rafmacalaba/datause-encoder-data huggingface dataset:
terms of welfare. The same reasoning can be made for the school meals, although in this case there is not a cut off level of attendance for benefits. When registration fees fall below a certain threshold, it is optimal for the household to register all her children and have them attend school only those times when the household values the school meals more than the child labor. Either way, absenteeism conditional on enrollment is likely to be higher with the program than without the program.

Overall it is plausible that with the program children who would have stayed out of school are enrolled but some of them attend less regularly. We can use the number of school age children (6-15) to empirically test this conjecture. We consider the presence of other (other than the student) children between 6 and 15 years old in the household at the time of the survey, independently of whether or not those other children were enrolled themselves. The number of children between 6-15 years old can be considered as given in the short term, and could not influenced by the program during the relatively short period it has been in place. To assess the effects of household labor supply, we construct three groups of households based on the number of children in the household and estimate the program impact (ATT) on absenteeism for each group. The estimation results are reported in table 10. Columns 1-3 show the results for households with no or at most one child in addition to the student (column 1), those with two or three children in addition to the student (column 2), and those with four or more children in addition to the student (column 3), respectively. We repeat the same regressions in columns 4-6 restricting the sample to younger students (i.e. 6-12 years old). Consistent with our conjecture, for households with at most one child in addition to the students (columns 1 and 4), the intervention has increased absenteeism for more than a full day compared to the control group. There is no significant effect for households with 2 or 3 children (columns 2 and 4). For households with 4 or more children in addition to the student (columns 3 and 6), THR has lead to a reduction of the number of days missed (decreased absenteeism) by about 1.4 day for all students (column 3) and by about 1.7 days for younger students (column 6).

# _5.5 Child labor_

Tables 11 to 16 look at the average intent to treat (AIT) impact of the interventions on 15
WPS4076

# Contagion and Firms’ Internationalization in Latin America: Evidence from Mexico, Brazil, and Chile

## Yaye Seynabou Sakho*

## Abstract

We investigate whether contagion matters when emerging market firms cross-list their stocks in a developed capital market. We develop a rational expectations model where financial markets are segmented along emerging markets’ borders and contagion spreads from one emerging market to another through the actions of international investors rebalancing their portfolio using stocks cross-listed in the developed market. We find that contagion is a cost of internationalization as cross-listed stocks are more affected by contagion than pure domestic stocks. Furthermore, a welfare analysis of international cross-listing versus financial autarky suggests that the benefits of internationalization in terms of less information asymmetry and better market efficiency offset the costs of contagion. The model is able to explain some transmission of the 1998 Brazilian crisis to Mexico and Chile.

JEL Classification: G15, G12, F3, F36

## World Bank Policy Research Working Paper 4076, December 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*Finance Cluster, Latin America and the Caribbean Region, World Bank. Contact: ysakho@worldbank.org, 1818 H Street NW Washington, DC 20433, Mail Stop I5-100. The author thanks Urban Jermann, Frank Schorfheide, and Amadou Sy for their guidance in preparing the document, and Karen Lewis for useful comments.
capita income. Third, we measure the amplification or dampening of the effects of external shocks depending on the degree of trade and financial openness. Section IV concludes.

# **II. Review of the Empirical Literature**

A growing empirical literature has analyzed the effects of financial openness, trade openness, and foreign shocks on growth levels and growth volatility. In this section we proceed to review briefly the analytical underpinnings and existing empirical results on the core relations that are the focus of this paper: those between financial openness, trade openness, foreign shocks, GDP growth, and GDP growth volatility.

## **A. Financial Openness and Growth**

The empirical literature on financial openness (FO) – as well as the research on trade openness (TO) reviewed below – is based on two classes of openness measures. Policy or legal measures reflect policy and regulatory restrictions or barriers imposed domestically on international trade volumes and financial flows and/or holdings. In contrast, outcome or _de facto_ measures reflect actual trade volumes and financial flows or stocks between the domestic economy and the rest of the world. On one hand, the strength of the first class of FO / TO measures is that it reflects policy restrictions while the second class is influenced by country-specific features – including size, distance, production specialization, and risk – in addition to domestic restrictions. Hence the first class may represent more exogenous policy conditions while the second class of FO/TO measures is likely to be endogenous to variables that are often explained by integration, including growth. On the other hand, outcome measures may reflect more truly country integration into world markets, while policy measures may not reflect binding policy restrictions and may be subject to measurement bias. Moreover, outcome indicators represent continuous variables that are more easily measured and are more widely available than the discrete measures of policy restrictions.

