rafmacalaba/datause-encoder-data huggingface dataset:
# **2. The current situation in developing countries is much worse than generally pictured on the basis just of school enrollment and attainment.**

Available measures of school attainment uniformly indicate that developing countries lag dramatically behind developed countries. This fact has driven a variety of efforts to expand schooling in developing countries, including the Education for All initiative. Yet, much of the discussion and much of the policy making has tended to downplay the issues of quality.

International testing indicates that, even among those attaining lower secondary schooling, literacy rates (by international standards) are very low in many developing countries. By reasonable calculations, a range of countries has fewer than 10 percent of its youth currently reaching minimal literacy and numeracy levels, even when school attainment data look considerably better.

Because of the previous findings – that knowledge rather than just time in school is what counts – policies must pay more attention to quality of schools.

For developing countries, the sporadic or nonexistent assessment of student knowledge is an especially important issue – correcting this shortcoming should have the highest priority. It is impossible to develop effective policies without having a good understanding of which work and which do not. Currently available measures of program “quality” frequently rely upon various input measures that unfortunately are not systematically related to student learning. Moreover, the existing international tests – such as the PISA tests of the OECD – may not be best suited to provide accurate assessments of student performance in developing countries. The evolving capacity for adaptive testing that can adjust test content to the student’s ability level seems particularly important in the developing country context. Adaptive testing offers the possibility of meaningful within-country variation in scores along with the ability to link overall performance with global standards.

# **3. Just providing added resources to schools is unlikely to be successful; improving the quality of schools will take structural changes in institutions.**

Even though attempts to improve quality have frustrated many policy makers around the world, we now have extensive research that reaches some clear conclusions. Research has delved deeply into the impact of adding resources within the current institutional structure (of both developed and developing countries). The overall finding is that simple resource policies – reducing class sizes, increasing teacher salaries, spending more on schools, and so forth – have little consistent impact on student performance when the overall institutional structure is not changed.

77
10 firmo rely hleavily on internal resources and informal credit arrangements. As well, they hoard<sup>4</sup> .nventories of goods to self-insure, in effect siphonina saving from productive investments. Commercial banks are the predominant financial institutions. Formal markets for direct credit -- particularly for long term debt and equity -- are virtually non-existent. These countries also borrow relatively little from abroad. A sizable fraction of external funds, moreover, is obtained from public sources.

Financialization appears to accompany growth in the real sector.<sup>6</sup> As economies develop, non-bank intermediaries crop up, offering borrowers and lendare a greater range of options. Another outcome is that more capital tends to flow in from abroad, in contrast to the prediction of the simple neoclassical model. Across developing countries, the ratio of external borrowing to GDP tends to rise with GDP.<sup>7</sup> Further, the composition shifts from public sources to private sources.

As development proceeds further, markets for direct debt and equity emerge.8 The variety and magnitude of financial institutions and services continues to grow, improving the allocation of saving and investment. For example, insurance companies and pension funds become important sources of long term credit. They also improve the allocation of saving by reducing the need for individuals to self insure.<sup>9</sup> Because less saving is needed for safe assets like government debt and durable goods inventories, more can flow to productive investments.

The observed link between financialization and growth is suggestive that financial factors may be important in development, but of course is not definitive about the exact nature and importance of the interaction, or about the rroper role of public policy. To explore these issues further, it is

> 6Gurtey and Shaw (1956) outline the stages of financialization.

7See Gertler and Rogoff (1990) for evidence.

%Equity market exist in some developing countries, but typically these markets are not very liquid. See the Wortd Sank Development Report (1989).

9sencevenga and Smith (1990) formaLize this point.
The remaining body of literature focuses on the impact of more isolated or ad-hoc type increases in protectionism. Recently, the IMF (2016) explores the effects of a hypothetical 10 percent increase in import prices driven by a symmetric rise in both tariff and nontariff barriers. The authors use Global Integrated Monetary and Fiscal Model (GIMF), a multi-region dynamic stochastic general equilibrium (DSGE) model and find that such an increase in protectionism will lead to a strong decline in trade flows by 16 percent, and on the long run translate into a fall in global output of 2 percent. Kutlina-Dimitrova (2017) assesses the impact of the Russian import ban on exports of agri-products from the European Union, the United States, Australia, Norway and Canada. The author shows that these protectionist measures weigh heavily on Russian consumers whose welfare declines by more than US$5.8 billion.

