rafmacalaba/datause-encoder-data huggingface dataset:
**_The scale transformation Command_**

10 the other half _K_ iterations are used for the minimization problem. Once the correct approach is selected, the program keeps the solutions for the correct specification and runs the remaining _N_ iterations indicated in the `iterations()` option.

## **3.7 Optimization Objectives**

The `scale transformation` command finds a 6th-degree polynomial monotonic transformation for a test score scale that optimizes one of the following:

1. Gap Growth: gap difference between the bottom and top groups from the compgroup variable in period 2, minus the same gap difference in period 1. In other words, it is the difference between the regression coefficient of the top compgroup indicator (controlling for groups in the middle) in period 2, minus the same regression coefficient of the top compgroup indicator (also controlling for other groups in the middle) in period 1. In both, the coefficient of the top compgroup indicator measures the difference between the top and bottom groups. Note that the Gap Growth is positive when the Gap widens and negative when the Gap shrinks (always with respect to the Gap in period 1). Thus, the Gap Growth will always be negative when the coefficient changes signs between period 1 and 2 (regardless of whether it is from + to -, or from - to +). Conversely, it will be positive when the gap widens, for instance, from -.1 to -.3;

2. Correlation: coefficient from regressing test scores in period 1 on test scores in period 2 (no controls allowed); 3. R-squared: from regressing test scores in period 1 on test scores in period 2 (no controls allowed); or 4. Controls Explanation: measures how much Gap Growth coefficients change when including controls in `controls1(` _varlist_ `)` and `controls2(` _varlist_ `)` - not in `controls(` _varlist_ `)` . This allows to test the explanatory power of only a subset of desired control variables. That means that, if you want to measure how much Gap Growth coefficients change by introducing time-invariant controls, you must include these variables in both `controls1(` _varlist_ `)` and `controls2(` _varlist_ `)` . The Controls Explanation term is estimated by taking the distance (i.e. absolute value of the difference) between the Gap Growth calculated without variables included in `controls1` and `constrols2` , minus the Gap Growth calculated with these two controls varlists. To make the interpretation easier, both Gap Growths coefficients - with and without controls - are included in the results.

# **4 Resulting Output**

## **4.1 Example**

In this section, we use the `timss` ~~`t`~~ `estscores.dta` ancillary data set file to find the Gap Growth maximization solution for test scores between year 1 and 2, comparing males
Consultancies (to obtain personalized consulting) – did significantly improve the business practices of firms in our sample. This raises the question of why firms needed the government’s GEM project to get them to use these services, and why more firms do not use a marketplace for business services on their own?

# **6.1 Why don’t more firms use the market for business services?**

There are multiple potential reasons why firms may not use the market for these services. Many of the reasons fall into two sets of issues. The first set are informational frictions. Firms may not know what services they need, may not know of providers in the market that can offer these services, may find it hard to tell good from bad providers, and be wary of trusting outsiders when information is incomplete. The second set of issues are cost-benefit calculations. Firms may not think the returns from using such services outweigh the costs, may not be able to afford the costs, or may have considerable uncertainty about what these returns are. Finally, with family-run firms, there may be pressure to hire relatives for positions instead of going to the market.

Our first follow-up survey asked firms to consider different reasons why SME owners in Nigeria (including themselves) might not go to the open market to obtain HR services that help them find a full-time employee (i.e., insourcing a worker), or to contract outside accounting and marketing service providers (i.e., outsourcing a professional). Table 8 summarizes their views.

<u>Cost-Benefit Considerations. The two most common reasons firms give are lack of funding to pay</u> the costs of these services (approximately half of firms think this), and uncertainty about what the returns are (approximately one-third of firms acknowledged this). At least one type of cost-benefit reason is considered “very likely” or “extremely likely” as a reason for not using a marketplace by: 56% of firms for marketing services; 63% of firms for accounting services; and 67% of firms for HR services. And interestingly, marketing is the service where the fewest firms think there is a low expected return from using the service (only 12% say this is a key reason that stops firms from contracting a marketing professional).

<u>Informational Frictions. Individual frictions are expressed by up to 20% of firms. But taken as</u> whole, 47% of firms view some form of informational friction as “very likely” or “extremely likely” for explaining the lack of use of outside marketing services, while 49% do for accounting services and 59% do for HR services. In particular, across the three business services, 21% of firms 27
Policy Research Working Paper 6914

# **Abstract**

This paper examines the extent to which the destination of exports matters for the input prices paid by firms, using detailed customs and firm-product-level data from Portugal. The authors use exchange rate movements as a source of variation in export destinations and find that exporting to richer countries leads firms to charge more for outputs and pay higher prices for inputs, other things equal. The results are supportive of the hypothesis that an exogenous increase in average destination income leads firms to raise the average quality of goods they produce and to purchase higher-quality inputs.