The policy measure of FO is largely based on information on capital controls in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions, which has spawned construction of several discrete indicators of capital-flow restrictions on cross2
|2007-08<br>984.7<br>1686.7|
|---|
|2008-09<br>1275.9<br>6122.6|
|2009-10<br>1640.7<br>-6163.7|
|2010-11<br>2805.0<br>278.1|
|Source: Nominal figures were obtained from the Annual Reports of the|
|Delhi Metro Rail Corporation Ltd. The CPI-IW, published by the Labour Bureau,|
|Government of India was used to convert to real terms.|
|1. These are before tax, not accounting for prior period|
|adjustments and after accounting for depreciation and interest.|

# **APPENDIX S2: Calibration Exercise: Comparing with Doll and Balaban**

# **(2013)**

Doll and Balaban (2013) estimate reductions in several pollutants, including CO, for the year 2011 as a result of the Delhi Metro being available as an alternative mode of travel. Their methodology involves building a before and after scenario using data on various transport sector parameters such as total travel activity in Delhi (passenger kilometers travelled), total number of vehicles of various kinds (cars, buses, two-wheelers etc.), average distance travelled, vehicle occupancy, composition of pre-metro travel modes of DM ridership, fuel efficiencies, and fuel emission factors.

They estimate an annual DM ridership of 651 million passengers in 2011. They also estimate an average trip distance of 14.7 kilometers, which equates to 9.66 billion passenger kilometers. This they calculate to be 6.6 percent of Delhi's motorized travel demand. Using a primary survey they estimate that 44 percent of the DM ridership is from buses, 22 percent from cars, 25 percent from two 69
<mark>exposure to natural disasters and climate variability; social, economic, and institutional characteristics of households and regions that affect their adaptive capacity; and health, livelihood, food security, and demographic characteristics that determine sensitivity to climate change impacts.</mark>

# **<mark>From concept to choice of variables</mark>**

<mark>A number of judgments have to be made when translating the concepts into estimates of vulnerability at the sub-national level; this is particularly so for adaptive capacity. For example, does migration and urbanization reduce or increase vulnerability? Vincent (2004) interprets growing urbanization as a sign of weak rural resilience and therefore high vulnerability. However, in the Tajik context, migration is accompanied by remittances vital to the livelihoods of the household members left behind. At least to the migrant households, remittances help reduce vulnerability. We therefore treat remittances as adding to income diversification and improving adaptive capacity. We construct a variable measuring the extent of diversification of non-agricultural income sources at the household level which we believe to be a good indicator of how well communities have already adapted: well- diversified communities (including those with remittance incomes from migrants) are displaying adaptive behaviors and might be expected to continue to do so in the future.</mark> <mark>A well-educated population with reasonable and diversified income sources and developed institutional structures is better able to manage risks and prevent biophysical impacts from translating into human impacts. We therefore also include in the adaptive capacity sub-index average per capita household consumption and the share of population with education above secondary.</mark><sup>1</sup> <mark>Institutional strength and stability are also important for determining the coping range of a population. Governance and political stability are important criteria here but, unfortunately, no direct indicator is available at the sub-national level. However, three good proxies for institutional strength are available in our data and all contribute to adaptive capacity:</mark>

- <mark>Social capital and trust is measured in the form of generalized trust. The question "Generally speaking, would you say that most people can be trusted, or that you can’t be too careful in dealing with people?" has been used in many settings to assess general interpersonal trust as a dimension of social capital. It is available for Tajikistan from the Life in Transition (LiTS) survey. We use the</mark>

