rafmacalaba/datause-encoder-data huggingface dataset:
3. _The interaction between physical and human capital_ , affecting the returns of a given mix of the two main production factors.

As a result of this framework, any policy related to education, job training, health but also early childhood development and other areas influencing the productivity of an individual in the economy affects human capital accumulation and, ultimately, growth.

If the direction of the impact is unambiguously positive - the accumulation of human capital is associated with positive growth - the magnitude of the impact - what is the return of a given human capital investment - is much more difficult to evaluate. Two strands of the literature attempt to provide an answer. The empirical macro literature takes a direct approach, considering output (GDP) as the dependent variable of interest and using aggregate measures of human capital investments as inputs. The typical example are crosscountry regressions where GDP is the dependent variable. Observed measures of human capital investments (such as years of schooling and life expectancy) are the regressors of interest [Barro and Sala-i-Martin (1995).] Equally relevant is the literature on development accounting where output growth is decomposed into the growth of its measurable components (physical capital and human capital) with the residual interpreted as TFP [Caselli (2005).] The empirical micro literature takes a more indirect route because the dependent variable is typically a measure of labor income observed at the level of the individual agent. If the impact on growth is more indirect (wages or labor income are only one component of overall GDP) there are numerous advantages of using this approach. First, since they do not need to aggregate at the country level, micro-level estimates use all the information available in the data. Second, they can estimate more flexible specifications. For example, each country is allowed to have different returns to human capital investments. Third, the range of additional controls that can be added to the specification is larger because survey data typically include more detailed information than aggregate level data. Fourth, micro level estimates may better incorporate the individual heterogeneity which is an integral part of the original human capital model.

The paper provides a survey of quantitative results in both the empirical macro and micro literature, considering human capital components ranging from years of schooling to disability indexes, from early childhood development indicators to multidimensional measures of skills.

Both theory and evidence show that investing in human capital is a promising strategy to attain stable positive growth. But the magnitude of the effects remains country-specific, varying based on the population of interest, the policy under consideration, and the human capital component considered. As a result, the paper suggests the use of micro data tailored to evaluate the specific policy of interest in the country under consideration, in conjunction with economic models able to aggregate the micro-level effects.

25
The last rows of Panel C of Table 5 show the decomposition of long-run export growth of continuing exporters in their continuing destinations across the HS 6-digit product dimension. Continuing products account for 77% of the growth of continuing exporters in their continuing destinations during the whole 2002-2008 period. But the introduction of new products by continuing exporters in their continuing destinations in gross terms plays a meaningful role, accounting for more than a third of that growth. This implies that the new products that survive see their export volumes grow fast subsequently. During the whole 2008-2011 period the thin export growth of continuing exporters in continuing destinations (1.6%) is entirely driven by the positive contribution of net entry of products given that growth in continuing products is actually negative (-1.2%).

Overall, there is a high degree of churning across HS 6-digit products, thus a high degree of reallocation of resources within continuing Turkish exporters in their continuing destinations. The pattern of turnover across HS 6-digit products for Turkish continuing exporters in their continuing destinations shows a similar role for the product extensive margin as what is shown for Portuguese continuing exporters for which Amador and Opromolla (2012) report that exports of continuing products account for about 75% of short-run export growth in continuing destinations.

Finally, to exploit the unique highly disaggregated nature of the product classification in the Turkish dataset, we present in Panel D of Table 5 the decomposition in Eq. (3) focusing on products at the HS 12-digit level. While continuing HS 12-digit products represent the bulk of short-run export growth in continuing destinations by continuing exporters in every pair of consecutive years from 2002 to 2011, the role of net entry is much larger than was the case in Panel C for HS 6-digit products. This can be rationalized by the fact that firms can innovate in their continuing destinations by introducing small variants of their products through a new HS 12-digit variety within the same HS 6-digit and dropping other variants of their products at relatively low cost. Interestingly, the last rows of Panel D of Table 5 show that over the long-run continuing HS 12-digit products make up only 40% of export growth in continuing destinations by continuing exporters between 2002 and 2008 while the majority of growth is due to the experimentation with success and failure of HS 12-digit products. During the crisis and recovery period the pattern is even more dramatic as growth of continuing exporters in their continuing 17
-