This paper is a product of the Trade and International Integration Team, Development Research 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 pbastos@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1. Introduction**

Two of the most salient trends surrounding the issue of migration and development over the last two decades are the large rise in remittances, and an increased flow of skilled migration. Officially recorded remittances to developing countries have more than tripled over the last decade, rising from US$85 billion in 2000 to US$305 billion in 2008 (World Bank, 2008, 2009). The number of highly educated emigrants from developing countries residing in the OECD doubled between 1990 and 2000 (Docquier and Marfouk, 2005) and is likely to have grown since as developed countries have increasingly pursued skill-selective immigration policies.

However, despite this positive association at the global level between rising remittances and rising high-skilled emigration, there are concerns that the increasingly skill-selective nature of immigration policies may hamper the rise in remittances, due to a belief that more educated individuals may remit less. This belief is accepted as fact by many: for example, the OECD (2007, p. 11) writes “low-skilled migrants tend to send more money home”. The main empirical evidence to support this across a range of countries comes from two recent papers (Faini, 2007 and Niimi, et al. 2008) which use cross-country macroeconomic approaches to claim that the highly skilled (defined as those with tertiary education) remit less.

Yet there are many reasons not to believe these cross-country estimates nor to consider them useful for policy. Both studies relate the amount of remittances received at a country level to the share of migrants with tertiary education, at best telling us whether countries which send a larger share of highly skilled migrants receive less or more remittances than countries which send relatively fewer skilled migrants.<sup>1</sup> This does not answer the factual question “do more educated individuals remit more or less?” There are a host of differences across countries which could cause a spurious relationship between remittances and skill level across countries. For example, if poverty is a constraint to both migration and education, we may find richer developing countries being able to send more migrants (yielding more remittances) and that the migrants from these countries also have more schooling. In addition to this, by focusing on the relative share of migrants who are skilled rather than the absolute number, these papers are not

> 1 A further concern is that the macroeconomic data on remittances covers only remittances through formal sources, and the share of total remittances which are thus reported by country will differ, and may differ in a way which is correlated with their share of tertiary-educated migrants if migrants differ in their propensity to use formal remittance channels according to education level.

