rafmacalaba/data-use-mentions-extended huggingface dataset:
their spending by as much as their revenue fell. As noted below, the CFA franc devaluation of 1994 - external adjustment - was followed by a return of economic growth and increased trade.

The CFA zone experience also casts doubt on the claim that countries should avoid devaluations in order to retain international investors. The zone certainly had stable prices and exchange rates, but its failure to solve the problems brought on by the overvalued real exchange rate decreased its attractiveness to foreign investors substantially. Capital flight increased in anticipation of an eventual devaluation (Clement _et al.)._

**Chile 1999.** Faced with weak demand from Asia and falling prices for its principal commodity export, copper, Chile saw its current account deficit widen and its currency come under attack. At first the central bank raised interest rates, but the high interest rates cut domestic demand on top of the decline in demand for exports, and plunged the country into its worst recession in 16 years and its highest unemployment rate in 10 years. On September 2, the Central Bank allowed the currency to float, suspending its formal commitment to intervene in the foreign exchange markets to keep the peso within a band. The currency immediately fell by a small step, from 519.35 to the dollar to 523.25 to the dollar (Torres, 1999).

# **Real Exchange Rate Overvaluation and Cross-Country Economic Performance**

Cottani, Cavallo, and Khan (1990) investigated the effects of real exchange rate misalignment and variability on the economic performance of 24 developing countries from 1960-1983. They found that exchange rate misalignment was strongly related to low growth of per capita GDP. Furthermore, misalignment was also related to low productivity. Capital did not go to the companies or sectors that could make the best use of it. Finally, misalignment was also related to slow export growth and slow agricultural growth.

A study of growth in 12 countries from 1965-1985 (Edwards, 1988 and 1989), reinforced these findings.'<sup>2<
<!-- Start of picture text --> Work in agricultural sector for the employed<br>08<br>E> 6<br>3E .051<br>2<br>=]<br>S 04<br>oS<br>=<br>S<br>£ .028<br>. .026<br>= 02 ° 015 017 015<br>0087 |<br>0042 ® Pre-covid cohort<br>9 ®@ Covid cohort<br>4 months later*** 8 months later 12months later 16 months later<br><!-- End of picture text -->

<!-- Start of picture text --> Work in industry sector for the employed<br>02<br>0<br>2 .0027 0046<br>€ ~0044 0034 .0059<br>3 oo13<br>FS ~<br>3I} -.02<br>£<br>£<br>x<br>S<br>= 04 -.039<br>@® Pre-covid cohort<br>-.06 a Covid. cohort<br>4 months later*** 8 months later 12 months later 16 months later<br><!-- End of picture text --> <!-- Start of picture text --> Work in service sector for the employed<br>01<br>2e -ot0 . .000046 005 t= -.0085<br>g3 -.012 -.015<br>3 -.019<br>=<br>5<br>=<br>-.03 -.03<br>@ Pre-covid cohort<br>~.04 ®@ Covid cohort<br>4 months later 8 months later 12 months later**16 months later***<br><!-- End
FIGURE 5 Exchange rate responses to global shocks