80 -

# * 4 Fig.4 Oil Prices and the Petroleum Cycle

<!-- Start of picture text --> 4"S REAL U.S. CRUDE OIL PRICES<br>4*3 tUlI EQUALS 100<br>asa<br>421<br>£73<br>OT70 So"0 Late LO £34 £6 £310 stag Lose £330 £6 139, 1366<br><!-- End of picture text --> <!-- Start of picture text --> " A" PROJECTION OF THE PETROLEUM CY=<br>166<br>34 go 4 *a so o Na ; Io s b_ a r008 t<br><!-- End of picture text --> Spimc **P**~n F.m. CwMp,1967**
12. Kim K, Moody P. More resources better health? A cross-national perspective. _Soc Sci Med_ 1992; **34** :837-842. doi:10.1016/0277-9536(92)90253-M 13. Hertz E, Hebert J, Landon J. Social and environmental factors and life expectancy, infant mortality, and maternal mortality rates. _Soc Sci Med_ 1994; **39** :105-114. doi:10.1016/02779536(94)90170-8 14. Cochrane A, Leger AS, Moore F. Health service ‘input’ and mortality ‘output’ in developed countries. _J Epidemiol Community Health_ 1997; **51** :344-348. doi:10.1136/jech.51.4.344 15. Robinson J, Wharrad H. Invisible nursing. _J Adv Nurs_ 2000; **32** (1):28-40. doi:10.1046/j.1365-2648.2000.01458.x 16. Robinson J, Wharrad H. The relationship between attendance at birth and maternal mortality rates. _J Adv Nurs_ 2001; **34** :445-455. doi:10.1046/j.1365-2648.2001.01773.x 17. Anand S, Bärnighausen T. Human resources and health outcomes: cross-country econometric study. _Lancet_ 2004; **364** :1603-09. doi:10.1016/S0140-6736(04)17313-3 18. Speybroeck N, Kinfu Y, Poz MD, Evans D. _Reassessing the relationship between human resources for health, intervention coverage and health outcomes_ . Geneva: World Health Organization; 2006.

19. Farahani M, Subramanian S, Canning D. The effect of changes in health sector resources on infant mortality in the short-run and the long-run. _Soc Sci Med_ 2009; **68** :1918–1925. doi:10.1016/j.socscimed.2009.03.023 20. Chen L, Evans T, Anand S, et al. Human resources for health: overcoming the crisis. _Lancet_ 2004; **364** :1984-90. doi:10.1016/S0140-6736(04)17482-5 21. WHO. _The Ebola outbreak in Liberia is over_ : World Health Organization; 2015. WHO statement, 9 May 2015.

22. WHO. _The World Health Report 2006: Working together for health_ . Geneva: World Health Organization; 2006.

> i The coefficients for under-five mortality used in our chosen specification as well as all robustness test are calculated using mortality rates for children aged between one and five years as per Speybroeck et al.<sup><u>18</u></sup> and Anand and Bärninghausen.<sup><u>17</u></sup> ii For robustness, we also ran our model with data on the stock of health care workers from the World Development Indicators database,<sup><u>9</u></sup> which are similar and produced identical effects on mortality.

13
thing, the association is negative for the case of individuals living in municipalities with stronger provision of basic public goods (column 2 in Panel C). Perhaps unsurprisingly, individuals living in municipalities experiencing improving economic conditions do consider that the economic situation of the country at the moment of the interview was better than a year before (column 3). This association is exclusively explained by responses from individuals living in municipalities characterized as “strong state capacity” (column 3 in Panel C).

I next analyze how individuals’ attitudes toward democracy respond to variation in income at the municipality level. Higher income levels in the municipality are associated with preferences for democracy over other types of government (column 4). This effect is essentially explained by individuals living strong state capacity (albeit standard errors for the implied effect are large, see column 4 in Panel C). Further, income does not appear to shape people’s satisfaction with democracy (column 5).

