rafmacalaba/datause-encoder-data huggingface dataset:
technology and have more capacity to finance technology adoption in imperfect financial markets settings (Cohen and Klepper, 1996). Note, however, that our regressions already control explicitly for firm access to finance. Second, the economies of scale from which large firms benefit allow them to operate with a more efficient division of labor resulting in better conditions for mechanization and technological upgrade.

The relationship between governance and technology adoption in Table 2 is more nuanced. Better governance (indicated by a higher value of the index) is negatively associated with all types of technology adoption, significantly so for ISO certification in 2002 and for web-use in 2005. In Table 3 we show that these results for governance actually differ across country groups, while the results on other proxies for market incentives are similar across country groups. For firms in EU-8 countries, better governance is associated with more frequent ISO certification and more frequent web-use. This finding is also true to some extent in South Eastern European countries and Turkey for web-use. In these two country groups, and particularly in the EU-8, predation and rent seeking opportunities have been reduced and thus better governance provides the right market incentives for more technology adoption. That is not the case in CIS countries, which drive the negative correlation between governance and technology adoption shown in the ECA-wide regressions in Table 2. While this result may appear counter-intuitive, we can rationalize it as follows. First, this result may reflect reverse causality. Firms that are ISO-certified or use the web are likely to perform better, be more profitable, and hence be more visible to government officials and more subject to inspections, visits, and bribe extraction. Indeed proxies for bribe extraction enter the calculation of the governance index. Second, the governance result is consistent with the results for competition. In locations where governance is weaker due to poor property rights enforcement, high potential for expropriation and corruption, the firms that are more able to extract rents are those that have more capabilities to adopt advanced technology.

Finally, we should note that our results on market incentives do not seem to suffer from multicollinearity problems since the coefficients for the BEEPS 2002 and the BEEPS 2005 samples are qualitatively unchanged when we estimate a variant of Equation (2) where we exclude the complementary inputs factors and the factors related to access to international knowledge.

# **_IV.3 Access to International Knowledge – Cross Sectional Results_**

Table 2 shows that foreign ownership is significantly positively associated with the adoption of new technology. Relative to their domestic counterparts, fully foreign-owned firms are 4.9% more

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_Low-Income Countries’ Access to Private Debt Markets_

# **Figure 23 Share of external debt owed to official creditors versus GNI per capita, 2007**

<!-- Start of picture text --> Percent<br>100<br>75<br>R 2 = 0.52<br>50<br>25<br>Low-income cut-off<br>0<br>4 5 6 7 8 9<br>log(GNI per capital)<br>Source: World Bank Debtor Reporting System and World Development Indicators.<br>Percent<br>100<br>75<br>50<br>R 2 = 0.44<br>25<br>Low-income cut-off<br>0<br>4 5 6 7 8 9<br>log(GNI per capita)<br>Official / total debt<br>Average grant element<br><!-- End of picture text --> _Source:_ World Bank Debtor Reporting System and World Development Indicators.

**Figure 24 Average grant element of new external debt commitments versus GNI per capita, 2007**

_Source:_ World Bank Debtor Reporting System and World Development Indicators.

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- Ethiopia’s Rural Capacity Building Project (RCBP) was implemented in 10 regions of the country beginning in 2007.  RCBP strengthens agricultural extension services through raising awareness of improved and environmentally sustainable techniques, as well as better targeting farmers’ needs, including a focus on women farmers — who constitute (depending on the data source) between 29 percent and 45.5 percent of agricultural labor (FAO, 2011; Palacios-Lopez et al, 2017).

- Malawi’s national Farm Input Subsidy Program for smallholder farmers, part of the country’s <u>Agriculture Sector-Wide Approach (ASWAp), includes female-headed households as targets for access to inputs,</u> extension and on-farm technologies, as well as supporting access to input and output markets (Deijl et al, 2017).

<u>Access to finance for self-employment</u>

- Mexico’s Expanding Rural Finance project, launched in 2015, is targeted towards small farmers and micro, small and medium enterprises (MSMEs) in rural areas. In 2017, women agricultural producers and MSMEs accounted for more than 70 percent of beneficiaries.

