rafmacalaba/datause-encoder-data huggingface dataset:
In the rest of the section we analyze the components of the employment growth formula in turn, gathering clues as to factors contributing to the poor job creation record of the Moldovan economy.

# **II.2 Firm entry and exit**<sup>**8**</sup>

A growing body of literature on labor market performance emphasizes the role of entrepreneurship and firm dynamics. In an environment which is conducive for entrepreneurship and where starting-up a business is easy labor market outcomes, such as the employment ratio, tend to be significantly better than in an environment where there are restrictions on entrepreneurs an it is difficult to open a firm (Krueger an Pischke, 1997; Boeri and Martins, 2000; Scarpetta et al., 2002; Lopez-Garcia, 2003).

The rate of firm creation has been and still is low in Moldova, especially by the standards of other transition economies. In addition, the already modest rates of firm creation observed in mid- to late 1990s, plummeted in 1999 and since then stabilized at a low level (Table 1). Presently, the rate of firm creation is 7-8 percent for firms which are legal persons, and around 5 percent for firms which are natural persons.<sup>9</sup> These figures should be contrasted with the average enterprise creation rate of 16-18 percent prevailing in transition economies of Central Europe (Eurostat, 2000). In Romania – Moldova’s neighbor – the firm creation rate in the late 1990s exceeded 20 percent. So, it was three times as high as in Moldova. This is a striking difference. Against this background Moldova’s record of firm creation is bleak indeed.

Particularly indicative is the low rate of creation of firms which are natural persons. In most transition economies the number of such firms has mushroomed, and they account for the majority of all firms. Enterprises that are natural persons have a sole proprietor and are small.<sup>10</sup> As such, this is a legal form well suited to the needs of small

> 8 The empirical analysis in this Section is based on the results of the Annual Survey of Enterprises, which covers firms registered with the official RENIM register.  The survey is carried out by the Moldovan Department of Sociology and Statistics (DSS).  A detailed description of the survey is provided in Annex 2.

> 9 This rates are underestimated insofar the overall number of firms is overstated due to the artificially low rate of firm closures.  This does not alter the qualitative nature of the conclusion that the rate of firm creation is low in Moldova.

> 10 Firms that are natural persons include the self-employed (in this case they have no salaried employees).

6
Table 4 summarizes the sectoral net trade flows for the regions in the APEC model. The final line shows that trade surplus (+) or deficit (-). Japan generates a $89b surplus, matched largely by the deficits in the US (-$42b) and EU (-$38b). Among the developing countries, only China and Thailand have notable deficits, while Indonesia shows a $7b trade surplus. From a sectoral perspective, among the advanced countries, minerals and energy are the major net import sector. Japan generates an enormous net export ($210b) in the capital goods sector, while the US has the biggest surplus in services and agriculture. Indonesia exports substantial energy and minerals, and imports capital goods; China exports apparel and light manufacturing, and imports intermediates and capital goods.

The data on trade and production structure are consistent with intuition based on conventional international trade theory. At one extreme, China and ASEAN are major competitors in labor-intensive nondurable manufactured exports and an important current and future importer of capital/technologyintensive products for their industrialization program. At the other extreme, Japan, the EU, and the US are major suppliers of capital/technology-intensive goods and major importers of labor-intensive consumer products. Asian NIEs are an intermediate case between the two extremes. They are important suppliers of all manufactured goods to China and ASEAN4, and growing demanders and suppliers of technology/capital-intensive products from Japan, EU, and the United States, while still remaining important suppliers of labor-intensive goods to industrial countries.

Most general equilibrium analyses of regional economic liberalization focus on the removal of _ad valorem_ equivalent price distortions against imports that arise from existing trade barriers and other sources. This is also the primary focus of the simulations conducted in this paper, since the pattern and degree of protection are important determinants of the impacts of trade liberalization. The larger the initial distortion, the greater the response to a particular policy change.

Table 5 presents _ad valorem_ import protection (tariff plus NTB) rates for each region by sector, along with other sectoral taxes and subsidies on exports and production. While the GTAP data base contains detailed tariff information, this was compiled prior to the conclusion of the Uruguay Round negotiations. The primary source for our tariff data is information obtained by the World Bank following the Uruguay Round from the GATT on the specific tariff commitments made by each participant. After reconciliation of this information to the sector and regional aggregation available in our model data, the result for each region is a set of sectoral tariff rates by origin (although only the average from all sources is shown in Table 5).'O

> **1'** Because information **on** tariffs for Taiwan and China was not available from the GATT, the earlier GTAP rates were used for these countries.

