rafmacalaba/datause-encoder-data huggingface dataset:
whereas they matter less for more homogenous, bulk type commodity trade. Beck (2002) finds a positive association between economies with more developed financial systems and export-oriented manufacturing industries, as the former allows financing of largescale, high-return investment projects.

Numerous “services inputs” therefore affect the volume and composition of trade, whether in goods or services. Many of these input costs will factor into the overall level of trade costs confronting firms. Actions to reduce these excess costs and improve quality will enhance the competitiveness of firms located in the markets concerned, with an aggregate effect that is akin to a depreciation of the real exchange rate.<sup>19</sup> Which factors are more important than others will vary across countries. Wilson, Mann and Otsuki (2005) use a gravity model to estimate the effects of four “trade cost” variables, two of which are services-related: port efficiency, customs clearance, the regulatory environment more broadly, and service sector infrastructure (telecommunications, e- business) across 75 countries for the 2000-2001 period. The total potential expansion in trade in manufactures from trade facilitation improvements in all the four areas – raising performance of “underperformers” to the average in the sample – is estimated to be $377 billion. On average, their port efficiency variable – which includes both maritime transport and airports -- account for more than half of the trade costs imposed by policies in their four areas.

# **_CGE analyses_**

Much of the (limited) literature on trade in services is simulation based, reflecting the paucity of data on both policies and outcomes. The limitations of CGE studies are well known, and in the case of services a major issue is the lack of detailed data on both policies and flows. However, they have the virtue of being forward looking in that they seek to identify potential impacts of reforms. For example, Konan and Maskus (2006), build a CGE model to investigate the potential effects of removing barriers to trade in services in Tunisia. They argue that increasing international competition on service markets will reduce the “cartel effect”—the markup of price over marginal cost that

> 19 This aspect of measures to reduce trade costs is an additional reason to target development assistance for this purpose: it can help attenuate the real exchange appreciation that may otherwise result (Dutch disease).

20
# **1. Introduction**

Achieving gender equality and the empowerment of women and girls by 2030 has been enshrined as a global goal by development actors including the United Nations.<sup>1</sup> To work toward this objective, policymakers will need to increase the participation of women in the economy and society. For genderrelated policies and programs to be designed and implemented effectively, it is necessary to equip policy makers and project teams with appropriate knowledge and instruments. Moreover, it is important to identify the relative size and drivers of gender gaps across the economy in order to determine how best to boost economic growth through efforts to achieve gender parity.

This approach is valuable for a country like Burkina Faso, where over 40 percent of the population lives below the poverty line (World Development Indicators, 2022). Burkina Faso ranks 144th out of 157 countries in terms of its human capital, and 184th out of 191 countries in terms of its Gender Development Index<sup>2</sup> . Previous research from Burkina Faso has found evidence of discrimination in favor of boys within the household (Haddad and Reardon, 1993), widespread male control of household assets (van den Bold et al., 2015) and limited decision-making power for women (Kieran et al., 2015). In this paper, we provide a systematic analysis of gender gaps across Burkina Faso’s main economic sectors; examine how they vary over time, between age groups and across regions; identify the primary drivers of these gaps; and provide evidence-based policy recommendations.

Using nationally representative household data from 2019, we find that women’s participation in paid work is 20 percentage points lower than that of men, and 10 percentage points lower when looking at both paid and unpaid work. Women in Burkina Faso earn 82 percent less in monthly wage earnings compared to men, while their business revenues are on average 61 percent lower. Within agriculture, women’s harvest values and crop sales are both approximately 60 percent lower than that of men’s. We further find that between 2014 and 2019, the overall gender gap in wage earnings increased, along with a small increase in the harvest value gender gap—but stayed the same for crop sales, business revenues and overall labor force participation. This means that Burkina Faso has not witnessed progress in lifting this enormous structural barrier to economic growth in the country.

We run a series of decompositions to understand and explain the causes of the current gender gaps, which allow us to identify how much of the gender gap is driven by differences in endowments (differences in the

> 1 Goal 5: https://sdgs.un.org/goals/goal5.

