rafmacalaba/datause-encoder-data huggingface dataset:
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Goldberg, Pinelopi, Amit Khandelwal, Nina Pavcnik, and Petia Topalova (2010a), "Multiproduct Firms and Product Turnover in the Developing World: Evidence from India," Review of Economics and Statistics, 92(4): 1042-1049.

–– (2010b), "Imported Intermediate Inputs and Domestic Product Growth: Evidence from India," Quarterly Journal of Economics, 125(4): 1727-1767.

Griffith, Rachel, Elena Huergo, Jacques Mairesse, Bettina Peters (2006), “Innovation and Productivity across Four European Countries,” Oxford Review of Economics Policy, 22(4): 483-498.

Griffith, Rachel, Stephen Redding, and Jon Van Reenen (2004), “Mapping the Two Faces of R&D: Productivity Growth in a Panel of OECD Industries,” Review of Economics and Statistics, 86(4): 883-895.

Grossman, G. M. and Helpman, E., 1991, _Innovation and growth in the global economy_ . Cambridge, Massachusetts, MIT Press.

Haddad, Mona and Ann Harrison (1993), "Are There Positive Spillovers from Direct Foreign Investment? Evidence from Panel Data for Morocco," Journal of Development Economics, 42: 51-74.

Hsieh, Chang-Tai and Peter J. Klenow (2009), “Misallocation and Manufacturing TFP in China and India,” Quarterly Journal of Economics, 124(4): 1403-1448.

Katayama, Hajime, Shihua Lu, James R. Tybout (2009), “Firm Level Productivity Studies: Illusions and a Solution,” International Journal of Industrial Organization, 27(3): 403-413.

Keller, Wolfgang (2004), “International Technology Diffusion,” Journal of Economic Literature, 42(3): 752-782.

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19
Policy Research Working Paper 9919

# **Abstract**

With only 32 percent of working-age women in the labor market, Guatemala is an upper-middle-income country with one of the lowest rates of female labor force participation in the Latin America and the Caribbean region, and in the world. The rate of female labor force participation is especially low in the poor regions of the North and the Northwest. This paper explores information from different micro data sets, including the most recent Population Census (2002 and 2018) to assess the drivers of the recent progress. Between 2002 and 2018, the female labor force participation rate increased 5.7 percentage points, from an average of 26 to 32 percent nationwide. This increase was partly explained by the drastic increases in the educational attainment of women, the reduction in fertility, and the country’s structural transformation toward services. However, a large component remains unexplained. Exploring 2018 data, the paper shows that social norms, attitudes toward women in society, and public policies are important determinants of these changes. The analysis suggests that, taken together, these factors can all become an important source of increased female labor force participation moving forward.

This paper is a product of the Human Development Team and the Central America and the DR Country Management Teams, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ralmeida@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **I.  INTRODUCTION**

Although the short-run benefits of global trade and financial integration remain a matter of debate, there is broad consensus among economists and policymakers that the longer-run effects of trade liberalization can be substantial. By eliminating distorted incentives (to engage in rent-seeking activities, for instance), trade openness may lead to improved allocation of resources among sectors. By promoting competition in domestic markets, it may force domestic firms to improve the quality of their products: If a foreigner produces a better product that can be imported, domestic firms must make a better product at a lower price to keep selling their product at home. Reducing barriers to imports may also help to promote exports; increased competition from imports lowers the profits that firms can earn by focusing solely on the domestic market, and may increase incentives to export. By facilitating the acquisition of new inputs, intermediate goods, and improved technologies, and by increasing knowledge spillovers from more advanced trading partners, trade integration may also exert beneficial effects on productivity of the economy.

