rafmacalaba/datause-encoder-data huggingface dataset:
# **3.2 Transport costs**

Trade and transport cost data are also not widely available for Africa.<sup>24</sup> In the international trade literature, trade costs are sometimes estimated from a gravity equation based on trade flows (Anderson and van Wincoop, 2004), or price dispersion (Donaldson, 2010) but trade flow data between cities and city-level price data are also not widely available. Furthermore, city growth may endogenously decrease transport costs. Among other reasons including the allocation of paved roads (discussed below), more transport companies are likely to compete on a route to a growing city than on a route to a stagnant one.

I deal with this by decomposing variable transport costs into two components: 1) the world price of oil, which varies across time but not across cities, and 2) the road distance between a city and its country’s primate, which varies across space but not time.<sup>25</sup> Figure 7 shows the evolution of oil prices during the study period. In general, they were relatively steady until a consistent rise beginning in 2002. However, there was some movement in the previous period, including substantial decreases (as a fraction of the initial price) in 1992–1994, 1996–1998, and 2000–2001.

Oil is a convenient proxy for transport cost per distance because no countries in the sample are individually capable of influencing its price substantially. However, motorists consume refined petroleum products, mostly gasoline and diesel, not oil, and some countries, especially oil producers, subsidize their prices. Country-specific diesel prices, surveyed in November in the main city, are available for most countries roughly every two years (Deutsche Gesellschaft f ̈ur Technische Zusammenarbeit, 2009). As shown in Figure 7, diesel prices averaged over a balanced panel of 12 countries from the main estimation sample generally rise in parallel with oil prices. Nigeria, Gabon, and Angola, the three sample countries for which oil production represents the largest fraction of GDP, show similar, though somewhat noisier time trends (Appendix Figure A.1) despite the fact that they typically had lower prices than average in most years, most likely because of subsidies. Using data from a survey of truckers in several African countries, Teravaninthorn and Raballand (2009) estimate that fuel represented roughly 35 percent of transport costs for trucks in 2005, when oil prices were roughly the mean of the minimum and maximum annual price for the period.

Rudimentary national statistics like road density and percentage of roads paved,

> 24Teravaninthorn and Raballand (2009) provide figures for several important routes from landlocked country capitals to the ports that serve them.

> 25Specifically, the oil price used is the annual average Europe Brent Spot Price FOB, in dollars per barrel, from the United States Energy Information Administration (http://tonto.eia.doe.gov/; accessed 5 Jul 2010).

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Miscellaneous”. As shown above, these are important export sectors in China. The table also reveals that positive demand shocks were relatively more important in “Vegetable products”, “Chemicals”, “Textiles and clothing”, “Machinery and electrical”, “Transportation” and the residual category “Miscellaneous”. The fact that the latter three sectors were characterized by both negative supply shocks and positive demand shocks is likely to reflect the fact that these are sectors in which trade in parts and components tends to be relatively important (recall that the similarity indexes are computed at the 6-digit level, and hence supply and demand shocks may be separately identified even within sectors).

**Table 10: Impacts of China shocks on exports of B&R industries, 2SLS, 2005-2015**

|Industry|Δ Supplycjt<br>Δ Demandcjt|
|---|---|
|Live animals and animal products|18.87<br>48.68<br>(311.63)<br>(390.83)|
|Vegetable products|-78.68<br>-9.99<br>(214.56)<br>(13.17)|
|Food products|50767.62<br>-61056.80<br>(1296326.28)<br>(1561593.31)|
|Minerals|1949.85<br>121.94|
||(2894.08)<br>(86.04)|
|Fuels|175.08<br>89.45|
||(171.33)<br>(60.93)|
|Chil|-14.93<br>44.86|
|emcas|(205.04)<br>(36.90)|
|Plastic or rubber|-337.67<br>*<br>35.11<br>(200.25)<br>(35.09)|
|Hid d ki|-296.44<br>-1600.08|
|es an sns|(554.72)<br>(4074.67)|
|Wood|448.51<br>39.63|
||(527.67)<br>(36.40)|
||-29.21<br>*<br>62.32<br>*|
|Textiles and clothing|(16.30)<br>(33.53)|
|Footwear|-17.85<br>-518.96<br>**|
||(28.17)<br>(247.46)|
|Stone and glass|-866.39<br>864.16|
||(1263.91)<br>(1894.30)|
|Metals|-84.47<br>56.83|
||(83.74)<br>(41.79)|
|Machinery and electrical|19.93<br>-44.91|
||(21.55)<br>(31.02)|
|Ti|-366.66<br>43.76|
|ransportaton|(774.46)<br>(76.70)|
|Miscellaneous|1.88<br>360.29<br>*|
||(38.86)<br>(212.71)|
|Robust standard errors clustered by|country, *** p<0.01, ** p<0.05, * p<0.1|

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construct measures of parental education, and a measure of male sibling education for two sub-samples (corresponding to 2,355, and 2,851 men, respectively). These subsamples can be useful to help control for various biases-in particular, possible biases induced by the correlation between unobserved ability, own schooling, and selection into the wage-earning sector-but they have important limitations, both because of their much smaller size, and because they are arguably non-random: Information on parental education is only available for wage-earning children of the household head in households in which both parents are still present, while information on the education and earnings of male siblings is only available for children of the household head in households with at least two male children, both of whom are living at home, and both of whom earn wages. As a result, these samples will tend to be younger, and may be biased if formation of an independent household is endogenous.

