rafmacalaba/datause-encoder-data huggingface dataset:
- Type: Catholic, other religious or non-sectarian. Source: PSS.

- School enrollment: total school enrollment, excluding ungraded and adult students. Source: own calculations based on PSS.

- Grade-level enrollment: number of students in each grade between K and 12.

- Percent of white students: source: own calculations based on the reported number of white students and the total enrolment. “White” includes other ethnicities as well.

- Percent of black students, percent of Hispanic students: constructed similarly to percent of white students.

- Percent of low income students: since PSS does not collect this information, we impute it based on the following logistic regression. Using data for public and charter schools, we regress percent low income on school percent white and percent Hispanic, school enrollment, and average household income of the school’s tract. We use this regression to predict percent low income in private schools. We check our predictions by comparing OSSE data on the percent of students receiving free and reduced lunch in private schools. Our predictions compare favorably with OSSE data.

- Tuition: annual tuition for the 2002/03 school year. Source: Salisbury (2003). Tuition is expressed in dollars of 2000.

# **A.2 Market Size and the Outside Good**

Since a market is a grade-year combination, market size _Mgt_ is equal to the number of children eligible for grade _g_ in year _t_ between 2003 and 2007. This number is not available, and neither is the number of children by age. Hence, we estimate market size based on the following, available data for Washington, D.C.:

1. The 2000 Census count of children by age;

2. The intercensal estimates of the number of children in the 5-13 and 14-17 year old brackets; 3. The 2000 Census count of enrolled and not enrolled children by age group. The resulting percent of children not enrolled in school is our best proxy for the outside good share in 2000 .

4. Observed enrollment for each grade and year between 2003 and 2007. Use _Ngt_ to denote aggregate enrollment in grade _g_ and year _t_ .

We estimate market size as _Mgt_ = _Ngt_ ∗ _θgt_ , which implies an outside good share equal to _Ogt_ = 1 − _Ngt_ / _Mgt_ . Adjustment factors _θgt_ are chosen so that _Ogt_ matches the 2000 Census fractions of children not enrolled in school. We adjust these fractions slightly to account for the fact that our enrollment data is based only on regular schools and excludes early childhood centers. Thus, we use fractions equal to 3 percent for ages 5-14 (corresponding to grade K-8) and 10 percent for ages 14-17 (grades 9 through 12). For computational reasons we impose the following constraint: _Ogt_ ≥ 0.01 (see Appendix E.3).

An appealing feature of our solution is the consistency of _Mgt_ with the growth rate for child population implied by intercensal Census estimates. In particular, the 45
**... and risk taking is not rewarded.** The well-connected have easier and relatively unscrutinized access to credit, as banks – usually associated to the key conglomerates - do not thoroughly assess the expected returns on their investment; as a result of their inadequate knowledge to pick “winners”, average or mediocre projects get implemented. On the other hand, farmers under-invest in high-risk but high-return crops because they cannot afford insurance.

**Figure 14. Rice: a domestic price above the world price produces rents**

<!-- Start of picture text --> Domestic and World Rice Prices<br>25.00<br>World price<br>Domestic price<br>20.00<br>15.00<br>10.00<br>5.00<br>0.00<br>1987 1987 1988 1988 1989 1989 1990 1991 1991 1992 1992 1993 1994 1994 1995 1995 1996 19961997 1998 1998 1999 1999 2000 2001 2001 2002 2002 2003 2003 2004 2005 2005<br>Pesos per kg<br><!-- End of picture text --> _Source:_ World Bank, 2006 and World Bank, 2007b.

**The maritime and aviation sectors, dominated by conglomerates, are not open to**

**competition.** The resulting high transportation costs undermine competitiveness, trading, tourism, and agriculture. For example, the price for exporting a 20-foot container from the Philippines is 60-300 percent higher than in other Asian countries (Table 5). By opening up its seas and skies - as long as competition is protected by rules and policies - the Philippines could become in the next few years a transport-and-logistics-hub in the Asia-Pacific region, and a competitive destination for tourism, medical services and retirement.