> 2 Human capital is measured through the Human Capital Index, which captures the amount of human capital that a child born today can expect to attain by age 18. The Gender Development Index was accessed at: <u>https://hdr.undp.org/gender-development-index#/indicies/GDI.</u>

2
# **C.** CREDIT POLICY IN KOREA '3

# 1. Objectives

Korean experiences are different from Japanese experiences in several respects. Unlike Japan, credit policy in Korea involved significant subsidization of the cost of borrowing. The coverage of the programs also was much more extensive. The programs heavily involved commercial banks as well as development banks. Both types of banks were owned by the government and were directed to channel their loans to priority sectors. An important feature of credit policy was the coercive nature of government intervention as firms with minimum equity funds were encouraged to enter particular industries, with a strong package of tax and financial incentives. The government also had strong leverage toward firms through its control over the banking system as additional credit could be denied to, or existing credit withdrawn from, faltering firms.

The operation of credit policy in Korea has also gone through three distinct phases:

- In the 1950s and 1960s, credit policy was oriented toward particular activities, mainly exports and industrial investment.

- In the 1970s during the drive for the development of heavy and chemical industries (HCI), credit policy became more geared toward promoting specific industries. During the HCI drive, the Korean authorities relied heavily on their control of the entire credit system and provided "strategic" industries preferential access at substantially subsidized rates. The use of policy-based loans was pervasive.

- The successes and failures of the HCI drive induced a change in government approach in the 1980s. First, the authorities became involved in industrial and financial restructuring of industrial sectors and companies in distress. As in Japan, Korean credit (and industrial) policy started to be preoccupied not only with "picking winners" but also with "phasing out losers". Second, the focus of policy was reoriented towards producing a more balanced industrial sector that would not be dominated by a few business conglomerates. As a result, lending to small and medium-size firms received greater attention. And credit policy was reoriented toward functional activities such as research and development and equipment investment.

> 1 3 This section draws on Cho (1989), Cho and Kim (1994), Vittas and Wang (1991), and World Bank (1987).

17
# **4. Results**

Table 2 presents the results for an OLS regression of average GDP per capita growth on the aggregate, normalized Doing Business index and a number of control variables for the year 2009. The table reports results for five-year and 10-year average growth respectively. Data availability restricts the sample size to 175 countries at its maximum to 166 when controlling for FDI and exports.

**<u>Table 2:</u>** <u>GDP per capita growth – the Doing Business ranking (1)</u>

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. Variable<br>|Average<br>|GDPper ca<br>|pitagrowth<br>|<br>|||||
|# Years|5|10|5|10|5|10|5|10|
|DBI|2.685<br>(2.010)|5.195***<br>(1.769)|3.635**<br>(1.752)|5.513***<br>(1.728)|3.593**<br>(1.764)|5.502***<br>(1.736)|3.406*<br>(1.796)|4.573**<br>(1.980)|
|initial log<br>GDP/cap|-0.350<br>(0.272)|-0.658***<br>(0.225)|-0.276<br>(0.247)|-0.637***<br>(0.226)|-0.253<br>(0.253)|-0.615***<br>(0.232)|-0.419<br>(0.260)|-0.651***<br>(0.228)|
|initial deviation<br>fr. GDP deflator|||0.153***<br>-0.04|0.008*<br>-0.005|0.151***<br>-0.041|0.008*<br>-0.005|0.151***<br>(0.0375)|0.007<br>-0.004|
|Civil war|||||0.858<br>(0.697)|0.409<br>(0.606)|1.080*<br>(0.635)|0.652<br>(0.587)|
|FDI (%GDP)|||||||-0.005<br>-0.007|-0.001<br>-0.005|
|Exports (%GDP)|||||||0.026***<br>-0.001|0.025*<br>-0.013|
|Constant|5.345***|5.591***|4.343***|5.279***|4.139***|5.075***|4.440***|4.769***|
||(1.428)|(1.102)|(1.365)|(1.126)|(1.420)|(1.218)|(1.478)|(1.301)|
|Observations|175|172|175|171|175|171|166|166|
|R-squared|0.017|0.083|0.130|0.111|0.134|0.112|0.187|0.145|

Notes: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Table 3 repeats the analysis but includes additional controls. Models 9 and 10 add general government consumption as a percentage of GDP. Whilst this reduces the sample size to 164 countries, the results still uphold. Adding dummy variables for Africa, East Asia, and Latin America reduces the magnitude of the DBI coefficient and renders it insignificant. This is an interesting finding. To explore it further, the analyses was repeated for poor countries and rich countries separately (the sample was divided on median log GDP per capita). Models 13 to 16 show that the DB effect on economic growth is generally associated with developing countries. The DBI coefficient is insignificant for the developed country sample, both for 5-year and 10-year growth. It is large and significant at the 1% level for developing countries. This effect remains when removing the regional dummies. The analysis thus clearly shows that it is particularly developing countries that can benefit from DB-inspired reform.