# **4. Estimation strategy**

The analysis in this paper is limited to men only. This is somewhat arbitrary, although the much larger sample size for wage-earning men (21,882) than women (11,228) in the APIS is an important advantage. In addition, limiting the sample to men may minimize two econometric problems. First, it is likely that non-random selection into the labor market is more of a concern for women than for men. Second, like most household surveys, the APIS did not collect information on labor market experience, so I approximate actual experience with "potential experience", defined as X - A - S - 6, where A stands for age in years, and S is the years of completed schooling. This 9
# **1. Introduction**

While women are beginning to get ahead of men in selective countries in a few areas, such as college admission, in both high-paying and leadership jobs, women continue to lag significantly behind (Goldin et al. 2006; Blau and Kahn 2017). For instance, women remain severely under-represented both among corporate directors (Bilimoria and Piderit 1994, Westphal and Stern 2006, 2007) and conductors of symphony orchestras (Goldin and Rouse 2000). Among the largest U.S. firms (i.e., the Standard & Poor’s 1,500 firms) in the 1990s, only 2.5 percent of top executives were women, and these female executives tended to serve in the smaller of these large firms (Bertrand and Hallock 2001). Things have improved only slightly since then: in a large sample of privately held and publicly listed firms, about 9.4 percent were found to have female CEOs (Faccio et al. 2014). Women’s share in businesses ranges from 20 to 40 percent in the United States and European countries, and much lower in many developing countries (Klapper and Parker 2010). Based on the World Bank Enterprise Survey data, which we use in this paper, in some countries the share of firms led by women approaches zero.

Why do women not fare as well in labor markets as men in general and in top-level jobs in particular? Some attribute the gender disparity to differences in human capital and discrimination, which results in lower pay and responsibility (Polachek 1981, Goldin and Rouse 2000; Black and Strahan 2001). A new literature considers the roles played by psychological and non-cognitive traits such as self-confidence, risk aversion, acceptance of competition, and inter-personal skills, which are extensively surveyed in Gneezy et al. (2009), Bertrand (2011), and Blau and Kahn (2017). This new literature comes largely from laboratory and field experiments (Gneezy et al. 2009), with subjects often being college students, and the stakes involved being minor. Whether or not the findings generalize into the real world is uncertain but of critical consequence. Moreover, even when studies on women’s labor outcomes are from the real world, the findings may not be relevant for women business leaders, who might differ significantly from the population of women in general. For example, self-confidence is rewarded differently among executives than clerical workers (Cattan 2014), female executives may be particularly self-confident among women, and they often do not fit female stereotypes (Adam and Funk 2012). For 2
limited skills and education but a good nose for business could become the exception as the urban region becomes technologically more sophisticated; the majority of businesses will be run by talented people who also tend to hire people well equipped with skills.<sup>31</sup> The last question follows from the above three: the Singapore-IDR urban region will need to be innovative to compete and catalyze clusters as the “dedicated silicon fab” was able to do for Hsinchu Park (Chen 2007).<sup>32</sup> Sustained innovativeness is of a different order from the technological learning during the catch-up phase. It requires a high level skills and heavy spending on R&D backed by incentives and checks which maximize the flow of innovation. While the resources ploughed into research surely matter, there are no ready answers to the above questions but it is only by tackling them that an urban region could clear the political handles and come to grips with economic opportunities. If the S– IDR region is to reap the advantages of an early start over its rivals in Southeast and South Asia, there is little time to lose. Innovativeness depends seemingly also on a local culture which tolerates questioning and a healthy amount of dissent; on a circulation of talented people many from outside the region who inject new ideas and stimulate new thinking; and on incessant networking and collaboration by knowledge workers in the urban region with their peers around the world. Innovation is increasingly done by teams and less and less by the lone inventor.<sup>33</sup>

> 31 See Berry and Glaeser 2005.

> 32 As recently as 2006, Singapore was rated as the leading Asian city with respect to the quality of life, a significant advantage where knowledge based development is the objective ("Singapore Ranked 'Top Asian City' for Expats" 2006). Moreover, according to 2D Net Asia’s Top Tech Index, Singapore was the preferred location for high tech companies locating in East Asia ("Singapore Outranks China, Hong Kong" 2005).