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Policy Research Working Paper 5488

# **Abstract**

Seasonal migration programs are widely used around the world, and are increasingly seen as offering a potential “triple-win”—benefiting the migrant, sending country, and receiving country. Yet there is a dearth of rigorous evidence as to their development impact, and concerns about whether the time periods involved are too short to realize much in the way of benefits, and whether poorer, less skilled households actually get to participate in such programs. This paper studies the development impacts of a recently introduced seasonal worker program that has been deemed to be “best practice.” New Zealand’s Recognized Seasonal Employer program was launched in 2007 with an explicit focus on development in the Pacific alongside the aim of benefiting employers at home. A multi-year prospective evaluation allows measurement of the impact of participation in this program on households and communities in Tonga and Vanuatu. Using a matched difference-in-differences analysis based on detailed surveys fielded before, during, and after participation, the authors find that the Recognized Seasonal Employer program has indeed had largely positive development impacts. It has increased income and consumption of households, allowed households to purchase more durable goods, increased the subjective standard of living, and had additional benefits at the community level. It also increased child schooling in Tonga. This should rank it among the most effective development policies evaluated to date. The policy was designed as a best practice example based on lessons elsewhere, and now should serve as a model for other countries to follow.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the department to study the determinants and consequences of migration and remittances. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmckenzie@ worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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
<u>Table 17. Summary of results of random effects wage regressions</u>

|**Ln(wage)**|**Immigrants**<br>**Total**|**Native**<br>**Danes**|**Immigrants**<br>**Danish**<br>**education**|**Immigrants**<br>**foreign**<br>**education**|
|---|---|---|---|---|
|**Regression I**|||||
|Schooling|0.061***<br>(0.001)|0.074***<br>(0.001)|0.072***<br>(0.002)|0.043***<br>(0.002)|
|Foreigneduc.|-0.061***<br>(0.005)||||
|Danish Track 1||||-0.074*<br>(0.031)|
|Danish Track 2||||-0.045***<br>(0.013)|
|Danish-missing||||0.015<br>(0.009)|
|Danish-missing<br>and arrived<br>after 1.1.1999||||0.018*<br>(0.086)|
|R2Overall|0.219|0.127|0.259|0,145|
|Obs.|43,702|380,943|23,189|20,513|
|**Regression II**|||||
|Adequate|0.077***<br>(0.001)|0.079***<br>(0.001)|0.079***<br>(0.002)|0.072***<br>(0.003)|
|Overeduc|0.023***<br>(0.002)|0.054***<br>(0.001)|0.037***<br>(0.003)|0.011***<br>(0.003)|
|Undereduc|-0.010<br>(0.005)|-0.047***<br>(0.002)|-0.032***<br>(0.007)|0.019*<br>(0.008)|
|Foreigneduc.|-0.051***<br>(0.005)||||
|Danish Track 1||||-0.067*<br>(0.029)|
|Danish Track 2||||-0.036**<br>(0.012)|
|Danish-missing||||0.020*<br>(0.009)|
|Danish-missing<br>and arrived<br>after 1.1.1999||||0.154<br>(0.081)|
|R<sup>2</sup>Overall|0.278|0.141|0.288|0.238|
|Obs.|43,702|380,943|23,189|20,513|

Note: (a) In the regression for native Danes, controls variables included, but not reported here, are age, (age squared)/100, experience in Denmark, (experience in Denmark squared)/100, number of children, marital status and year dummies. In addition to these, immigrant equations include YSM (years since migration), YSM<sup>2</sup> and ethnicity dummies. Legend: * p<0.05; ** p<0.01; *** p<0.001 (b) The estimations are performed using a sub-sample consisting of workers with either a vocational education or a short, medium, or long higher education.

(c) The reference category for ethnic origin is “Other non-Western countries”.