- Afghanistan’s <u>Women Economic Empowerment - Rural Development Program</u> (WEE-RDP), launched in 2018, will cover 34 provinces. A main focus will be on improving access to finance for women-owned MSMEs.

# Understanding how rural employment responds to market factors

Better data on factors enabling women’s economic mobility, and opportunity costs for employment, can also shed light on how rural women respond to changes in broader economic shifts. Using recent crosscountry data from developing-country labor force surveys, Figure 1 shows that LFP among younger rural women follows a U-shaped pattern (discussed in Goldin, 1995; Mammen and Paxson, 2000) — where women’s LFP is higher in poorer countries, declines initially with growth as they pursue more education and leisure, and then rises again with increases in income/expected earnings opportunities. However, employment for older age groups is less responsive to economic growth (right panel of Figure 1). This is an important policy question which countries like India have recently been grappling with (Fletcher et al, 2018). Younger women, who are less likely to have children and may have greater mobility (Including opportunities for education), could be more responsive to changes in income and market conditions — but better data are needed on economic opportunities and constraints faced by rural women overall.

Figure 1. Rural women’s labor force participation against log GDP per capita (2016-17), by age group Younger women (aged 15-24) Women aged 25+ <u>Source: LFP data obtained from ILOSTAT (Key Indicators of the Labour Market (KILM) data), 2016 and 2017. GDP data obtained from WDI.</u> 4
have an economic worth as well. Globally of course, higher returns to education for women are borne out in a number of studies including Psacharopoulos’ (1994) crosscountry review and Schultz (1994) and from such diverse settings as Taiwan (Gindling et al, 1994), Czech Republic and Slovakia (Chase, 1997) and India (Malathy and Duraisamy, 1993; Duraisamy, 2000).

# **DATA AND METHODS**

One of the reasons why the empirical literature on changing norms in South Asia has not progressed much is due to limited data sets that allow for such analyses. Individual questions in the Demographic and Health Surveys on attitudes to violence, fertility preferences and to individual diseases have allowed for some analysis of attitudes to these areas, but very few questions allow for an analysis of attitudes to gender inequality. We use a unique data set – the World Bank Survey on Gender Norms in Bangladesh (WBGNS) 2006, which has a number of questions on attitudes to gender equality. Our aim is to understand whether two cohorts of women display differences in gender norms with regard to education based on two questions – “should girls be equally or better educated than boys” and “should wives be equally or better educated than husbands.” The WBGNS 2006 is the first comprehensive nationally representative survey of gender norms and practices in Bangladesh. It is based on a sample adults that include married women in the 15-25 and 45-59 year age range, married male heads of households men in the 25-50 year age range, and 500 community leaders (such as Union Parishad (UP) members, Imams/Moulvis (religious leaders), primary school teachers and 10
Figure B5: Distribution of poor population across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Share of poor population (%)<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: GHSPOP 1km is used for the DOU and DB methods. Each boxplot shows the distribution of the share of the poor population over different geographic areas in 16 SSA countries.

Figure B6: Comparison of urban shares of poor populations between official and DOU/DB urban definitions <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>100 100 100<br>ETH<br>80 80 80<br>60 60 AGO 60 AGO<br>ETH ETH<br>UGATZA AGO<br>40 40 TZA 40 TZA<br>CIV GAB GAB<br>20 GINMWIGNBSENMRT GAB 20 SENMRT 20 MRTSEN<br>BFANERTCDLSOGHA BFANERGINMWITCDGNBUGALSOGHACIV LSOTCDBFANERGHAGINGNBMWICIV UGA<br>20 40 60 80 100 20 40 60 80 100 20 40 60 80 100<br>Share of poor in urban area (%) Share of poor in urban area (%) Share of poor in urban area (%)<br>Official definition Official definition DOU<br>DOU DB DB<br>Share of poor in urban area (%) Share of poor in urban area (%) Share of poor in urban area (%)<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: GHSPOP 1km is used for the DOU and DB methods. Dashed lines are 45-degree lines. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.

