rafmacalaba/datause-encoder-data huggingface dataset:
3 allocation effects of trade liberalization in Korea. A CGE model is particularly relevant for such an exercise because of the relatively high and dispersed protection in the Korean economy, and because of the importance of economies of scale in several sectors. Our calculations are derived from a seven sector model calibrated to 1982, a year which has especially good protection estimates. Three sectors - consumer goods, producer goods, and heavy industry - are calibrated to increasing returns to scale (IRTS). In some simulations, in line i*ith the empirical evidence, we allow these sectors to earn super-normal profits when protected. To anticipate our results, the welfare gains from a move to free trade reach up to 10 percent of GDP, **an** estimate tenfold larger than the corresponding gains under constant returns to scale (CRTS). Even if, when protected, these sectors cannot earn above normal profits, our estimates of the welfare gains reach up to 5 percent of GDP.

Our results stand in sharp contrast to other estimates of the costs of protection, one exception being the work of Harris (1984) on Canada. To judge the plausibility of these results, one must question whether our model of the Korean industrial organization structure is a reasonable one. Therefoee, in section 2 we go into some detail on recent Korean industrial organization and industrial policies, as we believe they provide good support for our modelling of trade policy in the Korean environment. Section 3 discusses our modelling of imperfectly competitive markets and how we calibrated the model to 1982 data. Results are in section 4 and conclusions follow in section 5.

2. Trade Policies, Industrial Structure, and Industrial Organization Policies in Korea Until the move to a sectoral development strategy focusing on heavy and chemical industries (HCIs) between 1973 and 1979, Korea's outward-oriented
Acemoglu, D., and F. Zilibotti (2001) “Productivity Differences,” Quarterly Journal of Economics 116(2), pp. 563–606 Baldwin, R., and J. Harrigan (2011) “Zeros, Quality and Space: Trade Theory and Trade Evidence,” American Economic Journal: Microeconomics, 3, pp. 60–88 Bastos, P., and J. Silva (2010) “The Quality of a Firm’s Exports: Where you Export to Matters,” Journal of International Economics, vol. 82(2), pp. 99–111 Bastos, P., J. Silva and E. Verhoogen (2017) “Export Destinations and Input Prices: Evidence from Portugal,” forthcoming American Economic Review Bernard, A. B., and J.B. Jensen (1997) “Exporters, Skill Upgrading, and the Wage Gap,” Journal of International Economics 42, pp. 3–31 Bernard, A., and B. Jensen (1999) “Exceptional Exporter Performance: Cause, Effect, or Both?,” Journal of International Economics 47 (1), pp. 1–25 Bernard, A., B. Jensen, S. Redding and P. Schott (2007) “Firms in International Trade,” Journal of Economic Perspectives, Volume 21, Number 3, Summer 2007, pp. 105–130 Bernini, M., S. Guillou and T. Treibic (2016) “Firm Export Diversification and Labor Organization,” Science Po, Working Paper Brambilla, I., and G. Porto (2016) “High-Income Export Destinations, Quality and Wages,” Journal of International Economics, vol 98, pp. 21–35 Brambilla, I., D. Lederman and G. Porto (2012) “Exports, Export Destinations and Skills,” American Economic Review, Vol. 102, No. 7, pp. 3406–3438 Brambilla, I., D. Lederman and G. Porto (2016) “Exporters, Engineers, and Blue-Collar Workers,” Policy Research Working Paper No. 7686, World Bank Caliendo, L., and E. Rossi-Hansberg (2012) “The Impact of Trade on Organization and Productivity,” Quarterly Journal of Economics, 127(3), pp 1393–1467 Caliendo, L., F. Monte and E. Rossi-Hansberg (2015) “The Anatomy of French Production Hierarchies,” Journal of Political Economy, 123 (4), pp 809–852 Caliendo, L., F. Monte and E. Rossi-Hansberg (2017) “Exporting and Organization Change,” mimeo Yale University Caliendo, L. G. Mion, L. Opromolla and E. Rossi-Hansberg (2016) “Productivity and Organization in Portuguese Firms,” NBER Working Paper No 21811 Caron, J., T. Fally and J. Markusen (2014) “International Trade Puzzles: A Solution Linking Production and Preferences,” Quarterly Journal of Economics, vol. 129, pp. 1501–1552 12
|**Table 1. Summa**<br>**Author (Date)**|**ry of studies that e**<br>**Type of**<br>**intervention**<br>**examined**|**xamine the im**<br>**Outcome of**<br>**interest**|**pact of prenatal sex**<br>**Data**|**selection on postnatal outcomes f**<br>**Empirical strategy**|**or girls**<br>**Observed impact on**<br>**female child survival**|**Observed impact**<br>**on other female**<br>**child outcomes**|
|---|---|---|---|---|---|---|
|Goodkind (1996)|Rising SRB<br>(indicator of<br>rising prenatal<br>sex selection)|Postnatal<br>survival of<br>girls|Vital registration<br>data, census data,<br>and other<br>secondary data<br>sources. Countries:<br>China, Hong<br>Kong, Japan,<br>Korea, Malaysia,<br>Singapore, Taiwan|Correlation study: Examine<br>how changes in the male-<br>female sex ratio of infant and<br>child mortality correlate with<br>changes in SRB before and<br>after the 1980s across different<br>Asian countries (with and<br>without documented son<br>preference)|Female infant and child<br>survival improved after the<br>1980s in countries or sub-<br>populations with<br>documented son<br>preference|N/A|
|Hu and Schlosser<br>(2012)|Higher and<br>increasing SRB<br>(indicator of<br>higher and<br>increasing<br>prenatal sex<br>selection)|Girls’<br>nutritional<br>outcomes|Pooled individual<br>data from 1992-93,<br>1998-99, and<br>2005-06 Indian<br>National Family<br>Health Survey<br>(NFHS)|Quasi-experimental study:<br>Triple difference-in-difference<br>that exploits geographic<br>variation in the prevalence of<br>prenatal sex selection over<br>time, and changes in outcomes<br>for boys v/s girls across states<br>over time|N/A|Larger reduction<br>in % of<br>malnourished<br>(wasted, stunted,<br>underweight) girls<br>in states with<br>increasing<br>prenatal sex<br>selection. This<br>result is not<br>explained by<br>changes in<br>mortality|
|Lin et al. (2014)|Access to sex-<br>selective<br>abortion in 1985|SRB and<br>girls’<br>neonatal<br>mortality|Individual-level<br>data from national<br>birth and death<br>registries, Taiwan<br>(1980 – 1996)|Quasi-experimental study: Uses<br>timing of legalization of<br>abortion, and cross-sectional<br>variation in cost of having more<br>children (variation in cost<br>across birth-order and mother’s<br>age) to isolate treatment effect|Reduced female neonatal<br>mortality for higher-order<br>births, and no change in<br>post neonatal mortality for<br>girls|N/A|
**Table 3.3: Decomposition of Annual Changes in Employment by Economic Activity and Position**