There is indeed broad empirical evidence indicating that trade openness has positive effects on productivity and economic growth; among recent studies are Lee, Ricci, and Rigobon (2004), Winters (2004), and Wacziarg and Welch (2008). The latter study, in particular, found that over the 1950-98 period countries that liberalized their trade regimes experienced average annual growth rates that were about 1.5 percentage points higher than before liberalization. Post-liberalization investment rates rose 1.5-2 percentage points, whereas the average trade to GDP ratio increased by roughly 5 percentage points.<sup>1</sup> At the same time, however, there has been growing recognition that supply-side bottlenecks in developing countries (particularly the poorest ones) may seriously constrain the ability to reap the benefits from trade liberalization. In particular, the lack of basic transportation infrastructure (ports, roads, and airports) may prevent businesses from sending their goods abroad. Given the lumpy nature of these investments, and the difficulty of

> 1 They also noted, however, that there were large differences across countries.

3
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Table 4. Benchmarking port indicators: Tema and Takoradi compared with other ports**

||**Takoradi (Ghana)**|**Tema (Ghana)**|Apapa (Nigeria)|Dakar (Senegal)|Abidjan (Cote<br>d’Ivoire)|Cotonou (Benin)|Mombasa (Kenya)|Durban (South<br>Africa)|
|---|---|---|---|---|---|---|---|---|
|**CAPACITY**|||||||||
|Actual container handled (‘000sTEU/year)|**51**|**471**|336|306|500|158|437|1,899|
|Actual general cargo handled (‘000s tonnes/year)|**4,300**|**7,900**|3,400|6,109|NA|1,100|12,980|16,100|
|General cargo handling capacity (‘000s tonnes/year)|**5,500**|**8,500**|5,000|NA|NA|2,500|1,500|NA|
|**EFFICIENCY**|||||||||
|Average container dwell time in terminal (days)|**NA**|**25**|42|7|12|12|5|4|
|Average truck processing time for receipt and delivery<br>of cargo (hours)|**NA**|**8**|6|5|2.5|6|4.5|5|
|General cargo vessel pre-berth waiting time (hours)|**3**|**9.6**|NA|24|2.9|48|36|NA|
|General cargo vessel turnaround time (hours)|**52.8**|**48**|NA|60|2.2|48|48|NA|
|Average container crane productivity (containers<br>loaded/unloaded per crane hour)|**NA**|**13**|12|NA|18||10|15|
|Average general cargo crane productivity (tons<br>loaded/unloaded per crane working hour)|**10**|**13.5**|9|NA|16|15|20.82|25|
|**TARIFFS**|||||||||
|Average container handling charge, ship to gate<br>(US$/TEU)|**NA**|**168**|155|160|260|180|67.5|258|
|Average general cargo handling charge, ship to gate<br>(US$/tonne)|**7.0**|**10.0**|8.0|15.0|13.5|8.5|6.5|8.4|
|Average dry bulk handling charge, ship to gate or rail<br>(US$/tonne)|**2.5**|**3.0**|NA|5.0|5.0|5.0|5.0|1.4|
|Average liquid bulk handling charge, ship to gate or<br>rail (US$/tonne)|**1.5**|**1.5**|1.0|4.0|NA|NA|NA|NA|

Source: Mundy and Penfold 2008. Derived from AICD ports database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data TEU = 20-foot equivalent units.

Across the region, there has been a tendency for aircraft fleets to be renewed and scaled down in size to facilitate consolidation of routes toward a hub and spoke system. The aircraft fleet serving Ghana has renewed rapidly in recent years, and the share of the fleet that comprises aircraft of recent manufacture rose from 52 percent in 2001 to 97 percent in 2007, which puts Ghana ahead of many of its neighbors. On the other hand, only about 16 percent of aircraft serving Ghana are in the small or medium size category, a share much lower than that of neighboring countries.

