rafmacalaba/datause-encoder-data huggingface dataset:
# **(iv) Productivity and Infrastructure**

**In the next stage of this analysis, we shift the attention to the infrastructureproductivity link as infrastructure has emerged as a major constraint to Russia’s firms in many recent firm surveys** . The 2008 BEEPS survey highlights the severity of the infrastructure challenge, relative to other countries within the region.  About 14.2 percent of Russian firms identify insufficient water as a major or severe constraint, compared to a regional average of 9.6 percent. Likewise, more firms in Russia complain about electricity and telecommunication facilities than in Ukraine, Romania, and Belarus (see Figures 8 to 11). Recent analyses of infrastructure and productivity growth, especially those based on detailed firm-level data and a wider menu of infrastructure measures (e.g., Fedderke and Bogetic 2009) have shown strong association between infrastructure and productivity that has sometimes proven elusive in more aggregated studies.

<!-- Start of picture text --> Figure 8: % of firms experiencing Figure 9: % of firms indentifying<br>insufficient water electricity as major or severe constraint<br>18 60<br>16 55<br>14<br>50<br>12<br>45<br>10<br>40<br>8<br>6 35<br>4 30<br>2 25<br>0 20<br><!-- End of picture text --> Source: BEEPs 2008.

15
The literature has offered several explanations for the missing middle. One strand of this literature suggests that onerous regulation and bureaucracy associated with being formal that particularly disadvantages small firms (e.g. Rauch, 1991), coupled with weak demand and poor institutional infrastructure, to support large scale production (e.g. Tybout, 2000). The literature surveyed above on credit constraints faced by small firms also suggests that small firms face difficulties in becoming middle-sized firms especially in low income countries giving, rise to the missing middle. A second strand of the literature favors a dual-economy model of large high-productivity firms and small low-productivity firms (e.g. Harris and Todaro, 1970) and argues that large firms are subject to constraints and regulations which small firms avoid. Along these lines, Dharmapala, Slemrod, and Wilson (2010) have argued that the missing middle may be the result of optimal tax policy where the government economizes on administrative costs by exempting small firms but in turn intermediate sized firms reduce their output to tax-exempt levels.

Empirical research on the size distribution of firms in developing countries has been limited by the absence of available census data and most studies have had to rely on small survey samples. Thus, while there are several case studies analyzing the missing middle in a single country context such as Côte d’Ivoire (e.g. Sleuwaegen and Goedhuys, 2002), there has been no systematic research/data on the prevalence of the missing middle across countries. Recently however, Hseih and Olken (2014) obtain census microdata from India, Indonesia, and Mexico and argue that there is no "missing middle" in the sense of a bimodal distribution in any of these three countries. They show that the missing middle pattern emerges only when 27
- 45 -

# **_Predicted demand response to global economic downturns_**

There are two possible consequences of a worldwide slowdown in economic activity.<sup>43</sup> First, it can reduce the general demand for energy and thus improve the global energy balance. International energy prices would decline. Therefore, energy-scarce countries could purchase electricity or fuel sources for domestic power generation at relatively low costs and might be able to have excess supply of energy. If governments translate this favorable market condition into their domestic energy tariffs, the demand for energy would increase. This may be particularly relevant to Macedonia where the energy-intensive industries are significant in production and exports. In the rest of the SEE countries, the firms’ response on the real side would be limited. But the downward pressure on domestic energy prices would be large if the shock is accommodated (Figure 20).

Second, however, there is another consequence for the demand for products, especially for material industries. Albania and Croatia, where the conditional energy demand elasticities with respect to output are estimated to be high, would experience large reductions in the nonresidential demand for energy, given the global economic downturn. On the other hand, let us assume that the energy demand elasticities would be negligible in the rest of countries. In Albania, the expected impact of reduced energy tariffs would be potentially large, but the effect might be offset by an energy demand shift caused by demand reduction in products. In Croatia, the pricing effect is expected to be small, but the country may experience a large reduction in energy demand because of the economic stagnation.