> 1 Access to information and communications infrastructure is also arguably important in <mark>influencing vulnerability (Blake et al, 1994). In the past, authors have used telephone access measures as proxies for information sharing, early warning, and general connectivity. However, now that most households have cell phones, the value of telephone access as a proxy variable has arguably diminished. This leaves us with no usable data source on access to information.</mark>

<mark>6</mark>
<!-- Start of picture text --> Global gas flaring and oil production 1996 to 2021 (flaring only at upstream oil & gas and LNG plants)<br>Hl cas faring J Oil production<br>100<br>225<br>90<br>200<br>80<br>75<br>70<br>150 Ss<br>i 6o z=<br>3@ 125 an4E<br>é:2 400 0 =A<br>bd 30 3<br>50 20<br>25 40<br>° °<br>1996 2001 2006 2011 2016 2021<br>Source: NOAA, Payne Institute and Colorado School of Mines. GGFR<br><!-- End of picture text -->
||Post-Int<br>|ervention<br>Refo<br>|Tab<br>Change in<br>nns in De<br>|le 3.3<br>M2/GDP,<br>scendingG<br>|Regressio<br>roup<br>|n Results<br>|||
|---|---|---|---|---|---|---|---|---|
|Explanatory<br>Variable|Macro +<br>Financia<br>Descend|<br>l +<br>ing|Macro +<br>Financia<br>+ Interes<br>Distortio|<br>l + Trade<br>t Rate<br>n|Macro +<br>Financia<br>+ Non-B<br>Financia<br>Regulati|<br>l + Trade<br>ank<br>l<br>on|Macro +<br>Financi<br>+ Prude<br>Regulat|<br>al + Trade<br>ntial<br>ions|
||(17)|(18)|(19)|**(201**|(21)|(22)|(23)|**(24)**|
|Constant|-12.90|-7.52|-12.95|_-6.65_|-13.60|-4.88|-19.73|-8.34|
||(1.96)|(1.83)|(1.98)|(1.54)|(1.83)|(1.86)|(2.99)|(2.16)|
|Group 1|6.77<br>(1.66)||7.17<br>(1.81)||9.51<br>(2.03)||9.81<br>(2.83)||
|Group 2|<br>3.09<br>(0.69)||2.66<br>_(0.59)_||5.89<br>(1.25)||5.86<br>(1.60)||
|Group 3<br>DBPC/GDPx Infl.|3.20<br>(0.74)|-.0043<br>(1.59)|2.91<br>(0.67)|-.0049<br>(1.77)|5.34<br>(1.10)|-.0053<br>(1.83)|7.05<br>(1.81)|-.0050<br>(2.03)|
|IMP/GDP|0.34|0.37|0.38|0.41|0.40|0.38|0.44|0.39|
|Descending|(2.11)<br>4.00<br>(1.26)|(2.60)<br>3.91<br>(1.23)|(2.35)|(2.67)|(2.11)|(2.33)|(3.02)|(2.90)|
|Interest Rate Dist.|||2.61<br>(1.31)|1.50<br>(0.76)|||||
|Non-Bank Fin.|||||3.20|.0089|||
|Reg.<br>Prudential Reg.|||||(1.80)|(.004)|4.74<br>(2.31)|3.72<br>(1.75)|
|Adj. R-Squared|.405|.356|.412|.411|.480|.275|.552|.422|
|Number of<br>Observations|16<br>**_**|16<br>**I**|16|16<br>**_I**|16<br>**III**|16|16|16|

Note: t-statistics in parentheses; group dununies defined in Table 2.2.

In many ways, the regression results **merely** help formalize notions suggested by the summary statistics of the first two sections. For example, a relatively under-developed financial sector and macroeconomic stability (as reflected in a low inflation rate) are both associated with growth in M2/GDP. Even after controlling for those factors, however, there are systematic differences in the performance of interventions. In particular, relatively open economies appear to benefit more from financial sector interventions than 38
-3-

another limitation of the previous studies is that they **assume** the responses of commodity prices to news are the **same** over different stages of the business cyccl..

