rafmacalaba/datause-encoder-data huggingface dataset:
concentration. Most of the included countries were able to increase diversification to access different markets. China’s performance stands out because of the significant number of new destinations.

In terms of products, the picture is not so favorable. As in many countries, Brazilian exports showed increased concentration for products in recent years. Commodity products gained significant relevance. Other commodity exporters, such as Chile and Russia, also showed similar performance with nonnegligible increases in the degree of concentration. China presented a slight deterioration, but from a low level of concentration. Anyway, as shown in figure 5, despite some worsening in recent years, Brazil still shows low levels of concentration when considering the level of GDP per capita.

Figure 5. Herfindahl Index and GDP per Capita (2008–10) <!-- Start of picture text --> R U S<br>C H L<br>P E RC O L<br>IN D J OR B R AU R YA R GM E X K O R<br>C H N<br>0 1 0 0 0 0 2 00 00 3 00 0 0 4 0 0 0 0<br>G D P p e r ca p ita (P P P , a v .)<br>.3<br>.2<br>H H In d e x<br>.1<br>0<br><!-- End of picture text --> _Source_ : World Development Indicators; authors’ calculation.

Given the recent trend toward more concentration, a key question is how Brazil is dealing with international competition. Figure 6 shows the main products exported, as introduced in table 2, between 2006 and 2011. The circle size shows the representativeness of this specific product in total exports. If above the line, it shows that Brazilian sales grew more than exports of this specific product in international trade between 2006 and 2011. Therefore, among the top 20 products exported, Brazil is losing international market share in only two of them (oil exclusive crude and automobiles)—a very positive result, which reflects the high competitiveness achieved in commodity products.

Figure 6 also shows how the growth of Brazilian exports to each market can be compared with the growth of exports of all other countries to that same market. Brazil increased its participation in 11 major markets, representing 58.7 percent of the total, with exception of the United States, Chile, and Argentina. Thus, we can argue that Brazil gained presence in most markets, as well as in more relevant products. Interestingly, Brazilian export growth to China more than doubled the other countries’ expansion in the recent period. This performance has a direct connection with the increasing Chinese demand for commodities.

8
Chart 15 <!-- Start of picture text --> Trade Integration Before and After the Trade Poflcy Reform of the 1980s<br>(Latin America)<br>Mex1com<br>Brazil<br>E C~~~rgnhie-<br>.0 ~ 5U 5 la 16 25id 30<br>Growth Rate of lmport/G DP Ratio (percent)<br>Real Effective Exchange Rate<br>(1980=100)<br>1980 - 1981 1982----- 198 184 ----- 98 196 98 198 98 -99 199 199 1993--<br>The tade egime of oth Arica FinncialCommnity CFA)membesBananon<br>CFA members of S--ahrn-fic -- -- efr 198 wer chrce-e y h eeiyo<br>100 33 - - - - - - - - - - - -<br><!-- End of picture text -->
manufacturing plants. However, the specification in Column 2 does not take into account potential endogeneity and thus the effect of services FDI on productivity growth is likely to be over-estimated. Indeed, the use of the two-period lag of the services FDI linkage in Column 3 reduces substantially the magnitude of the effect. However, the estimated coefficient on the services FDI linkage is still positive and significant at the 1% confidence level. In Column 4 we add the manufacturing and mining FDI linkages and find the coefficient on the services FDI linkage to be unaffected. Finally, Column 5 estimates the complete empirical specification in (5) which controls for plant-level observables, industry-year and region-year interaction effects and is our preferred specification. The standard errors in Table 1 and all subsequent tables are robust and clustered at the plant-level to allow for possible correlation across observations belonging to the same plant.<sup>27</sup> The plant fixed effects are found to be jointly significant in all our specifications. Our choice of plant fixed effects estimation is driven by the results from Hausman tests which reject random effects in favor of fixed effects for our specifications. Finally, note that the estimated input growth coefficients corresponding to the specification in Column 5 of Table 1 are shown in Appendix Table A.3 and are generally significant and have magnitudes in line with those in previous studies.

