rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 5 Construction costs for US nuclear plants, 1960-2000**

Cost per kilowatt (2010 dollars) <!-- Start of picture text --> Three Mile Island Chernobyl Comanche Park<br>6,000<br>Clinton<br>Hope Creek<br>Harris<br>Limerick<br>4,000<br>Nine Mile Point<br>Seabrook<br>Braidwood<br>Pilgrim<br>Byron<br>Oyster Creek<br>2,000 Davis Besse<br>LaSalle<br>Big Rock Point McGuire<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br><!-- End of picture text --> _Source_ : Davis (2011).

Since construction accounts for most of the total generating costs of nuclear power, the pronounced escalation in those costs, especially after TMI, had important implications for the economic future and competitiveness of nuclear power. Economic cost-competitiveness is an indispensable precondition for the successful deployment of any electricity generation technology. After all, utilities, especially profit-maximizing ones, make their various business decisions by comparing the costs of generating electricity from alternative energy sources and by determining how these alternatives fit with their current portfolio of technologies. Reactor orders fell sharply after 1974, less than half of the reactors on order in 1974 were ever completed. By the time the Chernobyl disaster occurred in 1986, the U.S. nuclear industry was already dilapidated (Davis, 2011).

16
# **II. In-service teacher training in Ecuador**

In 2011, Ecuador enacted a new education law ( _Ley Orgánica de Educación Intercultural_ ) that provided the education sector with a renewed vision of the country’s educational processes and actors. The law recognizes and gives high value to the work of teachers and introduces important elements to promote and develop the teaching career. For instance, Chapter 4 of the law specifies that teachers in Ecuador shall have free access to professional development opportunities, such as in-service training, to continuously update their knowledge and pedagogical practices. Chapter 10 establishes the need for the creation of a National University of Education, whose objective would be to support and develop the teaching profession and to better train new teachers. The law also establishes a more transparent teaching career path, whereby promotions are based on obtaining satisfactory results in teaching evaluations and on active participation in continuing education programs.

In 2008, the MINEDUC began making important efforts to promote in-service teacher training through its Comprehensive <mark>System for the Development of Education Professionals</mark> (SIPROFE), overseen by the MINEDUC’s Undersecretariat of Professional Development. SIPROFE’s main objective is to improve the country’s education quality through the provision of training to teachers and school administrators. To deliver in-service teacher and administrator training, the SIPROFE offers a variety of courses, including remedial and developmental courses, orientation to new teachers, and school management. The duration of courses varies between 8 and 60 hours, although most courses have a duration of either 20 or 40 hours (MINEDUC 2016). Some courses are sequential (that is, are a prerequisite for others) to promote and incentivize the sustained participation of teachers. Training programs under the SIPROFE have traditionally been delivered through agreements with universities and pedagogical institutes ( _Institutos Superiores Pedagógicos_ , ISPED) that satisfy predetermined quality and service delivery requirements.<sup>4</sup> Data from the MINEDUC suggest that during 2010–18, the SIPROFE program delivered in-service teacher training to about 113,000 teachers (representing about 75 percent of the total teacher population). According to the MINEDUC, between 2015 and 2018, the program benefited

> 4 ISPEDs do not operate anymore. Also, universities that can participate as providers of training under the SIPROFE program have to satisfy the maximum level of accreditation (Level A). This requirement limits the territorial supply of training courses, as not many regional universities satisfy this requirement.

5
- _income_ = a country’s per capita GDP (USD at 2000 prices) in natural logarithms from the World Bank’s World Development Indicators (WDI) database. It captures national competition, but also other aspects of the national and institutional environment.

# **4 Regression Results**

## **4.1 FDI Spillover Potential**

## **_Overall Results_**

In the following, we apply OLS and IV 2SLS regressions. All regression results follow equation (3) and include sector, subnational region, and year fixed effects. Standard errors are robust to heteroscedasticity and clustered at the country-sector level.<sup>16</sup> Foreign output share is defined as in equation (2). The summary statistics are reported in Appendix 4. Our data sample only includes domestic manufacturing firms, i.e. all firms with a foreign ownership share of less than 10 percent (see Appendix 2). Table 1 shows how the FDI spillover potential influences domestic firm productivity. Columns 1 to 4 report the OLS regression results, while columns 5 to 10 report the IV 2SLS regression results<sup>17</sup> . Foreign firm characteristics are averages across all foreign firms within a specific sector of a country. To rule out the possibility that the impact of a foreign firm characteristic on FDI spillovers which we observe is not the result of another foreign firm characteristic for which we do not simultaneously control, we assess the correlation of foreign firm characteristics with each other. The correlation matrix (available upon request) reassures us that foreign firm characteristics show a very low correlation of at most 0.26.