2
|P100531|2009|2016|12/31/2015|13 Closed|India|
|---|---|---|---|---|---|
|P100531|2009|2017|10/13/2016|14 Closed|India|
|P100531|2009|2017|4/20/2017|15 Closed|India|
|P100531|2009|2018|7/16/2017|16 Closed|India|
|P100531|2009|2019|7/10/2018|17 Closed|India|
|P100534|2008|2008||0 Closed|Jordan|
|P100534|2008|2009|11/10/2008|1 Closed|Jordan|
|P100534|2008|2009|6/18/2009|2 Closed|Jordan|
|P100534|2008|2010|12/14/2009|3 Closed|Jordan|
|P100534|2008|2010|4/21/2010|4 Closed|Jordan|
|P100534|2008|2011|12/4/2010|5 Closed|Jordan|
|P100534|2008|2012|7/17/2011|6 Closed|Jordan|
|P100534|2008|2012|11/4/2011|7 Closed|Jordan|
|P100534|2008|2012|5/21/2012|8 Closed|Jordan|
|P100534|2008|2013|7/6/2012|9 Closed|Jordan|
|P100534|2008|2013|1/6/2013|10 Closed|Jordan|
|P100534|2008|2013|2/25/2013|11 Closed|Jordan|
|P100534|2008|2013|6/18/2013|12 Closed|Jordan|
|P100534|2008|2014|9/29/2013|13 Closed|Jordan|
|P100534|2008|2014|11/25/2013|14 Closed|Jordan|
|P100546|2008|2008||0 Closed|Jordan|
|P100546|2008|2009|11/5/2008|1 Closed|Jordan|
|P100546|2008|2009|6/18/2009|2 Closed|Jordan|
|P100546|2008|2010|12/29/2009|3 Closed|Jordan|
|P100546|2008|2010|5/26/2010|4 Closed|Jordan|
|P100546|2008|2011|12/26/2010|5 Closed|Jordan|
|P100546|2008|2012|10/26/2011|6 Closed|Jordan|
|P100546|2008|2013|7/11/2012|7 Closed|Jordan|
|P100546|2008|2014|7/21/2013|8 Closed|Jordan|
|P100564|2007|2007|6/25/2007|1 Closed|Haiti|
|P100564|2007|2008|12/18/2007|2 Closed|Haiti|
|P100564|2007|2008|6/30/2008|3 Closed|Haiti|
|P100564|2007|2009|12/22/2008|4 Closed|Haiti|
|P100564|2007|2009|6/24/2009|5 Closed|Haiti|
|P100564|2007|2010|12/18/2009|6 Closed|Haiti|
|P100568|2007|2007|12/28/2006|1 Closed|West Bank and Gaza|
|P100568|2007|2007|4/16/2007|2 Closed|West Bank and Gaza|
|P100568|2007|2008|12/3/2007|3 Closed|West Bank and Gaza|
|P100568|2007|2008|12/11/2007|4 Closed|West Bank and Gaza|
|P100568|2007|2008|6/23/2008|5 Closed|West Bank and Gaza|
|P100568|2007|2009|11/15/2008|6 Closed|West Bank and Gaza|
|P100568|2007|2009|3/10/2009|7 Closed|West Bank and Gaza|
|P100580|2009|2009|6/10/2009|1 Closed|Ukraine|
|P100580|2009|2010|2/25/2010|2 Closed|Ukraine|
|P100580|2009|2011|11/24/2010|3 Closed|Ukraine|
|P100580|2009|2011|6/29/2011|4 Closed|Ukraine|
|P100580|2009|2012|2/13/2012|5 Closed|Ukraine|
25 virtually none of It Irrigated. There was some kIprovement after 1976, with 14 percent of the land added to the reform sector being sultable for cultivation (2 percent of all land distrlbuted was Irrigated). Currently, the total cropland In the reform sector accounts for 14 percent of the cumulative land reform area (I.e. taking Into account all land added to the sector from 1900 to 1984XINEGI,1986:273; Table 6). Non-crop land comprises forests, natural pasture, rough grazing and land that Is Inappropriate for any agricultural use.43. The 1981 Agricultural Census Indicates that the average size of the ejldo parcel was 7.3 hectares; in the crowded states of Mexico and Puebla (both bordering on the Federal District), average parcel size was respectively 2.1 and 3.5 hectares. While the average holding size for privately-owned land is much larger (73.6 hectares In 1981), Just under 60 percent of both private and ejido enterprises operate small holdings (up to 5 hectares) (Table 7). "Mlni holdings (2 hectares or less) are more common In the private sector (40 percent of all holdings) than In the ejido sector (31 percent), suggesting perhaps that inheritance procedures In the ejldos (the stipulatlon that parcels be passed on Intact to a single heir rather than divided between several heirs) have acted as a partial brake on holding fragmentation.

44. In 1981, proportionately more land was left out of productlon (idle) In the private sector than In the ejldo sector (27 percent and 15 percent respectively)(Table 5). However, If the comparison Is restricted to enterprises no larger than 5 hectares, the proportion of land left Idle was the same for both tenure categories (6 percent). In 5 states, more than half of the land In private holdings was Idle In 1981; In the ejldo sector, the same level of neglect was true only for the state of Sonora (Table 8).
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 62 Hinkel, J., Nicholls, R.J., Tol, R.S.J., and 7 others (2013) A global analysis of erosion of sandy beaches and sea-level rise: an application of DIVA. Global and Planetary Change, 111, 150-158.

Hinkel, J., Lincke, D., Vafeidis, A.T., Perrette, M., Nicholls, R.J., and 5 others (2014) Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences, 111, 3292–3297.

Hoozemans, F.M.J., Marchand, M., and Pennekamp, H.A., 1993. A Global Vulnerability Analysis: Assessment for Population, Coastal Wetlands and Rice Production on a Global Scale, 2<sup>nd</sup> Edition. Delft Hydraulics, the Netherlands.

Kebede, A.S., Nicholls, R.J., Brown, S. and Hanson, S.. (2010) Coastal zone. In Mozambique: Economics of Adaptation to Climate Change, World Bank, Washington DC, pp. 47-58.

Nicholls R.J. (2004). Coastal flooding and wetland loss in the 21st Century: Changes under the SRES climate and socio-economic scenarios. Global Environmental Change, 14(1), 6986.

Nicholls, R.J., Brown, S., Hanson, S. and Hinkel, J. (2010) Economics of coastal zone adaptation to climate change, Climate and Development, World Bank Discussion Papers, 10, World Bank, Washington DC, 48pp.