Using an econometric framework and changes in the incidence of protectionism documented in the Global Trade Alert database, Evenett and Fritz (2015) estimate that protectionist measures implemented between 2009 and 2013 slowed LICs exports growth significantly costing them an equivalent of about one-third of the total exports.

In an empirical exercise similar to the one in this paper, Bouet and Laborde (2010) use MIRAGE, global dynamic CGE model to estimate the impact of a scenario that implies an increase in tariffs in major economies up to WTO bound rates. The model is calibrated using a 27 region and 12 sector aggregation of the GTAP 7 database with base year 2007. The authors show that such an increase in worldwide protectionism translates into a decline in world trade of 9.9 percent and world welfare by US$353 billion – more pronounced in many developing countries.

Finally, a recent paper by Kutlina-Dimitrova and Lakatos (2014) assesses the impacts of the EU-Singapore free trade agreement (FTA) also by simulating the costs of not concluding the FTA. By doing so the authors go beyond the standard assessment of the impact of free trade agreements by introducing an alternative scenario which quantifies the economic impact of Singapore raising its tariffs to levels bound in the WTO. Along these lines, Kutlina-Dimitrova and Lakatos (2014) exemplify that there is an additional benefit from a free trade agreements namely the prevention of future tariff hikes in line with WTO commitments. Results show that considering the cost of non-insurance against protectionism is an important aspect of the likely gains from trade liberalization.

# **4 Modeling framework and simulation design**

The model underlying the simulations described in this paper is the dynamic GTAP model (GDyn). GDyn is a multi-sector and multi-region recursively dynamic computable gen9
Table 9. Comparing “no coal reduction (NCR)” with IEA “Current Policies” scenarios

||20|20<br>CPS|20|30<br>CPS|
|---|---|---|---|---|
||NCR|vs<br>NCR|Base case|vs<br>NCR|
|Variable|values|% change|values|% change|
|GDP (billion ¥2010)|80,494|-0.10|<br>126,235|-0.29|
|Consumption (bil ¥2010)|38,784|-0.13|<br>67,264|-0.35|
|Investment (bil ¥2010)|31,532|-0.07|<br>43,747|-0.25|
|Government consumption (bil<br>¥2010)|8,958|-0.14|<br>12,785|-0.27|
|Fossil energy use (million tons of<br>sce)|4,915|-2.1|<br>6,113|-3.5|
|Coal use (million tons)|4,147|-3.5|<br>4,794|-5.5|
|Oil use (million tons)|663|0.12|<br>836|-0.16|
|Gas use (billion cubic meters)|239,647|2.1|<br>361,272|-1.5|
|Electricity (billion kWh)|6,949|-2.7|<br>9,102|-4.3|
|Electricity price||1.3||3.4|
|CO2 emissions (inc cement; mil<br>|11,236|-2.3|<br>13,073|-4.0|
|tons)|||<br>||
|Cumulative CO2(2015-2030)||<br>|<br> <br>187,684|-2.8|
||share o|f total|share o|f total|
|Electricity generation|gener|ation|gener|ation|
|Coal|0.678|0.657|<br>0.706|0.653|
|Gas|0.037|0.037|<br>0.040|0.039|
|Nuclear|0.047|0.058|<br>0.049|0.076|
|Hydro|0.157|0.156|<br>0.127|0.128|
|Other|0.025|0.030|<br>0.022|0.031|
|Wind|0.046|0.048|<br>0.047|0.057|
|Solar|0.010|0.014|<br>0.009|0.015|

# **6. Conclusion**

As part of the Paris Agreement, China has set for itself targets for reducing energy intensity by 60-65% in 2030 compared to 2005 levels. This study examines the economic consequences if the targets were met through a carbon pricing mechanism, where all fossil fuels are taxed in proportion to their carbon contents. The study uses a recursive dynamic computable general equilibrium model of the Chinese economy for the purpose this analysis.