As could be expected, capital intensity shows a positive and significant effect on labor productivity. More importantly for this paper, intra-industry foreign output share mostly shows a negative effect on labor productivity of domestic firms, although in Table 1 the impact is only significant in column 2. What may explain the negative FDI spillovers? In the short term, local firms might face losses in their market share due to increased competition (competition effect). Loss of market share, in turn, requires them to produce at higher average unit-costs, as a declining amount

> 16 We partial out fixed sector, subnational region and year effects in the IV 2SLS regressions to avoid that the covariance matrix of orthogonality conditions is not of full rank, which is common when using the cluster option. This also allows us to perform overidentification tests.

> 17 The number of observations is lower in the IV 2SLS regressions since not all instruments are available for all countries (see summary statistics in Appendix 4).

19
31 Figure 5 (continued): Relative contributions of different border policy instruments to welfare reduction from agricultural policies (IWRI), 1960 to 2004 (percent) <!-- Start of picture text --> (c) All focus countries<br><!-- End of picture text --> <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Import tax Export tax Import subsidy Export subsidy<br><!-- End of picture text --> <!-- Start of picture text --> 1970<br>Import tax<br><!-- End of picture text --> <!-- Start of picture text --> 1975<br><!-- End of picture text --> Source: Authors’ calculations using data in Anderson and Valenzuela (2008).
**Decoupling and recoupling**

# **1. Introduction**

Emerging markets (EM) advocates often emphasize their newly gained resilience to external shocks. Resilience, however, can be interpreted in several ways. In particular, it is expected to manifest differently according to whether it refers to economic performance, market interdependence, or the link between markets and economic activity. In this paper, we revisit the debate surrounding the different varieties of EM decoupling to document the recent behavior (in both the pre-crisis bonanza and the turbulent crisis period) on these three fronts, discuss the economic factors underlying the specific patterns in each case, and draw market and policy implications from these findings.

In particular, we examine decoupling: (i) in the traditional sense, namely, business cycle synchronicity and sensitivity to the world economy (real decoupling); and (ii) as cross-market financial interdependence (financial decoupling), in the sense of the “betas” of EM credit, equity and currency returns to returns on the global market portfolio, as well as within-EM betas (a typical feature of the 1990s); Finally, we center the debate around EM financial integration to the world (as the deepening of financial capital inflows) and its impact on financial ties of EM assets to those in the developed world.

The discussion is centered on EM assets. More specifically, we look at a sample of countries that represents what is usually labeled as emerging markets in the financial industry, following the Morgan Stanly Capital Index benchmark (MSCI) for individual countries and groups of countries, and focus our analysis on equity and, to a lesser extent, currency and hard currency sovereign bonds. As a comparison group, we include in the study a sample of advanced markets detailed in the Appendix.

We find that: (i) on the real front, rather than actually decoupling from the world, EM have diversified away from the US and the euro zone into emerging Asia (through international trade and the growing economic importance of China); (ii) on the market front, despite the real decoupling, the co-movement between EM and global assets (as measured by the EM betas to developed markets return) has risen in the late 2000s, even before the 2008-2009 crisis.

Why has real decoupling not provided more “room” for assets from fundamentally sound EM to decouple as well? A priori, financial globalization (FG), to the extent that it internationalized <u>2</u>
openness of US immigrants to their returns to home country experience. Assuming a linear relation between openness and return to experience, this question translates into where Openj is some measure of openness in country j. A positive (negative) sign of the estimator of the coe¢ cient bH indicates that trade increases (decreases) on-the-job human capital accumulation. The marginal e¤ect of openness on returns to home country experience is restricted to be constant across countries in speci...cation (2) : This assumption will be relaxed during the empirical analysis for some speci...cations and bH will be allowed to vary for sub-groups of countries.<sup>4</sup> Relying on the fact that the return to experience captures productive capabilities attributable to on-the-job human capital investment, this estimation strategy requires to exclude that factors that are not related to on-the-job human capital investment may generate the upward slope of the experience earnings pro...le at the beginning of a worker’s career. In a search environment, for example, wages may grow with labor market experience because workers may improve the quality of their jobs by means of job search (see for example, Jovanovich (1979)). Moving to the United States is likely to imply for immigrants that the search capital is lost. Employer schemes to economize on costs of monitoring (Lazear (1981)) and turnover costs (Salop and Salop (1976)) may also generate an upward sloping experience earnings pro...le but are unlikely to explain positive returns to home country experience of US immigrants.<sup>5</sup> It is therefore reasonable to assume that returns to home country experience of US immigrants are related to on-the-job human capital investment.

