rafmacalaba/datause-encoder-data huggingface dataset:
significant at less than the 1 percent level. The associated marginal effect is an increase of 4.5 percentage points in the likelihood of a firm having a female manager when we move from manufacturing to the retail sector. This is a large effect even though it is lower than the corresponding marginal effect of 5.3 percentage point we found in column 4.

As above, the construction services dummy and _Employment_ continue to show a statistically significant negative correlation with the likelihood of having a female manager; however, none of the service sectors or the firm-level controls, including city size, shows any significant correlation with the dependent variable.

We also checked for any significant difference in the proportion of female managers between retail and the remaining service sectors. We find that for all the specifications discussed above, the likelihood of having a female manager is much higher, economically and statistically (significant at less than the 1 percent level) among retail firms compared with any of the remaining service sectors (not shown), and this holds irrespective of the set of controls used above. Hence, retail is special not just vis-à-vis manufacturing but also when compared with the other services sectors.

# **4. Extensions**

In this section, we explore how the strength of the relationship between _Retail_ and _Female_ varies, if at all, across firms of different types. Specifically, we consider how the strength of the relationship varies between small vs. large firms (that is, with _Employment_ ) and for firms located in small vs. large cities (that is, with _Large city_ ). To this end, we interact _Retail_ with _Employment_ and _Retail_ with _Large city_ and add these interaction terms to the specifications discussed above.

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**Table M2: Intrasectoral Allocaion of Sector Resources** (Percentage of reports in each category)

|Country Groupings|Insufficient<br>Allocation to<br>Primary<br>Education|Excessive<br>Allocation to<br>Higher<br>Education|No<br>Assessment|
|---|---|---|---|
|Low PE (17)|71|77|12|
|High PE (19)|26|53|37|
|Low SRM (20)|40|60|30|
|High SRM (16)|56|69|19|
|Total (36)|47|64|25|

Note: Parantheses indicate the number of reports in each category.

3.24 Judgments in the reports about the allocative equity and<sup>efficiency of</sup> government spending on the respective levels of education<sup>are based on two types of</sup> criteria: (i) the distribution of the sector budget (recurrent, capital,<sup>or total) relative to the</sup> corresponding distribution of student enrollments among the various<sup>education levels; (ii)</sup> the ratio of public expenditures per student at the various levels<sup>to unit expenditures at the</sup> primary level, or to per capita GNP. In both approaches, regional<sup>comparisons sometimes</sup> complement the analysis. Neither approach explicitly considers<sup>internal efficiency or quality</sup> differences between the various levels and types of education.<sup>As a result, assessments of</sup> the adequacy of the intrasectoral allocation of the education<sup>budget, even though well-</sup> intentioned and seemingly rational, all reflect a varying degree of<sup>arbitrariness.</sup> 3.25 As an example of the first approach, both the Burkina Faso and<sup>the Senegal</sup> reports consider the intrasectoral allocation of public resources<sup>skewed in favor of higher</sup> education. Burkina Faso allocates 32 percent of its education<sup>budget to higher education;</sup> Senegal, only 19 percent. But higher education serves about 1.5 percent<sup>of the total student</sup> population in Burkina Faso, and close to 2 percent of it in<sup>Senegal. Both countries</sup> combine a high degree of sector resource mobilization with a low<sup>primary enrollment rate.</sup> Whereas the diagnosis of a misallocation of sector resources may<sup>appear intuitively credible</sup> in the case of Burkina Faso, it is less so for Senegal, where the<sup>overriding issue might be</sup> the inefficient use of resources at all education levels.