> 33 The Internet and increase specialization have prompted much more collaborative research and joint publication in the natural and social sciences. Moreover, although national and regional innovation systems still have primacy, corporate research is becoming more globalized with large MNCs taking the lead in sourcing research from different parts of the world, setting up laboratories in different countries, creating multinational research teams and entering into far flung research alliances (Carlsson 2006; Gerybadze and Reger 1999).

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familiarity between partners was a strong component. Conceivably, such selection bias could be avoided by working with each firm to generate a list of its partners and then choosing one randomly. But this would increase the length and complexity of questions and the survey implementation overall. Asking firms about their agreements with transactional partners in general avoids these types of conceptual and practical complications, reflecting the plausible assumption that the approach taken by a firm will transcend one specific relationship, being based on the firm's overall comparative advantage.

Moreover, the WBES surveys establishments (which we have called 'firms', for convenience). The WBES is meant to broadly capture firms' experiences, and so it includes questions on a variety of topics, which are generally pitched at the level of overall operations. This provides a fitting context in which to embed a question about agreements with transactional partners in general. Importantly, establishments usually produce a narrow range of products with a uniform technology. Hence, our approach is analogous to that taken in many studies where establishments are viewed as a single entity with a single technology, one example being the estimation of establishment-level production functions. We therefore use a level of aggregation that is common in the economics and business literatures by posing questions about the establishments' transactional partners in general. Finally, our question was preceded in the WBES questionnaire by questions that specifically asked respondents to focus on the whole set of transactional partners. Therefore, respondents begin to answer our question on supplier (or customer) relationships having been primed to think about the circumstances under which they engaged with suppliers (or customers) in general.<sup>30</sup>

> 30 Our question on customer relations was identical to the one for suppliers, except for the obvious substitution of 'customers' for 'suppliers'.

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has a significant positive effect on transitional GDP per capita growth. The estimates in her paper, see column (1) of Table 3, show that the level of GDP per capita is around 5 percent higher in the long run due to a one percentage point increase in the Gini coefficient.<sup>3</sup> Using our instrumental variables approach and a model specification that does not include an interaction term between the Gini and initial income (as in Forbes), we find that the estimated coefficient on the Gini is positive and significantly different from zero. Instrumental variables estimates of a model which restricts the effect of inequality on GDP per capita to be the same across countries' initial incomes show that the level of GDP per capita is around 6 percent higher in the long run due to a one percentage point increase in the Gini.

Forbes also reported estimates for different income groups (e.g. below and above $1,000, $3,000, and $6,000, respectively). Only in the group of countries below the specified threshold (i.e. the low-income group) is there a significant positive effect of inequality on transitional growth; in the group of countries above the specified threshold the effect is insignificant. Forbes's finding that inequality has a positive effect on transitional growth in poor countries is qualitatively the same as in our paper. What Forbes's analysis does not show is the effect of inequality on growth at relatively high levels of income. The advantage of our model that includes an interaction between inequality and initial income is that this model examines the effect of inequality on transitional growth for various levels of initial income. This matters as we find that for high levels of initial income, such as for example those of OECD countries, inequality has a statistically significant and quantitatively large negative effect on transitional growth.

The paper's methodological contribution is to propose an instrument for inequality that is strong in the econometric sense, i.e. it has a highly significant first-stage effect. The first stages in

> 3 The long-run effect is calculated as 0.0036/0.076=0.047 (see column (1) of Table 3 in Forbes). The relevant equation is lnyt=γlnyt-1+ βInequalityt-1; see equation (2) in Forbes where control variables have been left out to simplify. The equation can be rewritten as Δlnyt=κlnyt-1+ βInequalityt-1, where κ=(γ-1). Because |γ|<1, a permanent increase in inequality has a permanent effect on the level of GDP per capita. This follows from solving the first-order difference equation and differentiating with respect to inequality, i.e. ∂ln(y)/∂Inequality=β/(1-γ)= β/-κ.

6
fragility hypotheses. Specifically, using standard panel logit models, they assess whether the probability that a country suffers a systemic banking crisis in a specific year depends on the concentration of the banking system, controlling for other banking system, macroeconomic and institutional factors that the literature has shown to be associated with the probability of a banking crisis They find that more concentrated banking systems are less likely to suffer systemic banking crises, a finding that is robust to a number of different specifications and controlling for an array of other factors potentially associated with crises. Table 1 presents these results for a sample of 69 countries and 47 crisis episodes over the period 1980 to 1997. These findings hold when they control for general measures of bank competition. When analyzing the channels through which concentration might be positively associated with banking system stability, they find tentative evidence that more concentrated banking systems allow better possibilities for banks to diversify risk. On the other hand, they do not find any evidence, that it is easier for bank supervisors to monitor more concentrated banking systems or that the higher stability results from the market power and consequent franchise value of banks in more concentrated banking systems. Bank concentration is thus not an indicator of the lack of competition. Rather, more competitive banking systems are also less likely to suffer systemic banking distress.