To obtain an estimate of the return to home country experience, I substitute potential experience Eij � Hij + Uij for home country experience Hij;

> 4 Openness does not only vary across countries but also over time t, that is

Identifying the home country ...xed e¤ect (ajH ) and the time e¤ect (gtH ) requires to construct a panel data set on returns to experience. This is severely constrained by the limited amount of observations available for a large number of countries in the US Censuses.

> 5 In these models wages grow because ...rms defer compensation in order to prevent workers from shirking (Lazear (1981)) or in order to induce a self-selection of heterogeneous workers that enhances productivity (Salop and Salop (1976)).

6
# Table Al-2 continued:

Data availability: celi is marked with an -x- if at lesat one of the variables in the category is available.

||Year of<br>survey|Number<br>of women<br>__________<br>|Marriage<br> <br>and<br>Fertility<br>_**_________**j<br>j|Educa-<br>tion<br>**_** ________<br>_<br>_______|Infant /<br>Child<br>mortality<br> <br>___________|Vaccina<br>tions<br> <br>______|- Incidenc<br>of illnes|e Consulta-<br>s tion / No<br>treatmnen<br>o f illn ess|Anthrop-<br>ometrics<br>t|
|---|---|---|---|---|---|---|---|---|---|
|Pakistan||||||||||
|Baluchistan|1990/91|<br>941|<br>x|x|x|x|x|x|x|
|NWFP|1990/91|<br>1665|<br>x|x|x|x|x|x|x|
|Punjab|1990/91|<br>2207|<br>x|x|x|x|x|x|x|
|Sindh<br>.|1990/91|<br>1798|<br>x|x|x|x|x|x|x|
|India||_||||||||
|AndhraPradesh|1992/3|<br>4276|<br>x|x|x|x|x|x|x|
|Arunachal Pradesh|1992|<br>882|<br>x|x|||x||x|
|Assam|1992/93|<br>3006|<br>x|x|x|x|x|x|x|
|Bihar|1993|<br>2067|<br>x|x|x|x|x|x|x|
|Delhi|1993|<br>3457|<br>x|x|x|x|x|x|x|
|Goa|1992/93|<br>3141|<br>x|x|x|x|x|x|x|
|Gujarat|1993|<br>3832|<br>x|x|x|x|x|x|x|
|Himachal Pradesh|1992|<br>2962|<br>x|x|x|x|x|x|x|
|Haryana|1993|<br>2846|<br>x|x|x|x|x|x|x|
|Jammu region of J&K|1993|<br>2766|<br>x|x|x|x|x|x|x|
|Karnataka|1992/93|<br>4413|<br>x|x|x|x|x|x|x|
|Kerala|1992/93|<br>4332|<br>x|x|x|x|x|x|x|
|Madhya Pradesh|1992|<br>4283|<br>x|x|x|x|x|x|x|
|Meghalaya|1992/93|<br>1137|<br>x|x|||x||x|
|Manipur|1993|<br>953|<br>x|x|||x||x|
|Maharashtra|1992/93|<br>4106|<br>x|x|x|x|x|x|x|
|Mizoram|1993|<br>1045|<br>x|x|||x||x|
|Nagaland|1993|<br>1149|<br>x|x|||x||x|
|Orissa|1993|<br>4257|<br>x|x|x|x|x|x|x|
|Punjab|1993|<br>2995|<br>x|x|x|x|x|x|x|
|Rajasthan|1992/93|<br>5211|<br>x|x|x|x|x|x|x|
|Tamil Nadu|1992|<br>3948|<br>x|x|x|x|x|x|**x**|
|Tripura|1993|<br>1100|<br>x|x|||x||x|
|Uttar Pradesh|1992/93|<br>8722|<br>x|x|x|x|x|x|x|
|West Bengal|1992|<br>1036|<br>x|x|x|x|x|x|x|

