rafmacalaba/datause-encoder-data huggingface dataset:
Second, domestic firms can imitate foreign technologies or managerial practices either through observation or by hiring workers trained by the foreign company–the demonstration channel.<sup>2</sup> The analysis employs firm-level information from 122 developing economies across 50 sectors from the World Bank Enterprise Surveys (WBES) to construct sectoral measures of these transmission channels and relates them to the performance of indigenous enterprises operating in the sector. The proxy for the linkages channel is the average share of inputs that MNCs source domestically; the proxy for the demonstration channel is the share of MNCs’ output in total sectoral output.

The absorptive capacity of domestic firms explains their ability to capture knowledge spillovers and endure competitive pressures. Absorptive capacity is the ability to recognize the value of new information, assimilate it, and apply it to improve production processes (Jimenez-Barrionuevo et al. 2011). Because absorptive capacity differs across firms and countries, I estimate FDI spillovers for the average domestic firm and for a subset of firms that accounts for a significant share of job creation in each economy. The distinctive characteristics of these enterprises, henceforth called high-growth firms, have been the subject of study both from the perspective of individual firms, interested in sales and revenue growth, and from the perspective of policymakers, interested in job creation and economic growth (Coad et al. 2014). Additionally, I estimate FDI spillovers transmitted through linkages and demonstration channels separately for six regions of the world and for the manufacturing and services sectors.

This paper recognizes that the different motivations of MNCs to invest in the developing world have different implications for FDI spillovers. I employ the typology proposed by Dunning (1993) and implemented empirically by Perea et al. (mimeo) to test whether FDI motivated by access to natural resources (natural resource-seeking), the size of the host market (market-seeking), or global value chains considerations (efficiency-seeking) generates different FDI spillovers to high-growth firms in developing countries. Spillovers steaming from different types of investment may differ not only because the linkages and demonstration channels vary, but also because competitive pressures are likely to be different.

> more stringent requirements from the foreign firms. Third, competition from other local firms for foreign consumers may also spur productivity upgrading. The analysis in this paper focuses on the knowledge diffusion impact of linkages.

> 2 See Alfaro et al (2006), Alfaro and Chen (forthcoming), Lipsey (2004), Barba Navaretti and Venables (2004) and Alfaro and Rodriguez-Claire (2004) for an overview of the empirical literature about the channels of FDI spillovers.

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The evolutions in Figure 4 for the entire textile industry and for apparel in particular, show that the experience of Bangladesh is mirrored in the overall export success of China and the entire South Asian region, but does not extend to sub-Saharan Africa. The graph shows a lot of lines, but the most important feature is they are trending up for China (in blue) and for South Asia (in green), but not for sub-Saharan Africa (in orange).

The share of global exports in textiles—summing over five segments of the textile industry<sup>4</sup> —for sub-Saharan Africa was only 1% in 1995. This fraction rose slightly to 1.2% by 2004, but subsequently it declined and the region ended the sample period in 2016 with a share of global textile exports of only 0.7%. The evolution was markedly different for China and South Asia (India+) which both saw their exports almost double as a share of global trade in this sector (shown on the right scale). As these two regions developed their textile sectors, they took away market share from more advanced countries, which in turn specialized in more advanced products than textiles. The combined market share of China and South Asia in textile exports grew from 27% to 45% over two decades.

It is not the case that this export increase in China and South Asia was merely a by-product of these regions’ rapid development over this period. Textile imports also increased, but that growth rate was much more subdued. As a result, their net trade balance in the textile sector evolved very favourably, at least from the perspective of boosting local industrialization. In China, it grew from a surplus of +9% in 1996 to a surplus of +28% in 2016. In South Asia, the net trade surplus doubled from +4% to +8% over the same 20-year period.