Our finding of positive and generally significant effects of electricity growth and of services growth in Appendix Table A.3 indicates that above and beyond those positive contributions to plant output growth, increases in services FDI in Chile during the 19922004 period led to a significant increase in productivity growth for the plants that use services more intensively. Our preferred coefficient (0.153) implies that the average

> 27 The use of non-clustered robust standard errors or other levels of clustering – at the year level, at the 3-digit industry level and at the 3-digit industry-year level – does not affect the significance of our results.

21
Trujillo, L. and Nombela, G. - _Privatization and regulation of the seaport industry_

**_Box 13._ Comparison of container handling charges across world regions (1996)**

<!-- Start of picture text --> Region Port Price per loaded TEU ($)<br>North Europe Antwerp 120<br>Felixstowe 173<br>Hamburg 182<br>Rotterdam 156<br>l ___________________ Zeebrugge 123<br>South Europe Algeciras 193<br>Barcelona 211<br>La Spezia 240<br>Marseilles 233<br>Pireus 203<br>Asia Pusan 175<br>Kaohsiung 140<br>Manila 118<br>Singapore 117<br>North America Halifax 168<br>Los Angeles 256<br>Australia Melbourne 199<br><!-- End of picture text --> _Source: Drewry Shipping Consultants._ An advantage of this mixed-form of revenue for port authorities (from port tariffs and concession fees) is that part of the demand risk is left to the private operators, who then have correct incentives to provide efficient low-priced services in order to minimize that risk. Additionally, concession fees provide port authorities with a safe continuous cashflow, and therefore they have the possibility of financing general port costs or even part of the facilities' construction/rehabilitation costs.

In European ports, revenues obtained from port tariffs are in general higher than revenues from concession fees, with these latter averaging 37% of total income received by port authorities. However, there are examples in other regions, as in the case of the port of Baltimore (US), where more than half of total income is obtained from concession fees (58%), therefore indicating a higher presence of private operators (PDE, 1998).

There are no established procedures to determine the level of concession fees to be paid by private firms. An optimal rule should be to relate payments to the opportunity costs of the infrastructure and those superstructure elements that the concession might be associated with. For infrastructure, an approximation for the opportunity cost could be the market price of port adjacent land, although modified by the specific characteristics of the surface used by the concessionaire. Meanwhile, the opportunity costs of equipment granted by a concession are easier to estimate, since they would be equal to the price that they could reach in a rental market.

To the basic objective of concession fees reflecting opportunity costs, it is possible to add other aims, like, for example, sharing the risk of demand fluctuations between operator and 41
input prices they face? We regress ln(K/L) at the firm level on the female-owned dummy, the lnSales of the firm, and then add the capital constraint and cost variables sequentially. The estimated coefficients on the female-owned dummy in Table 8 suggest that the average capital labor ratios of male and female entrepreneurs do not differ significantly, within an industry and country (column 1) or when the size of firm is held constant (column 2). The capital-labor ratios fall with an increase in sales revenues and this relationship does not differ for men and women (column 2). This suggests that the firms do not have homothetic production technology or that the relative price of capital to the price of labor rises with the scale of operation (sales). Next we find that firms with access to bank financing, and which have received a loan, tend to use more capital per unit of labor; this relationship does not differ by gender. The coefficient on the interest rate is negative but not significant for men and positive and not significant for women. Taking the results in the table together, it appears that the K:L ratio is more sensitive to access to capital than to its price.

# **6. Conclusions**

Using firm level data from the 2005 BEEP Survey of 26 countries in the Eastern European and Central Asian (ECA) region, we explore various questions regarding differences in firm performance of male and female entrepreneurs and test various hypotheses offered to explain the observed patterns.