12
Table 4. Pollution Intensities and Effluent Concentrations for Unrated Firms and Firms Rated for the First Time

||||Pollution I|ntensity||||Effluent C|oncentratio|n|
|---|---|---|---|---|---|---|---|---|---|---|
||Water|TSS|COD|SO2|Dust/Smo|ke<br>Gas|TSS|COD|SO2<br>|Dust/Smoke|
|**_Zhenjiang launched_**|**_its pilot Gre_**|**_en Watch P_**|**_rogram in 19_**|**_99_**|||||||
|Unrated|50.06|68.05|63.44|0.58|0.38|6.38|115.44|170.43|183.52|199.82|
||(103.13)|(190.49)|(177.37)|(1.70)|(0.81)|(13.39)|(51.90)|(97.95)|(343.55)|<br>(166.58)|
|Rated|6.93|26.71|23.82|0.04|0.01|2.30|93.54|179.51|413.45|485.60|
||(19.94)|(63.85)|(67.41)|(0.11)|(0.06)|(5.68)|(79.60)|(227.12)|(522.20)|<br>(709.19)|
|Equal mean test|3.64*|0.13|0.82|0.42|3.21*|0.52|3.77**|1.30|0.73|0.67|
|Equal mediantest|5.14**|0.15|2.73*|0.17|0.48|0.10|6.71***|3.25*|1.99|2.58*|
|**_Three cities (Huaian_**|**_, Wuxi and_**|**_Yangzhou) l_**|**_aunched Gr_**|**_een Watc_**|**_h program_**|**_s in 2001_**|||||
|Unrated|76.74|8.79|1.89|0.16|0.11|24.17|95.83|278.24|967.66|253.96|
||(245.71)|(28.67)|(6.67)|(0.49)|(0.31)|(102.11)|(56.86)|(304.49)|(999.83)|(218.00)|
|Rated|143.92|30.34|119.72|0.13|0.06|28.81|120.27|229.84|1284.55|295.01|
||(492.41)|(94.04)|(556.27)|(0.68)|(0.28)|(148.89)|(335.57)|(650.73)|(2476.77)|(784.40)|
|Equal mean test|2.93**|0.06|6.83***|1.67|1.25|2.42|5.06**|8.71***|0.01|0.22|
|Equal mediantest|7.54***|1.23|2.58*|0.05|0.83|1.52|7.02***|10.57***|0.14|0.23|
# Table 2b: Côte d'Ivoire

Firm Count by Industry <!-- Start of picture text --> Number of Firms Number of Firms<br>2003 2014<br>Tobacco 2 Tobacco 2<br>Paper 5 Paper 4<br>Other Transport 7 Other Transport 5<br>Textiles 9 Furniture 7<br>Non-metallic metal 10 Non-metallic metal 10<br>Leather 12 Textiles 12<br>Petroleum 12 Petroleum 26<br>Electrical Machinery 12 Leather 35<br>Furniture 13 Electrical Machinery 45<br>Fabricated metal 31 Chemicals 54<br>Chemicals 35 Rubber & Plastics 55<br>Printing 37 Fabricated metal 57<br>Rubber & Plastics 41 Wood, not Furniture 69<br>Wood, not Furniture 54 Printing 70<br>Food & Bev 63 Food & Bev 168<br>0 50 100 150 200 0 50 100 150 200<br>Graphs by year Graphs by year<br><!-- End of picture text -->

# Table 3b: Côte d'Ivoire

Firm Shares by Industry <!-- Start of picture text --> Share of Firms Share of Firms<br>2003 2014<br>Tobacco 0.6 Tobacco 0.3<br>Paper 1.5 Paper 0.6<br>Other Transport 2.0 Other Transport 0.8<br>Textiles 2.6 Furniture 1.1<br>Non-metallic metal 2.9 Non-metallic metal 1.6<br>Leather 3.5 Textiles 1.9<br>Petroleum 3.5 Petroleum 4.2<br>Electrical Machinery 3.5 Leather 5.7<br>Furniture 3.8 Electrical Machinery 7.3<br>Fabricated metal 9.0 Chemicals 8.7<br>Chemicals 10.2 Rubber & Plastics 8.9<br>Printing 10.8 Fabricated metal 9.2<br>Rubber & Plastics 12.0 Wood, not Furniture 11.1<br>Wood, not Furniture 15.7 Printing 11.3<br>Food & Bev 18.4 Food & Bev 27.1<br>0 5 10 15 20 0 10 20 30<br>Graphs by year Graphs by year<br><!-- End of picture text --> 40
point of each administrative area to the coordinates of the capital (from the Global Insights (v 6.1) data), which we call _dist2cap_ . Likewise, we calculate distance from the center point of each administrative area to the border ( _dist2border_ ) using the boundary file provided by the NSO.