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TABLE 2

# BALANCE SHEET COMPOSITION OF A REPRESENTATIVE MOSHAV ASSOCIATION, SEPTEMBER 30, 1981

(in percent of total assets)

|ASSETS||LIABILITIES AND MEMBERS'|EQUITY|
|---|---|---|---|
|Fixed assets|3.7|Equity|0.7|
|Long-term investments<br>and loans to members|3.5|Long-term debt to<br>Settlement Dept.|2.1|
|Inventories|4.0|Other long-term debt|2.1|
|Accounts receivable<br>from non-members<br>Members' debit balances|12.2<br> <br>76.7|Short-term bank ioans<br>Short-term loans from<br>regional supply coop.|0.6<br>76.9|
|||Suppliers' credit|3.9|
|||Members' credit balances|13.5|
|||Other payables|0.2|
|Total|100.0||100.0|

Source: Zusman (1988, Table G.2).

Note: The data are for an average association in a sample of 13 moshavim in the "Mountain Region" (a fictitious name).

inflation-protected bank deposits or in government bonds--often with the blessing and the advice of the association.

Long term credit in agriculture comes mainly from the Settlement Department of the Jewish Agency and from the government. The 'Settlement Department is responsible for newly established moshavim supplying them with start-up capital for the early stages of their development--until they take off and can operate independently, generating savings and raising credit. The government supports investments in preferred agricultural development projects. By 1981 6
# **Labor Market Regulations and Outcomes in Sweden: A Comparative Analysis of Recent Trends**<sup>**1**</sup>

Hulya Ulku World Bank Group Silvia Muzi World Bank Group

**Keywords:** Labor market regulations and flexibility; wage determination; temporary employment; unemployment; productivity.

**JEL classification codes:** J3, J24, J41, J51, J64, J65.

> 1 The corresponding author can be contacted at hulku@worldbank.org. The authors thank Pontus Braunerhjelm, Johan Eklund and Augusto Lopez Claros for their constructive feedback as well as the participants of the launch seminar of the Swedish Economic Forum Report 2014 for their comments and suggestions. The original version of this paper was published in Swedish Economic Forum Report 2014. All remaining errors are ours.
**Table 1: Capabilities and the Indicators Used to Represent Them**

|**Category**|**Capability**|**Indicator**|**Units**|**Source**|
|---|---|---|---|---|
|Fixed|Proximity to markets|GDP‐weighted<br>distance|Kilometers*|CEPII|
||Natural capital|Total value of<br>natural capital|Current $US<br>billion|World Bank Wealth of<br>Nations|
|Long‐term<br>Policy<br>Variables|Human capital|Average years of<br>schooling (>15 years<br>oldpopulation)|Years|Barro and Lee (2010)|
||Physical capital|Capital stock per<br>person|2005 $US<br>thousands|World Development<br>Indicators (WDI) and<br>World Bank staff<br>calculations|
||Institutional capital|Rule of Law|Rating from ‐<br>2.5 to 2.5|World Governance<br>Indicators|
|Short‐<br>term|Logistics/connectivity|Logistics<br>Performance Index|Rating from 1<br>to 5|World Bank LPI|
|Policy<br>Variables|Wage<br>competitiveness|Minimum wage for a<br>19‐year old worker<br>or an apprentice|$US*|World Bank Doing<br>Business|
||Market access|Overall Trade<br>Restrictiveness Index<br>(of Trading Partners)|Uniform tariff<br>equivalent of<br>partner<br>country tariff<br>and non‐tariff<br>barriers*|Kee, Nicita and<br>Olarreaga (2008, 2009)|
||Access to inputs|Overall Trade<br>Restrictiveness Index|Uniform tariff<br>equivalent of<br>country tariff<br>and non‐tariff<br>barriers*|Kee, Nicita and<br>Olarreaga (2008, 2009)|

*Two variables were inverted from the source data to give all indicators have the same direction; a higher figure for all indicators signifies a higher capability level.