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# **Section 2: Literature Review**

The increased risk of being HIV positive for widows and those divorced or separated is well documented in a number of studies. Naibutu et al. (1994) find an overall HIV prevalence rate of 8.2% in a rural population in Uganda but then a twofold increase of infection risk in divorced or separated individuals 25 or older compared with those currently married. In the same study area, 10.1% of the population was divorced or separated at the time of the survey in 1989/90. 15 years later, Lopman et al. (2009) show in a study in Zimbabwe that the prevalence of HIV among widowed men was 54% and widowed women was 63% compared to married men who had never been widowed at 19% and married women who had never been widowed at 28% . Many other studies have found that individuals who are divorced, separated or widowed have a higher risk of contracting HIV (Nunn et al. 1994, Quigley et al. 1997, Gregson et al. 2001; Gresenguet et al. 2002; Boerma et al. 2003; Porter et al. 2004; Bongaarts 2007; Mermin et al. 2008; Reniers 2008; Boileau et al. 2009).

The HIV prevalence rates among widowed and divorced individuals become even more alarming when we examine the pervasiveness of divorce and widowhood in Sub-Saharan Africa. In a rural area of Tanzania, 5% of men and 10% of women were divorced or separated at the time of a survey interview (Boerma et al. 2002). These numbers may not seem very high; however, among those married at the time of the interview, 44.8% of men and 33.9% of women had been divorced at least once. 11.1% of men and 5.6% of women had a least two broken marriages. Boileau et al. (2009) found similar results among rural women in Malawi; by the follow-up interview, 6.4% had gotten divorced or separated and 4.8% had been widowed in the previous three years. However, 34.4% of the women in the study had been divorced at least once during their married life and 9.8% had been widowed at least once. In another longitudinal survey, 32.5% of women and 28.6% men were divorced or separated and 7% of women and 4.3% of men were widowed (Reniers 2008). The findings in this study come from the Malawi 3
Figure 3: Occupation level among the 5-17 year-old population (Percentual of total 5-17 population) <!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Brazil North Northeast Southeast South Mid-West<br>2004 2005 2006 2007<br>15.9<br>14.8 14.9<br>14.4<br>13.8 13.1 13.4 14.0 13.6 13.6<br>12.2 12.4<br>11.8 11.5 10.8 11.3 11.1 10.5<br>9.9 9.8<br>8.6 8.4<br>% 7.9 7.9<br><!-- End of picture text --> Source: IBGE, PNAD 2007 However, child labor is still a major issue for Brazilian policy makers and PNAD (IBGE, 2007) also indicates that. For example, 60.0% of the children between 5 and 13 years old are involved in non-paid activities. In the rural areas of Brazil, 40.0% of the 5-17 year old population works between 30 and 40 hours per week. In the Southeast, the richest region in the country, 30.0% work at least 40 hours per week.

Economic science has developed a range of potential theories to explain child labor. As Becker and Lewis (1973) state, child labor is an activity that generates current benefits in terms of income, but also creates future costs by reducing study and leisure. Given that, families evaluate the cost-benefits related to sending their children to school or to work. Rosenzweig (1981) has demonstrated that children's time allocation depends on the production capacity of the children and their parents and the substitution degree of the work force between both.

Basu & Van (1998) have built a model using one basic assumption: luxury. They consider that poverty is the main factor that makes parents send children to work. Hence, the children's time that is not allocated (school and leisure) to generate income is luxury, which low-income parents cannot afford. Ray (2001) has created a theory for emerging economies: child labor occurs mainly because of poverty and credit market imperfections. He has shown that if poor families had access to credit, in the presence of high returns for education, they would be willing to send their children to school instead of work. Furthermore, the same study showed the relationship between income inequality and child labor under credit constraints. The main conclusion states that a more equal income distribution would reduce child labor.