3.26 A few of the reports appeared to advocate reallocating resources<sup>to primary</sup> education solely because of the distribution of the education<sup>budget, not because of the</sup> corresponding distribution of enrollments. Among the countries advocating such a 28
# **1. Global value chains in the automotive industry: a nested structure**

From a geographic point of view, the world automotive industry, like many others, is in the midst of a profound transition. Since the mid-1980s, it has been shifting from a series of discrete national industries to a more integrated global industry. In the automotive industry, these global ties have been accompanied by strong regional patterns at the operational level (Lung et al, 2004; Dicken, 2005, 2007). Market saturation, high levels of motorization, and political pressures on automakers to “build where they sell” have encouraged the dispersion of final assembly, which now takes place in many more places than it did 30 years ago. While seven countries accounted for about 80 per cent of world production in 1975, 11 countries accounted for the same share in 2005 (Automotive News Market Data Books, various years). The widespread expectation that the markets in China and India were poised for explosive growth generated a surge of new investment in these countries. The recent economic crisis is serving to reinforce and accelerate many of these trends.

Consumer preferences sometimes require automakers to alter the design of their vehicles to fit the characteristics of specific markets.<sup>1</sup> In other places, automakers want their conceptual designers to be close to “tuners” to see how they modify their production vehicles. These motivations have led automakers to establish a series of affiliated design centers in places such as China and Southern California. Nevertheless, the heavy engineering work of vehicle development, where conceptual designs are translated into the parts and sub-systems that can be produced by suppliers and assembled into a drivable vehicle, remain centralized in or near the design clusters that have arisen near the headquarters of lead firms.<sup>2</sup> The automotive industry is therefore neither fully global, consisting of a set of linked, specialized clusters, nor is it tied to the narrow geography of nation states or specific localities as is the case for some cultural or service industries. Global integration has proceeded at the level of design and vehicle development as firms have sought to leverage engineering effort across products sold in multiple end markets. As suppliers have taken on a larger role in design, they in

> 1 Examples include right vs. left hand drive, more rugged suspension and larger gas tanks for developing countries, and consumer preferences for pick-up trucks in Thailand, Australia, and the United States.

> 2 The principal automotive design centers in the world are in Detroit, USA (GM, Ford, and Chrysler, and more recently Toyota and Nissan); Cologne (Ford Europe), Rüsselsheim (Opel, GM’s European division), Wolfsburg (Volkswagen), and Stuttgart (Daimler-Benz), Germany; Paris, France (Renault); and Tokyo (Nissan and Honda) and Nagoya (Toyota), Japan

2
# **Figure 2.  The gender gap in account ownership persists**

<!-- Start of picture text --> 100%<br>80%<br>High‐income economies<br>60%<br>Developing economies<br>40%<br>20%<br>0%<br>2011 2014 2017<br>Share of adults with an account<br><!-- End of picture text --> _Source_ : Global Findex database. The lines represent the share of male and female adults aged 15 and above who own an account at a financial institution or through a mobile money provider.

In some ECA countries, the gender gap is much larger than the developing country average of 9 percentage points (table 2and figure 3). The largest gap is in Turkey, where 83 percent of men but just 54 percent of women have accounts (figure 4). The gender gap is about three times as large as the average gap in developing countries. The income gap in account ownership is about 21 percentage points in Turkey, much higher than the global average. Younger adults are also less likely to own an account than older adults (see box 1). Such countries include Armenia (15 percentage points), Kosovo (17 percentage points), Tajikistan (10 percentage points), and Turkmenistan (10 percentage points).

**Table 2. Gender gap in account ownership varies by country**

||_2_|_011_|_2_|_014_|_2_|_017_|
|---|---|---|---|---|---|---|
|_Country_|_Men_|_Women_|_Men_|_Women_|_Men_|_Women_|
|_Central Asia_|22|22|39|39|46|43|
|Kazakhstan|40|44|52|56|57|60|
|Kyrgyz Republic|4|4|18|19|41|39|
|Tajikistan|3|2|14|09|52|42|
|Turkmenistan|0|1|..|..|46|36|
|Uzbekistan|24|21|42|39|38|36|
|_Central Europe and the Baltics_|69|67|77|72|80|78|
|Bulgaria|50|55|63|63|71|74|
|Croatia|90|87|84|88|90|83|
|Czech Republic|81|81|85|79|84|79|
|Estonia|96|97|98|97|98|98|
|Hungary|72|73|72|72|78|72|

