rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1. The Value, Share, and Changes in Sub-Saharan Africa's Major Non-Oil Export Products in OECD Markets: 1%2-64 to 1991-93.**

|l___________________________________|Valu<br>($mil|es<br>lion)|Share of<br>Expor|African<br>ts(%)|Africa's<br>OECD Im|Share of<br>ports (%)|Growth<br>Export|Rates for<br>s from|
|---|---|---|---|---|---|---|---|---|
|Export Product (SITC)|1962-64|1991-93|1962-64|1991-93|1962-64|Share change<br>(points)|Africa|World|
|Unwrought copper alloys (682.1)|510.8|780.8|14.73|5.16|32.4|-22.5|1.47|5.69|
|Green or roasted coffee (071.1)|447.9|1,053.0|12.91|6.95|22.7|-7.2|2.99|4.36|
|Cocoa beans raw or roasted (072.1)|337.3|1,338.0|9.72|8.83|80.1|-9.9|4.87|5.34|
|Groundnuts green (221.1)|185.5|11.1|5.35|0.07|81.6|-79.9|-9.24|3.68|
|Non-conifer saw logs (242.3)|176.6|734.2|5.09|4.85|36.1|-16.1|5.04|7.20|
|Raw cotton (263.1)|161.0|379.5|4.64|2.51|11.4|1.8|3.00|2.48|
|<br>Unmanufactured tobacco (121.0)|119.9|589.7|3.46|3.89|13.8|-1.6|5.65|6.09|
|Iron ore (281.3)|115.0|247.3|3.32|1.63|9.5|-6.3|2.68|6.65|
|Raw beet and cane sugar (061.1)|93.0|415.1|2.68|2.74|10.0|5.8|5.29|3.64|
|<br>Palm nuts and kernels (221.3)|84.2|2.6|2.43|0.02|92.3|-69.2|-11.28|-6.95|
|Natural rubber and gums (231.1)|77.8|191.1|2.24|1.26|10.3|-2.7|3.15|4.22|
|Fresh bananas (051.3)|61.3|202.8|1.77|1.34|14.2|-9.8|4.21|8.52|
|<br>Palm oil (422.2)|57.5|53.0|1.66|0.35|59.0|-54.1|-0.28|8.63|
|Vegetable oil residues (081.3)|54.7|68.7|1.58|0.45|10.1|-8.8|0.79|8.06|
|Agave fibers (265.4)|52.7|15.4|1.52|0.10|33.3|18.5|-4.15|-5.60|
|Manganese ore (283.7)|44.8|176.2|1.29|1.16|27.8|4.2|4.83|4.33|
|<br>Groundnut oil (421.4)|39.9|78.2|1.15|0.52|55.3|-19.1|2.35|3.85|
|Shaped lumber (243.3)|38.6|418.1|1.11|2.76|15.5|-6.7|8.56|10.69|
|Tea (074.1)|36.7|246.0|1.06|1.62|8.5|13.7|6.78|3.31|
|<br>Base metals nes (689.5)|36.4|252.4|1.05|1.67|29.2|-16.0|6.90|9.88|
|Posts and poles (242.9)|32.5|1.4|0.94|0.01|57.5|-56.2|-5.50|3.40|
|Fixed vegetable oils (422.9)|31.2|6.5|0.90|0.04|48.4|-46.8|-5.26|6.41|
|Nonindustrial diamonds (667.2)|26.4|1,792.7|0.76|11.84|5.2|4.3|15.65|13.27|
|<br>Unwrought tin alloys (687.1)|26.0|2.9|0.75|0.02|8.9|-8.5|-7.28|3.45|
|Inorganic bases (513.6)|25.2|35.3|0.73|0.23|12.1|-11.4|1.17|11.93|
|Industrial diamonds (275.1)|23.2|23.0|0.67|0.15|21.0|-16.3|-0.03|5.31|
|Unwrought aluminum alloys (684.1)|21.3|272.0|0.61|1.80|4.1|-1.6|9.18|11.09|
|Tin ores (283.6)|20.4|6.9|0.59|0.05|19.1|26.2|-3.67|-6.49|
|Crude asbestos (276.4)|19.3|23.3|0.56|0.15|10.1|0.3|0.65|0.55|
|<br>Natural gums and resins (292.2)|18.6|76.7|0.54|0.53|28.4|11.8|5.15|3.90|
|<br>All Above Items|2,975.7|9,495.6|85.79|62.69|20.8|-11.1|4.08|7.37|
|MAJOR PRODUCT GROUPS|||||||||
|Foods and feeds|1,775.4|6,044.1|51.19|39.90|8.0|-5.7|4.31|8.84|
|Agricultural materials|<br>629.8|<br>2,094.9|18.16|13.83|5.6|-2.7|4.23|6.60|
|<br>Ores, minerals and nonferrous metals|919.5|2,600.5|26.51|17.17|10.1|-7.3|3.65|8.28|
|Manufactures|135.2|4,230.0|3.90|27.93|0.3|-0.1|12.61|13.50|
|ALL NONFUEL PRODUCTS|3,468.4|15,146.3|100.00|100.00|3.7|-3.1|5.21|11.83|