15
for each product from each partner country, multiply that by the bilateral import demand elasticity to obtain the change in trade at tari¤ line level and constrain the fall in trade to be no more than the level of imports in 2008. Summing all changes in trade at the tari¤ line level across all partners gives us the total change in trade, change in trade using tari¤s = Xn<sup>max [mn;c;2008"n;c;2008 (tn;c;2009 �tn;c;2008) ; �mn;c;2008] :</sup> (5)

# 3 Data

We obtained both the MFN tari¤ and bilateral tari¤ data for 135 countries from the International Trade Center (ITC) in Geneva. For India, Japan and South Korea we supplemented the ITC data with MFN schedules from other sources.<sup>4</sup> Table 1 presents some summary statistics of these schedules. In terms of the MFN tari¤s, the countries that have the highest simple average tari¤ in 2009 are Sudan (20.5%) and Morocco (20.2%). However, once we factor in the presence of preferential tari¤s in most bilateral trade, the average tari¤s in 2009 are lower.<sup>5</sup> Countries that have the highest average bilateral tari¤s in 2009 are Maldives (20.2%), Gambia (18.7%) and Sudan (18.5%).

Between 2008 and 2009, many countries actively adjusted their tari¤ policies. Countries that have had the largest percentage of tari¤ lines with increased tari¤s during the two-year period are

> 4 India’s 2008 and 2009 MFN schedule as well as Japan’s 2008 MFN schedule come from TRAINS. Japan’s 2009 MFN schedule was obtained from <http://www.customs.go.jp/english/tari¤/2010/index.htm>. South Korea’s 2009 MFN schedule comes from <http://english.custom s.go.kr/kcsweb/user.tdf ?a= user.customtari¤.Custom Tari¤App& c= 1001& m c= ENG LISH_ INFO RM ATIO N_ KO REA >. For these three countries, we lacked ad-valorem equivalents of 2009 speci...c tari¤s, hence we used the 2008 values.

> 5 The simple averages bilateral tari¤s for most countries are less than those of the MFN tari¤s, because of the presence of preferential tari¤s in most bilateral or regional trade agreements. However, given that the MFN data we obtained from the ITC are in tari¤ line level, which for some countries are HS 8 or HS 10 digit level, while the bilateral tari¤ data are in HS 6 digit level, the average MFN tari¤ may appear lower than the bilateral tari¤s.

8
II firm's ability to earn above-market returns on the tangible positions shown in its tangible balance sheet. In principle, financial economists seek to partition a corporation's intangible net worth into three time-related portions:

- a) the value of "identifiable" intangible assets such as a corporate reputation, staff, business locations, and a customer base whose value is built up from wise expenditures made in the ps;

- b) the capitalized value of ongoina. net regulatory subsidies or burdens that flow from existing and projected laws and supervisory practices;

- c) the present value of growth opportunities (PVGO), which varies according to strategic adaptations and innovations that the corporation might make in the fu.

Stock market values or takeover bids that diverge greatly from book values of tangible net worth make U.S. regulators uncomfortable. However, when U.S. regulators choose to give accounting relief to troubled institutions, they typically do so by selectively authorizing these institutions to record the values of specific categories of intangible assets. For example, in 1982 federal regulators authorized beleaguered thrift institutions to take into their balance sheets appraisal-based estimates of the extent to which theL office buildings had appreciated above their book value.

A skeptical attitude toward intangible net worth is embodied in the itemization rules established by the Basle agreement. To the extent that intangible sources of net worth are recognized, such items are classified as "secondary" rather than "primary" capital.

Downplaying intangible net worth is traditional among Western European regulators, who have long preferred that their banks maintain off-balance-sheet sources of strength. But it is doubtful that East European and deve'oping countries can afford to neglect intangible items. The desirability of assuring that intangible positions are valued fairly around the world should lead the World Bank to conduct research aimed at identifying reliable procedures for valuing intangible bank assets in different financial environments.

# Valuzalodn s

In different countries, professional accounting standards boards are free to authorize different valuation principles. For U.S. deposit institutions, GAAP bal.,nce
Table 2 –Parameter Estimates from Main Effects Models