‐28‐
41 Communications Commission, 1990.) 7. For additional thoughts on how a regulator with limited information should design the set of options he offers to a regulated firm, see Baron (1988), Besanko and Sappington (1987), Caillaud et al. (1988), Laffont and Tirole (1993), or Sappington (1991).

8. For instance, if the regulatoiy goal is simply to ensure that consumers are not misled by false advertising, the regulator can focus on developing the ability to assess the veracity of advertisements and to impose penalties (e.g., monetary fines) when misleading advertising is identified. In contrast, if the regulator wishes to protect consumers more broadly, for example by ensuring that they are able to secure high-quality service at low prices, then a more elaborate system of regulation will generally be required (e.g., a system to monitor quality and to limit prices to 'reasonable" levels).

9. For a detailed analysis of the interaction between the properties of a country's institutions (such as the strength of its judiciary) and the design of regulatory policy for the country, see Levy and Spiller (1993, 1994).

10. See Levy and Spiller (1993), and Vickers and Yarrow (1988) for additional thoughts on this matter.

11. For an overview of the many regulatory considerations associated with the compatibility issue, see Besen and Saloner (1990), for example.

12. See Baron (1985) for one formal treatment of this issue. Baron's analysis illustrates the important observation that losses to society can result when regulatory policy is not
- 12 -

**<u>Table 1. Summary statistics for firm cost function estimation</u>**

|Variable|Abb.|Obs. Mean St|d. Dev.|Min|Max|
|---|---|---|---|---|---|
|Total operating cost<sup>1</sup> <sup>2</sup>|C|903 25.13|138.60|0.00009|1686.80|
|Factor share: Labor|SL|903 0.36|0.28|<br>0|<br>1.00|
|Electricity|SE|903 0.06|0.09|<br>0|<br>0.71|
|Materials and goods<sup>3</sup>|SF|903 0.36|0.29|<br>0|<br>1.00|
|Wage (average per full time employee)<sup>1</sup>|WL|903 0.05|0.21|<br>0.00|<br>3.50|
|Electricity cost<sup>1</sup>|WE|903 0.54|6.67|<br>0.00|193.00|
|Material and goods cost<sup>1</sup>|WF|903 5.85|41.72|<br>0.00|587.88|
|Other input cost<sup>1</sup>|WM|903 4.12|58.60|<br>0.00|1650.00|
|Output (total sales)<sup>1 2</sup>|Y|903 67.49|331.65|<br>0.00|5079.49|
|Average duration of power outage (hours)|ZE|903 2.10|7.02|<br>0|<br>96.00|
|Number of days without water supply per year|ZW|903 0.26|1.71|<br>0|<br>30.00|
|Level of inventory measured by days of production|ZT|903 30.48|59.79|<br>0.00|365.00|
|Transport connectivity variables:||||||
|Local market areas measured by the number of people<br>|<br>LOCAL|903 0.14|0.16|<br>0.01|<br>0.57|
|who live within driving distance for 30 minutes<sup>2</sup>||||||
|<br>Market Access Index (Normalized to the max of|MAI|903 0.72|0.45|<br>0.27|<br>1.63|
|accessibility in 2007)||||||
|Transport costs to bring goods to the nearest port|PORT|903 7.49|6.57|<br>0.58|<br>30.72|
|($/ton)||||||
|Firm characteristics:||||||
|Number of firms that exist in the same province|N|903 25340|15785|<br>2104|40638|
|Number of years since the firm was established|YEAR|903 14.86|12.61|<br>1|<br>110|
|Dummy for firms having a female owner||903 0.25|0.43|<br>0|<br>1|
|Dummy for owner’s education level: Secondary||903 0.34|0.47|<br>0|<br>1|
|Tertiary||903 0.45|0.50|<br>0|<br>1|
|Share of exports in total sales (%)||903 5.89|18.68|<br>0|<br>100|
|Dummy for firms importing goods and materials||903 0.21|0.41|<br>0|<br>1|
|Dummy for firms owning a power generator||903 0.22|0.42|<br>0|<br>1|
|Sector dummy variable:||||||
|Agro business (food and wood products)||903 0.22|0.41|<br>0|<br>1|
|Chemical and metal||903 0.19|0.39|<br>0|<br>1|
|Light manufacturing||903 0.22|0.41|<br>0|<br>1|
|Service||903 0.24|0.43|<br>0|<br>1|
|Dummyfor 2018 data|_t_|903 0.44|0.50|<br>0|<br>1|
|1In constant 2018 metical.||||||