Source: Computed from UN Comtrade Statistics
interested in the role of IPRs in attracting trade flows and not in creating trade flows, we only use the Park and Ginarte index of the destination country of the trade flow as explanatory variable (that is countryj in the case of exports and country i in the case of imports).

# **IV. Empirical Estimates**

Our estimation results are presented in Tables 2 through _5._ The overall performance of the rnodels is quite good. Most gravity variables have the expected signs and are statistically significant. Exceptions are for total non-fuel trade (Tables 2 and 3) the coefficient on the border dummy which has the wrong sign in the probit equation and is not statistically significant in the gravity equation; and the wrong signs of the coefficients on the dummies indicating EC and EFTA membership in the gravity equation, which are, however, never significant. For the high technology aggregate (Table 4 and 5), the exceptions are similar: the coefficients on the border dummy in the probit equation and on EC and EFTA membership in the gravity equation are never statistically significant and sometimes have the wrong sign. Likelihood ratio tests indicate that for all alternative specifications estimated the explanatory variables are join,tly significantly different from zero.

The estimated correlation coefficients between the probit and gravity equations p are always close to zero and not statistically significant based on a likelihood ratio test for both total non-fuel and high technology trade. This suggests that for our data it would have been possible to estimate the equations independently and that the exclusion of zero observations in the gravity equation does not lead to a bias stemming from a non-random sample selection.

Recalling the theoretical ambiguity of the effect of IPRs on bilateral trade described in Section II, we had no prior expectations regarding the sign of the coefficient on the Park and Ginarte index. F'or both total non-fuel imports and exports, the Park and Ginarte index has only a small effect on the probability of positive trade flows between countries, although the effect is positive and statistically significant at the 5% level for total non-fuel exports. Turning to the gravity equation, IPRs have a significantly positive impact on bilateral trade flows for both total non-fuel imports and exports. Comparisons of models (I) and (II) in Tables 2 and 3 suggests that inclusion of IPRs leads to relatively small changes in the coefficients of most gravity variables. The biggest changes occur in the coefficients on GDP and population of the destination country of the trade flow. These changes can be explained by the strong correlation between the strength of IPRs protection and the level of economic development as 8
World Bank) and the _Index of Economic Freedom_ (the Heritage Foundation). Appendix 2 provides full definitions and sources of all variables used in the paper, and Appendix 3 presents basic descriptive statistics for the data used in the regressions.

We then extend the regression specification by allowing the growth effect of openness to vary with the country characteristics represented by the control set. We do this by interacting the openness measure with each of the control variables in turn. The regression equation with an interaction term is the following, where _cv_ represents one of the control variables in particular. We interact openness with the control variables one at a time in order to both simplify the interpretation of the results and not to overextend the parameter requirements on the data.

The interpretation of the coefficients on the time-varying variables and on their interaction term with openness is straightforward. However, the interpretation of coefficients related to the variables that are constant per country requires some explanation. In linear regression models, they are well captured by the country-specific effect and, in general, would not be incorporated into the regression specification. In our case, however, we need to include them in the regression in order to analyze their interaction with openness. The coefficients on the constant variables _themselves_ cannot be identified unless we have additional information on their relationship with the countryspecific effect or are willing to make assumptions about it. Nevertheless, in order to complete the information set, we include them in the regression as explanatory variables on their own whenever their respective interaction with openness is analyzed. In order to avoid confusion, we do not report the estimated coefficients on the constant variables themselves but only the coefficients on their respective interaction terms.

# **3.2. Estimation Methodology**

The growth regression presented above poses some challenges for estimation. The first is the presence of unobserved period- and country-specific effects. While the inclusion of period-specific dummy variables can account for the time effects, the common methods 17
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Policy Research Working Paper 11069

# **Abstract**

This paper estimates the effect of electricity prices on firm performance, focusing on firm productivity, sales, and employment. Using the World Bank Business Pulse Survey data for a sample of 24 emerging markets and developing economies during 2019–23, the paper estimates the average effect and the heterogeneous effects across industries of varying energy intensity and firms that implemented (or did not implement) energy efficiency measures (self-reported in the Business Pulse Survey). The findings show that increasing electricity prices by 1 percent reduces employment at firms in energy-intensive industries that did not adopt energy efficiency measures by about 1.5 percent, compared with similar firms in energy-non-intensive sectors. In parallel, energy-intensive firms may increase sales and productivity but this result is robust to all alternative specifications. Firms may increase sales while reducing employment after energy price hikes, by adopting energy-efficient technologies and by passing through costs to consumers in inelastic markets while reducing employment in energy-intensive sectors due to cost pressures. These results highlight the adoption of energy efficiency measures by firms as an important employment protection policy action to cope with future volatility in energy (electricity) prices.