**Figure 4:  Evolution of exports and the trade balance for the broad textile industry in 3 regions**

<!-- Start of picture text --> 2% 40%<br>1% 20%<br>0% 0%<br>1995 2000 2005 2010 2015<br>-1% -20%<br>-2% -40%<br>Total exports (SSA) Trade balance (SSA) Apparel bal. (SSA)<br>Total exports (China) Trade balance (China) Apparel bal. (China)<br>Total exports (India+) Trade balance (India+) Apparel bal. (India+)<br><!-- End of picture text --> Note: All fractions and growth rates are calculated from nominal values in current USD. Total exports sum over five product categories in the textile industry and is expressed as a fraction of global trade. The trade balance subtracts total imports from exports. The apparel balance shows net exports limited to apparel products (the

> 4 The five segments of the textile industry: agri-inputs, textile equipment, textile, apparel, leather & footwear.

8
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5 Table 1. Government Employment, _early 1990s_ (as percent of population)

||_No. of_|_General_|_Government A_|_dministration_|_Teaching &_|
|---|---|---|---|---|---|
||_Countries0_|_Government_|_Central_|_Local_|_Health_|
|Africa|:iW20|2.0|0.9|0.3|0.8|
|Asia|11|2.6|0.9|0.7|1.0|
|Eastern Europe & former USSR|17|6.9|1.0|0.8|5.1|
|Latin America & Caribbean|9|3.0|1.2|0.7|1.1|
|MiddleEast & North Africa|iMI|3.9|1.4|0.9|1.6|
|OECD|21!|7.7|1.8|2.5|3.4|
|Overall|86|4.7|1.2|1.1|2.4|

Overall _Source:_ Table A-2. All averages are unweighted.

<!-- Start of picture text --> Figure 3. Govermnent employment, early 1990s<br>(various recent years)<br>8.0<br>7,0<br>g6.0<br>5.0 O~~~~~~~~~~~~~~~~~Teaching & Health1<br>4.0 ULocal<br>Z3.0 E aCentral<br>2.0<br>1.0<br>00 Africa Asia ECA LAC MENA OECD<br><!-- End of picture text -->

# **3.** THE **KEY FINDINGS ON GOVERNMENT WAGES**

Table 2 and Figure 4 show the fiscal weight of the central government _wage_ bll, and average wages as a multiple of per capita GDP and in relation to private wage levels. The reader should keep in mind the qualifications made earlier concerning international comparisons of public wages and the relative nature of the notion of "adequacy". Note also that, while the data for the wage bill ratio and for average wages - as multiple of GDP are averages for over 90 countries and therefore highly - represe-ntative the ratios of public to private sector wages come from a handful of countries for which evidence (primary or secondary) can be found. The reader is
It is important to note that, while the results of this study provide some evidence on the efficacy of gasoline and diesel price changes, many other avenues for reducing air pollution are available. Nonprice measures, such as technology mandates and catalyzer regulations, are underused and could improve air quality without diminishing consumption levels (UNEP 2021). And while 64 percent of countries have ambient air quality standards in legislative instruments, their distribution is lopsided toward developed countries. For example, while all EU countries have such regulation, only 40 percent of Commonwealth countries do. Therefore, encouraging the implementation of air quality legislation—such as mandatory filters and fuel mileage requirements for cars—could improve urban air quality. A single price adjustment alone will not curb air pollution; rather, a combination of measures need to work in tandem. This paper documents the role prices could play.

# 3. Data and methods

# 3.1. Data

To evaluate the relationship between fossil fuel prices and air pollution, we combine three datasets to construct a panel dataset with PM2.5 readings, GDP per capita, and fossil fuel prices and consumption for 133 countries.

First, we take surface PM2.5 concentrations in μg/m3 from van Donkeelar et al. (2021), who estimate mean annual air pollution levels globally using satellite imagery from 1998 to 2019. PM2.5 measures anthropogenic fine particulate pollution, such as exhaust emissions from fuel combustion, and is adjusted to remove naturally occurring particles, such as desert dust and sea salt. As it is possible to choose among a host of locations within a country that could affect readings, we homogenize our data to the center of each country’s capital city.

Second, we merge GDP per capita values, measured in 2017 international $ adjusted for purchasing power parity (PPP), from the World Bank’s World Development Indicators database (World Bank 2021a).