10
# **References used only in the Online Appendix**

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labor force surveys conducted in 1988, 1998 and 2006; (b) test-scores in Trends in International Mathematics and Science Study (TIMSS) in 2003 and 2007; and (c) national examination scores at completion of primary, preparatory and secondary education. Finally, the paper looks at the role of two potential factors of learning inequities: (a) the extent to which exam score differentials are associated with attendance of different school systems; and (b) the role of private expenditures in tutoring on learning outcomes.

The paper is structured as follows. Section 2 focuses on changes in educational attainments and their association with an individual background. Section 3 provides measures of the opportunity-shares of learning achievement inequalities. Section 4 provides empirical evidence on the contributions of attendance of schools from different systems and of private expenditures in tutoring to the formation of learning inequities. Section 5 concludes the paper.

# **2. Access to Education and Educational Attainment**

Here we look at access to education by individuals from different socioeconomic and geographic backgrounds over the last two decades. We use data from three labor force surveys: the Egypt Labor Force Sample Survey (LFSS) of 1988, the Egypt Labor Market Survey (ELMS) of 1998, and the Egypt Labor Market Panel Survey (ELMPS) of 2006.<sup>2</sup> The surveys are representative of the total population and consist of about 28,000, 24,000, and 37,000 individuals, respectively. They are based on similar sampling design and questionnaires, ensuring data comparability. The surveys collected information on educational attainments, measured by completed levels of education and school enrollment, as well as on gender, region of birth, parents’ educational attainments, and father’s occupational status. The paper looks specifically at a subsample of young people aged 21-24 years, most of whom have already completed their education.<sup>3</sup>

**Table 1** presents the completion rates at preparatory, secondary and college levels for 21-24

> 2 The LFSS 1988 was conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS). The ELMS 1998 and ELMPS 2006 were conducted by the Economic Research Forum (ERF) in cooperation with CAPMAS.

> 3 A small number are still enrolled in college but this is not a concern to the extent that they already attained some higher education.

4
# **3 Evidence on the emergence and dissemination of green technologies identified in patents**

In this section, we present the main findings of our analysis of innovation and diffusion of green technologies based on patent data from the WIPO green database. This analysis allows us to assess the dissemination of the most novel and influential green technologies. As discussed above, we follow a recent literature seeking to infer innovation and dissemination of technologies using general-text recognition methods for identifying patents associated with specific technologies.

Figure 1 reports the share of each WIPO green sector in the total mentions of green technologies in patents. The evidence in this figure reveals that green technologies belonging to the WIPO green sectors energy and construction account for the vast majority of mentions in patents over the period 2000-2021, accounting for about 80 percent of total mentions in most years. Transportation is the third most important sector, and its relative importance experienced significant growth in the mid-2000s, remaining relatively stable thereafter. Technologies from the PMP and Farming and Forestry sectors are the least important, accounting together for less than 10 percent of total patent mentions in most years.

Figure 2 reports the percentage of patents mentions by technology within each WIPO green sector over the same period. Among green technologies from the construction sector, we observe a clear downward trend for the relative importance of air conditioning, and a growing importance of LED technology, sensors, and construction material. Since 2005, LED technology accounted for about 40 percent of total mentions in patents within the construction sector, while the other technologies stabilized at around 20 percent each. In the energy sector, we observe a growing importance of biofuels, geothermal energy, and inverter technology. In transportation, drones accounted for a progressively higher share of total mentions, reaching about 80 percent in the early 2000s and stabilizing around 70 percent between 2015 and 2020. Among the PMP sector, carbon capture became the most important technology since 2005, reaching about 60 percent of total mentions at the end of the sample period. In Farming and Forestry, transgenic crops became the most important technology (as measured by mentions in patents) since 2012, reaching about 70 percent of total mentions in this sector at the end of the sample period.

7
**Figure 4.4 Pension-system assets invested in government securities: Signs of a more diversified portfolio approach**

Percent of total <!-- Start of picture text --> 100<br>90<br>80<br>70 2001<br>60 2006*<br>50<br>40<br>30<br>20<br>10<br>0<br>Ghana Kenya Madagascar South Africa Zambia<br>%<br><!-- End of picture text --> Sources: Pension system asset data sourced from national pension funds and financial authorities. * End-2006 or most recent available; end-2005 for Madagascar and South Africa; end-2007 for Zambia.