S. This paper coatributes to the existing literature on the impact of macroeconomic shocks on commodity prices in a number of ways. First, the respenses of commodity prices to economic news are allowed to vary over different stages of the business cycle. Second, it analyzes the simultaneous **impacts** of news from U.S. macroeconomic announcements and surprises from daily exchange and interest rate shocks on daily c; aodity price movements. For economic variables which are announced periodically (money stock, inflation and real activity indices), survey data are used to divide macroeconomic announcements into expected and unexpected components, with the latter measuring news. For other independent variables whose values are realized on financial and credit markets (exchange rates and interest rates), autoregressions are used to model their daily behavior and the residuals from these autoregressions are taken to be exogeneous shocks to commodity markets. Third, recent data (01/02/85-05/31/89) are used. All existing studies analyning the impact of economic shocks on short-run coamodity price behavior have used data that date from the late seventies to the early eighties. Fourth, the price behavior of important commoditi.s (e.g. energy products) not considered by the existing studies are analyzed.

6. The important role of exchange rate fluctuations in short-run commodity price dynamics cannot be ignored. The association of movements in daily commodity futures prices with movements in the U.S. dollar is
the system are permanent in nature but initially unanticipated. The<sup>external disturbance</sup> is an _import price shock_ involving a 50% increase In the import<sup>price of primary goods</sup> which Include oil. This is a reasonable replication of the 1974 oil crisis<sup>since it causes an</sup> Immediate worldwide inflation which raises the cost of imported raw<sup>materials everywhere.</sup> Another type of major disturbance is an interest rate shock much like in<sup>the late seventies.</sup> However, the effects of the latter, which is an outright shift in the intertemporal<sup>rate of</sup> transformation of resources, are straightforward and will lead to a decline<sup>in investment</sup> and a contraction in the economy as what happened to many middle-income countries after 1979.15 Hence, we consider only the import price shock.

Several questions are raised in this exercise. Do the simulation results illustrate<sup>the</sup> observed responses of middle-income countries in relation to the import price<sup>shocks during</sup> the 1970s? Does the introduction of forward-looking and dynamic behavior<sup>make any</sup> difference? There are several dimensions to this issue. In relation to a comparable<sup>static</sup> CGE model, what are the _qualitative_ differences in behavior? Are there changes in the _signs_ of the effects? Are the magnitudes of these differences large?

The results are presented in graphical form for easier .nterpretation. In<sup>each graph, a</sup> horizontal line equal to one along the vertical axis is taken as the reference<sup>balance growth</sup> run. Each deviation from this reference traces the effects on a<sup>key variable of a change in</sup> policy or the advent of an external shock.

I<sup>5</sup> See Go (1988) for details of this simulation.

15
- 3 -

The existing evidence is supportive of theory: In the United States, the level of firm inventory declined by 7 cents for every additional dollar of investments in the highway capital (Shirley and Winston, 2004). In China, each dollar of road spending is estimated to reduce firms’ held inventories by 2 percent (Li and Li, 2013). Using the Golden Quadrilateral project liking the country’s four largest cities as a quasi-natural experiment, Datta (2012) finds that firms in cities located along the improved highway reduced their inventory by 7 operating days’ supply on average in India.

In Africa, the expected impact of improved transport infrastructure is likely to be larger than in other regions, reflecting the different densities, costs and reliability of the comparable networks currently. But there is little hard evidence available in the literature. The paper casts light on the East African Community, in which the member countries have different levels of transport connectivity to markets. While Kenya and Tanzania have the advantage of having major seaports situated on the Indian Ocean, Burundi, Rwanda and Uganda are landlocked countries and dependent on transit across the former countries for the closest international access.

The paper examines the firm inventory behavior in these five countries, using firm-level data from the Business Environment and Enterprise Performance Survey (BEEPS) and integrated spatial road data.<sup>1</sup> In the paper, a new instrumental variable is considered to deal with an endogeneity issue related to infrastructure placement. That is, governments may invest more in transport infrastructure where more efficient firms are located. More reliable transport infrastructure can reduce firm inventory, raising their productivity. Even after controlling for this problem, it is shown that firm inventory is crucially determined by the transport cost to the port.