As alluded to above, some of these capabilities vary with one another across countries. The two fixed capabilities ‐ natural capital and proximity to markets – seem to have no linear relationship (corr = 0.02). On the other hand, many of the “long‐term policy variables” – human, physical and institutional capital – are correlated (Annex Table A2). In particular, physical and institutional capital often occur together (corr = 0.83). This means products found to be intensive in physical capital will typically also be intensive in institutional capital, and vice versa, because countries making those products will usually be abundant in both of these capabilities or neither of them. As for the policy variables, low labor costs and good logistics often exist in the other’s absence (corr = ‐0.71), but the other variables do not have a strong linear relationship with one another.

7
In sum, credit seems to have an important relation with household welfare in Vietnam. It works both directly on income growth and through increased market integration as evidenced by cash crop income shares and commercialization indices.

# **3. REFORMS AND THE DETERMINANTS OF INCOME RESPONSE**

The overall growth of income in Vietnam between 1993 and 1998 was remarkable. Real total household income in rural areas increased by almost 50<sup>20</sup> percent; most of this increase was due to increases in agricultural incomes (Isik-Dikmelik 2006). This was a broad-based expansion in the entire rural sector rather than growth in only food crops or cash crops. The role of policy changes in this were corroborated by the perceptions of households as well. In the 1998 survey, as part of the community questionnaire, it was asked whether the communes felt that there had been an improvement in the economic situation. More than 95 % of the communes (106 communes out of 111) replied that things had improved while 3 communes said that things had gotten worse and 2 said they had stayed the same<sup>21</sup> . Among the 106 communes where they felt that things had improved, 92 communes or 87 percent replied that it was due to the changes in agricultural policies. Another 6.7 percent said that the reason for the improvement was the expansion of the non farm economic activity. The overwhelming acknowledgement that the improvements were caused by policy changes supports the hypothesis that it was the reform that led to the supply response.

The analysis of the impact of reforms on household welfare generally falls under one of two main approaches 1) ex-ante studies where change in prices due to a trade liberalization are simulated and then in turn used to estimate the change in household welfare and 2) ex post econometric studies where the data before and after the policy change is used to estimate changes in household welfare<sup>22</sup> . Dercon (2005), Nicita (2004),

> 20 This growth rate is the unweighted averages of the household growth. The growth in mean (weighting the total growth by incomes) for the rural areas turns out to be 60%, (Isik-Dikmelik-2006, Benjamin&Brandt2004)

> 21 Of the 3 which felt that things had gotten worse said it was because of a disaster and the 2 communes that have said that things had stayed the same gave disasters or bad weather as the reason. 22 For both of these approaches, both impact analysis and analysis allowing for behavioral responses have been used in the literature.