||year end<br>|ingthe secondq <br>|uarter of...<br>|
|---|---|---|---|
||2008|2009|2010|
|**TOTAL**|**960,040**|**(522,415)**|**1,307,551**|
|**BY ECONOMIC ACTIVITY**||||
|**Primary**|**(30,714)**|**(77,160)**|**191,338**|
|Agriculture|(13,856)|(113,755)|254,482|
|Mining and electricity|(16,858)|36,595|(63,144)|
|**Secondary**|**164,498**|**(795,552)**|**235,583**|
|Manufacturing|109,083|(672,193)|257,594|
|Construction|55,415|(123,359)|(22,011)|
|**Tertiary**|**805,433**|**375,750**|**890,581**|
|Trade and commerce|101,366|12,870|364,390|
|Restaurants and hotels|166,157|(45,601)|186,286|
|Transport and communications|51,613|76,379|(46,833)|
|Financial services|191,658|37,599|131,194|
|Social services|46,923|168,234|18,605|
|Other services|120,403|24,889|259,965|
|Government services|127,313|101,380|(23,026)|
|**Not specified**|**20,823**|**(25,453)**|**(9,951)**|
|**BY FIRM SIZE**||||
|Agriculture firms|(13,856)|(113,755)|254,482|
|Micro firms|468,692|(221,427)|728,288|
|Small firms|156,744|11,997|268,388|
|Medium firms|4,337|(106,252)|(5,214)|
|Large firms|55,688|(379,747)|(21,111)|
|Government|127,313|101,380|(23,026)|
|Other|11,072|(1,367)|121,036|
|Not specified|150,050|186,756|(15,292)|

**Source: Own calculations using data from ENOE.**

Flows by employment characteristics accentuate the image of a severe recession affecting good jobs and a recovery concentrating in not-so-good employment creation (see table 3.4). Salaried formal jobs declined by 403,000 during the recession but only increased by 180,000 during the recovery. On the other hand, job positions characterized as salaried informal, self-employed, or nonpaid worker all increased by more than double the net flows observed the previous year. Furthermore, during the crisis the loss of jobs with health coverage was three times the loss of jobs without health coverage, and the latter grew more than six times the former during the recovery. Finally, crisis job losses concentrated among those with wages between two and five times the minimum wage in 2009 while 17
improving financial access for these groups must therefore include provisions for building an enabling, inclusive infrastructure. It starts by addressing these known barriers to financial inclusion. Doing that should start with pro-inclusion regulation.