Nicholls, R.J., Hanson, S. and Hinkel, J. (2013) The economics of adaptation in the coastal sector - In Economics of Climate Change in East Asia - Westphal, Michael L., Hughes, Gordon A. and Brömmelhörster, Jörn (eds.) Asian Development Bank, Manila, pp: 60-83.

Sadoff, C.W., Hall, J.W., Grey, J.C.J.H., D. an. Aerts, Ait-Kadi, M., Brown, C., Cox, A., Dadson, S., Garrick, D., Kelman, J., McCornick, P., Ringler, C., Rosegrant, M., Whittington, D., Wiberg, D., 2015. Securing Water, Sustaining Growth: Report of the GWP/OECD Task Force on Water Security and Sustainable Growth.

Spencer, T., Schuerch, M., Nicholls, R.J., Hinkel, J., Lincke, D., Vafeidis, A.T., Reef, R., McFadden, L. and Brown, S. (2016) Global coastal wetland change under sea-level rise and related stresses: the DIVA wetland change model. Global and Planetary Change, 139, 1530.

Yohe, G. and Tol, R.S.J., 2002. Indicators for social and economic coping capacity—moving toward a working definition of adaptive capacity. Global Environmental Change, 12, 25-40.
and only 20 percent of female Ninis transition into work; while around 5 percent of Ninis, both men and women, go back to school exclusively.

Taken together, Figures 1 to 3 illustrate the high short-term persistence of Nininess and the high probability of youth transitioning from being Ninis into employment, and seldom back to school.

To bring this section to a close, we delve deeper into the previous results investigating the composition of states from which new Ninis come. Figure 4 (left panel) shows that, among those youth aged 15 to 24 years old, roughly four out of five transition into Nininess from work, while the remainder mainly go from studying to becoming Ninis. Differences between genders are small for this cohort. Coming directly from studying is more common amongst younger new Ninis than older ones, for both men and women. Nonetheless, most new Ninis aged 15 to 18 still come from working rather than directly from school: 7 out 10 young men and 6 out 10 young women come from work, while the rest come mainly from school (Figure 4, right panel).

Figure 4. Which states do _new_ Ninis today come from? Status at time of first interview for those who became Ninis in the second interview (as percentage) <!-- Start of picture text --> 3 3<br>4 4<br>22<br>17 Women<br>26<br>41<br>15 to 24 y.o. 15 to 18 y.o.<br>56<br>Men<br>70<br>79<br>75<br>Work Study Study+Work Work Study Study+Work<br><!-- End of picture text --> _Notes_ : Weights are used in the estimation. A person is considered a _Nini_ if s/he is not working nor studying. This graph uses information for all cohorts included in the sample. _Source_ : Authors' calculations based on the _Encuesta Nacional de Ocupación y Empleo_ (ENOE).

Finally, Figure 5 shows the states that those who are Ninis in the first interview transition into in the second interview, conditional on transitioning out of Nininess. The left panel shows the results for youth aged 15 to 24 years old, and the right panel for those aged 15 to 18 years old. Figure 5 makes it clear that once Mexican youth are Ninis it is very difficult for them to 15
# Determinants of Choice of Migration Destination<sup>*</sup>

Marcel Fafchamps<sup>†</sup> Forhad Shilpi<sup>‡</sup>

> * We thank Mans Soderbom and seminar participants at University of Gothenburg for their excellent comments. We are very grateful to Prem Sangraula and Central Bureau of Statistics of Nepal whose assistance with data was essential for the success of this endeavor. Financial support for this research was provided by the World Bank. The views expressed here are those of the authors and should not be attributed to the World Bank.

> † Department of Economics, University of Oxford, Email: marcel.fafchamps@economics.ox.ac.uk.