Our first finding is that there is a significant gender gap in the scale of operations, as measured by sales revenues. This finding is consistent with the literature from individual country studies which show that female business owners (entrepreneurs) run smaller businesses (measured in various ways, including sales and number of employees). Our finding holds even when controlling for the industry in which the firm operates, although the gap falls from being 63% (when controlling for country fixed effects) to 37% (when controlling for both country and industry fixed effects).

However, counter to studies which have found large gender gaps in profits (Robb and Wolken, 2002 for the US and Bosma et al., 2004 for Holland), we find that the unconditional average gender gap in profits in the ECA region is small to begin with and falls to 3% of men’s profits after controlling for country and industry. Moreover, we find that the gap vanishes when we control for the scale of the firm (sales) in addition to 22
KR,i,t = (1-δ)KR,i,t-1 + IR,i,t-1 where KR,i,t ≡ technology development stock of firm i at time t; IR,i,t-1 ≡ technology development expenditures of firm i at time t-1; and δ ≡ depreciation rate (assumed to be 15%).

The NBS data set provides the flow of technology development expenditures by firm over the period 1995-2004. We estimate the technology development stock in the initial year 1995 as follows: KR,i,1995 = IR,i,1995 / (δ+γ) where γ is the growth rate of IR, estimated as the average annual growth rate of the 2-digit industry of firm i over the period 1995-2004.

Obviously, after narrowing our merged dataset to the four industries that are the focus of this study, we lose a considerable number of firms. Specifically, we have 49 firms and 294 observations in the pulp and paper industry; 115 firms and 690 observations in the cement industry; 70 firms and 420 observations in the iron and steel industry; and 27 firms and 162 observations in the aluminum industry.

China’s National Bureau of Statistics classifies enterprises into seven ownership types (stateowned, collective-owned, HKMT (Hong Kong SAR, China; Macao; and Taiwan, China), foreign, 16
> - 47 -

Table 2: Sectoral Decomposition of Aggregate Poverty Alleviation

||Poverty<br>Line|Sectoral<br>Urban|Poverty<br>Gain:<br>Rural|alleviation du<br>Population<br>Shift|e to (2)<br>Interaction<br>Effect|
|---|---|---|---|---|---|
|Headcount<br>index<br>(a-<br>0)|11000<br>15000|9.83<br>15.58|84.99<br>74.96|7.05<br>10.30|-2.02<br>-0.93|
|Poverty<br>gap<br>(a-<br>1)|11000<br>15000|7.81<br>10.13|89.50<br>84.89|5.21<br>6.86|-2.45<br>-1.99|
|Preferred<br>measure<br>(a -<br>2)|11000<br>15000|7.18<br>8.54|90.78<br>88.25|4.46<br>5.65|-2.58<br>-2.31|

Notet Urban population shares were .235 in 1984 and .264 in 1987.
-15-

**Laa** 1: The credit constraint faced by the debtor, Dt, in the constant returns to scale range kj2ki grows with the capital stock k., and the income level, 4(k,).

The proof can be found in Cohen and Sachs (1986), and is summarized in the appendix. I am implicitly assuming that kN is sufficiently large so that the debtor nation avoids the range of "non-binding" credit constraint identified by those authors.

An intuitive argument for Lemma 1 can be obtained by considering the creditor's lending decision. Given the debtor country production function, Q(kt), a debt ceiling will exist for any kt, based upon the debtor agents choosing their utility-maximizing consumption path (Ct). This debt ceiling function, Dtsh(k.), defines an optimal investment program for debtors with access to capital markets. The value of remaining a debtor in good standing satisfies equation (2.9) above, where D1 is constrained by the credit ceiling. On the other hand, the value of defaulting in any given period sati.sfies equation (2.10). Cohen and Sachs show that in the range of constant returns to scale, the credit constraint will be linear in the debtor nation capital stock: h*(kt)-c.kt, where c is a constant and Dtsh*(kt) if and only i!. Vr(kt,Dt)2Vd(kt,D.). This linear debt ceiling thus satisfies bth debt constraint criteria.