How the authorities should react to these expected consequences is out of the scope of the current paper. But the paper provides some insight on what they could do. The expected reduction in energy demand may create some room to carry out other energy policies for

> 43 An important implicit assumption underlying this argument is that the demand response to output would be symmetric and marginal. Since our models are mainly estimated with the 2005 data, it is not necessarily clear whether it would be appropriate to interpret the results in the context of the current global economic downturn since 2008. The demand response could be asymmetrically rigid or elastic, and given a significant slowdown, the cost structure might change. The discussion here is valid as long as a marginal shock in output is considered.
Table 21: IV estimates of the effect of the share of labor with Internet on labor outcomes by quartile of income-per-worker growth: Manufacturing

||1st quartile|2nd quartile|3rd quartile|4th quartile|
|---|---|---|---|---|
|**Dependent variable:** ∆**ln(Numb**|**er of white-c**|**ollar workers**|**)**||
|∆Share of labor that uses Internet|0.0298***|0.0189**|0.0089**|-0.0011|
||(0.0085)|(0.0074)|(0.0042)|(0.0026)|
|F- frst stage|10.96|7.94|16.55|67.13|
|Sargan p-value|0.2230|0.8503|0.8674|0.6233|
|**Dependent variable:** ∆**ln(Numb**|**er of blue-co**|**llar workers)**|||
|∆Share of labor that uses Internet|0.0496***|0.0163***|-0.0070**|-0.0184***|
||(0.0133)|(0.0060)|(0.0032)|(0.0030)|
|F- frst stage|8.83|8.48|16.55|53.53|
|Sargan p-value|0.9703|0.4196|0.6502|0.8251|
|**Dependent variable:** ∆**ln(Wage **|**of white-coll**|**ar workers)**|||
|∆Share of labor that uses Internet|-0.0209**|-0.0068|0.0166***|0.0283***|
||(0.0081)|(0.0055)|(0.0047)|(0.0032)|
|F- frst stage|8.83|8.48|16.55|67.28|
|Sargan p-value|0.1862|0.4234|0.6731|0.6110|
|**Dependent variable:** ∆**ln(Wage **|**of blue-colla**|**r workers)**|||
|∆Share of labor that uses Internet|-0.0331***<br>(0.0093)|0.0082*<br>(0.0042)|0.0216***<br>(0.0045)|0.0292***<br>(0.0031)|
|F- frst stage|8.83|8.48|16.55|67.12|
|Sargan p-value|0.5190|0.5803|0.5705|0.3109|
|**Dependent variable:**∆**white/blu**|**e-collar work**|**ers**|||
|∆Share of labor that uses Internet|-0.2234|0.1585|0.4136***|0.2322***|
||(0.2294)|(0.1476)|(0.1391)|(0.0743)|
|F- frst stage|8.83|8.48|16.55|59.71|
|Sargan p-value|0.2196|0.8847|0.2659|0.7336|
|**Dependent variable:**∆**white/blu**|**e-collar wage**|**s**|||
|∆Share of labor that uses Internet|0.0034|-0.0449**|-0.0313***|-0.0100*|
||(0.0135)|(0.0184)|(0.0115)|(0.0052)|
|F- frst stage|8.83|8.48|16.55|67.12|
|Sargan p-value|0.8468|0.2699|0.4443|0.7944|
|Observations|2,860|2,859|2,860|2,859|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

50
|Capital city Y:1 N:0|12.4%|29.8%|-0.3%|4.3%|-1.4%|13.6%|-0.1%|9.9%|
|---|---|---|---|---|---|---|---|---|
|Obstacles severity|-0.3%|4.5%|0.0%|-3.7%|-1.2%|2.8%|9.5%|-360.0%|
|Sole proprietorship Y:1 N:0|-39.6%|41.3%|-3.6%|10.2%|2.3%|1.3%|31.2%|-34.3%|
|Shareholding company (non-<br>traded) or Partnership Y:1<br>N:0|10.6%|9.1%|1.5%|-9.1%|1.6%|-10.6%|-16.9%|-15.4%|
|R&D and New|0.7%|-1.2%|0.6%|-0.3%|-16.9%|-38.9%|14.9%|15.3%|
|product/process|||||||||

Huber-White robust standard errors clustered on country-industry pairs. *** (1%), ** (5%), * (10%). Without much loss of generality, only the combined contribution of R&D and New product/process development is shown.