2 Divided by 1 million for presentation purposes.

3 Including expenditures on raw materials and intermediate goods, and finished goods.
**Traditionally, global FDI has mostly flowed between developed countries—for example, the United States investing in Western Europe and vice versa; in 2010, for the first time recent history, more than half (51.6 percent) of the world’s total FDI inflows were received by developing countries (Figure 2).** This reflects more integrated and diversified markets as well as the gradual shift of the global economy’s center of gravity toward developing countries. A similar but less pronounced trend has occurred in FDI outflows. In 2000, developed countries were the largest source of outward FDI, with 90 percent of the total, but their share has fallen to 70 percent (Figure 3). This also reflects greater globalization of capital markets and the growing prominence of developing economies in global supplychain linkages, with their growth-enhancing spillover effects.

**Figure 2: Inward FDI Flows, 1995-2010**

<!-- Start of picture text --> P roportion from Developing Economies Proportion from Developed Economies<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: UNCTAD statistics and World Bank staff calculations<br>Note: Developing economies include both developing economies and transitional economics in UNCTAD.<br>Percent<br><!-- End of picture text -->

**Figure 3: Outward FDI Flows, 1995-2010**

<!-- Start of picture text --> P roportion from Developing Economies Proportion from Developed Economies<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: UNCTAD statistics and staff calculations<br>Note: Developing economies include both developing economies and transitional economics in UNCTAD.<br>Percent<br><!-- End of picture text --> 7
# **2** **<u>Data construction and methodology</u>**

## **2.1** **<u>Data sources</u>**

The data used in this paper consist of country-year average decile income/consumption covering the period 1988 to 2008. This means average per capita income for a given decile in country _i_ and year _t_ . The data come from a number of sources. PovcalNet is the starting point of our database, contributing more than two-thirds of the surveys.<sup>26</sup> PovcalNet is the compilation of a large number of household surveys stored by the World Bank research department. It has been mostly used to compute estimates of world poverty, as in Chen and Ravallion (2010b), and thus lacks data on rich countries. From PovcalNet we obtain average per capita incomes, already converted in 2005 $PPPs, and decile shares, which we combine to compute decile average incomes.<sup>27</sup> Next, we merge the updated World Income Distribution (WYD) data (Milanovic 2012). PovcalNet and WYD provide almost 98 percent of all data. We convert these data into countryyear deciles in order to obtain a consistent database.<sup>28</sup> Where possible we fill remaining gaps with data from the Luxembourg Income Study (LIS), the British Household Panel Survey (BHPS), the European Union Survey of Income and Living Conditions (SILC) and data from country statistical offices.<sup>29</sup> Overall we end up with 565 surveys across the five benchmark years 1988, 1993, 1998, 2003 and 2008 (Table 1).

26 PovcalNet is the on-line tool for poverty measurement developed by the Development Research Group of the World Bank, http://iresearch.worldbank.org/PovcalNet. Data downloaded on 29 July 2012, which refers to the last data update on 28 February 2012.

27 PovcalNet uses grouped data derived from household surveys to derive these decile shares. They are estimated from Lorenz curves fitted to the population-weighted (accounting for household size and sampling weights) distribution of per capita household income or consumption. Both Generalised Quadratic and Beta Lorenz curves are estimated and the functional form with the better fit is chosen.

28 The vast majority of country-year observations are already in deciles or in equally spaced quantiles (e.g., ventiles), so they can be easily converted. In total 13 country-years were imputed by fitting a log-normal Lorenz curve using the “ungroup” command included in the DASP Package (Abdelkrim and Duclos, 2007). This procedure implements the Shorrocks and Wan (2008) adjustment thus ensuring that the fitted Lorenz curve matches the original points. We choose a log-normal functional form and fitted the Lorenz curve on 2000 observations, as suggested by Shorrocks and Wan (2008). Minoiu and Reddy (2012) show that for estimating the global income distribution it is better to fit a parametric Lorenz curve than to estimate the kernel density.