In consideration of the initial conditions of administrative areas for regional trade, we construct distance to major rivers ( _dist2major_ _~~r~~ iver_ ) and distance to coast ( _dist2coast_ ). Also here we use the center point of the administrative areas as the points of origin and destination. Major rivers and coastline data are from Natural Earth<sup>12</sup> .

In addition, we consider the terrain with regards to the elevation, slope and ruggedness. The elevation data are from the Shuttle Radar Topography Mission (SRTM) Digital Elevation dataset (version 4) and is measured in meters at 3 arc-seconds and 30 arc-seconds (approximately 1km<sup>2</sup> ) (Jarvis et al. 2008). We summarize mean and max: _elev_ and _elev_ _~~m~~ ax30_ . We derive elevation in populated places using a 1% threshold level, which is approximately 10 people per km2 from the Landscan 2012 dataset (described in the population count and density section). We take advantage of the slope function on Google Earth to process the 3 arc-seconds elevation data into a slope variable: _slope_ .<sup>13</sup> Figure A2.1 (left panel) illustrates the variation of slope, where the Great Rift Valley is seen to run from North to South. The Ruggedness Index we use is developed by Nunn and Puga (2012) which measures the level of ruggedness in hundreds of meters of elevation difference for each grid cell. We summarize the pixel mean ( _rugged_ ) and maximum ( _rugged_ _~~m~~ ax_ ) of this ruggedness index evaluated over the entire administrative areas as well as over populated areas ( _rugged_ _~~p~~ opmask_ and _rugged_ _~~m~~ ax_ _~~p~~ opmask_ ). Populated areas are defined as areas with any population in the Landscan 2012 population model (described in population count and density section below). Figure A2.1 (right panel) shows the spatial variation of the log of _rugged_ .

# **Agriculture:**

Agriculture and arable land can indicate higher economic growthpotential, or conversely, imply a lack of urbanization and infrastructure. Agricultural activities provide evidence of economic activity, which in turn may indicate a presence of water infrastructure. In order to measure agricultural land-use we use the Global Hybrid dataset (0611-2012 V2) produced by Fritz et al. (2015), which estimates the percentage share of land used for agriculture within a one square kilometer pixel. Expert assessment of five existing global land cover products along with national and subnational crop statistics as inputs provides the likelihood of a pixel indicating agricultural land use. By mul-

> 12Large scale (1:10 million) vector data are available for download from: http://www.naturalearthdata.com/downloads/

> 13Similarly, Verdin et al. (2007) derive slope measures at 30-arc-seconds derived from the 3-arcseconds SRTM data.

40
wages. A 10 percentage point increase in the share of sales that are exported causes female employment levels to increase by 1.2 percent and female wage levels to decrease by 1.5 percent. The insignificant OLS yet significant IV results indicate that reverse causality is likely a concern, such that the average correlation is not significant.

Exploring further the positive relationship with female employment but negative relationship with female wages for Georgian exporters is limited due to data constraints. Nevertheless, the results are in line with the global literature. In a cross-country data set, World Bank (2019, 2020) shows that exporters tend to hire more women than non-exporters, but these women are more likely to be in labor-intensive production jobs or occupations that require lower skills and pay less. At the same time, these firms are significantly less likely to be majority female owned and are also significantly less likely to have a top female manager. Women’s placement stems, in part, from disadvantages in endowments—such as assets, access to credit, education, skills training, and social capital, among others. These arise because of gender-biased regulations or discriminatory social norms (World Bank 2019, 2020).

Segregation of female employment across sectors may be another explanation for higher female employment but lower female wages on average for Georgian exporters if women are more likely to work in sectors that pay lower wages. As identified in World Bank (2018), women tend to be employed in lower-productivity sectors such as agriculture in Georgia, which is confirmed with the data used for this paper’s analysis, as in Figure 1. Because the regressions control for industry fixed effects at the 1-digit industry level, they would capture this type of segregation of women across broad industries. Nevertheless, if this segregation also happens within more closely defined industries that also have pay gaps for women, such as garment production within the manufacturing industry, which also has higher female labor shares, this could be one possible explanation of the results. The inefficient allocation of female employment across sectors therefore may similarly prevent female workers from benefiting as much as they could from gains in employment driven by exporters’ performance.<sup>11</sup>

> 11 We also explored whether the results change when restricting the sample to 2007-2013 when Russia had imposed an embargo on trade with Georgia, but the overall results hold.