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**Figure 6: Location of commercial sorghum farms in 2003/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands**

<!-- Start of picture text --> Legend<br>Kebele boundary 2004<br>Yield (q/ha) by quintile<br>0 - 5<br>6 - 8<br>9 - 11<br>12 - 15<br>16 - 28<br>Region boundary<br>Buffer around Sorghum farm (04)<br>Distance (km)<br>25<br>50<br>100<br>150<br><!-- End of picture text --> 31
EV ERY = (max [integer � LNO] � START )=(SHHS � 1) (7) Intuitively, we determined EVERY such that the sequence of households to be sampled is evenly distributed over the list of households in the village, i.e. evenly distributed over HHSVC.<sup>47</sup> A replacement strategy was also designed. The replacements are selected as follows. If a selected household with HHSVC code x could not be surveyed, the household with HHSVC code x+1 should be selected. If that is not feasible (because there is no x+1 household or because that household could not be interviewed, or because that household has already been interviewed), the household with HHSVC code x-1 should be selected. If that is not feasible, the household with HHSVC x+2 should be selected, and thereafter x-2, etc.

# A.1.3 Ex-post Survey

The same sample of health facilities, villages and households that were sampled and surveyed in 2004 were re-surveyed in the ex-post survey at the beginning of 2006, and there was likely to be cases where the previously surveyed household could not be interviewed for some reason (i.e. the household had moved or died etc.), a replacement strategy was designed. The replacements were selected as follows. If a selected household with HHSVC code x could not be surveyed, pick the household residing to the right of household x. If that is not feasible (because there is no household to the right or because that household could not be interviewed either, or because that household has already been interviewed), pick the household residing to the left of household x. If that is not feasible, pick the household residing two houses to the right of household x, and then two houses to the left of household x, etc.

In total, 4,996 households were surveyed in the ex-post survey, 4,373 of which were resurveyed.

> 47Denote LAST as the last household in the list to be surveyed (i.e. the sampled household with the highest HHSVC). Then LAST = START + EVERY*((SHHS -1).

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their services sectors but also promotes manufacturing and other goods producing sectors. Countries like China that may be concerned with the sustainability of their manufacturing export success may consider building stronger services sectors as a way to upgrade their manufacturing sectors to an even higher level. According to Appendix 1B, China’s business services exports in value added terms, relative to its exports in gross terms, are less impressive compared to the corresponding figure for financial services shown in Appendix 1A. Both its direct and indirect business services exports are only 8-9 percent of the corresponding numbers of the U.S. Drawing from the firm-level data in ORBIS, Miroudot and Cadestin (2017) show that China is the only country in their sample which has a majority of the manufacturing firms (77 percent in 2013) selling only goods, with little bundling of goods and services such as manufacturing and distribution services, as seen with Apple iPhones/iPads and Apple Stores. To strengthen the manufacturing sector, countries need to focus not just on manufacturing production, but also on services upgrading including but not limited to R&D, marketing, advertising, inventory management, quality control, production scheduling, after-sale technical supports, and follow-up customer services.

With significant improvement in transportation and communication technologies and increasing services outsourcing activities, some developing countries such as India have developed competitive services sectors. For example, Indian services RCA calculated based on DVA in gross exports are either greater than one (financial services) or close to one (business services), much bigger than the corresponding numbers for other developing economies such as China. For countries like India, our paper suggests that the manufacturing sectors that use these services intensively tend to have a comparative advantage. However, different from most of the other WIOD countries, Indian gross exports of business services are actually larger than its total value added exports, suggesting relatively little embodied business services in other sectors, as shown by the direct and indirect ratio of Indian business services exports in Appendix 1B. There is plenty of room left for India and similar countries to take advantage of their competitive services sectors during their industrialization process. This illustrates the importance for policy makers and entrepreneurs to understand the implications of inter-sectoral linkages.