# A. Global demand shocks

# B. EMDEs: Global demand shocks

<!-- Start of picture text --> Percentage points Percentage points<br>Interquartile range Median<br>2.5 Interquartile range Median 4<br>3<br>2.0 2<br>1<br>1.5<br>0<br>1.0 -1<br>0.5<br>0.0<br>-0.5<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>C. Global supply shocks D. EMDEs: Global supply shocks<br>Percentage points Percentage points<br>1.0 Interquartile range Median 3 Interquartile range Median<br>2<br>1<br>0.5<br>0<br>-1<br>0.0<br>-0.5<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>E. Oil price shocks F. EMDEs: Oil price shocks<br>Percentage points Percentage points<br>1.0 Interquartile range Median 3 Interquartile range Median<br>2<br>0.5 1<br>0<br>0.0 -1<br>-2<br>-0.5<br>-1.0<br>-1.5<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>Low High Importer Exporter Deficits Surpluses<br>Low High Peg. IT Importer Exporter Deficits Surpluses<br>Float Peg. Non-IT IT<br>Float Non-IT<br><!-- End of picture text --> Note:
Figure 4: The impact of the decile of consumption on the economic standing <!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10<br>Decile of consumption<br>Responses 1/2 Responses 3/4<br>Responses 5/6 Responses 7/8<br>Responses 9/10<br>.6<br>.4<br>.2<br>Probability of responding<br>0<br><!-- End of picture text --> Note: LiTS data from 2005, 2010 and 2015. The y-axis represents the average adjusted predictions of economic standing for each decile of consumption. Responses on a 10-step scale are aggregated to a 5-step scale for presentational purposes having been employed at least one month in the previous year increases the probability of respondents to position themselves in the middle steps of the economic ladder and decreases the probability of positioning in the bottom, while no effect is visible for the probability of positioning in the top. A sense of the magnitude of this effect can be given by the fact that the probability of choosing the steps 1 and 2 of the ladder for someone in decile 7 with no employment is the same as for someone in decile 4 with full employment - that is, the effect of not having an employment on the probability of “feeling poor” is equivalent to a change in three deciles of objective consumption.

The effect is stronger when looking at the (self-rated) health status as shown in figure 6. For the same objective level of consumption, individuals who declare to be bad health have up to 10 percentage points more probability of ranking themselves in the bottom of the ladder than those who declare to be in good health. The effect is of the opposite sign and larger magnitude for the probability of ranking themselves in the middle of the ladder: respondents declaring to be in good health are 20 percentage points more probable to position in the middle than those declaring
# **References**

- ABEBE, G., A. S. CARIA, M. FAFCHAMPS, P. FALCO, S. FRANKLIN, AND S. QUINN (2021): “Anonymity or Distance? Job Search and Labour Market Exclusion in a Growing African City,” _The Review of Economic Studies_ , 88, 1279–1310.

- ABEBE, G., S. CARIA, M. FAFCHAMPS, P. FALCO, S. FRANKLIN, S. QUINN, AND F. SHILPI (2020): “Matching Frictions and Distorted Beliefs: Evidence from a Job Fair Experiment,” _Department of Economics, Oxford University (mimeo)_ .

- ACEVEDO, P., G. CRUCES, P. GERTLER, AND S. MARTINEZ (2020): “How Job Training Made Women Better Off and Men Worse Off,” _Labour Economics_ , 65, 101824.

- ADHVARYU, A., N. KALA, AND A. NYSHADHAM (2023): “Returns to On-the-job Soft Skills Training,” _Journal of Political Economy_ , 131, 2165–2208.

- AGENCE NATIONALE DE LA STATISTIQUE ET DE LA DÉMOGRAPHIE SÉNÉGAL, AND ICF (2020): “Senegal Demographic and Health Survey 2019 [Dataset].”

- AGHION, P., A. BERGEAUD, R. BLUNDELL, AND R. GRIFFITH (2019): “The Innovation Premium to Soft Skills in Low-skilled Occuptions,” CEPR Discussion Paper 14102, C.E.P.R. Discussion Papers.

- ALAN, S., T. BONEVA, AND S. ERTAC (2019): “Ever Failed, Try Again, Succeed Better: Results from a Randomized Educational Intervention on Grit,” _The Quarterly Journal of Economics_ , 134, 1121–1162.

- ALDEROTTI, G., C. RAPALLINI, AND S. TRAVERSO (2023): “The Big Five Personality Traits and Earnings: A Meta-Analysis,” _Journal of Economic Psychology_ , 94, 102570.

- ALFONSI, L.
but the average value per exporter is not affected. They argue this is strong evidence that quality standards act as a fixed cost to trade. Relative to these studies, our novelty is to examine the integration of regulatory barriers in PTAs and to show that PTAs alleviate the regulatory burden with a particular benefit for small firms.