# **5 Conclusion**

Trust and confidence in most Salvadoran institutions have declined over time. This decline started in the aftermath of the 2008 global financial crisis and has particularly accelerated over recent years, coinciding with a path of low economic growth. In this paper, I study whether individuals’ attitudes toward Salvadoran institutions are shaped by respondents’ municipality-specific business cycles. Exploiting multiple waves of two opinion surveys and a two-way fixed effect model, I find that individuals’ trust towards both the president and the municipal government are positively associated with a higher level of income at the municipality level. These documented empirical relationships mask relevant heterogeneity from a rural-urban divide and differences in municipal state capacity. Most of the positive association between income and trust in the Salvadoran president is explained by individuals living in rural areas and municipalities with low levels of state capacity, whereas most of the positive association between income and trust in the municipal government comes from individuals living in urban areas.

I also find that municipality-specific business cycles strongly predicts trust in mass media and, to a lesser extent, trust in the national legislature and confidence in the judicial system. Interestingly, income does not affect trust in the Catholic Church. Moreover, views of income distribution fairness and preferences for democracy over other types of government are positively shaped by improving economic conditions at the municipality level. On the contrary, neither generalized trust nor satisfaction with democracy is empirically associated with the business cycle.

Except for general attitudes towards the government, the implied associations between income at the municipality level and attitudes towards institutions do not seem to emanate from respondents’ own economic situation. That is, respondent’s unemployment status does not predict trust 20
Sintomer, Yves, Carsten Herberg, Giovanni Allegretti, Anja Röcke, and Mariana Alves. 2013.

- “Participatory Budgeting Worldwide – Updated Version.” Global Civic Engagement – Service for Development Initiatives).

- Sjoberg, Fredrik M. 2012. “Making Voters Count: Evidence from Field Experiments about the Efficacy of Domestic Election Observation.” Columbia University Harriman Institute Working Paper, no. 1.

- Sled, Sarah Marie. 2008. “It’s in the Mail: The Effect of Vote by Mail Balloting on Voter Turnout and Policy Outcomes in US Elections.” Massachusetts Institute of Technology.

- Smith, Aaron. 2013. “Civic Engagement in the Digital Age.” Pew Internet & American Life Project.

- Stein, Robert M., and Greg Vonnahme. 2011. “Voting at Non-Precinct Polling Places: A Review and Research Agenda.” Election Law Journal 10 (3): 307–11.

- Stortone, Stefano, and Fiorella De Cindio. 2014. “From Online to Offline Participation and Viceversa: A Software Platform for New Political Practices.” In Communities and Technologies (C&T), Digital Cities 8.

- Touchton, M., and B. Wampler. 2013. “Improving Social Well-Being Through New Democratic Institutions.” Comparative Political Studies, December.

- Trechsel, Alexander, Guido Schwerdt, Fabian Breuer, Michael Alvarez, and Thad Hall. 2007. “Report for the Council of Europe: Internet Voting in the March 2007 Parliamentary Elections in Estonia.” Strasbourg: Council of Europe.

- Vassil, K., and T. Weber. 2011. “A Bottleneck Model of E-Voting: Why Technology Fails to Boost Turnout.” New Media & Society 13 (8): 1336–54.

- Vie, Stephanie. 2014. “In Defense of ‘slacktivism’: The Human Rights Campaign Facebook Logo as Digital Activism.” First Monday 19 (4).

- Vissers, Sara, and Dietlind Stolle. 2014. “Spill-over Effects between Facebook and On/offline Political Participation? Evidence from a Two-Wave Panel Study.” Journal of Information Technology & Politics,

- Vitak, Jessica, Paul Zube, Andrew Smock, Caleb T. Carr, Nicole Ellison, and Cliff Lampe. 2011.

- “It’s Complicated: Facebook Users’ Political Participation in the 2008 Election.” CyberPsychology, Behavior, and Social Networking 14 (3): 107–14.

- Wampler, Brian. 2007. Participatory Budgeting in Brazil: Contestation, Cooperation, and Accountability. University Park, Pa: Pennsylvania State Univ Pr.

- Wolchok, Scott, Eric Wustrow, Dawn Isabel, and J. Alex Halderman. 2012. “Attacking the Washington, DC Internet Voting System.” In Financial Cryptography and Data Security, 114–28.