||(1)<br>CL|(2)<br>MMNL<br>Cost Random|(3)<br>MMNL<br>Cost Fixed|
|---|---|---|---|
|Mean||||
|ASC|3.405<sup>***</sup>|19.25<sup>***</sup>|20.10<sup>***</sup>|
||(0.348)<br>|(4.119)<br>|(5.711)<br>|
|Durability|0.0530<sup>***</sup>|0.0708<sup>***</sup>|0.0759<sup>***</sup>|
||(0.00774)|(0.0105)|(0.0105)|
|Time Reduction|0.0128<sup>***</sup>|0.0193<sup>***</sup>|0.0153<sup>***</sup>|
||(0.00187)|(0.00254)|(0.00223)|
|Fuel Reduction|0.0175<sup>***</sup>|0.0263<sup>***</sup>|0.0217<sup>***</sup>|
||(0.00175)|(0.00318)|(0.00284)|
|Smoke Reduction|0.0119<sup>***</sup>|0.0195<sup>***</sup>|0.0177<sup>***</sup>|
||(0.00307)|(0.00433)|(0.00431)|
|Cost|‐0.00266<sup>***</sup>|‐0.00402<sup>***</sup>|‐0.00384<sup>***</sup>|
||(0.000543)|(0.000822)|(0.000670)|
|Standard Deviation<br>ASC||‐7.972<sup>***</sup>|‐8.265<sup>***</sup>|
|||(1.804)<br>|(2.368)<br>|
|Durability||‐0.0553<sup>***</sup>|0.0861<sup>***</sup>|
|||(0.0190)|(0.0129)|
|Time Reduction||0.0118<sup>*</sup>|‐0.00807|
|||(0.00687)|(0.00711)|
|Fuel Reduction||0.0431<sup>***</sup>|0.0410<sup>***</sup>|
|||(0.00378)|(0.00357)|
|Smoke Reduction||0.0200<sup>*</sup>|0.0281<sup>***</sup>|
|||(0.0118)|(0.00791)|
|Cost||0.00814<sup>***</sup><br>(0.000977)||
|Observations|9054|9054|9054|
|Log lik.|‐1936.2|‐1824.8|‐1844.4|
|Chi‐squared|2758.9|222.7|183.6|

Standard errors in parentheses<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01 To better interpret the results, we calculate the respondents’ WTP for each attribute. Table 3 presents marginal WTP estimates for each attribute for each model (holding other attributes constant). Durability has the highest marginal WTP per unit of each attribute, with respondents’ WTP 19.11 ETB on average (across the three models) for each additional year of use, followed by fuel reduction at 6.27 ETB on average for each additional percentage of fuel reduction, smoke reduction at 4.63 ETB on average for each additional percentage of smoke 14
There is also work on panel data-based analysis of the impact of the crisis for Indonesia. For instance, Beegle, Frankenberg and Thomas (1999) estimate how changes in per capita consumption and transitions into and out of poverty during 1997 and 1998 were related to a set of household and community covariates in 1997. Such panel data analysis however also comes with its own set of problems. First, a potential advantage of panel data is that we can eliminate potential bias due to any omitted observed or unobserved household level determinants of welfare, using household fixed or random effects. However, without an independent measure of the household-specific shock, the presumption is that everyone was hit by the crisis-induced shock, and this rules out the use of "difference-in-differences" estimation that has often been used in impact evaluation analysis.<sup>8</sup> Second, while panel data holds the promise of providing a direct measure of welfare change, there is also the thorny issue of measurement error. It is not clear how much of the observed change in household welfare or transitions into or out of poverty are signal rather than noise. Thus, even with panel data, empirical determination of the distributional impact of the crisis is not easily resolved.

There have also been estimates of self-rated poverty for the Philippines based on quarterly surveys conducted by the Social Weather Stations (Mangahas 1999). According to these surveys, the incidence of poverty<sup>9</sup> averaged 59% for the period 1996-97 while the average for 1998 was 61%. Similarly, Reyes et al. (1999) reported an increase in self-rated poverty from 40% just before the crisis to 43% in January l999."0 Quite apart from the use of a very different

> **8** A useful discussion of the properties of this and other estimators commonly used in the impact evaluation literature can be found in Angrist and Krueger (1999).

> 9 The incidence of self-rated poverty is not calculated using a pre-determined poverty line but by asking households where would they place their family on a card marked with the words "poor" and "nonpoor" and a line in between. See Mangahas (1999) for further details.