10
# **References:**

Bello, G. A., J. Chipeta and J. Aberle-Grasse. 2006. Assessment of trends in biological and behavioral surveillance data: is there any evidence of declining HIV prevalence or incidence in Malawi? Sexually Transmitted Infections 2006;82(1): i9-i13.

Benson, T. 2002. Malawi: An Atlas of Social Statistics.

Boerma, J. Ties, Ghys D. Peter, Walker Neff. 2003. Estimates of HIV-1 Prevalence from National Population-based Surveys as a New Gold Standard. The Lancet 363, 1929-30.

Crampin A.C., Glynn J.R., Ngwira B.M.M., Mwaungulu F.D., Ponnighaus J.P., Warndorff D.K. and Fine P.E.M. 2003. Trends and measurement of HIV prevalence in northern Malawi. AIDS 17(12), 1817-25.

Eckert E. L., Damisoni H., Bicego G., Martin R., 2002. A Validation of Malawi’s Sentinel Surveillance Data. Memo. XIV International Conference on AIDS, Barcelona, July 7-12.

Elbers, C., Lanjouw J. O., Lanjouw P., 2003. Micro-Level Estimation of Poverty and Inequality. Econometrica 71(1), 355-64.

Elbers, C., Lanjouw, P., and Leite, P. G., 2008. Brazil within Brazil: Testing the Poverty Mapping Methodology in Minas Gerais. Policy Research Working Paper 4513, the World Bank.

Garbus, L. 2003. HIV/AIDS in Malawi. Policy Research Paper. AIDS Policy Research Center, University of California San Francisco.

Hentschel, J., Lanjouw, J.O., Lanjouw, P., and Poggi, J. 2000. Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador. World Bank Economic Review 14(1), 147-65.

Mishra, V., Vaessen M., Boerma J. T., Arnold F., Way A., Barrere B., Cross A., Hong R., and Sangha J. 2006. HIV Testing in National Population-based Surveys: Experience from the Demographic and Health Surveys.

National AIDS Commission of Malawi. 2003.http://www.policyproject.com/pubs/countryreports/MALNatEst2003.doc Estimating National HIV Prevalence in Malawi from Sentinel Surveillance Data: Technical Report.

National Statistical Office (NSO) of Malawi and ORC Macro. 2005. Malawi Demographic and Health Survey, 2004. Calverton, Maryland: NSO and ORC Macro.

UNAIDS. 2006. Report on the Global AIDS Epidemic. Geneva.

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**Table** 5. **Differences in regional poverty headcounts and their statistical significance**

|Reeion|H-anoi&<br>HCMC_|Other<br>urban|Rural<br>N Uplands|Rural<br>RedRDelta|Rutal<br>NCCoast|Rural<br>SCCoast|Rural<br>Hiehiands|Rural<br>Southeast|
|---|---|---|---|---|---|---|---|---|
|Otherurban|-0.109******<br>(0.012)||||||||
|RuralNUplands|-0.561*******<br>(0.013)|-0.452* *<br>(0.016)|||||||
|RuralRedRDelta|-0.343*******<br>(0.009)|-0.234*******<br>(0.013)|0.218***<br>(0.012)||||||
|RuralNCCoast|-0.477**"8**<br>(0.013)|-0.368*******<br>(0.016)|0.084***<br>(0016)|-0.134***<br>(0.013)|||||
|RuralSCCoast|-0.438*******|-0.330*******|0.123*|-0.096*******|0.038***||||
||(0.012)|(0.015)|(0.016)|(0.012)|(0.014)||||
|RuralCHighlands|-0.481*******<br>(0.017)|-0.372*******<br>(0.020)|0.081*<br>(0.021)|-0.138<br>(0.018)|-0.<sup>0</sup>04n.s.<br>(0.020)|-0.042******<br>(0.019)|||
|RuralSoutheast|-0.0S9***9***<br>(0.008)|0.020 n.s.<br>(0.012)|0.472*******<br>(0.012)|0.254*******<br>(0.007)|0.388***<br>(0.012)|0.349*<br>(0.011)|0.392*******<br>(0.017)||
|RuralMekongDelta|-0.360'*|-0.252***|0.201*******|-0.017******|0.117"'+|0.078*******|0.120*|-0.271*******|
||(0.009)|(0.013)|(0.013)|(0.008)|(0.013)|(0.012)|(0.017)|(0.008)|

Source: I)ata from 1998 VLSS and 3% sample of 1999 Population and Housing Census

> Note: Differences expressed as povcrty headcount of column region minus poverty headcount of row region. Standard errors in parentheses.