36
TABLE 4B Panel regression results for subsamples: Manufacturing

||Manufacturing||||||
|---|---|---|---|---|---|---|
||Advanced<br>economies only|Advanced<br>economies<br>and EMDEs|EMDEs<br>only|Commodity<br>importers<br>only|Commodity<br>importers and<br>exporters|Commodity<br>exporters<br>only|
|Liner Shipping|-0.215***|-0.235*** †|-0.206*** †|-0.234***|-0.227*** ‡|-0.198***|
|Connectivity<br>Index|[0.0266]|[0.0117]|[0.0104]|[0.00947]|[0.0119]|‡[0.0258]|
|Logistics|-1.788***|-0.298*** †|-0.336*** †|-0.686***|-0.327*** ‡|-0.237** ‡|
|Performance<br>Index|[0.123]|[0.058]|[0.0539]|[0.0486]|[0.0525]|[0.107]|
|Distance|0.353***|0.239*** †|0.247*** †|0.303***|0.236*** ‡|0.255*** ‡|
||[0.0179]|[0.0122]|[0.0103]|[0.011]|[0.0107]|[0.0199]|
|Tariffs|-0.225|0.804***|0.218***|0.243**|0.621***|0.258***|
||[0.302]|[0.11]|[0.0569]|[0.107]|[0.0827]|[0.099]|
|Regional trade<br>|-0.005|-0.0243***|-0.108*** †|-0.0478***|-0.0515***|-0.107***|
|agreement<br>membership|[0.0131]|[0.00791]|[0.0122]|[0.00841]|[0.00981]|[0.0255]|
|Trade policy<br>|0.0287*|0.0744***|0.0382***|0.104***|0.0615***|-0.000122|
|uncertainty|[0.0171]|[0.00867]|[0.00701]|[0.0069]|[0.00748]|[0.0132]|
|Common border|-0.283**|-0.371**|-0.466*** †|-0.325***|-0.511*** ‡|-0.354***|
||[0.111]|[0.145]|[0.0364]|[0.0561]|[0.0609]|[0.0626]|
|Common<br>language|-0.136***|-0.0924***|-0.183***|-0.153***|-0.146***|-0.254*** ‡|
|Observations|642|538|632|134|382|408|

Source: World Bank.

Note: * p<0.05, ** p<0.01, ***p<0.001,. Robust standard errors are shown in parentheses. † indicates statistically significant difference of the coefficient estimate from the coefficient estimate for a sample of advanced economies only. † indicates statistically significant difference of the coefficient estimate from the coefficient estimate for a sample of commodity importers only. The table shows estimated coefficients from a gravity panel regression estimated for up to 95 countries using annual data for 2007-18 where the dependent variable is the log of bilateral trade costs. The regression includes time fixed effects. Standard errors are clustered by country pairs.
# **Table 1: Private Sector Share of GDP**

||**1990**|**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Czech Republic**|10|15|30|45|65|70|75|75|75|80|80|80|80|80|80|80|80|
|**Hungary**|25|30|40|50|55|60|70|75|80|80|80|80|80|80|80|80|80|
|**Poland**|30|40|45|50|55|60|60|65|65|65|70|75|75|75|75|75|75|
|**Slovak Republic**|10|15|30|45|55|60|70|75|75|75|80|80|80|80|80|80|80|
|**Slovenia**|15|20|30|40|45|50|55|60|60|60|65|65|65|65|65|65|65|
|**Estonia**|10|10|25|40|55|65|70|70|70|75|75|75|80|80|80|80|80|
|**Latvia**|10|10|25|30|40|55|60|60|65|65|65|65|70|70|70|70|70|
|**Lithuania**|10|10|20|35|60|65|70|70|70|70|70|70|75|75|75|75|75|
|**Bulgaria**|10|20|25|35|40|50|55|60|65|70|70|70|70|75|75|75|75|
|**Romania**|15|25|25|35|40|45|55|60|60|60|60|65|65|65|70|70|70|
|**Russia**|5|5|25|40|50|55|60|70|70|70|70|70|70|70|70|65|65|
|**Ukraine**|10|10|10|15|40|45|50|55|55|55|60|60|65|65|65|65|65|

Source: European Bank for Reconstruction and Development (EBRD), Transition Reports
Perhaps one consequence of a diminished role of MFIs as credit providers for the poor may be an enhanced role for the government. In order to assess whether this would be desirable, and given its relevance, a closer look at the evidence regarding the effectiveness of institutions with significant government involvement in providing access to credit for the poor is clearly warranted.

In this article we report findings from a survey conducted in three districts in AP in 2007 designed to assess the performance of rural credit cooperatives and in particular Primary Agricultural Credit Cooperatives (PACS).<sup>3</sup> The experience of PACS may prove useful to shed light into the debate about the role of government intervention in access to credit for the poor.

We find evidence that PACS were used as political instruments and that borrowers responded by prioritizing all debt obligations (MFIs, informal lenders, etc) before repayment of PACS loans. This indicates that the culture of repaying institutions with significant government involvement may have been low and that a private sector solution that is shielded from the government’s interference may be desirable.

The article proceeds as follows. We first describe the data and the sources of credit used by the sample of households interviewed, we then discuss the PACS management practices and the impact of politics in the culture of repayment. Section 4 concludes.