# **_5.1 The role of financial regulation_**

As alluded to previously in this paper, governments and financial authorities around the world are adopting new programs and regulations to spur financial inclusion. Regulatory frameworks encompass policies that ease the opening and maintenance of accounts, entry and licensing of service providers and agents, monitoring and enforcement of consumer protection rules, and overall safeguarding of consumer funds.

Bank regulations, especially for consumer protection, can lead to higher financial inclusion. For example, one study finds that individuals in economies with the best regulations are 12.4 percent more likely than those in economies with the worst regulations to have an account at a financial institution such as a bank (Chen and Divanbeigi, 2019). Cross-country analysis has found that higher financial inclusion is correlated with lower costs of financial services, closer proximity to financial institutions, strong legal protections, and political stability (Allen, et al., 2016). These studies suggest that market conditions and the policy environment can have a significant impact on financial inclusion.

Beyond direct government programs and policies, innovation-friendly regulation also plays a role. The most obvious example is the regulation of fintechs such as mobile money providers, which are playing an increasingly important role in the financial sector. Sub-Saharan Africa offers a case in point for the impact that pro-innovation regulation can have. A supportive environment for mobile money has inarguably played a role in enabling Sub-Saharan Africa to become the global leader in mobile money account ownership. Enabling policies include not only licensing and oversight of mobile money providers—often mobile network operators—but also general support for enabling infrastructure, such as widespread telecommunications networks, mobile phone access, payments networks, and reliable electricity and power grids.

One of the mechanisms by which effective regulation appears to drive financial inclusion is by increasing consumer trust. Early evidence for this idea came from Allen et al. (2016), who used data from the inaugural 2011 edition of the World Bank's <mark>Global Findex Database to study the</mark> 16
# **Australia: National Schools Specific Purpose Payments, 2009**

The National Education Agreement specifies the following objectives for this grant program:

- ensuring that all children are engaged in and befitting from schooling, with a goal of lifting the Year 12 attainment rate to 90 percent by 2015;and

- ensuring children meet basic literacy and numeracy standards and continuing to improve overall literacy and numeracy achievements.

These are equal per capita payments for both government and non‐government schooling to all states. Out of this the government schools component for each state is based upon each state’s share of full time equivalent student enrollments in government schools. The growth factor for government school component is the product of the growth in average government schools recurrent costs and the growth in full time equivalent in government schools. The grant funds are required to be spent on schooling but states have full budget flexibility to allocate funds as they see fit to achieve mutually agreed objectives. The program has not set nay benchmarks for performance but as part of an overall reporting and accountability framework, states must provide performance data to the Commonwealth and to the general public. The COAG Reform Council will collate data for all states and will publish such statistics and analytical reviews of performance for government and general public use (The Australian Treasury, 2009).

# **United States:  Race to the Top Competitive Grant Program, 2009**

This $4.35 billion program was launched by President Obama on July 24, 2009 to mark a new federal partnership in education reform with states, districts and unions to 15
in 2004. Under full trade and subsidy liberalization, global cotton markets would change dramatically: the value of production would fall by one-third or more than $5 billion per year in high-income countries (mostly in the US), and the value of their exports would fall by $3.6 billion. The world totals would hardly change though, as developing country output and exports of cotton would expand by about the same amounts, with Sub-Saharan Africa enjoying more of that gain than any other region (Table 14). Indeed cotton is so important in Sub-Saharan Africa minus South Africa that it contributes one-quarter of that region’s net gain in agricultural value added from full liberalization.<sup>8</sup> The bottom line, therefore, is that if all current distortions to world trade in merchandise were phased out over the rest of this decade then, according to the latest GTAP database and the Linkage model, by 2015 developing country agricultural production, employment and real net income would be greater than without such reform. And it would be greater in most regions within the developing country group, the exceptions being South Asia and Eastern Europe where import tariffs are higher for agricultural than non-agricultural goods. This does not _necessarily_ mean that if each individual developing country were to unilaterally liberalize we would find farmers benefiting in all cases except in South Asia and Eastern Europe; but if we were to run those many individual model simulations we may well get that result also, since, as is apparent from Table 3, terms of trade effects of reforms by others are usually dominated by efficiency gains from own reforms except for the least-distorted economies.