> * statistically significant at the 10%Ju level, 1* at the 5% level, and *** at the 1% level.

n.s. not statistically significant at the 10% level
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 2.3. Time required to import goods from Douala to Ndjamena via road and rail**

<!-- Start of picture text --> a. Total time (hours) b. By step (% of total time)<br>1000 100%<br>800 80%<br>600 60%<br>400 40%<br>200 20%<br>0 0%<br>Rail/road Road Rail/road Road<br>Port Transport<br>Port Transport Locomotive interchange Administrative<br>Locomotive interchange Administrative<br>Border Border<br>Source: Data collected from World Bank 2008; World Bank 2007; AICD ports and railway databases; and the Camrail Web site<br>(http://www.camrail.net/en/prestation_fret.html).<br>Figure 2.4. Cost of importing goods from Douala to Ndjamena via road and rail<br>a. Total cost (US$ per tonne) b. Composition of cost (% of total cost)<br>300 100%<br>200 75%<br>100<br>50%<br>0<br>25%<br>Road/Rail Road<br>0%<br>Port Travel Road/Rail Road<br>Locomotive interchange Administrative<br>Border Port Travel Locomotive interchange Administrative Border<br>modes<br>goods via alternative transport<br>Costs (US$ per tonne) of importing<br>alternative transport modes<br>Share of costs of importing goods via<br>alternative trasnport modes alternative transport modes<br>Time (hours) to import goods via Share of time to import goods via<br><!-- End of picture text --> _Source:_ Data collected from World Bank 2008; World Bank 2007; AICD ports and railway databases; and the Camrail Web site <u>(http://www.camrail.net/en/prestation_fret.html).</u> The corridors with the lengthiest import times are also those with the highest total costs for importing freight. It costs between $230 and $650 per tonne to import freight on intraregional corridors (figure 2.6a). The expensive transport costs along Central African corridors account for at least 50 percent of total costs, followed by ports, which compose 20 to 40 percent of total costs. Easing the restrictive trucking regime and improving port efficiency can alleviate the burden of transporting freight to landlocked countries (figure 2.6b).

15
firm-perceived obstacles may be endogenous—that is, firm-level determinants of growth may directly determine firms’ perceptions of the degree of obstacle in a particular aspect—they instrument firm-perceived obstacles with country-level measure of institutions, and find the results to be robust. This instrumental variable approach hinges on the assumption that country-level measure of institutions affects firm growth only through the corresponding firm-perceived obstacles.

In contrast, findings from a large firm-level data set in China around 2003 suggest that both property rights institutions and contracting institutions are important (Cull and Xu 2005)—that is, both sets of institutions are statistically and economically significant in predicting firm reinvestment rates. Here property rights institutions are captured by the percentage of total sales spent on informal payments to various government agencies, the share of government officials that the firm interacted with that are helpful (instead of hindering) to the firm; the contracting institutions are captured by the likelihood that the legal system will uphold the firms’ contracts and property rights in business disputes, whether the firm signed at least one formal contract with a client, and the share of business disputes that were resolved by court action.

# **The Effects of Corruption on Firm Performance**

A visible symptom of bad property rights institutions is corruption—the abuse of public office for personal gain. How does corruption affect firm performance? On the one hand, the grease view implies that corruption acts like an auction that allocates licenses and government contracts to the highest bidders, which is efficient since the most efficient firms can afford the highest bribes (Lui 1985; Myrdal 1968). On the other hand, corruption is potentially much more dangerous (Shleifer and Vishny 1993). It diverts resources from public uses such as the provision of public goods. Moreover, when regulators are decentralized and uncoordinated, cumulative bribe burden on private agents may become excessive, and efficient economic activities such as FDI entry may not occur, resulting in efficiency losses. In addition, corruption by its nature entails secrecy. To avoid exposure, corrupt regulators may divert a country’s investment away from the highest-value projects into potentially useless but secrecy-preserving projects. The demands of secrecy also induce government officials to maintain monopolies, to prevent entry, and to discourage innovation by outsiders in order to prevent the expansion of the ranks of the elite and preserve the secrecy of the existing corruption practices. Distinguishing these two views is best achieved by micro, objective data. In macro data, corruption is measured as perception and is not directly comparable to tax rates. Yet with micro data, bribes and taxes can all be measured in monetary values and are directly comparable.

14