Kassouf (2002) demonstrates that an increase in the household's income reduces the probability of child labor and increases school attendance. Another element that affects the probability of child labor is the parent's education degree. Bhalotra & Heady (2003) find a negative effect given the mother's level of education and the child’s labor participation in Ghana. The effect of the mother's education profile is higher compared with the father’s. Kassouf (2002), in Brazil, obtains the same negative effect. Family composition is another relevant factor. Patrinos & Psacharapoulos (1994), for Paraguay, and Bhalotra & Heady (2003), for Pakistan, conclude that the more people there are in the family, the greater the chances of having child labor.

6
year’s demand matches supply or, in other words, a price such that inventory variations are zero. Moreover, it is even possible to assume that inventories could reach zero at the end of the year, given that, in practice, in the last quarter it usually reaches very low values with respect to harvest size. It proceeds to compare these theoretical prices with observed ones and estimate the di¤erence, and ...nd that after 1996, di¤erences are small and do not last more than a year or two. Additionally, this document ...nds that the increase in price levels in the 2000s seems to be explained mostly by the presence of an increasing demand for bioethanol.

In the quarterly model, it follows a market structure that mimics the typical seasonality seen in agriculture markets ( with production occurring only during one quarter). Given that the time lapse between decisions is shorter, it also allows for information shocks to occur by introducing them to the agent’s expectations through private and public signals. Results show that around 70% of price volatility can be explained through the shocks cited above, leaving a 30% measured space for other non fundamental sources of variation.

Regarding model speci...cs, the long run case assumes isoelastic forms Dt = Ztp<sup>�</sup> t<sup>fordemand</sup> and St = Atp<sup>�</sup> t<sup>forsupply,the...rsttermrepresents(exogenous)levelsandthesecondtermthe</sup> endogenous response to price with the respective elasticity. Therefore, shocks are captured as changes in Zt; At. These will be de...ned as the current fundamental source of price changes. Time series for these variables can be obtained using USDA corn usage and production data. They are then integrated into a market clearing equation with no storage decisions. The short run case considers demand as a whole aggregate and assume identical isoelastic functions for both demand and supply, as before. The model is linearized, calibrated and simulated with real shocks as inputs. It then estimates the impact of di¤erent shocks (demand, supply, and information) on price changes and quanti...es them.

One additional analysis that results from this paper is related to the issue of prices’being fully informative and appropriate for business-cycle measurement. Romer (2006) raises this question and shows evidence of ...rms that do not always behave in pro...t-maximing ways by analyzing the case for professional football teams. This paper shows that although in the long run, prices seem to be very close to fundamentals, when going to a quarterly frequency, market prices can sometimes be very far away— as much as 50%— from them. These results are in line with Hussman (1992), who shows that once imprecise signals are introduced in a rational expectations framework, market prices become 4
14 strong support to the finding that integration among developing countries leads to _divergence_ in economic performance.<sup>2</sup> ' Although the unique feature of trade liberalization in the SA framework is that it simultaneously embraces both a European and an intra-regional dimension, interesting insights can be gained by focusing on SEE-5 intra-regional trade liberalization. In fact, these insights reinforce the earlier conclusion that the simultaneous pursuit of liberalization in its three dimensions-multilateral, European and intra-regional-would maximize economic gains for the SEE-5 countries. Moreover, ill-designed SEE-5 FTAs may bring about greater harm than the earlier discussed negative effects of the hub-and-spoke pattem.

* There are arguments suggesting that regional integration moving much faster than European integration would not make economic sense, as it would lead to divergence in economic growth. Although SEE-5 is a very heterogeneous region, not a single country qualifies as high-income economy. Croatia, the most developed among SEE-5 economies, with the GDP per capita at around 30 percent of the average EU level has capital/labor ratio well below the EU average. In consequence, it may not have comparative advantage in most industrial products in world markets. But many of its products may become highly competitive within a SEE-5 free trade area. Simultaneously Croatia has the GDP per capita around four times higher than that of Albania and Bosnia and Herzegovina and around three-times as high as in remaining SEE-5 economies. Its total GDP of US$20 billion accounts for 40 percent of the aggregate region's GDP (Table 1).