The solution found within this range looks much like the Cohsn and Sachs model at high capital stock levels. As in their model, h(kt) is increasing in f'(kt), the marginal product of capital, P, the rate of discount of the debtor nation, and 0, and the "default penalty," while h is decreasing in r, the world rate of interest.
# 1. INTRODUCI1ON

Though there is growing evidence that increased openness<sup>improve economic performance</sup> in developing countries, there is considerable skepticism<sup>about the relevance of this relationship for</sup> Sub- Saharan Africa. Notwithstanding a good<sup>deal of country-specific evidence that African</sup> producers respond positively to improved incentives, the skeptics<sup>argue that increased openness does</sup> not work in Sub-Saharan Africa for various reasons.<sup>Some argue that reforming trade regime to</sup> enhance efficiency and exports contributes less<sup>to output growth than increasing aid or promoting</sup> faster technical progress in agriculture (Helleiner 1991).<sup>Others argue that efficiency gains from</sup> reforms may be small because domestic producers<sup>cannot reallocate resources sufficiently due to</sup> weaknesses in the human resource base, in infrastructure<sup>and in institutions (Elbadawi, 1992, p. 5).</sup> This skepticism is fuelled, in large measure, by<sup>the dismal economic performance of the Sub-</sup> Saharan Africa (SSA) region as a whole.' Nearly<sup>half the countries of the region are poorer today</sup> than they were a generation ago (World Bank<sup>1991). More then three quarters of them have</sup> undertaken policy reforms during this decade. Thus, on the face<sup>of it, recent reforms, including trade</sup> reforms, do not appear to be associated with improved<sup>economic performance in the region.</sup> There may be at least three reasons why increased<sup>openness resulting from trade reform may</sup> not be associated with higher aggregate output levels<sup>and faster growth. First, if producers do not</sup> respond to the trade reforms because inconsistent<sup>macroeconomic-policies</sup> may have undermined their credibility, then greater openness would not generate<sup>the expected gains in productivity. Second,</sup> even when trade rxforms are credible, producers may<sup>be unable to respond to reforms because of</sup> domestic regulations in product and factor markets or<sup>because of intrinsic weaknesses in institutions,</sup> infrastructure and the human resource base. Again greater<sup>openness would not lead to productivity</sup> gains. Third, even if reforms are credible and producers<sup>do respond, the resulting productivity gains</sup> 'Growth in GDP per capita has averaged 1.5 percent<sup>and -1.0 percent over the 1965-80 and</sup> 1980-90 periods, respectively.
_Table 9: Probability of having recovered after two years, as a function of various housing characteristics, magnitude of losses, and coping mechanisms. N.B. Logistic regression is done after CEM. Details can be found in the Appendix._

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Did not lose more than 5% of household ex-<br>penditure due to the flood||||||
|Lost more than 5% of HH expenditure due to||||||
|the flood|-0.541*|-0.592*|-0.620*|-0.655**|-0.748**|
||(0.316)|(0.312)|(0.326)|(0.323)|(0.323)|
|Log of real per capita expenditure||-0.182||-0.131|-0.134|
|||(0.197)||(0.210)|(0.223)|
|Main income source: Monthly salary||||||
|Main income source: Casual labor|||-1.026***|-1.016***|-0.917**|
||||(0.382)|(0.386)|(0.396)|
|Main income source: Hawking|||0.124|0.0994|0.0874|
||||(0.725)|(0.726)|(0.754)|
|Main income source: Remittances|||0.192|0.174|-0.451|
||||(0.541)|(0.543)|(0.611)|
|Main income source: Stable business|||-0.182|-0.194|-0.212|
||||(0.289)|(0.290)|(0.298)|
|Main income source: Other|||-0.169|-0.182|-0.598|
||||(0.619)|(0.618)|(0.657)|
|Does not anyone to borrow money from in cas|e of emergenc|y||||
|Can borrow money from at least one or mul-||||||
|tiple persons in case of emergency|||||0.473*|
||||||(0.256)|
|Did not receive remittances in the past year||||||
|Received remittances in the past year|||||0.989***<br>(0.304)|
|Constant|0.858***|2.422|1.106***|2.236|1.730|
||(0.139)|(1.682)|(0.270)|(1.810)|(1.933)|
|Observations|393|393|393|393|393|
|Prob > F|0.0886|0.142|0.0515|0.0893|0.00263|