Two countries’ insurance sectors reported sizeable investment allocations in government securities (Kenya, 45 percent, and Uganda, 65 percent), but insurance sectors overall held a relatively smaller proportion in government securities than their counterpart pension systems. In addition to Kenya and Uganda, Madagascar had holdings in government securities exceeding 20 percent. Tanzania’s insurance sector reported that a combined 33 percent of assets were held in a combined category of “bank deposits and government securities.” Bank deposits and cash constitute a large proportion of insurers’ asset holdings in several countries: the West African Economic and Monetary Union (WAEMU) countries, where these holdings range from 29 to 47 percent, and Sudan, where 71 percent of total sector assets were held in deposits, according to the most recently available data<sup>**36**</sup> ). Although data are not available, in Chad, bank deposits reportedly constitute a “sizeable portion” of insurers’ investment portfolios. Nevertheless, bank deposits and cash make up a lower portion of holdings by insurance sectors than corresponding national pension systems in some countries: Ghana, Nigeria, Uganda, and Zambia.

Somewhat surprisingly, those national insurance sectors with relatively higher holdings in non- or low-yielding investment vehicles such as cash/bank deposits and government securities do not in all cases accord a smaller role to the private sector in the provision of insurance services. For example, the five WAEMU countries—Benin, Burkina Faso, Côte d’Ivoire, Niger, and Senegal—which overall have insurance sectors with a small or no role for the state, nevertheless hold large amounts in cash and deposits (see above). This could reflect the severely underdeveloped financial markets and lack of suitable investment vehicles, rather than a lack of appropriate emphasis on maximizing investment returns.

Institutional investors across the focus countries keep the vast majority, if not all, of their assets in domestic assets, with a few exceptions, although some countries are lowering ceilings on offshore 36 The most recently available data for Sudan’s insurance sector asset allocation are only current as of 2003, however (World Bank, FSAP database).

37
44 possible to carry out higher order dominance tests using "poverty deficit curves" which plot the area under the cumulative distribution at each expenditure level. Figure 3 depicts the first order dominance test for Nigeria using 1985 and 1993 information (as in Figure 3) 1/. In all classifications we find that poverty has declined between 1985 and 1993. The figures (in Appendix 2) show the cumulative distribution functions of national, urban, rural and Lagos urban.

In principle, the entire distribution of expenditure must be plotted, but in practice this can be restricted to the highest possible location of the poverty line. We restrict the distribution to 120% of the poverty line, a reasonable upper limit for the poverty line as seen in figure 3. Between 1985/86 and 1992/93 the two curves intersect between 40 percent and 60 percent of the poverty line, (around 45 percent) showing that the conclusions about poverty incidence are sensitive to where in that range the line is set. Any poverty line below 45 percent of the line, will result in an unambiguous increase in poverty.

The higher order dominance tests were carried out by plotting the poverty deficit curve and poverty severity curve. The deficit curve is the area under the incidence curve. A fall in poverty requires that the poverty deficit curve is nowhere lower for 1985 at all points up to the poverty line. As we see in Fig A2.4 (Appendix 2), the 1985 deficit curve intersects the 1992 curve around 35 percent of the poverty line, which makes the second-order dominance test inconclusive. The third order dominance test relies solely on the distribution sensitive measures such as P2. A poverty severity curve is the area under the poverty deficit curve; the dominance test is again inconclusive, since the 1985 severity curve intersects the 1992 curve around 55 percent of the poverty line. This means that conclusions about poverty distributions are not likely to be robust.

If the poverty line was atN 158.16 per annum per person then poverty would increase between 1985-92 from 2.67 percent to 3.69 percent. This indicates that in terms of poverty trends while the whole Nigerian population did not uniformally benefit from the economic growth achieved since the mid 1980s. There are substantial segments of the population, especially those in the bottom decile, who have actually neither participated nor benefited from the economic growth since the mid 1980s. Hence the future growth strategy for Nigeria needs more poverty focus if it has to ever prove beneficial to the extreme poor.

**7/** We also do the dominance tests in higher order (using the poverty deficit curve and poverty severity curve) and also in terms of broader regions and geographical zones. The results are presented in Appendix 2.