_Table 1: Population and GDP in SEE-5 in 1999_

||Population<br>(inmillion)|Share<br>intotal|GDP<br>(mil.ofUS$)|Share<br>intotal|GDPper<br>capita<br>(inUS$)|Croatia<br>=100|Tradeas%of<br>GDP"|
|---|---|---|---|---|---|---|---|
|Albania|3.4|14%|3,665|8%|1,102|25|52%(63%)|
|Bosniaand<br>Herzegovina|4.2|17%|4,387|10%|1,027|23|77%(95%)|
|Croatia|4.5|18%|20,211|46%|4,467|100|114%(143%)|
|FYRMacedonia|2.0|8%|3,401|8%|1,701|38|82%(99%)|
|FR Yugoslavia|10.5|43%|12,020|28%|1,562(98)|35|52%|
|<br>_Kosovo_|_1.6_|7%|_1,562_|_3%_|<br>_868_|_18_|_85%_|
|_Montenegro_|_0.8_|3%||||||
|<br>TOTAL|24.5|100%|51,033|100%|2,017|42|58%|
|_Memorando_||||||||
|Bulgaria|8.2||12,403||1,513|34|150%(176%)|
|<br>Romania|22.5||34,027||1,512|34|107%(116%)|
|GRAND TOTAL|55.2||97,463||1,766|40||

"Including services in parentheses.

_Source: World Development Indicators 2001,_ The World Bank, Washington D.C. 2001, Michalopoulos and Panousopoulos (2002) and World Bank and IMF staff estimates.

While GDP per capita alone may not fully capture the diversity in the levels of their economic development, other indicators corroborate this diversity. Despite similar levels of the GDP in Albania and Bosnia and Herzegovina, agriculture generated 53 percent of the GDP in the

**21** A number of FTAs among developing countries in Africa were terminated because of benefits accruing to a more developed partner at the expense of a less developed one (World Bank.2000b).
(tables 23.1 and 23.2). We also have unpublished data supplied by Rosstat on exports and imports by sector and by region of Russia.

We assume that the technology of production is common across regions, so that the same input output coefficients apply across all regions. We create an input output table for each region of Russia, where the shares of output and energy for each region are taken from the Regions of Russia and we have common technology across all regions. We infer regional demand (and supply) of services, assuming that intermediate and final demand for services have the same intensity of demand in all regions as in the national model.

We have to adjust the resulting regional import and export intensities so that regional exports in aggregate are consistent with national import and export values. We do not need to make any other adjustments, as the production technologies are assumed consistent across the regions.

# **FDI Shares**

We explain the methodology further in the appendix, but briefly, we first employed the NOBUS survey to obtain the shares of workers working in multinationals service sectors in each sector in each region. We used this as a proxy for the share of output in each service sector in each region. We also obtained information from (1) our estimates from Russian service sector institutes of the share by sector of multinational ownership in the key services sectors;<sup>17</sup> (2) _Regions of Russia (2003)_ by Rosstat; and (3) the “BEEPS survey. Only the NOBUS survey provides data that allows us to estimate shares of multinational ownership by both region and sector. We thus start with our calculations based on the NOBUS information.

When found, however, that when we aggregate the NOBUS shares across regions or sectors, the other three sources of information show considerably higher foreign ownership shares than the NOBUS survey. We believe that the NOBUS survey estimates are too small, and adjust them. We adjusted our estimates from the NOBUS to be consistent with the estimates of the service sectors institutes. The service sector institute estimates are lower than those from the BEEPS or _Regions of Russia,_ and thus involve less adjustment of the NOBUS data _._ We employed least squares adjustment of the NOBUS data so that the weighted average over all of Russia in each sector is consistent with the national estimates we received from the specialist service sector research institutes in Russia. This process will give as a _structure_ of ownership based on the NOBUS survey, with the economy-wide average by sector determined by the national data. Results are presented in table 12b.

> 17 We thank the service sectors institutes in Russia mentioned above for these estimates.

15