The remainder of the paper is structured as follows. Section 2 describes the data sources. Section 3 describes our empirical approach. Section 4 describes our main results while 5 presents results from several robustness checks and extensions. Section 6 concludes.

# **2 Data and Descriptive Statistics**

## **2.1 Deep Trade Agreements Database**

Our analysis is based on the novel database on the content of preferential trade agreements described in Mattoo et al. (2020).<sup>10</sup> The database contains detailed information on the commitments included in 18 policy areas across 283 PTAs signed and notified to the WTO between 1958 and 2017.<sup>11</sup> SPS and TBT provisions are covered in, respectively, 269 and 263 PTAs signed between 1960 and 2017.<sup>12</sup> The templates used for the mapping of these provisions cover areas such as (a) the reference to the WTO rules; (b) the type of integration approach (harmonization versus mutual recognition) for standards, technical regulations, and conformity assessment procedures, (c) transparency; (d) institutions or mechanisms to administer the agreement and solve disputes; and (e) cooperation among regional partners on standards-related issues.<sup>13</sup> A set of essential provisions are identified which comprise substantive provisions, that is,

> 10See https://datatopics.worldbank.org/dta/index.html.

> 11The policy areas are: anti-dumping, countervailing duties, competition policy, environmental laws, export taxes, investment, intellectual property rights, labor market, migration (visa and asylum), movement of capital
the 8-digit product level,<sup>39</sup> which are then aggregated to the 4-digit ISIC sectors by using simple averages.<sup>40</sup> The 4-digit ISIC level import data come from the COMTRADE dataset, United Nations Statistics Division and the industry production data at the same level are available through UNIDO’s Industrial Statistics Database.

The productivity estimates, value added, input, and materials prices data are obtained from Eslava, Haltiwanger, Kugler and Kugler (2004), where each variable (except the value added<sup>41</sup> ) is aggregated from the firm level to the 4-digit ISIC industry level with production shares used as

> 39The product classification code, called “Nabandina”, is due to the Andean Community of Nations. I thank Marcela Eslava at Universidad de Los Andes/CEDE, Colombia for generously sharing the data.

> 40I use simple averages to be consistent with the earlier literature. An alternative way would be to use the import or production shares of each product as weights but these data do not exist for all sample years at this level of disaggregation.

> 41The value added is used to compute a measure of scale economies where it is an unweighted total in each sector.

19
Australia, Austria, Belgium, Brazil, Canada, Czech Republic, Denmark, Finland, Germany, Iceland, Italy, Japan, Korea, Netherlands, Norway, Portugal, South Africa, Sweden, Thailand, United Kingdom, United States.

It should be remarked that our database includes some cases that deserve comment. For example, it includes Australia for which we obtained pension assets time series corresponding to the sum of both mandatory and voluntary pensions but we could not ...nd separate series for each of them. It also includes countries such as Japan, Canada, Korea, Philippines and Panama for which we found just a fraction of pension assets. Finally, it also includes the case of Netherlands. Although we consider it voluntary because there is no legislation on the contrary, some people consider it mandatory because pension plans are the result of collective labor agreements.

The old dependency ratio, odr, is de...ned as the number of people older than 65, op, divided by the number of people in working age (people older than 15 and younger than 65), mp. The young dependency ratio, ydr, is de...ned as the number of people younger than 15, yp, divided by the number of people in working age, mp. The urbanization ratio, ur, is de...ned as the ratio between the number of people living in urban areas, up, and total population, tp. Annual time series of each of the variables were obtained from The World Development Indicators.

The terms of trade, tot, is an index that shows the national accounts exports price index divided by the imports price index, with 1995 as base year. Annual time series of tot for each country were obtained from The World Development Indicators.