65
_Yij_ 2014 _− Yij_ 2000 and zero for _△Yij_ 2000 = _Yij_ 2000 _− Yij_ 1990.<sup>17</sup> The focus of the analysis is on the coefficient of the triple interaction term, i.e., _π_ 7. For night-lights, we can separate out the effects of the war years from that of the post-war years; the specification includes a dummy for war years [2002-2007] and another dummy for post-war years [2008-2011]. The omitted category is the pre-war years in this case. The error term _εijt_ could be spatially correlated. To correct for possible spatial correlations, we cluster standard errors at the route level.

The outcome variable _Yijt_ is expressed in shares for employment, built-up density, and skilled labor, and in logarithms for night-light luminosity. The dependent variables are differenced to wipe out the effects of time-invariant location heterogeneity. This is especially important in our analysis; for example, the labor-share in agriculture in a commune would be determined in part by the land quality and agro-ecological conditions. Part of the differences in employment shares observed between the Abidjan and non-Abidjan routes may be driven by these differences in agricultural endowment rather than the trade cost shock to the Abidjan route. For built-up density and skilled labor, the initial level of agglomeration economies may play a role in addition to the sunk investments in creating significant differences between the Abidjan and non-Abidjan routes. To address this concern, we include population density in 1975 as a measure of initial agglomeration economies in a commune/department. The DID regressions condition on a vector of pre-war locational attributes _Zij_ 0 which includes log of arc distance to the nearest route, a dummy indicating if the communes are from Mali, in addition to log of population density in 1975.<sup>18</sup>

# **(6.2) Defining Market Access**

Many studies rely on the distance to the nearest city/town as a measure of market access. It seems intuitive in the context of the simple theoretical model developed above where distance to the nearest city ( _K_ or _J_ ) is a sufficient statistic for market access of a location.

> 17We have data on 1975 which is used for initial conditions including the 1975 population density as a measure of agglomeration.

> 18The DID results do not change in any significant way if we exclude arc distance to the nearest route from the regressions.

20
Table 4: Plant-level effects of GQ access: estimation of plant exit/closure and performance outcomes

|||Sa|mple sche|me||
|---|---|---|---|---|---|
|DV-Loggedvaluesof|;<br>Exit[0/1]|Output|Avg.<br>.<br>plant size <br>(emp.)|Labor<br> productivity|TFPR|
||1|2|3|4|45|
|(0.1) District 0-10 km fromGQ|-0.11444<br>” (0.054)|(0.123444<br>" (0.040)|0.016<br>(0.021)|0.11377<br>~—(0.045) ~—-|«(0.015<br>(0.020)|
|N<br>|18321<br>|"<br>10213<br><br>|10211<br>|10172<br>|9853<br>|
|rR|0.022|"<br>0.947|0.926|0.912|0.907|
||B.|Young||||
|(0.1) District0-10km fromGQ|-0.140¢+ <br>|)<br>—0.145++<br>|0.071<br>|0.050<br>|0.013<br>|
||” (0.057)|" (0.063)|(0.046)|(0.061)|(0.033)|
|N<br>|6255<br>|"<br>3486<br><br>|3486<br>|3479<br>|3373<br>|
|rR|0.026|"<br>0.944|0.905|0.910|0.912|
||C.|Mature||||
|(0.1) District0-10km fromGQ|-0.100+<br>|O.111+<br>|-0.014<br>|0.14577<br>= <br>|(0.017<br>|
||” (0.056)|" (0.058)|(0.025)|(0.057)|(0.029)|
|N<br>|9459<br>|"<br>5245<br><br>|5243<br>|5222<br>|5051<br>|
|rR|oo19|"<br>0.950|0.936|0.911|0.909|
||D|. Old||||
|(0.1)District0-10kmfromGQ|-0.106++<br>|=0.114+<br>|-0.019<br>|0.1647<br>|0.015<br>|
||<br>” (0.050)|<br>" (0.061)|(0.044)|= (0.064)|(0.052)|
|N<br>|2607<br>|"<br>1482<br><br>|1482<br>|1471<br>|1429<br>|
|rR|0.024|"<br>0.939|0.937|0.915|0.878|