'° This is however based on a much smaller survey of 430 households in 31 communities. The sample households were selected on the basis of spatial and sectoral categories using purposive rather than a probability sampling (Reyes et al. 1999).

_5_
same currency is crucial to allow for comparison across projects.

Unit costs are provided for programs or sections; a program is a part of a loan or credit, or a number of road sections combined. Sections define unit costs for road works on particular segments of a road. In either case, we have information on the name of the project that the program or section is part of. Considering that a range of reports is used for the data collection, 44% of entries are estimated costs, 27% are contracted costs, and 29% are actual costs.<sup>13</sup> Unit costs from these different sources often differ by a large extent, so knowledge of the source is critical to compare unit costs. Individual road works activities also sometimes form part of a larger roads project. In order to account for the fact that there might be various costs types for the same projects, as well as various different work activities for the same project, we cluster the standard errors by country to allow for arbitrary correlation of costs within the same country. Table S.2 in the appendix shows the mean, median, maximum and minimum cost of various work types and work activities for both preservation and development works. The most expensive development work type is a new six lane expressway followed by a four lane expressway, while for preservation works the most expensive work type is concrete pavement restoration followed by strengthening.

Table 2 shows the range of average unit costs for a precisely defined work activity: asphalt overlays between 40 and 59 mm between 1996 and 1998 and 2006–08 ranked by the cost in US$ per km. We limit the time window in order not to conflate differences in unit costs with changes in input prices that might affect economies differently. What is striking is that even for a narrowly defined time window and work activity, there are differences in unit costs of a factor between three and four. Using these unit costs, an asphalt overlay for a length of 100 km would cost US$3,300,000 in the Dominican Republic in 1997, compared to US$11,000,000 in Tanzania in 1996, or US$10,500,000 in Pakistan in 1998. Two sources of heterogeneity remain. While costs per square kilometer of a precisely defined work activity in a short time window are likely to be comparable, one could argue that different road widths might contribute to higher unit costs. The ranking is largely unaffected when we

> for these countries we use the unit cost data provided in the database.

> 13For some projects the database contains costs on all of the three categories. We include all of the available cost data.

10
associated with higher income. This finding suggests that some aspects of mobility may be more relevant for development in some contexts than others.

The following section reviews relevant studies of intergenerational educational mobility. Section 3 examines measures of mobility. Section 4 describes the study’s empirical methodology. Section 5 describes the data. Section 6 presents the main patterns of intergenerational educational mobility. Section 7 presents the main results of the empirical analysis and associated robustness tests. Section 8 concludes.

# **2. Literature review**

Multiple studies carried out a descriptive exploration of the patterns of intergenerational educational mobility. Hertz et al. (2007) use data from 42 countries between 1992 and 2005 to measure the persistence of educational attainment across generations by birth cohort. They find significant variation in the cross-country levels of intergenerational educational mobility, with high persistence of educational attainment in Latin America and much higher educational mobility in countries of Northern Europe. Chevalier, Denny, and McMahon (2009) study the patterns of intergenerational educational mobility in 20 developed countries, including the United States, Canada, New Zealand, and 17 European countries, and find that expanding access to higher education is not always associated with increased mobility. <u>Causa and Johanson (2010)</u> find a positive and significant correlation between parental human capital and their children’s educational and wage outcomes in countries of the Organisation for Economic Co-operation and Development (OECD).

Financial constraints may hold back high-ability individuals from disadvantaged backgrounds. <u>Razzu and Wambile (2022)</u> study intergenerational educational mobility in 34 countries of SubSaharan Africa for cohorts born in the 1960s–1990s. They find that children’s educational outcomes depend strongly on their parents’ educational status but that the strength of this association is weaker for younger generations. The authors point to high heterogeneity in educational mobility across African countries. Van der Weide and others (2023) compile a global database of intergenerational educational mobility in 153 countries and demonstrate that such mobility is estimated to be lower in the average developing country than in the average highincome country.

4