16
_2_ _The reasons why I am such a strong believer in the virtues of investment are spelled out in the paper. And, in turn, investment, which determines longer-term growth is strongly linked with technological progress, and is a function of optimistic expectations. be these of profits or be Liese of future demand. I don't want co enter into the Keynesian v .sus Neo-classical controversy. I just say that, in fact, the two tend to be correlated over the cycle and over the medium-run anyway._ _Therefore, it is optimistic expectations that generate upswings in investment, and it is upswings in investment, in turn, which lead to acceleration (or deceleracions in the opposite case) in the growth path of the world economy._ _What are these expectations function of? Well, there the field is more open, as far as I am concerned. There are various views on what may generate favorable expectations. One of chem is the idea that we are subject to virtually inevitable cycles in optimism, pessimism, investment activity and so on, which have gone under the name of Kondratieff cycles._ _rmportance of System Shocks_ _But there are two other views which I think are more interesting, one of which would stress the importance of deregulated markets and spontaneous market forces, very much the present policy orthodoxy in stimulating entrepreneurial activities. The other one, on the other hand, would stress what I call system shocks, but I do not only want to call them system shocks, special impulses which appear from time to time, often in an ad hoc way, which can come from wars, from change in trading regimes, but also importantly from economic policies._ _So it is really between these two, in a sense, opposite views, I think, that the debate may be placed._ _What does the empirical evidence show, if anything? I don't think it shows that much. It shows what Paul Armington and Shahrokh Fardoust call in thelr paper the sort of ineluctability of 2-1/2 percent per annum. If I take the Angus Maddison's data for 16 industrialized countries [see Phases of Capitalist Development, Oxford University, Press, 19821, which go back at least a century and possibly a bit more, one finds that indeed over most of the time che growth rate of industrialized countries has been roughly 2-1/2 percent per annum. And I have trled with a certain amount of ingenuity. I think, to find periods of acceleration and deceleration within this long-run historical experience and basically, apart from the obvious 1950s and 1960s, which are a clear exception to the general trend because of very sharp sudden acceleration, I can at best come up with two periods of very mild acceleration in the 1850s and 1860s and again at the end of the 19th century. The accelerations are mild;_
<mark>we know with certainty whether they were living in refugee affected areas or not for at least part of the crisis. The sub-sample of households who migrated after 2001 is only used for our placebo tests. The reason is that there is no way to tell if those who migrated to their current place of residence after 2001 were previously in refugee affected areas or not prior to 2001 (the DHS does not provide information on multiple migration spells, only migration to the current place of residence).</mark> <mark>In an effort to provide some explanations to the findings of the main analysis, we also use older DHS data to study the socio-economic characteristics of grandmothers of children in 2015/2016. We use the 1991/1992 DHS to empirically assess labor market outcomes of grandmothers prior to the refugee influx in 1993. We also use the 1996 DHS data to assess the short-term impacts of refugees’ influx on under five years old children’s health outcomes and grandmothers’ labor market participation during the first phase of the influx from October 1993 to December 1996.</mark>

# **5.** **<mark>Empirical Strategy</mark>**

<mark>Our empirical approach follows the main strategy used by the literature on measuring the impact of forced displacement on host communities where the interaction between the treatment group (</mark> _<mark>T</mark>_ <mark>) and the refugee shock (</mark> _<mark>R</mark>_ <mark>) is the variable of interest.</mark><sup>16</sup> <mark>The general equation is described as follows:</mark> <mark>where Y ����</mark><sup><mark>is the Height for Age Z score or the probability of Stunting (HAZ <-2 SD) of the child</mark></sup><sup>_<mark>i</mark>_</sup> <mark>living with parents (mothers or fathers) in the cluster k and born in year</mark> _<mark>t</mark>_ <mark>from the mother</mark> _<mark>j,</mark>_ <mark>γ</mark> �<sup><mark>is the</mark></sup> <mark>child’s year of birth fixed effect, m</mark> �<sup><mark>is the mother’s year of birth fixed effect,</mark></sup> <mark>X</mark> �<sup><mark>are a set of children</mark></sup> <mark>and household level characteristics including the gender and birth order of the child, the gender of the household head and the age of the mother</mark> _<mark>j</mark>_ <mark>when giving birth to the child</mark> _<mark>i</mark>_ <mark>, θ</mark> �<sup><mark>are cluster fixed effects</mark></sup> <mark>and ε ����</mark><sup><mark>is an idiosyncratic error term. The cluster fixed effects allow us to control for unobserved</mark></sup> <mark>heterogeneity at the geographic level. However, we are mindful that this heterogeneity might be nonadditive.</mark> <mark>As in previous literature, R</mark> �<sup><mark>is our refugee shock variable instrumented using the inverse of household</mark></sup> <mark>distance from refugee camps as follows:</mark><sup>17</sup>

> 16 See <mark>Verme and Schuettler (2019) for a review of these models.</mark>

> 17 See <mark>Verme and Schuettler (2019) for a review of similar instruments.</mark>

13