These results show that building resilience is not only about increasing income (and expenditure levels), and that monetary poverty and resilience are different dimensions that are not perfectly correlated.<sup>11</sup> There 11 With a broader definition of poverty that would include financial inclusion, social capital, and stability of income, poverty would affect the ability to recover. Here, we define poverty only through the level of annual expenditure.

23
DISCRIMINATION AGAINST SEXUAL MINORITIES IN EDUCATION AND HOUSING: EVIDENCE FROM TWO FIELD EXPERIMENTS IN SERBIA Figure 2: Acceptance and refusal of feminine and non‐feminine boys—simplified <!-- Start of picture text --> NON‐FEMININE BOYS<br>FEMININE BOYS<br>5%<br>15%<br>85% 95%<br>Accepted Refused Accepted Refused<br><!-- End of picture text --> <mark>Database: Cases with clear outcome (unclear cases omitted), n=172, 93% of schools, feminine boys n=85 (92%), non‐feminine boys n=87 (95%)</mark> <mark>The chances of a non‐feminine boy being accepted into a school without hesitation are more than twice as high (72 percent) as the chances for feminine boys (35 percent; figure 3).</mark><sup>xxii</sup> Figure 3: Feminine and non‐feminine boys accepted without hesitation <!-- Start of picture text --> FEMININE BOYS NON‐FEMININE BOYS<br>28%<br>35%<br>65% 72%<br>Accepted without hesitation<br>Accepted without hesitation<br>Accepted with hesitation<br>Accepted with hesitation<br><!-- End of picture text --> Database: Accepted boys only; n=155, 84% of schools; feminine boys n=72 (78%), non‐feminine boys n=83 (90%) 7
||Business<br>**Table A8. Impac**|Income and<br>**t of Handholdin**|**g**||Decision-|
|---|---|---|---|---|---|
||Knowledge<br>|Assets<br>|CO Cohesion<br>|Outlook on<br>|Life<br>Making<br>|
||(1)|(2)|(3)|(4)|(5)|
|**Panel A: Intent to Treat Effect**||||||
|Handholding (1=Yes)|-0.043|-0.019|-0.022|0.000|-0.194|
||(0.050)|(0.044)|(0.051)|(0.048)|(0.172)|
|Lottery Winner (1=Yes)|-0.035|-0.007|-0.017|0.075|-0.221|
||(0.054)|(0.049)|(0.069)|(0.065)|(0.227)|
|HH and LW|0.015|-0.076|-0.017|0.048|-0.137|
||(0.068)|(0.059)|(0.060)|(0.069)|(0.231)|
|P-value of t - test of ...||||||
|BT = BT and LW|0.33|0.29|0.92|0.39|0.81|
|LW = BT and LW|0.47|0.22|0.99|0.72|0.74|
|BT = LW|0.89|0.8|0.95|0.23|0.91|
|**Panel B: Intent to Treat Effects with Gender In**|**teractions**|||||
|Handholding (1=Yes)|-0.083|-0.023|-0.044|0.012|-0.177|
||(0.070)|(0.053)|(0.071)|(0.070)|(0.288)|
|HH x Female|0.075|0.013|0.048|-0.018|-0.051|
||(0.099)|(0.082)|(0.097)|(0.095)|(0.355)|
|Lottery Winner (1=Yes)|-0.069|0.055|0.075|0.165**|-0.267|
||(0.067)|(0.052)|(0.102)|(0.080)|(0.330)|
|LW x Female|0.064|-0.134|-0.204*|-0.193|0.100|
||(0.105)|(0.089)|(0.114)|(0.118)|(0.417)|
|HH and LW|-0.057|-0.056|-0.048|0.073|-0.359|
||(0.096)|(0.074)|(0.082)|(0.088)|(0.320)|
|HH and LW x Female|0.158|-0.047|0.072|-0.051|0.530|
||(0.122)|(0.102)|(0.104)|(0.128)|(0.445)|
|P-value of t - test of ...||||||
|HH = HH and LW|0.75|0.65|0.95|0.36|0.57|
|LW = HH and LW|0.89|0.12|0.28|0.30|0.80|
|HH = LW|0.84|0.17|0.28|0.05|0.79|
|HH + HH x Female = 0|0.90|0.88|0.95|0.92|0.26|
|LW + LW x Female = 0|0.96|0.31|0.06|0.77|0.56|
|HH and LW + HH and LW x Female = 0|0.22|0.21|0.75|0.84|0.60|
|Mean of dependent variable among controls|0.06|-0.07|0.08|-0.01|0.01|
|N. Observations|1140|1140|1140|1140|1140|

