/kg, an oil extraction rate of 28%, and a processing cost of 21.2 US$/ton.<sup>b</sup> The original ranges are reported in 2004 dollars per 1.09 liters of biodiesel to facilitate the comparison with its fossil alternative. The numbers reported here are in 2008 dollars per liter. Costs for management overhead were not included.

**Table 17: Estimated costs of producing biodiesel from jatropha trees**

||Yr 4|Yr 5|Yr 6<br>($/l|Yr 7 Yr 8 Yr 9 Yr 10<br>t<sup>a</sup>)|
|---|---|---|---|---|
|Delivered Jatropha oil cost<sup>b</sup>|1.16|0.44|0.36|0.33 0.33 0.31 0.31|
|Biodiesel production (total refining costs)|0.16|0.16|0.16|0.16 0.16 0.16 0.16|
|Methanol|0.11|0.11|0.11|0.11 0.11 0.11 0.11|
|KOH|0.00|0.00|0.00|0.00 0.00 0.00 0.00|
|Electricity, water and other|0.03|0.03|0.03|0.03 0.03 0.03 0.03|
|Yield loss (10%)|0.01|0.01|0.01|0.01 0.01 0.01 0.01|
|Depreciation of fixed costs|0.11|0.03|0.02|0.01 0.01 0.01 0.01|
|Sub-total costs for biodiesel before|||||
|distribution to end-users|1.43|0.63|0.53|0.51 0.50 0.48 0.48|
|Distribution to end-users|0.07|0.07|0.07|0.07 0.07 0.07 0.07|
|Producer's margin|0.07|0.07|0.07|0.07 0.07 0.07 0.07|
|Assumed tax(excise and sales)|0.10|0.10|0.10|0.10 0.10 0.10 0.10|
|**Total cost of biodiesel(delivered)**|**1.67**|**0.87**|**0.77**|**0.75 0.74 0.72 0.72**|

Source: Kukrika (2008).<sup>a</sup> The original figures are in 2007 Rs/lt, and were converted to $/lt using and exchange rate of 42.4 Rs/$ as of 2007 and inflation adjusted to 2008.<sup>b</sup> From Table 11.

One of the most explicit estimates available for the costs of producing jatropha based biodiesel in India was provided by Kukrika (2008) (see Tables 11 and 17). The estimates 31
strategy were also considered in cost comparison. Alatas et al. (2012) suggest that there are synergies in targeting approaches so that combining approaches is less costly than the sum of the costs of the two approaches individually, but costs are certainly greater than that of CBT targeting alone.

Our cost analysis finally relies upon administrative data from the TUP program. The TUP program in its entirety served 7,500 households across six provinces of Afghanistan. While there is no data available on the total number of households screened by the TUP program, the portion of the program in Balkh province that was enrolled in the RCT identified 1,235 ultra-poor households out of 20,702 households screened (Bedoya et al., 2019). Assuming similar eligibility rates across Afghanistan, we estimate that the TUP program as a whole likely screened around 125,721 households. We use this value to estimate total targeting costs for the TUP program under counterfactual targeting approaches. Eligible households received benefits totaling $1,688, including a productive asset, cash transfers, a health voucher, training, biweekly social worker visits, and veterinarian visit once every two months during the year of intervention. The total benefits dispersed by the program were therefore on the order of 12.7 million (although the total program costs, including overhead, were closer to 15 million (Bedoya et al., 2019)); we use the total value of benefits to compare the costs of program targeting using our set of counterfactual targeting approaches to the direct costs of program benefits in Table S9. We find that targeting costs for a PMT or asset-based approach would represent approximately 3.97% of the total benefits delivered in the program; costs for a CBT approach would represent approximately 2.18% of the total benefits. In comparison, costs for the phone-based approach would be negligible.