We also provide evidence for a bypass effect, that is, countries may bypass their inefficient domestic services sectors by relying more on imported services inputs. This suggests that nations 24
In (8), the operator ∆ represents the difference of a variable between 2015 and 2008. ∆ _ETR_<sup>_oecd_</sup> _r_ is the change in instrumental variable obtained by substituting OECD average robots’ exposure in (7). ∆ _Yr_ is the change of an outcome variable in regency _d_ . The change in regency-level demand shifter is denoted by ∆ _Dr_ . The vector _Xr_ includes a series of regency-level characteristics in the 2007 base year, which may affect the outcome variables including: i) population; ii) shares of workers with tertiary and no education (separately); iii) real output per capita, and iv) the shares of manufacturing employment. Finally, equation (8) includes province fixed effects _up_ to account for differences in local economic development.

This approach is in line with Acemoglu and Restrepo (2017), Dauth et al. (2017) and Giuntella and Wang (2019), which use long differences to capture aggregate longrun trends in local labor markets. That is different from the plant level regressions that instead exploit yearly variation in robots’ import. We argue that the yearly analysis is appropriate for two reasons. First, running the plant level specifications in long difference would reduce the sample size by almost two-thirds relatively to the yearly regression model as it would only focus on surviving plants throughout the entire period.<sup>33</sup> Second, dropping plants that were in the sample in 2006 but exited before 2015, might introduce selection bias.

In the baseline specifications, standard errors are robust to arbitrary heteroskedasticity within each regency. We also experiment with two alternative specifications. First, we calculate shift-share standard errors following the methodology of Adao et al. (2019). They show that shift-share designs, such as those used in this part of the analysis, might lead to residuals that are correlated across regions with similar industry shares and so over-rejection of the null hypothesis of no impact. In the second alternative specification, we provide errors clustered at the province-level.

Table A4 presents the first stage regression of regency-level exposure on the instrument based on the OECD region. In column (1), we compute the share of employment at high risk of automation based on GM. The coefficient of OECD exposure is significant at the

> 33Labor force survey data are not subject to this problem, which allows us to estimate models in long

31
combined with district level indicators such as concentration of industry in the district, urban population density, and potential access to urban markets.

# **1.5 Summary information on spatial variation**

Before moving on to discussing the results from the empirical analysis, we provide a general overview on the concentration and basic characteristics of firms in the study sectors. We first divide the economic landscape to comprise of non urban areas, urban areas, and large metropolitan areas. The metropolitan areas include the following cities and their urban agglomerations-Delhi, Mumbai, Kolkata, Chennai, Bangalore, and Ahmedabad. Using the sample data from the ASI for 1998-99, we see that average wages across industries are the highest in metropolitan areas (see Table 1). In comparison to a nationwide average annual of Rs. 60,000 per employee, labor remuneration is Rs. 74,000 for metropolitan areas, Rs. 54,000 for other urban areas and Rs. 50,000 for non urban areas. Among various industries, annual wages are the highest in electronics and computers (Rs. 101,000 per employee) and lowest in the leather industry (Rs. 41,000 per employee). Even within sectors (not shown in this table), wages tend to be higher as we move up the urban scale.

Productivity indicators such as output per employee and value added per employee show interesting trends. While per employee output is relatively quite high in several industries, the value added figures show quite a different situation. For example, per employee output in computing and electronics is Rs. 344,000 but value added per employee is only Rs. 65,000. Similarly, the numbers for output and value added per employee are Rs. 376,000 and Rs. 79,000 for 22
Management Agency, the Dumping and Subsidies Investigation Management Agency, the Injury and Safeguards Investigation Management Agency, the Accounting and Financial Analysis Management Agency and the International Legal Proceedings Management Agency.