> 8 If full cotton liberalization was included in the Doha package, it would nearly double both the net welfare gain and the boost in net farm income in the SSA region. Furthermore, the share of all developing countries in global cotton exports would be 85 percent instead of 56 percent in 2015 – all of which vindicates the efforts to ensure cotton receives specific and substantial attention in the Doha negotiations (see Anderson, Martin and van der Mensbrugghe 2005b).

16
epidemic. This reversal of the gradient might be driven by the fact that the countries in southern Africa with the highest HIV prevalence are also among the richest in SSA.

The HIV/AIDS pandemic is certainly a major underlying factor for these changes. The bottom panel of Figure 3 separates out the Sub-Saharan African countries into those in which the prevalence of HIV was above 5 percent in 2001 and those where it was below 5 percent (according to UNAIDS 2010). The effects of high levels of HIV on adult mortality are readily apparent, with the average adult mortality rate almost tripling between 1985-89 and 2000-04 (from a probability of around 1.7 percent to almost 5 percent) in the high prevalence SubSaharan African countries. Importantly, however, mortality increased somewhat as well in the lower prevalence Sub-Saharan countries as well, from around 1.6 percent in 1985-89 to over 2 percent in 2000-04—a point we return to below.

# **3.2) Country-specific trends in adult mortality**

The aggregated results presented so far mask a substantial amount of heterogeneity across countries, and may be driven by a few large countries (note that while the figures presented here aggregate using population weights, the regression analyses are not weighted). Figure 5 shows the trends in adult mortality for each country in our study. Countries are grouped by region, although we separate out two groups of countries (regardless of region): those in which there has been an episode of civil or international conflict, and those in which that episode resulted in a period of genocide. (Note that the vertical scales in these graphs differs from graph to graph: while this downplays differences across groups of countries, for example the countries where mortality is surging versus those where it has remained relatively stable, it allows the maximum amount of information to be visible in each graph.) Panel (A) of Figure 5 shows countries in the southern region of Sub-Saharan Africa. The increases in adult mortality are (with the exception of Madagascar) very steep. In the 1980-84 period, the 5-year probability of death was less than 2 percent in all these countries. By 1995-99, for the six countries with data, the probabilities range from 2.3 percent (Lesotho) to 5.5 percent (Zambia). For the four countries with data for 2000-04, the 5-year probability of death ranges from 3.8 (Lesotho) to 7.8 percent (Swaziland). These increases are unparalleled in other 12
technologies provides some indication of the degree to which renewables can compete with fossil fuel-fired generation once the tax is imposed (Timilsina et al. 2012).

There exist several studies analyzing the costs of electricity generation for different countries, regions, projects, or at the global level. Examples are Shea and Ramgolam (2019), Cartelle Barros et al. (2016), Ouyang and Lin (2014), Candelise et al. (2013), Timilsina et al. (2013), Timilsina et al. (2012), Darling et al. (2011) and Branker et al. (2011). These studies are focused on different sets of technologies with differing geographical coverage. Shea and Ramgolam (2019) compare the LCOE of wind and solar with other technologies in Mauritius and show that the LCOE for bagasse, landfill gas, and utility-scale solar PV is smaller than that of coal and fuel oil. Ouyang and Lin (2014) analyze the LCOE of various renewable energy technologies to suggest the expansion of financial incentives (feed-in-tariffs) to other renewable energy technologies besides wind and solar. Yuan et al. (2014) analyze the LCOE of distributed PV in China and find that these technologies are not economically viable under the current electricity tariff even if these technologies receive government subsidies. Cartelle Barros et al. (2016) compare the LCOE of renewable energy technologies and conclude that conventional sources of electricity generation (e.g. coal, oil, natural gas, and nuclear) are still the most competitive options despite the falling costs of renewable energy technologies. Candelise et al. (2013), Darling et al. (2011) and Branker et al. (2011) analyze the LCOE of solar PV technologies under various conditions. All of these studies either focus on a particular technology or a region. The findings may not hold for other locations or contexts. Timilsina et al. (2012) compare, from a global perspective, the LCOE of solar energy technologies with that of many different technologies under various assumptions, including imposing carbon taxes on fossil fuels. Timilsina et al. (2013) do the same for wind power technologies. Timilsina et al. (2012) and Timilsina et al. (2013) consider multiple values for the main input variable, the capital cost; however, those papers do not consider variations in other input variables.