When it comes to speed, in-person data collection for an asset-based (or PMT) targeting approach typically takes months or years to prepare and implement (World Bank, 2020). The CDR-based approach can be rolled out comparatively quickly — but there are still practical hurdles to implementation. First, training data for the CDR-based poverty prediction model must be collected, preferably shortly prior to program roll-out (Aiken et al., 2021). While in the TUP project training data was collected in-person in a household survey, in other contexts training data collection was expedited via a phone survey (Blumenstock et al., 2015; Aiken et al., 2021). Even if data is collected over the phone, it will typically take several weeks to design a survey instrument and collect data. Second, the CDR-based method requires data from mobile network operators. Data sharing agreements with mobile network operators take at minimum a few weeks to arrange, and substantially longer in the worst case (Milusheva et al., 2021). Third, and finally, training a CDR-based poverty prediction model is expensive in 33
These validation results provide rather encouraging support for the application of predictionbased method to obtain poverty estimates in the absence of consumption data. Put differently, if consumption data in the 2010 survey round were not available, we could provide reasonable estimates using the consumption data in the 2008 survey round in combination with the household characteristics in the 2010 round.

Another interesting result is that, since household assets are known to have a positive correlation with household consumption (as empirically indicated by the regression results in Appendix 2, Tables 2.1. and 2.2), if we know that asset ownership rates are generally increasing over time (as seen in Appendix 2, Table 2.3), we would also know from Proposition 2 that estimation models that omit assets would provide upward biased estimates of the true poverty rate. Indeed, Models 1 to 3’s estimates are around 1.5 percentage points higher than the true rate of 14.4 percent. Thus with additional knowledge on the trend of asset ownership over time, Proposition 2 practically offers a way to obtain a bound estimate on poverty where Assumption 2 is not satisfied.

# **III.2.4. Alternative Imputation Methods**

We provide other modelling options to our imputation framework in Table 4. The imputation framework that provides the estimates in Table 3 relies on the assumption of a normal distribution for the error terms μ _j_ and ε _j_ , j= 1, 2. Is this a valid assumption? We provide a robustness check by assuming no functional form for these error terms and use their empirical distribution instead. Estimation results (Table 4, row 1) provide accurate estimates only for Models 5 to 7, and higher poverty rates for the remaining models, suggesting that this assumption is reasonable and help improve our estimates. Conversely, we provide another check 25
# 3.5 Brain drain and human capital accumulation

Let us now use our empirical ...ndings to simulate the e¤ect of skilled migration on human capital accumulation. We use a counterfactual experiment, which consists of setting the 1990 skilled emigration rate to the level of the unskilled rate, <u>m</u> ~~9~~ 0<sup>.8</sup> The unskilled migration rate may be considered as a minimal emigration rate given country characteristics such as size, geographic proximity, colonial and linguistic links with OECD members and political factors at origin. Hence, our experiment consists of minimizing the size of the brain drain. We ...rst use (13) to simulate the impact b on education susbsidies, �90, and then use (12) to predict the 2000 level of human capital among natives, H<sup>b</sup> 00. Note that a similar method is used in Beine et al (2007a) to compute the e¤ect of skilled migration on human capital accumulation between 1990 and 2000. However, taking education subsidies as exogenous (BDR method) potentially overestimates the size of the incentive e¤ect on human capital formation.

We compare the observed and hypothetical proportions/numbers of skilled residents. Figure 2 gives the simulated impact of skilled emigration on the proportion of skilled among residents (intensive measure of human capital) and Figure 3 gives the impact on the number of skilled residents (extensive measure). A positive amount means that the country bene...t from skilled migration, i.e. has more human capital than in the counterfactual scenario where the brain drain is reduced. The grey lines reports simulations with exogenous subsidies (BDR method).

> 8For a couple of countries where the unskilled emigration is very close to zero, we impose a lower bound equal to one tenth of the skilled emigration rate. Since we use a log speci...cation, this avoids unrealistic e¤ects.