This paper is a product of the Finance, Competitiveness and Innovation Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mmelecky@worldbank.org, miootty@worldbank.org, and raterido@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
In seeking health care for oneself, getting permission is a constraint faced by at most 5- 6% of Senegalese women, and appears not to be strongly associated with marital status. A much larger share of women point to a cost constraint, with the health care of remarried and current widows being the most liquidity constrained, at 71% and 68% respectively, and oncemarried women the least constrained at 55%.

Incomes are not typically pooled across the members of a Senegalese household. Women contribute their labor to the household enterprise and its overall care, but their own earnings are in general for their own and their dependents’ non-food needs. Women are asked what share of their earnings is spent on the household. The data suggest that on average around one-third of women contribute none of their own earnings to household needs. The one exception among marital statuses is for remarried widows, of which only a low 17% surrender none of their earnings. Indeed, they are more likely to give up more than half of their personal earnings than other women (34% versus 16% for married and 21% for ever-divorced women). The one exception is widows, who, since they more often head their households, are expected to do so. These statistics are consistent with the idea elaborated in Section 5 that widowed women without resources or a place to live often remarry poor men who can provide a degree of social insurance, but not much more than food and shelter.

Adult female DHS respondents were asked about whether a husband is justified in beating his wife if she goes out without telling him, neglects the children, argues with him, refuses sex, or burns the food. The responses follow the same patterns across women (only two are shown). Widows and divorcees are least likely, and remarried widows most likely to agree that a husband is justified in beating his wife. Here again, remarried widows stand out as in a particularly weak position.

Among women, a larger share of remarried widows never watch television (43%) followed by women in their first marriage (32%) and remarried divorcees (27%). Current widows and divorcees are most likely to watch TV and access information and entertainment on a frequent basis. Such statistics could reflect economic constraints although alternative explanations are also possible (such as a lower burden in terms of household chores).

The above are simple correlations for which one should of course be cautious about making causal interpretations. Note, however, that the associations between the descriptive statistics and potential indications of decision making power and constraints faced are consistent with our simple theoretical framework and many of the relationships that emerge from PSF in what follows for women by marital status.

17
primary school age were attending school: here, the existence of such a road does not matter. Only one village, in contrast, had a secondary school, so that the trip may then come into play. It is seen that the attendance rate is a little higher in villages with roads, but the difference is not statistically significant ( _χ_<sup>2</sup> (1) = 2 _._ 093 _, χ_<sup>2</sup> 0 _._ 05<sup>(1) = 3</sup><sup>_._84).</sup> The same applies to attendance at high school ( _χ_<sup>2</sup> (1) = 0 _._ 613).

Table 8: School attendance by age group and all-weather road, _kharif_ 2009<sup>_a_</sup>

|Villages:||With road||W|ithout roa|d|All|
|---|---|---|---|---|---|---|---|
|Status:|In|Not in|Total|In|Not in|Total|Total|
||school|school||school|school|||
|Age group||||||||
|6 - 10|40|0|40|121|2|123|163|
|11 - 15|36|7|43|66|25|91|134|
|16 - 19|10|19|29|18|50|68|97|

> _a_ Included are 13 children who were at boarding schools or living with relatives elsewhere.

The length of the daily trip to school is also a potentially important factor. As noted above, two villages had no primary school, so it is only in these that distance was likely to matter where normal attendance is concerned. Since there were but six children in this sub-sample, five of whom were attending school regularly some 2 km. away, nothing much can be said.<sup>5</sup> Attending secondary school, however, involved a daily trip elsewhere in all but one village.<sup>6</sup> The distance from home to the secondary school in villages with and without an all-weather road averaged 3.98 km. (33 pupils) and 3.59 km. (74 pupils), respectively, a difference that is not statistically significant ( _t_ = 0 _._ 52, clustering on villages). Although the _t_ -test is fairly robust, both distributions look distinctly non-normal. Two non-parametric tests for their equality are the (combined) Kolmogorov-Smirnov, _D_ = 0 _._ 2314 ( _p_ = 0 _._ 122), and Kruskal-Wallis, _χ_<sup>2</sup> (1) = 0 _._ 006 ( _p_ = 0 _._ 938). On balance, the _t_ -value being so small, it seems fairly safe to conclude that the presence of an all-weather road had no influence on whether children were normally attending school.