- Zeglovits, Eva, and Sylvia Kritzinger. 2013. “New Attempts to Reduce Overreporting of Voter Turnout and Their Effects.” International Journal of Public Opinion Research..

26
**_Figure 9:  Average KLEMS Labor Productivity across Countries – By Disaggregated Sub-Sector_**

<!-- Start of picture text --> Wholesale Trade Retail Trade Inland Transport<br>50 50 70<br>45<br>60<br>40 40<br>35 50<br>30 30 40<br>25<br>20 20 30<br>15 20<br>10<br>10<br>10<br>5<br>0 0 0<br>1997-2000 2001-2004 1997-2000 2001-2004 1997-2000 2001-2004<br>Post & Telecommunications Financial Intermediation Real Estate Activities<br>110 120 3200<br>100 2800<br>90 100<br>2400<br>80<br>70 80 2000<br>60 1600<br>60<br>50<br>1200<br>40 40<br>30 800<br>20 20 400<br>10<br>0<br>0 0<br>1997-2000 2001-2004 1997-2000 2001-2004 1997-2000 2001-2004<br>Computer & Related Activities R&D Legal, Technical & Advertising Activities<br>160 120 120<br>140 100 100<br>120<br>80 80<br>100<br>80 60 60<br>60 40<br>40<br>40<br>20<br>20<br>20<br>0<br>0 0<br>1997-2000 2001-2004 1997-2000 2001-2004 1997-2000 2001-2004<br>Lithuania EstoniaSlovenia LatviaHungary Poland EU-15 LatviaHungaryCzech Rep.Slovak Rep. EstoniaSloveniaLithuania Poland EU-15<br>Slovak Rep. Czech Rep.<br>Lithuania Estonia PolandSlovak Rep. Czech Rep. LatviaHungarySlovenia EU-15 LithuaniaSlovak Rep. LatviaHungaryCzech Rep. Poland EstoniaSlovenia EU-15<br>Estonia LatviaLithuaniaHungarySloveniaCzech Rep.Slovak Rep. Poland EU-15<br>Estonia Latvia PolandLithuaniaCzech Rep.Slovak Rep. SloveniaHungary EU-15<br>LithuaniaCzech Rep.Slovak Rep. LatviaSloveniaPoland EU-15HungaryEstonia Czech Rep.Slovak Rep. LatviaLithuania PolandSloveniaHungary EU-15Estonia LithuaniaSloveniaCzech Rep. Latvia PolandSlovak Rep. EU-15HungaryEstonia<br>Thousands of PPP Euros per Employee Thousands of PPP Euros per Employee Thousands of PPP Euros per Employee<br>Thousands of PPP Euros per Employee Thousands of PPP Euros per Employee Thousands of PPP Euros per Employee<br>Thousands of PPP Euros per Employee<br>Thousands of PPP Euros per Employee Thousands of PPP Euros per Employee<br><!-- End of picture text --> Source: KLEMS database 2007. Note: In this figure, financial intermediation is NACE 65 category.

Finally, we consider the performance of services sub-sectors in the ECA region from a dynamic perspective by examining average annual growth rates of labor productivity. Figure 10 shows average annual growth in WIIW labor productivity for each sub-sector across EU-5 countries, South Eastern European countries and Ukraine in 1997-2000 and in 2000-2004. Four main stylized facts that emerge from the figure. First, in all services sub-sectors there are important disparities in labor productivity growth rates across countries. Second, all sub-sectors with the exception of real estate, renting, 21
<!-- Start of picture text --> 25<br>244<br>ry°<br>sé<br>5s<br>& Sfe) o-<br>st2oSa.<br>ES<br>=ofayO44ec<br>8<br>22> <——_Serndrier in ethe recentrecent pest_—past near normal wetter in the recent past<br>4 -2 0 2 4<br>Cumulative z-score (past three years)<br><!-- End of picture text --> <!-- Start of picture text -->
-.2<br>2<br>ges -4<br>s= .<br>5s<br>=a€ 8<br>soeasone -6<br>S68<br>“oOSSeoco) -8<br>8<br>-1<br>TM) Less forest cover<br>BE More forest cover<br>1.2<br>Dry shock (1SD shock)<br><!-- End of picture text -->
and the Caribbean and Sub-Saharan Africa make up nearly a quarter of all available data. This provides the largest representation of developing countries to date in a learning database.