# **2. Data and Sources of Credit**

Our data includes 72 PACS from the districts of Anantapur, Mahabubnagar and Vizianagaram in Andhra Pradesh with audited recovery rates as of June 2004 between 25 and 75 percent.

We chose a sample of PACS in each of the 3 districts with repayment rates in 2004 just above and below the thresholds of 30 and 50%. We then chose a representative sample of borrowing households and another sample of non-borrowers from the villages where the PACS operates. Since we have the proportion of PACS members in each village, we can weight responses from

> 3 Primary Agricultural Cooperative Societies (PACS) are the lowest tier of the rural cooperative banking system. They focus primarily on providing short term (seasonal) credit for agricultural purposes and are regulated by NABARD. According to the Reserve Bank of India, there were 94,942 PACS in 2008, each one serving 7 villages on average, and a total membership of 131 million individuals, 79 million of which borrowers (Johnson and Meka 2010).

3
<!-- Start of picture text --> — Total Reserves — Foreign Currency Assets<br>°<br>a<br>iS)=<br>°<br>S Inflation Targeting<br>°<br>y<br>PTOAUKPFOAKTMATKT TMAH TOATTMATTMATY<br>TOUCTOTCOOSOSoCoCoOoOoOoOoOoOoe Oooo os ooOOD<br>DHOK KANT THORRADOOKNMNMNTHOORAODO<br>Mnooo000000q00g00g0MMSOOSDOCODODDDCODSCDDODDDDDODOGDOOOONOcoor KrTrYrrrrrrTrrrre<br>HH ANAANANANAANANAANANANANANNANANANANANAANAA AA<br><!-- End of picture text --> <!-- Start of picture text --> — Total Reserves ©— Foreign Currency Assets<br>a<br>°N<br>2<br>iS) Inflation Targeting<br>re}<br>PTOAKATOAKTMOAKTOMOAKTMATTMATTMATY<br>oooMoo Mo MoR MoM MoM Romo M- BoB m Romo MoM Rom -momomomos<br>DHOKKAMNTTOHOORNRODOOKAMNNTHOONDDO<br>MDAAROOCDDCDDDDOCOCOCOOOFTMHRSOSOSCSCSCSCOSOSCODSCSODSDDDODDDDCOOOOCOSere rrrrrrr ree<br>HOH ANNAAANAAAAANANANANANANAAAANANANANAAAA<br><!-- End of picture text -->
||(1)|(2)|(3)|
|---|---|---|---|
|Serv. Lib.|1.348*|0.446***|0.133*|
||(0.483)|(0.086)|(0.061)|
|Exporter|3.114***|0.294|0.094|
||(0.330)|(0.223)|(0.080)|
|Serv. Lib. _×_|-1.241|-0.007|0.078|
|Exporter|(0.558)|(0.126)|(0.078)|
|Industries|11|102|238|
|N|77|498|1574|

* p<0.05, ** p<0.01, *** p<0.001

Notes: Dependent variable is ln( _TFP_ ) estimated by Olley-Pakes procedure. Robust standard errors are reported in parentheses. Each estimation is performed with industry and time fixed effects which are not reported for brevity.

Table 7: Industry level results productivity within the industry, which exceeds the effect for a particular firm in the industry, and to higher dispersion of output, revenues, and profits.<sup>9</sup> The industry productivity is defined as output-weighted average TFP of firms operating in the industry. The measure of TFP is taken from the baseline estimation of the production function presented in Table 3. Figure 4 shows kernel densities of TFP for each manufacturing industry in 2001 and 2006<sup>10</sup> . The distribution has shifted to the right for Textile and Leather, Printing, High-tech machinery, Vehicles and transport. At the same time, productivity has shifted to the left in Food and tobacco, Wood and Paper, Coke, chemistry and plastics, Non-metallic minerals, and Metallurgy.

Industry level regressions, presented in Table 7, confirm our conjecture of large and positive impact of services liberalization on _within_ industry productivity. Results are presented for industry-level aggregation in column (1), NACE 2 digit aggregation in column (2), and NACE 3 digit aggregation in column (3).

Finally, we estimate the effect of productivity, capital, and liberalization on probability of exit. We expect that services liberalization should encourage exit of low productive firms. We

> 9The analysis is almost identical to the analysis of trade liberalization presented by Melitz. The important difference is that Melitz assumes that the distribution of productivity is constant over time, while we consider the case when the distribution shifts exogenously.

> 10We have chosen TFP in 2006 rather than in 2007, because the sample of firms in 2007 is much smaller and comparison of distributions in 2001 and 2007 might be misleading.

29