Third, we use IMF fossil fuel price and consumption data for 220 countries from 1980 to 2021 for coal, LPG, diesel, petrol, natural gas, electricity, kerosene, biomass, and other oil products, all commonly used energy types (Parry et al 2021). This data is made available by the IMF, and combines source data from IMF and World Bank country desk datasets, as well as a range of secondary sources as detailed by Parry et al. (2021, Annex B). As not all countries have data for gasoline, diesel, and coal during the study period, we reduce the number of countries included to 133. Also drawing on Parry et al (2021), we use fuel consumption in tons of oil equivalent (toe) data from the International Energy Agency, adjusted to a per capita basis using population numbers from the World Bank database (World Bank 7
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2

# I. **INTRODUCTION**

Much has been written on the issue of aid fungibility—the notion that governments alter their planned expenditure allocations so that foreign aid does not necessarily get spent where it is allocated but rather is diverted elsewhere.<sup>1</sup> However, the bulk of the literature to date has focused on the issue of _whether_ aid is fungible, most focusing on intersectoral fungibility (the issue of whether aid intended for, say, the transport sector in fact ends up financing defense projects) rather than intrasectoral fungibility (the issue of whether aid intended for, say, projects in poor rural areas ends up financing projects in affluent urban areas).<sup>2</sup> By contrast, few studies to date have examined two key questions, namely: How can one estimate the impact of development assistance in the presence of fungibility? And how far does fungibility reduce the benefits of aid? The few that have are aggregate studies based on cross-country regressions; they examine whether the conclusions reached in previous cross-country regressions linking foreign aid to outcomes such as economic growth and child mortality are modified when the (estimated) degree of fungibility of aid is taken into account.<sup>3</sup> This paper presents a microeconomic perspective on the two questions mentioned above. The scenario is a common one facing multilateral and bilateral aid agencies, namely a donor wanting to target its efforts on a specific country, and a specific sector

> 1 Pack and Pack (1990; 1993) seem to be the first published articles on the subject.

> 2 Studies of intersectoral fungibility are several, and include Pack  and Pack (1993), Feyzioglu, Swaroop and Zhu (1998) and Swaroop, Jha and Rajkumar (2000). Studies of intrasectoral fungibility are rarer, and include van de Walle and Mu (2007), who examine fungibility across Vietnamese provinces in assistance for road rehabilitation.

> 3 Pettersson (2007b) re-estimates the macroeconomic growth regressions of Burnside and Dollar (2000) distinguishing between aid that is fungibile and aid that is non fungible; he finds no evidence of fungible aid being any less effective than nonfungible aid. Pettersson (2007a) includes the estimated fungibility of aid in a cross-country regression of child mortality on GDP and aid volumes; he finds the fungibility of aid has no independent effect on child mortality.
for heads of household, while a 1-digit classification was available for all members. The surveys and years for different countries are as follows: Indonesia – Sakernas Survey for years 1994, 1996, 1999, 2001, 2003, 2005 and 2007; Philippines – LFS Survey for years 1988, 1991, 1994, 1997, 2001, 2004, 2006; Thailand - - Socio-economic Survey for years 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004; Vietnam – VLSS for years 1992, 1998, 2004, 2006; Cambodia – Socio-economic Survey for years 1997, 2003-4 and 2007; China – China Urban Labor Survey for years 1999 and 2005; and Mongolia – LSMS 1998, 2002 and 2007-8.

In estimating equation (1), the datasets were standardized across countries and years in terms of variables used. The dependent variable is the logarithm of hourly wage, except for Thailand where the dependent variable is the monthly wage, as the data did not contain the necessary information for the calculation of the hourly wage. The estimation sample contains workers over the age of 15. Data issues did not permit standardization of the composition of the sample of workers across countries: thus for Indonesia and Vietnam earnings are reported for those in wage employment; for the rest of the countries the sample includes a mixture of workers employed for wages.