Boyd, de Nicoló and Jalal (2006) arrive at a different conclusion using bankindividual fragility data. Rather than focusing on systemic bank distress, they use the z- score, a bank-level measure of distance from insolvency as fragility indicator. Unlike Beck et al. (2006a,b), they find banks are closer to insolvency, i.e. more likely to fail, in countries with more concentrated banking systems. Cross-country results on the effect of concentration thus vary depending on whether one considers individual bank fragility or systemic banking distress. It is important to note, however, the different concepts these studies consider – actual systemic banking distress vs. the probability of individual bank fragility; the latter might not necessarily result in the former.

Schaeck, Cihak and Wolfe (2006) find a negative relationship between bank competition and systemic bank fragility using a more refined measure of competition in the banking market – the H-Statistics. Specifically, using a sample of 38 countries over the period 1980 to 2003, they show that more competitive banking systems are less prone
Table 12 reports the short-run findings on health outcomes. The control group means are similar to those reported in tables 3-5 for the _participation & information_ intervention, but the treatment effects are small and insignificant.<sup>21</sup> That is, there are no statistical differences in under-5 mortality (column i); infant mortality (column ii); neonatal mortality (column iii); number of births (column iv); number of pregnancies (column v); and in weight-for-age and height-for age z-scores (columns vi-ix) between the treatment and the control group. Thus, based on health outcomes only, the participation intervention had no impact.

### **Utilization**

Table 13 reports the results on utilization. There is no clear evidence of impact. For general outpatient service, the point estimate goes from –100.2 (p-value=0.38) to –20.3 (p-value=0.89) depending on the specification. For delivery, there is a significant negative effect in two out of three specifications, while the treatment effects are positive, albeit insignificant, for antenatal care and family planning. The average standardized treatment effects are small and insignificantly different from zero in all three specifications.

Columns (vi) and (vii) report changes in utilization patterns based on household data. There is some evidence that treatment households are less likely to use traditional healers and selftreatment, but the average standardized treatment effects are once more insignificant.

## **5.3 Processes and health treatment practices**

The _participation intervention_ did not result in improvements in child health or the quantity of health care used. In the appendix, tables A.2-A.6, using the same outcome variables as in section 4.3, we show that there is no systematic evidence that the intervention resulted in increased monitoring and information exchange in the treatment communities. Management of the facility and the extent to which clinical guidelines were followed also remained unchanged.

# **6 Mechanism and Discussion: The role of information**

The results of the _participation_ and the _participation & information_ interventions differ markedly. Without information, the process of stimulating participation and engagement had little impact on health workers’ performance or the quality of health care. In contrast, when community members

> 21The p-values for the null hypothesis of equal means in the two control groups are 0.65 (for under-5 mortality); 0.62 (for infant mortality); 0.74 (for neonatal mortality); 0.22 (for number of births); 0.28 (for number of pregnancies); 0.50 (for weight-for-age); and 0.17 (for height-for age).

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# _The working poor_

Looking at the poverty status of households and the labor force status of their members confirms the idea that the most disadvantaged individuals are not especially found among the unemployed and the inactive. While 14 percent of workers lived in households that were below the poverty line in 2008, only 10 percent of unemployed individuals and 9 percent of inactive did so (VHLSS). This is consistent with the idea that those who are unemployed or inactive can afford to do so because of their household situation.

There are two underlying issues here: (i) the extent to which work pays. In other words, what is the type of employment that workers are able to secure, does it differ across gender and compared to other lower-middle-income countries? (ii) The extent to which the labor supply of household members differs between poor and non-poor households.

# **4. Mobility and migration**

The results suggest that movers and migrants tend to live in urban areas, to be employed as wage workers, and to have moved for job related reasons. Women living in urban areas are more likely than urban men to have moved, while rural men and women are similarly likely to have moved. Men are more likely to have moved/migrated for jobrelated reasons than women; women are more likely to have moved/migrated in order to get married. Only a static view of mobility and migration is available.

# _Among respondents, few report having moved or migrated in recent years_

A small minority of individuals (6.6 percent) report having moved in the 5 years before 2009 (LFS, 2009).<sup>13</sup> Among those who have moved, 70 percent are working and 57.1 percent are women. Individuals living in urban areas are much more likely to have moved than those living in rural areas: while 12.3 of urban individuals have moved or migrated within the previous 5 years, only 4.1 of individuals in rural areas have moved or migrated in the previous five years. This is consistent with a move towards urban areas. Young people (up to the age of 30 years old) are more likely to have moved than older individuals.

> 13 Comparison with Census data for 2009 suggests that this proportion underestimates the share of movers in the population.

27