22
Table 8: Range of welfare gains ( _wi,j_ )

||||Rest|days|||
|---|---|---|---|---|---|---|
||1|_φ_= 0_._3<br>2|3|1|_φ_= 0_._4<br>2|3|
|1<br>s|[-0.29,-0.06]|[-0.61,-0.38]|[-0.75,-0.55]|[-0.19,0.07]|[-0.52,-0.25]|[-0.68,-0.43]|
|2<br>day|[-0.03,0.15]|[-0.3,-0.1]|[-0.49,-0.27]|[0.01,0.19]|[-0.2,0.03]|[-0.39,-0.13]|
|3<br>rk|[-0.02,0.17]|[-0.13,0.01]|[-0.31,-0.11]|[-0.05,0.13]|[-0.06,0.09]|[-0.21,0.01]|
|Wo<br>4|[-0.1,0.12]|[-0.04,0.03]|[-0.19,-0.03]|[-0.17,0.03]|[0,0.06]|[-0.11,0.06]|
|5|[-0.18,0.06]|[0,0]|[-0.12,0]|[-0.28,-0.07]|[0,0]|[-0.06,0.07]|
|6|[-0.26,0.02]|[-0.04,0.01]|[-0.11,0]|[-0.37,-0.14]|[-0.07,-0.02]|[-0.07,0.04]|
|ighlig<br> 5-2 sc|hted cells are <br>hedule. Welfa<br>1|schedules in <br>re gains are <br>Table 9: <br>_φ_= 0_._3<br>2|which the rati<br> computed usi<br>Expected we<br>Rest <br>3|o of rest days<br>ng equation 1<br>lfare gains <br> days<br>1|to work day<br>3.<br> (_wi,j_)<br>_φ_= 0_._4<br>2|s exceed that <br>3|
|1|-0.18|-0.49|-0.64|-0.07|-0.38|-0.55|
||[-0.26,-0.11]|[-0.57,-0.41]|[-0.71,-0.58]|[-0.15,0.02]|[-0.47,-0.28]|[-0.63,-0.47]|
|2|0.04|-0.19|-0.37|0.07|-0.08|-0.24|
||[-0.02,0.11]|[-0.26,-0.13]|[-0.44,-0.3]|[0.02,0.15]|[-0.15,0]|[-0.33,-0.16]|
|3<br>days|0.06|-0.06|-0.2|0.03|0.02|-0.09|
|rk|[0.01,0.13]|[-0.1,-0.01]|[-0.26,-0.14]|[-0.02,0.09]|[-0.03,0.07]|[-0.16,-0.02]|
|Wo<br>4|0.02|-0.01|-0.11|-0.06|0.03|-0.02|
||[-0.04,0.08]|[-0.03,0.02]|[-0.16,-0.06]|[-0.12,0]|[0.01,0.05]|[-0.07,0.03]|
|5|-0.03|0|-0.07|-0.14|0|0|
||[-0.11,0.03]|[0,0]|[-0.1,-0.03]|[-0.22,-0.1]|[0,0]|[-0.04,0.03]|
|6|-0.08|-0.02|-0.05|-0.22|-0.05|-0.02|
||[-0.17,-0.02]|[-0.04,0]|[-0.09,-0.02]|[-0.3,-0.17]|[-0.07,-0.03]|[-0.05,0.02]|

Highlighted cells are schedules in which the ratio of rest days to work days exceed that of a 5-2 schedule. Welfare gains are computed using equation 13.

See notes in Table 8. In each cell, the top row is the average welfare gain and the bottom row is the 95 inter-percentile range.

32
the appendix.

Total payouts from all five games were aggregated and made in one lump sum at the end of the session. The average payout was approximately 15 Sudanese pounds (roughly five US dollars), which corresponds to about one day’s wage in the rural areas where we worked. We present summary statistics of these measures when we discuss the estimates of program impact in section 7.

Game instructions were given entirely verbally according to a specific script in the local language. Illiteracy rates are very high in rural Sudan, and our respondents found the use of paper and pens very challenging, so we were forced to have the subjects complete the game tasks for four of the five games under the supervision of a facilitator/record keeper. This is a common practice when conducting games in the field in developing countries with illiterate populations (Karlan, 2005; Henrich et al., 2004). Such observation was not required for the public goods game. While we were concerned about Hawthorne effects, having the subjects play under supervision was the only way we could ensure that the subjects understood the decisions that were making.

We also gathered network data from all of our laboratory subjects. We completed a matrix of relationships among the subjects for each of several different categories of social relationships. Table 1 provides an overview of the questions we asked and a summary of their responses.<sup>7</sup> For example, every person was asked whether he or she is a family relation to another subject. We aggregate each person’s connectedness to the group we summed the number of relationships each person had in each category. If a person reported a relationship with four people in the group, that person would receive a score of four. We divided these scores by the total number of subjects in the village, which varied across villages due to attrition in subject recruitment. We instructed our enumerators to crosscheck 7We exclude networks in an irrigation group because it had a zero mean in both treatment and control communities.

19