Note: The table limits the sample to plants from the sample scheme observed in the data in the five before and after the nearest GQ segment being completed. “Exit” is an indicator variable measured as the failure to observe the same plant in the post-GQ period. Table reports OLS coefficient estimates of the effect on plant outcomes due to proximity to the GQ segment. All specifications include an unreported constant term, regressors for completion interacted with an indicator for nodal district and district 10 - 50 km from the nearest GQ segment, and plant and year fixed effects. Sample is restricted to comprise the same set of districts available in the repeated cross-section analysis above. Standard errors are clustered by district. Significance levels are indicated by + < 21,+7-<:05,++7<:01.
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definitions of the network in the following way. The core and reference is the most specific one, also used in the preceding section (i.e. peers in the same Lao province, exporting the same HS-6 product to the same country in a particular month). Each of the four dimensions involved has a corresponding layer of aggregation and specificity: geographical, sectorial, destination and temporal. We explore each with reference to the most specific level (see Table 3). To take an example: we compare the impact on survival of having peers in one's province (same HS-6 product to the same destination in the same month) to that of having peers in one's region but outside one's province and to that of having peers in the country but outside one's region. Similarly, for time, we compare the impact of having peers in a certain month (same province, same HS-6 product to the same destination) to that of having peers in the same year but in other months. A “placebo” is also constructed to count peers selling any product to the same destination. One expects this to be much less important for survival than product-peers, because information is assumed to travel via the product channel, not the country channel. Such an arrangement allows us to pinpoint the most relevant level at which networks function and to confirm some intuitive results.

Table 4 compares the coefficients of the various definitions of _peers_ in the same full RE logit used in Table 2. For ease of use, it groups together the layers of aggregation mentioned above under the four dimensions in Table 3. Moreover, models (2) and (3) also introduce controls at the same layers of aggregation to estimate, in (2), a specification with fixed effects for Lao region, world region, sector and year fixed effects, and in (3) a specification with fixed effects for Lao province, country, HS-2 product and month.

**Table 4 – Comparison of the coefficient on “peers” for different levels of aggregation**

|||RE logit||
|---|---|---|---|
||(1)|(2)|(3)|
|**peers (same province, HS-6, dest. and month)**|**0.928*****|**0.915*****|**0.959****|
|peersfromown Lao region (outside own province)|0.947|0.963|0.980|
|peersfromall of Laos (outside own region)|1.002|1.012|1.015|
|peerstoregion of world (besides dest. country)|0.982*|0.981|0.964|
|peerstoentire world (besides dest. region)|1.020|1.004|0.962|
|peersinHS-4 (outside own HS-6)|1.021|1.027*|1.045*|
|peersinsector (outside own HS-4)|0.995|1.003|1.001|
|peersoveryear (other than current month)|0.989|1.002|0.966|
|peers to same dest, any product (placebo)|1.001|0.999|0.998|
|_Controls sector, Lao region, dest. region and year_||_yes_||
|_Controls HS-2, Laoprovince, dest. country and month_|||_yes_|
|N|13986|9881|8454|

_* p<0.10 ** p<0.05 *** p<0.01. Source: Own calculations based on Lao PDR customs data. Model (2) includes controls for sector, Lao region, world region and year. Model (3) includes controls for HS-2_ 23
Policy Research Working Paper 7269

# **Abstract**

Several studies explore the differences in men’s and women’s labor market participation rates and wages. Some of these differences have been linked to gender disparities in education attainment and access. The present paper contributes to this literature by analyzing the relationship between the proclivity of a firm to have a female top manager and access to education among women relative to men in the country. The paper combines the literature on women’s careers in management, which has mostly focused on developed countries, with the development literature that has emphasized the importance of access to education. Using firm-level data for 73 developing countries, the analysis finds strong evidence that countries with a higher proportion of female top managers also have higher enrollment rates for women relative to men in primary, secondary, and tertiary education.