29 The sources for the final database (country-years in parentheses) are: PovcalNet (379), WYD (173), LIS (8), SILC (2), and one survey each from BHPS (Bardasi et al, 2012), Statistics Finland, and Statistics Portugal.

16
# **I. Introduction**

Around the world, over 1.2 billion people still lack access to grid electricity and globally, about 84 percent of those without electricity live in the rural areas (IEA 2016). Rural electrification has been widely recognized as a key driver for alleviating poverty and boosting economic development, and has been ranked highly on the agenda of governments, nongovernmental organizations (NGOs) and international development agencies. Recently, a growing amount of research seeks to empirically assess the effects of rural electrification on micro-level development outcomes. The recent literature, however, paints a somewhat mixed picture about the economic viability of rural electrification. While many studies find substantial benefits from electrification on income, labor supply and education results, others suggest that these benefits may be overstated.

For example, Burlig and Preonas (2016) find only modest impact of rural electrification on labor participation and no effects on asset ownership, housing stocks and village-level public good provision in India. Using a randomized field experiment in India, Aklin et al. (2017) find no evidence for changes in spending, business creation, time spent working or studying and other indicators of socioeconomic development within a year of electrification. Also using an experimental approach, Lee, Miguel and Wolfram (2016) suggest that rural electrification in Kenya may reduce social welfare as the costs of grid expansion significantly outweigh its benefits. Bensch, Kluve, and Peters (2011) find no effect of rural electrification on income and children’s home study using data from Rwanda. Among papers that reported positive net benefits of electrification, findings on magnitudes of the benefits are wide-ranging (Table A1).

In this paper, we argue that there are at least two explanations for the lack of conclusive evidence in the literature. We hypothesize that the size and type of benefits electrification can bring about are contextdependent. Electricity leads to improved living standards through services provided by electrical appliances. The benefits are likely to be low if the “connected” households do not receive an adequate level of services due to unreliable electricity supply. Power outages are particularly a concern in developing countries where businesses report at least 20 hours of electricity outages a month,<sup>2</sup> and millions of households receive only a few hours of electricity a day. Both technology failures and systematic power cuts during periods of power shortages cause frequent and long hours of blackouts in developing countries. Poor quality of electricity supply could constrain and even discourage the productive use of electricity by households and businesses.

> 2 World Bank Enterprise Survey.

2
TABLE 6. Are China and India the Exception Rather than the Rule?

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||PPML|PPML|PPML|PPML|
|||Co-inve|ntorship<br>||
||No BRICS|No US|No BRICS,<br>no US|No BRICS,<br>no US|
|ln(Diaspora)|0.192***|0.204***|0.200***|0.190***|
||(0.0384)|(0.0358)|(0.0419)|(0.0465)|
|ln(Distance)|-0.447***|-0.325***|-0.515***|-0.269***|
||(0.0631)|(0.0582)|(0.0654)|(0.0759)|
|Bilateral controls|Yes|Yes|Yes|Yes|
|Attribute controls|Yes|Yes|Yes|No|
|Constant|-5.626|-7.494|-5.900|1.753***|
||(7.610)|(5.929)|(7.425)|(0.667)|
|Observations|24,180|24,852|22,971|14,752|
|Pseudo R2|0.900|0.828|0.658|0.725|
|Sending FE|Yes|Yes|Yes|No|
|Receiving FE|Yes|Yes|Yes|No|
|Year FE|Yes|Yes|Yes|No|
|Sending FE*Time FE|No|No|No|Yes|
|Receiving FE*Time FE|No|No|No|Yes|
|Controls|Yes|Yes|Yes|Yes|
|LogLik|-14236.62|-15541.05|-11836.79|-10463.32|

Source: Authors’ analysis based on data described in the text. Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

# _Further Robustness Analysis_

To further check the robustness of the results, table 7 runs the baseline specification using different estimation methods and dependent variables—for the case of coinventorship only. In particular, it includes OLS and Negative Binomial models. For both cases the estimated coefficients are slightly larger compared to table 2, but they are fairly comparable. Column (3) takes on board the potential heterogeneity of international co-patents in terms of quality and weights them according to the number of forward citations received within five years after their priority year. As can be seen, results are robust for this different approach to computing the dependent variable— although further research, possibly at the micro level, should shed more light on this 33