> 1 The paper mainly uses the 2006 BEEPS for Burundi, 2007 for Kenya, 2005 for Rwanda, 2006 for Tanzania, and 2006 for Uganda. Note that newer data in 2011 are available for Rwanda. For consistency purposes, however, the analysis relies on the 2005 survey.
WPS 4234

# **Immigrant Overeducation: Evidence from Denmark**<sup>*****</sup>

## Chantal Pohl Nielsen AKF

## Danish Institute of Governmental Research

### **Abstract**

Anecdotes abound in the Danish public debate about well-educated immigrants that are in jobs they are formally overqualified for. Using a 1995-2002 panel data set based on Danish registers, this study attempts to find out how large a problem immigrant overeducation is in the context of the Danish labor market. More specifically, three questions are posed: First, to what extent are immigrants overeducated and are they more likely to be so than native Danes? Second, why are some immigrants more likely to become overeducated than others? And finally, what are the consequences of overeducation for individual wages? We find that among wage earners with at least a vocational education or higher, 25% of male non-Western immigrants are overeducated. The same applies for 15% of native Danes. Particularly immigrants with a foreign-acquired education risk becoming overeducated – here the share is 30% among those with a vocational education or higher. We find that Danish labor market experience is extremely important in reducing the likelihood of becoming overeducated. Years spent in the country without accumulating labor market experience do not improve an individual’s chances of an appropriate job-to-education match. In terms of earnings consequences, the study concludes that years of overeducation do increase wages for immigrants, but much less so than years of adequate education. This is also true for native Danes, but the relative penalty for overeducation is much larger for immigrants than for Danes.

### World Bank Policy Research Working Paper 4234, May 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org_

> * Contact Information: Danish Institute of Governmental Research, Nyropsgade 37, 1602 Copenhagen V, Denmark. Phone: (+45) 33 14 59 49 ext.156. E-mail: <u>CPN@akf.dk.  I would like to thank Caglar Ozden and an anonymous</u> referee for comments and the World Bank for financial support under the Danish Trust Fund.
**<u>Table 2: Productive performance of smallholders vs. commercial farms in different farm size classes</u>**

||**Maize**|**Sorghum**|**Teff**|**Wheat**|
|---|---|---|---|---|
|**Yield (Q/ha)**|||||
|Smallholder|27.07|21.29|13.62|21.85|
|Commercial farmers|37.691|27.005|8.294|25.578|
|< 20 ha|42.04|30.88|9.18|41.69|
|20-50|37.42|24.52|8.79|33.68|
|50-100|36.89|25.64|8.61|26.11|
|100-500|39.30|28.21|7.75|24.64|
|> 500 ha|33.81|29.51|9.90|28.32|
|**Use fertilizer (%)**|||||
|Smallholder farmers|38.76|15.40|65.24|73.18|
|Commercial farmers|67.73|23.48|73.52|65.25|
|< 20 ha|52.85|19.48|85.44|65.79|
|20-50|58.39|20.18|77.00|59.62|
|50-100|73.79|18.28|61.34|74.22|
|100-500|84.31|33.18|62.44|64.74|
|> 500 ha|82.70|44.79|48.01|74.45|
|**Use seed (%)**|||||
|Smallholder farmers|24.92|0.40|4.29|7.17|
|Commercial farmers|66.81|15.19|69.32|58.19|
|< 20 ha|51.99|17.58|85.44|65.79|
|20-50|57.87|18.66|76.23|58.25|
|50-100|69.57|15.88|58.66|74.22|
|100-500|86.21|4.21|45.86|40.32|
|> 500 ha|80.30|42.08|40.93|74.45|
|**Observations**|||||
|Smallholder kebeles|1,368|910|955|634|
|Commercial farmers|1,659|3,077|826|464|
|< 20 ha|358|295|291|162|
|20-50|479|1122|212|109|
|50-100|351|833|124|28|
|100-500|382|724|165|128|
|> 500 ha|89|103|34|37|

_Source:_ Own computation from 2013/4 CSA large farm and smallholder farm surveys 18
<!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2010 2011 2012 2013 2014 2015<br>Average PLF(%) —=——eThermalPLF(%) —=—=Hydro PLF (%)<br><!-- End of picture text -->