Per capita gdp, pcgdp, is de...ned as per capita gdp at constant prices 28
0.6|0.5|0.1*|
|High school|0.1|0.4|-0.3***|0.4|0.3|0.1|
|% of classes with full-time edu|0.0|0.0|0.0|0.0|0.0|0.0|
|Duration of school day|4.3|4.3|0.0|4.3|4.3|0.0|
|N|611|195||555|196||

_Sources: Prova Brasil,_ School Census, and INEP.

_Note:_ Table reports the average characteristics for the schools that joined _Mais Educação_ in 2008 and remained until 2011 that have information for all variables in 2005, 2007, 2009, and 2011 (columns (1) and (4)) and for the schools that joined _Mais Educação_ in 2008 and remained until 2011 but have missing information (columns (2) and (5)). Columns (3) and (6) report the mean differences of the variables between the two groups. _T_ tests were performed to detect the mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample.
# **_c. Economic migrants_**

Data on skill mobility between countries and regions are difficult to obtain for SSA, as in other parts of the world. However, this can be proxied using data on international economic migrants although such data do not dissociate skilled from unskilled economic migrants. An economic migrant can be defined as a person who leaves his or her place of origin to seek better livelihood in another place (for a short or long term). In other words, economic migrants search for better job opportunities in places where they can earn better income compared to their place of origin. These migrants include both skilled and unskilled workers who move from one place to another purely for economic reasons. Since data on international economic migrants are available for most countries in the world, we use net migration-to-population ratio as a proxy for labor mobility. This is defined as: A positive ratio for a given country means that more people (i.e. workers) are coming than leaving the country for work. In addition, a positive ratio means that more people join the domestic workforce since economic migrants add to the workforce of host countries. It is however important to note that economic migrants are different from refugees or asylum seekers and their worker statuses depend on host countries’ laws and openness to foreign workers. In this regard, economic migrants can work either legally or illegally in formal or informal sectors of host countries. Consequently, their skills can be incorporated in GVC activities. Thus, we expect the net migration ratio to have a positive and significant impact on countries’ participation in GVCs.

# **3.2 Methodology: Model specifications and estimation technique**

We use a gravity model to estimate the effects of skills and migration on the participation of African countries in GVCs. Since the pioneering work by Tinbergen (1962), gravity models have become an important tool used in explaining trade flows. For example, McCallum (1995) used gravity-type equations to understand the impact of borders on trade patterns
entry into force rather than signature.

Finally, we consider the role that trade liberalization via RTAs—and ODS-related commitments therein—might play in exacerbating spillovers from regulated ODS to other environmentallyharmful substances. To do so, we extend our triple-difference model in Equation (2) with target commitments under the Montreal Protocol in substitute substances. We consider two main substitution channels: from CFCs to HCFCs and from HCFCs to HFCs: where _y igt_ either refers to the country-year consumption of HCFCs as reported by UNEP or the country-year aggregated HFC emissions generated from EDGAR data. _target it_ refers to the specified target level under the Montreal Protocol for the substance being substituted away from—for the HCFCs regressions the previous substance is CFCs, and for the HFCs regressions the previous substance is HCFCs. We also include some specifications in which we interact _target it_ with our trade liberalization variables to measure changes in the spillover channel around trade liberalization.

Recall that HCFCs became subject to phaseout schedules under the Copenhagen Amendment in 1992, with 0% dates in 2020 and 2030 for non-Article 5 and Article 5 countries, respectively. HFCs became subject to phaseout schedules under the Kigali Amendment in 2016, with 15% dates in 2036 and 2045. Note that the RTA signatures in our database all occur before the passage of Kigali in 2016.

For all of our enforcement linkage specifications, as well as our spillover specifications, we report two-way, cluster-robust standard errors clustered at the country-RTA level—to account for temporal autocorrelation within treatment units—and at the country-year level—to account for correlation across RTA “stacks” that might arise from overlapping, adjacent RTA treatment at the country level.