The UPCI’s budget allocation grew substantially and it soon became the administrative unit with the largest qualified staff, outperforming any other department of the Secretariat in terms of both personnel and budget allocation.

The role of the competent authorities was both strengthened and diversified. In addition to conducting investigations, the UPCI would act as a technical advisory unit, cooperate in drafting any amendment to the Foreign Trade Act, support the SECOFI’s determinations submitted before dispute settlement bodies, and offer technical and legal assistance to Mexican exporters who were under investigation in a foreign country for unfair practices or safeguard measures.<sup>38</sup> The activities undertaken by this new entity —concerned with the administration of an instrument so distrusted in many important countries in the world because of its protectionist use at a time in which Mexico was committed to policies reinforcing its trade and economic liberalization process— were ambivalent and even contradictory. This can be inferred from officials’ communications as well as from governmental programs of that period.<sup>39</sup> In this context, the Program for Industrial and Foreign Trade Policy ( _Programa de Política Industrial y Comercio Exterior_ or PROPICE) clearly shows the Government’s continuing concern arising out of the conflict between the greater use of antidumping instruments and the trade policy objectives. Since the liberalization process had been undertaken with the key objective of increasing the manufacturing sector’s competitiveness, the government feared the negative impact that antidumping measures might have on capital and intermediate goods.

> 38 SECOFI 1997. _Informe de Labores_ UPCI 1991-1996, SECOFI.

> 39 While the National Development Plan 1995-2000 set forth that one of its key guidelines on foreign trade was to “penalize with utmost rigor and efficiency any unfair trade practice affecting our manufacturers,” the main objectives of the antidumping policy under the Program for Industrial and Foreign Trade Policy (PROPICE) revealed the government’s concern over the potential conflict between contingent protection measures and the new development model for the country: “...in designing our policy against unfair international trade practices, we should take into account that applying countervailing or antidumping duties may severely affect our manufacturers’ competitiveness in the coming years” on account of the substantial increase of investigations filed.

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- 46 -

production.

- Assess the economic and social benefits which result from women's production, e.g. improved nutrition, increased funds for school fees, better healthcare, etc.

- Assess and educate the public in the increase in production if women were fully included in agricultural support services.

- f. Institutional Arrangements

4.28 How can national and international research organizations best collaborate to develop a research agenda to meet the needs of women farmers? The International Agricultural Research Centres (IARCs), in their relationship with the national research organizations should:

- Sensitize policymakers and researchers to the importance of considering users in setting priorities and programming for research.

- Inform policymakers about research results and technologies or potential technologies and their policy implications.

- a Develop methodologies for survey work and for testing and validation of research that give full consideration to user needs.

- Train researchers in the use of these methodologies and in programming user-oriented research. (Murphy in Rockefeller ISNAR 1985)

4.29 While the IARCs clearly have a role in sensitizing national researchers and policy-makers to the importance of addressing women farmers' needs in research and technology, it is the national research institutions which should undertake the bulk of this research and inform policymakers about implementation and adoption. As ILCA has emphasized: "When policy constraints are a limiting factor in adoption, national agencies are in an advantageous position to encourage policy changes" (Jiggins 1986).

4.30 To develop the technology to raise the productivity of rural women, these national research systems must focus on the tasks, enterprises, constraints, and objectives of female farmers. A collaborative approach and an understanding of women farmers based on adequate socioeconomic and baseline agricultural data are crucial. As such data become available, special efforts will be needed to share it, since individuals and institutions that are not part of the CGIAR system are likely to be the dominant actors. One recommendation is to develop a "clearinghouse-cum-network.n (L.S. Hardin, in Rockefeller/ISNAR 1985, p.18).

# Conclusion

4.31 The extension service will only be able to "sell" the extension message if that message suits the farmer's situation. This 'suitability' has various aspects. Most farmers will consider changing an established practice or trying something new only if the proposed change technology answers a need the farmer has expressed, is appropriate and relevant to the individual