Note: The reported mean of the dependent variable is computed using CO members not offered the hand holding treatment nor assigned to be lottery winners.The dependent variables are aggregates of standardized z-scores. See Appendix B for a definition of the aggregates. All regressions are estimated using OLS methods and include as covariates the stratification variables (eligibility for loan lottery, business ownership at baseline and gender). Standard errors reported in parentheses are clustered at the CO level. The following symbols *, * * and ** * denote significance at the 10 , 5, and 1 percent level, respectively.
We use two competing hypotheses to help answer the third question. The misallocation-of-capital hypothesis states that diversified firms allocate capital to less profitable segments. The more diverse and complex the investment opportunities available, the more pronounced this misallocation is.' Such misallocation of capital should be associated with a reduction of short-term profitability and a pronounced diversification discount. In contrast, the learning-by-doing hypothesis argues that when firms diversify into new lines of business, there is an initial period during which labor is learning how to use the new technology, and a reduction in short-term profitability should be observed.<sup>2</sup> This learning-by-doing should not be associated, however, with a diversification discount since the forward-looking capital market fairly assesses the increase in profitability over time as the learning-by-doing pays off. We test these hypotheses by separating vertical and complementary diversification. Since vertical diversification is more complex in technology, management, and capital investment than complementary diversification (and thus involves more possibilities for misallocation of capital and learning-by-doing), we should observe a more pronounced effect on short-term profitability and diversification discount in vertical diversification than in complementary diversification.<sup>3</sup> Our East Asian sample allows us to effectively test these two competing hypotheses. First, some governments in the region have encouraged corporations to engage in vertical expansion to upgrade technology and general level of development (see World Bank (1998)). Since this is not a pure market-outcome, it may be more likely to find a differential effect of vertical integration and complementary diversification for countries with heavy intervention such as South Korea than for countries undertaking a natural transition into diversified businesses. Second, the nine East Asian countries are at different levels of development, which provides insights into the link between diversification patterns and outcomes and the general level of economic development.<sup>4</sup> This diversity

> ' See Shin and Stulz (1996), Scharfstein (1997), and Rajan, Servaes, and Zingales (1997). 2 See Stockey (1991) and Young (1993).

> 3 See discussion of vertical integration in Williamson (1971) and Klein, Crawford and Alchian (1978). 4 In particular, the nine countries can be thought of as a V-formation with Japan at the lead, flanked by Hong Kong, Singapore and Taiwan, then South Korea, Malaysia and finally Thailand, Indonesia and the Philippines.

2