15
Policy Research Working Paper 10966

# **Abstract**

This paper investigates the relationship between the expectations of economic mobility and support for tax-financed education reforms using data from the Life in Transition Survey, which covers 39 countries in Europe, Central Asia, and the Middle East and North Africa. The analysis demonstrates that individuals who expect themselves or their children to be upwardly mobile are more likely to support tax-financed education reforms. This correlation is robust to different formulations of mobility expectations and persists over a decade, encompassing both stable and post-crisis economic environments. The relationship is partially mediated by beliefs about the fairness of economic opportunities in society and individuals’ readiness to embrace risks.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region and the Poverty and Equity Global Practice. 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:// www.worldbank.org/prwp. The authors may be contacted at acojocaru@worldbank.org, mlokshin@worldbank.org, and itorre@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
<mark>more intensively. These skills, on the other hand, are less important for administrative and managerial workers, professionals and technicians, and clerical workers.</mark> Third, we use information from the National Socio-Economic Survey (Susenas) to generate technology adoption variables at the province level. The survey was conducted bi-annually over the period analyzed in this paper (in March and July) and we combine the March wave for years 2005 and 2006 with the July wave for 2007 onwards, these being the only waves representative at the province level in each year. The Susenas collects information on technology adoption at the household level, which we aggregate to obtain technology indicators at the province level. Using this survey, we construct the share of households having a computer in each province and year, which we use as a technology adoption variable in our econometric analysis.

Finally, measurements of trade in goods are taken from the UN Comtrade database from 2005 to 2015. We calculate the annual value of Indonesian exports at the industry level in constant 2010 U.S. dollars and the global demand for each industrial product (excluding Indonesian products) that we use as an instrumental variable. We match the industrial classification available in the UN Comtrade database (Harmonized Commodity Description and Coding System HS1) for the period under analysis with the classification of industries in the Sakernas survey (KBLI 2000, 2005, and 2009), resulting in a mapping to 22 tradable industries.

# **4. Descriptive Statistics**

Indonesia is in an early stage of digitization. ICT infrastructure is still weak, but the country has been increasing the rate of ICT use over time. Panel A of Table 2 provides some descriptive statistics on ICT adoption at the household level across the 33 provinces of the country. The percentage of households owning a cellphone more than tripled between 2005 and 2010 (from 20.3% to 71.6%) and continued to increase between 2011 and 2015, although at a slower pace (from 75.
and in the long term, as new products become available through innovation. Third, a period of higher prices with demand outstripping supply leads to induced innovation in the production of commodities. These three trends were observed throughout the events of the past twenty years. The dramatic changes in commodity demand were driven by China, which experienced rapid growth at the start of the 1990s, but has slowed more recently, and also saw a shift from being a lower‐income country to a middle‐income country over this period (World Bank 2018). As such, its income per capita growth, and income elasticity of demand, both slowed, from a high level. The period of strong com‐ modity demand (which was also associated with high prices) induced innovation and substitution in the use and production of commodities—the most notable example of in‐ novation being the development of the U.S. shale oil industry.

The objective of this paper is to shed light on the first two principles by estimating income elasticities of energy and metal commodities at the individual and aggregate level and incorporating cross‐price effects to test for substitution between commodities. Our approach, which takes into consideration both non‐linearities as well as short‐ and long‐ term substitution effects, extends the literature of commodity demand in four ways.<sup>**1**</sup> First, it utilizes a comprehensive data set and applies a common framework to three energy and six metal commodities for up to 63 countries over more than half a cen‐ tury (1965‐2017). The broad and long data coverage, which encompasses the two post‐ WWII price cycles (1970s and 2000s), enables us to obtain a consistent picture across the full spectrum of industrial commodities. This is especially pertinent in the context of EMDEs, for which the literature is not as rich as that of high‐income countries, even though EMDEs account for two‐thirds of world metals demand and nearly half of world energy