Our methodology leverages the growth of international assessments to construct globally comparable learning outcomes. These tests are derived from assessments conducted in the United States since the 1960s such as the SAT and the National Assessment of Educational Progress (NAEP). The tests are psychometrically designed, standardized assessments of cognitive skills. Since the 1990s, international assessments have been conducted by organizations such as the OECD. Two high profile examples are PISA and TIMSS which covered 71 and 65 countries in 2015. These assessments enable credible global comparison of learning across countries and over time. However, to date most analyses of these assessments focus mainly on OECD countries and cover few developing countries.<sup>7</sup> This limits the distribution of countries represented and has implications for our understanding of the link between human capital and economic development.

We include 164 countries, two-thirds of which are developing countries, by linking international assessments to their regional counterparts. Regional assessments cover much of Sub-Saharan Africa and Latin America, but have often been excluded from international comparisons. We employ methods to convert a regional test score to an international test score within subjects and schooling levels (primary and secondary) and within adjacent years. By including tests across the same testing round and at the disaggregated schooling and subject level, this minimizes the likelihood that test differences are a function of time, proficiency, schooling level, or data availability and are an accurate reflection of test difficulty. We then apply this conversion to a country that participates in regional test but not an international test to produce a comparable score (referred to as a Harmonized Learning Outcome (HLO) in the database). Means are also calculated for disaggregated groups such as by gender.

The success of this approach hinges on three key assumptions. First, linked tests must capture the same underlying population. This assumption is satisfied by using sample-based assessments representative at the national level where a country participated in both a regional and international assessment. This ensures that the underlying population tested is the same on average and we capture differences between tests. Second, tests should measure similar proficiencies. To this end, we link within subjects (math, reading and science) and schooling levels (primary and secondary)

> 7 Earlier studies include Barro and Lee (2001); Hanushek and Kimko (2000); Hanushek and Woessmann (2012a); Altinok, Angrist and Patrinos (2018).

4
# <u>4.3 What do individual country / region studies say?</u>

Individual country-level studies also provide a mixed picture on the impact of EU regional policy on regional economic performance. De La Fuente (2003) finds that EU investment in infrastructure post accession makes it more distributive, with a positive effect on convergence. He sees the EU Community Support Framework as a large positive shock that, over a period of 7 years, raised aggregate infrastructure, other capital stock and years of training. For objective 1 regions as a whole, the CSF added 6.9 percentage points to output and 3.4 points to employment in 2000.

Pereira (1997) shows that EU programs had a substantial impact on economic growth. During 1989-93, “Greece, Ireland and Portugal received grants corresponding to 2.3, 2.9 and 3.5% of their respective GDPs for the period. The private cofinancing requirements were 0.6, 1.4 and 2.1%, while the public co-financing requirements were 2.4, 1.6 and 3.4%, respectively. Accordingly, the total resources involved in these programs are estimated to be 5.8, 5.3 and 9% of their respective GDPs for the period.” According to the paper’s simulation results, these transfers induced an improvement in GDP growth of 0.3 percentage points for the Greek and Irish economies, and 0.5 percentage points for the Portuguese economy. However, their analysis is at a national level, and does not say much about what happened to regional economic performance within these countries.

According to Barry (2001) both the single market and EU funds played a role in Ireland’s economic performance. In addition, national economic policies, such as a lower tax rate also played an important role. On balance, it seems that national policies and the single market played a much more important role in Ireland’s impressive growth than EU policies. Farrel (2004) also finds that institutional and market forces had greater effect than EU redistribution in explaining the differential growth between Spain and Ireland. While Spain as a whole caught up, internal regional disparity actually increased. Further, Spain’s investment was primarily in infrastructure, while Ireland also focused on developing human capital. Both the latter points show the importance of strong regional governments. First, Ireland is small enough to be a region, and its government responsible for its national growth delivered. Similarly the Spanish government also 30