This paper is a product of the Global Indicators Group, Development Economics. 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://econ.worldbank.org. The authors may be contacted at mamin@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
this matter. To address this question surveys should ask about the firm's lines of business (defined as precisely as possible), whether the firm is a conglomerate (i.e., engaged in unrelated activities), the extent of vertical integration (i.e., produces its main inputs or has its own distribution channels), and whether the firm has any subsidiaries. With these measures, researchers can link productivity with the scope of the firm to see how firm scope changes under different institutional environments.

**The extent of markets.** Theory suggests that the extent of markets may fundamentally constrain firn development. Some believe that a small market, for instance, may help explain the so-called "missing middle" phenomenon in developing countries-the existence of some large firms and many small firms, but very few middle-sized firms, which are common in industrialized countries. Small markets have also been suggested as a barrier to technological change. Existing Bank surveys tend to neglect this issue. Many surveys can become more useful by asking certain questions about the extent of market: Where firms sell their products, the distance to a major city, and the firm's access to all types of transportation.

Two Bank surveys have made impressive efforts to explore market structure. One, the _RPED,_ asks two especially useful questions: (1) What characterizes a firm's competitors? (None, domestic firms, foreign competitor, or imports). (2) How does the firm set its prices? (Marketprice-taker, markup over costs, in line with imports, price discriminate in different markets, price increases with quality, follow largest competitors, government-set, negotiate-with-the-buyer, or set by a business association). Although the category of "domestic firms" in the first question is rather broad, the second question is especially rich.

The second, _Russia.- Economic and Civil Society,_ asks detailed information about market structure:

- How many Russian enterprises the respondent considers to be direct competitors.

- Whether the respondent's firm's most important products compete with imported products from other countries listed.

- Whether the firm is classified as a monopoly producer.

- The ownership structure of input suppliers.

- Whether distribution channels offer credit assistance.

13
enlarged to increase the precision and make it representative at the Tehsil level (a level below the district). The total number of surveyed households is 30,932 in 2003 and 91,280 in 2007-08. For the empirical models of this paper, the analysis uses pooled cross-sectional data comprised of girls aged 12-19 selected based on the criteria described above.

The surveys used to construct covariates for the school-level analysis are again used for the household-level analysis. This includes the PSLM, which relies on a similar sampling strategy as the MICS. Other data come from the PIHS, the National Education Census, administrative data of other programs under the PESRP, and the annual Punjab public school census collected in 2003.

Given the targeting of the program to low-literacy districts, stipend and non-stipend districts are likely to differ in aspects other than the treatment and a baseline comparison confirms this. Since literacy rates are likely correlated with other underlying socio-economic characteristics, such factors may, in turn, be associated with different education attainment and divergent trajectories in human capital accumulation between households in stipend and nonstipend districts. Indeed, as shown in Table 3, at the community level, households in stipend districts are more likely to be below the poverty line, to be located in a rural village, and to spend a little less on education. The stipend districts also have less access to public transport and to schools of different levels (defined as a distance of two kilometers or travel time of 15 minutes) as well as fewer private middle schools relative to public schools. At the school level, schools in stipend districts tend to have worse school facilities, but the differences are very small, except for electricity. On the other hand, stipend schools exhibit more favorable student-teacher ratios. Finally, at the household level, households in stipend districts appear to have more dependents and be headed by individuals with approximately 1.6 fewer years of education. They are also less likely to have dwellings connected to gas and electrical services, and to have access to family planning and health centers.

In order to deal with potential biases that may rise due to these differences, we run econometric specifications which control for factors at the community, school, and household levels that could contribute to the outcomes, as well as district and cohort-fixed effects. Control variables that may be endogenous to the stipend program are added to the models using their 10