The same cannot be claimed for involuntary absences due to bad weather. The

> 5All but a single child in this age-group attended primary school in the other 28 villages; the difference between the two types of villages is significant only at the 8 percent level (Fisher’s exact test).

> 6We discarded the 13 children who were at boarding schools, or living with relatives elsewhere, or attending local private schools. Recall also that some children in primary school were older than eleven, and some in secondary school, older than fifteen. Four villages had a primary school extended to include standards 6 and 7. Children attending the latter were also excluded from the comparisons that follow.

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**Figure 14: Percent change in wages related to a 10 percentage point increase in female labor share, trading vs. non‐trading firms**

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>‐1<br>‐2<br>‐3<br><!-- End of picture text --> Note: The graph shows the results of regression analysis described in Appendix 10 and 11 using the firm’s average wage rate (in natural logarithms) as dependent variable and the female labor share as independent variable. The model interacts exporter, importer, GVC participant and FDI dummies with the female labor share variable to assess the mediating role of trading/FDI status. Exporters are firms with an export share (direct or indirect) of at least 10% of total sales. Importers are firms with an imported input share of at least 10% of total inputs. GVC participants are firms that are classified as both exporter and importers. FDI refers to firms with a foreign ownership share of at least 10%. All regressions control for age, firm size, capital intensity, skill intensity and country‐sector, subnational region and year fixed effects and are at least significant at the 5% level.

# **5. Conclusions**

Using a cross‐section of more than 29,000 manufacturing firms in 64 developing and emerging countries from the World Bank’s Enterprise Surveys, this paper assessed whether trading firms have a female labor share premium relative to non‐trading firms. It focused on four types of trading firms: exporters, importers, GVC participants and FDI firms. The study found a female labor share premium for all four types of trading firms, controlling for firm output, capital intensity, total factor productivity and fixed effects. Exporters and GVC participants show the highest female labor share premium compared to non‐exporting and non‐participants, respectively, underlining the importance of the export channel. The results also held controlling for differences in relative wages between men and women, and excluding traditional export sectors (apparel and electronics) from the sample.

Interestingly, the female labor share premium is much higher for production workers compared to non‐production workers (i.e. managers, sales, administration), implying that women in our country sample specialize in low‐skill production. In line with these findings, the female labor share premium for exporters and GVC participants is highest in low‐tech sectors, suggesting the largest benefits for women in low‐skill intensive sectors. The paper also analyzed the role of female firm ownership and female top 27
On a related point, several cros-country studies have found that health outcomes have a sizable impact on growth (see Appendix A for a brief overview of the recent evidence). As can be inferred from the discussion in the previous sections, this may still underestimate the true impact of health, which may operate through a variety of indirect channels—such as the impact of better health on the incentives to acquire skills and accumulate human capital, and the effect of a higher expected lifetime on the rate of time preference and the propensity to save. At the same time, however, improvements in health outcomes themselves may be the consequence of greater access to public infrastructure, for the reasons outlined earlier. Because most cross-country studies do not account for these indirect effects, the true contribution of infrastructure to growth tends to be underestimated. Country-specific studies, such as the analysis of long-run growth in South Africa by Fedderke, Perkins, and Luiz (2006), suffer from the same shortcomings<sup>27</sup> . Simulation exercises aimed at evaluating, say, infrastructure needs and their impact on growth are bound to be misleading, because they are based on mispecified models.

Future work based on cross-country growth regressions must provide a more careful attempt to disentangle the various channels through which infrastructure affects growth, possibly through the use of simultaneous equations models. An alternative approach is to develop country-specific structural macroeconomic models, which have considerable advantages (compared to small econometric models), given the flexibility that they provide to account explicitly for the various externalities associated with public infrastructure. Important classes of models in this area are the SPAHD models developed by Agénor, Bayraktar, and El Aynaoui (2006), and Agénor et al. (2005), or the more advanced IMMPA framework described in the contributions contained in Agénor, Izquierdo, and Jensen (2006). A key feature of both types of models is indeed an explicit account of the composition of public capital (with at the

> 27Some of these studies suffer from other limitations as well. In particular, they do not always account for the fact that the impact of public spending on growth depends on how the increase in outlays is financed. Ignoring the government budget constraint invalidates the use of the model for a number of purposes, such as calculations of investment needs. Moreover, existing studies do not provide an adequate treatment of nonlinearities—which may be quite important in assessing the impact of infrastructure on growth, as a result of network effects. See Agénor (2006), Hurlin (2006), and Arestoff and Hurlin (2005) for a discussion of this last point.

35