> ‡ DECRG, The World Bank.
36

||Kili|manjaro|Ru|vuma|Kilim|anjaro|Ru|vuma|Kili|manjaro|Ru|vuma|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||OLS|Hh FE|OLS|Hh FE||OLS|Hh FE|OLS|OLS|Hh FE|OLS|Hh FE|
|**Social expenditures**|**Educatio**|**n**|||**Health exp**|**enditures**|||**Gifts sent**|**out**|||
|Farm income|0.002|0.002|0.006*|0.002|0.000|-0.002|0.000|0.001|0.000|0.000|0.001**|0.001|
||(1.092)|(0.842)|(1.862)|(0.873)|(-0.368)|(-1.383)|(0.091)|(0.385)|(-0.859)|(-0.897)|(2.920)|(1.046)|
|Earned non-farm income<br>d b l dl|0.008|0.006|0.011**|0.006|0.001|0.003|0.004|-0.001|0.001**|0.001|0.003**|0.003**|
|earne mae aus|||||||||||||
||(1.532)|(1.161)|(2.075)|(0.935)|(0.289)|(0.648)|(1.025)|(-0.260)|(2.223)|(1.639)|(2.625)|(2.063)|
|Earned non-farm income<br>d b fl dlt|0.033*|-0.001|0.008|0.014|-0.004|0.003|0.004|0.014|0.000|-0.001|0.006**|0.006*|
|earne y emae aus|||||||||||||
||(1.923)|(-0.093)|(0.608)|(0.768)|(-0.503)|(0.376)|(0.428)|(0.902)|(-0.053)|(-0.603)|(3.152)|(1.817)|
|Unearned non-farm income<br>|0.018|0.014|0.019|-0.058|0.041**|0.038*|0.072|0.038|-0.001|-0.003|0.019**|0.016**|
|received by male adults|||||||||||||
||(0.770)|(0.596)|(0.363)|(-1.034)|(2.674)|(1.673)|(1.164)|(0.583)|(-0.833)|(-1.544)|(2.945)|(2.143)|
|Unearned non-farm income<br>rid b fml dlt|0.040|0.029|0.131|0.185|0.001|-0.037|0.104|0.125|0.008**|0.004|0.019**|0.021|
|eceve y eae aus|(1.124)|(0.783)|(1.166)|(1.341)|(0.090)|(-1.524)|(1.381)|(1.390)|(2.897)|(1.044)|(2.211)|(1.571)|

Note: Total household expenditure on each expenditure category as dependent variable. T-values are in between brackets. All regressions include time varying village fixed effects. Regressions also include loans, (lagged) livestock, members of households holding elected office, gender, education and age of household head, household size, dependency ratio and female share of household labor as covariates. Observations with earned income, unearned income, loans, (lagged) livestock in the top 1% are excluded in all regressions. In each regression, observations with the dependent variable in the top 1% are excluded.
evolution. To identify this relation, I use alternative measures of firm evolution and I benefit from a rich set of control variables.

To measure firm evolution, I look at growth rates as explained in the descriptive analysis section. In addition to these, I look at variables that proxy for technological innovations. Enterprise surveys provide information on the probabilities of introducing new products, improving existing processes, using various quality certificates such as ISO 9000 or 9002, and use of foreign licenses. Since the seminal works of Grossman and Helpman (1991) and Aghion and Howitt (1992), it has been argued that firms grow as a result of innovation and technology adoption. These measures of innovation would be interpreted as firms’ adoption of production technologies, methods, or knowledge that were not available to them. This interpretation of innovation is more preferable than the interpretation we would consider for the developed countries. In other words, in developing countries firms’ innovations should be thought as approaching the frontiers of technology or production methods rather than extending these frontiers.

As was shown in the model introduced in section 2, innovations are the main drivers of firm growth. Since firm level output prices are not available, measured values of productivity and sales can be affected by both output and input price movements. These movements can affect the growth rates of sales and productivity. On the other hand, the measures of innovation as well as employment growth are not affected from such price movements. The use of various measures of firm evolution, growth rates and technological innovation rates improves the reliability of the analysis.

**<u>Table 8 Firm Evolution and Technological Innovation</u>**

||**Depe**<br>|**ndent Variables**<br>|<br>|
|---|---|---|---|
||EmployGrowth|Sales Growth|Log(Proy)|
|Prod Innov|0.021<br>(0.002)***|0.044<br>(0.003)***|0.167<br>(0.018)***|
|Proc Innov|0.025<br>(0.002)***|0.024<br>(0.004)***|0.226<br>(0.032)***|
|Foreign Lic|0.013|0.012|0.419|
||(0.003)***|(0.005)**|(0.039)***|
|Quality Cert|0.004|0.024|0.169|
||(0.002)*|(0.004)***|(0.040)***|

Robust standard errors clustered by country, industry, and year. In the regressions, I control for 2-digit industry, survey year, and country fixed effects. ***p<0.01, ** p<0.05, *p<0.1.

The data show that measures of technological innovation are positively and significantly correlated with firm growth. Table 8 shows the results of regressing growth rates of size and log of productivity on the proxy measures of technological innovation. Each cell in the table shows the 17