# **4. Results**

# _4.1. Educational Attainment and Skilled Labor_

Trends in educational attainment by country are reported in Tables A1- A7 in the Appendix. The comparative percentage change in highest education qualifications over time is reported in Table 1 below. At the beginning of the 1990s with the exception of Cambodia, Thailand had the lowest educational attainment of the working population, as reflected in the average years of schooling as well as the proportion of workers with secondary or higher qualifications. Between 1990 and 2004 educational attainment in Thailand increased dramatically at all levels, especially at the upper secondary and tertiary levels with the proportion of workers with upper secondary and tertiary qualifications doubling during the same period. A similar evolution of educational attainment is observed in Indonesia. The proportion of workers with tertiary 5
# Speed and flexibility of responss to training needs

Except possibly in some large-scale vestibule training programmes, ad-hoc enterprise-based training does not suffer from the problems of overlycentralized decision-making, or the imperfect links between trainer and. employer, that tend to afflict institution-based training.

## Adaptability

Since trainers and training equipment can often be borrowed from production and returned to it according to need, the provision of such training is flexible. Furthermore, the flexibility is operational: there is little risk of an ad-hoc course outlasting its need and surviving autonomously. Indeed it is not uncommon in larger firms to find that change is taken for granted, training being organized in cycles beginning with the development of a fresh curriculum and materials. The issue facing the training designer is not whether but when the new system will have to be replaced in turn and what initial development investment would be most cost-effective (Kearsley and Compton, 1981).

## Innovation

Employer-based training is criticized for perpetuating obsolescent skills, often justly in the case of simple on-the-job training (for example, Cuervo, 1985). Sometimes standards actually fall because older employees withhold some of their knowledge to protect their own jobs. As Thurow (1980) observes, the removal of this training disincentive may be an important benefit accruing frow a seniority system for hiring and firing in which each trainer's trainees is fired before he or she is.

Obviously new skills cannot be imparted by on-the-job training, but most enterprises surmount the problem by having recourse to various types of external training: short courses at government, industry or proprietary training centers, training by suppliers of equipment incorporating new technology, secondment to more advanced enterprises in other countries, etc. This is the case in both developing and industrialized countries, although it may be true that this infrastructure is weaker in the former. The problem is not so much with the implementation of a decision to innovate, as with the taking of the decision itself. The greater the weight of ad-hoc methods in the training of supervisory and managerial workers, the narrower their horizcns are likely to be. This is illustrated by the Daly, Hitchens and Wagner (1985) case-study of British and West German manufacturing plants in the metal-working trades. The senior staff in the British plants had a much lower level of formal technical training than their German counterparts and they were much less receptive to the timely introduction of new technology.

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the poverty levels obtained in the post-simulation case with those prevailing in the pre-simulation case. To calculate the poverty measures for the urban or the total population at t, we can either weigh the group-specific poverty measures of equation (11) by their initial population shares, wj0, or by their population shares at t, wjt, as in equation (6).

Given that the household survey data are discarded once the shape parameters are estimated, and given that the distribution function approach assumes implicitly that intra-group inequality does not change following a shock, we limit now the measurement of inequality to between-group inequality. The Theil index allows an exact decomposition of total inequality between within- and between-group inequality.<sup>12</sup> Between-group inequality, TBt, can be calculated as: Therefore between-group inequality depends only on sjt, the share of total income held by group j, y ̄jt, the mean income in group j, and y ̄t, the overall mean of income. Of course, to estimate changes in overall inequality, one may measure intra-group inequality with the survey that is used to estimate the shape parameters in the first place, and then add this measure in each period to between-group inequality. Figure 3 summarizes the simulation procedure when using the Beta distribution method.

# 3.3.2 Estimation Results and Goodness of Fit

Table A1 in Appendix B presents the estimated shape parameters of the Beta density distribution function for both disposable income and consumption expenditure, using our fictitious household survey. Initial experiments indicated that the estimation results were very sensitive to outliers. To increase the goodness of fit of our estimates, we eliminated for each group the five highest and five lowest values of the distribution. Table A2 in Appendix B compares the “observed” values for the poverty measures P0 and P1 for income and consumption with the predicted values using both sets of estimated shape parameters, that is, with and without dropping the extreme values (as defined above). Figures A1 and A2 show the corresponding “observed” and “fitted” (after dropping extreme values) cumulative distribution functions.

> 12See for instance Shorrocks (1984), Deaton (1997), and Cowell (1999).

14