Some governments, private sector actors, and international organizations regularly publish LCOEs at national, global, and regional levels.<sup>4</sup> For example, a private bank, Lazard, has been

> 4 Timilsina and Shah (2020) discuss LCOEs from Lazard (2019), IEA (2015), EIA (2018), IRENA (2020) and NREL (2019).

3
Table 13: Anthropometric measures

||(1)<br>Attrition:<br>core pupils<br>measured|(2)<br>Attrition:<br>Prob<br>core pupil|(3)<br>Height<br>for age<br>Z-score|(4)<br>Height<br>for age<br>percentile|(5)<br>Weight<br>for age<br>Z-score|(6)<br>Weight<br>for age<br>percentile|
|---|---|---|---|---|---|---|
||(out of 30)|measured|||||
||OLS|Probit|OLS|OLS|OLS|OLS|
|Treatment|-0.68|-0.16|0.21|3.37|0.79|8.56|
||(1.16)|(0.10)|(0.77)|(5.95)|(0.38)**|(3.62)**|
|Girl||-0.04|-0.18|-2.26|0.09|2.37|
|||(0.11)|(0.22)|(2.39)|(0.07)|(1.65)|
|Girl * Treatment||0.18|-0.21|-0.39|-0.44|-5.37|
|||(0.13)|(0.50)|(4.30)|(0.23)*|(2.66)**|
|Age 7||-0.91|-1.34|-24.30|-1.11|-22.16|
|||(0.09)***|(0.14)***|(2.14)***|(0.15)***|(1.78)***|
|Age 8||-1.3|-1.77|-29.30|-1.31|-25.49|
|||(0.10)***|(0.16)***|(2.00)***|(0.13)***|(1.64)***|
|Constant|10.84|0.62|0.04|40.18|-0.70|29.87|
||(0.82)***|(0.07)***|(0.51)|(4.21)***|(0.10)***|(2.39)***|
|Obs|50|2156|1184|1184|1184|1184|
|R-squared|0.01||0.06|0.11|0.09|0.13|
|F stat for Ho: Girl+|||0.00|0.51|2.66|1.37|
|Girl*treatment=0|||||||

# Notes:

The dependent variable is column 1 is the number of core pupils (out of 30) who were measured. For a description of the construction of the core sample, see Table 9. The dependent variable in column 2 is a discrete variable that takes on value 1 if a child was measured and 0 if a child was not, while the sample is the set of core pupils (30 pupils per school). In columns 3-6, Z-scores and percentiles are relative to a healthy US population and were computed using the anthro program (Centers for Disease Control and Prevention and World Health Organization). Ages in this table are ages in 2002. The regression model is OLS. Standard errors are clustered at the school level in columns 2-6. Robust standard errors are reported in parentheses. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent 36
# IV. **Some Particular &xperiences**

In this final section we attempt to complemen **t** he somewhat abstract chaacter of the analysis with some observations on experiences in different countries and regions of the world There is no particular principle underlying the choices made, which have been dictated partly by the availability of relevant litrature.

# (i) Tske

The case of Turkey is a particularly interesting one in the polidcal economy of development. Among other rasons this is because of her early experience with a "developmental dictatorship" under Mustapha Kemal Atatwuk durng the inter-war years, providing a preview of many recent Third World scenarios of development. My discussion is heavily indebted to the excellent historical study by Keyder (1987).

The ouiginal system of the Ottoman empire fits into our model of a "surplusmaximizing" traditional monarchy, with the surplus shared between the monarch himself and the class of state funcdonaries that administered the system. Unlike Eurpe of the Old Regime or Latin America there was no class of large landowners, the soil being mainly worked by peasants who were heavily taxed. Turkey was drawn into the grat expansion of world trade from 1870 t 1914 as an exporter of primary products, with Greek and Armenian merchants playing the role of intmediaies between the world madret and the Turkish peasant producers. Needless to say the state was not slow to take the opportunity to substantially inse its revenue, thus leading to an enhancement of the traditional domination of the bjeaucracy in Turkish society. Ihe "alien" merchant community was ceraiy not able to challenge this hegemony. The ravages of World War I and the associated conflicts with the Greeks and Armenians led to the almost total disappearance of the merchant community as a result of prsecutdon and flight. The twenties saw a condnuation of agricultural exports under the new regime of KemaL 29