rafmacalaba/datause-encoder-data huggingface dataset:
# **II. The Current State of SME Exchanges**

# **_A. What Is an SME?_**

There is no standard definition of what constitutes an SME. Definitions of an SME vary widely across countries and regions, although the criteria include a maximum level for one or more of the following criteria: number of full employees and/or annual sales turnover.<sup>3</sup> These differing SME definitions across countries can make an international comparison ambiguous. However, for the purpose of this paper, instead of taking into account any specific SME definition, we will rely on each country’s own national criteria for an SME definition (see Table 1).

||Table 1. SME Definitions<br>|<br>|
|---|---|---|
|Country|Number of Employees|Annual Sales Turnover|
|India|—|—|
|South Africa|<200|<$6.2 million|
|Taiwan|<200|<$3.3 million|
|Brazil<br>Industrial sector<br>Service sector|<500<br><100|—<br>—|
|Poland|<250|<$68 million|
|Turkey|<250|<$12 million|

_Source:_ WBG compilation of exchange information. _Note: — =_ not available.

- SMEs in the European Union (EU) are defined as enterprises that have fewer than 250 employees and do not have sales that exceed €50 million ($68 million) or an annual balance sheet that exceeds €43 million ($58 million). As a member of the EU, Poland adopts this definition.

- South Africa applies differing criteria for different business sectors. The definition of SMEs in South Africa is set out in the National Small Business Amendment Act of 2003. The classification distinguishes between different sectors and takes full-time employees (less than 200), annual turnover, and total gross asset value into account.

- Brazil adopts the number of employee criteria for the definition of SMEs; this differs according to business sector (industrial sector fewer than 500 employees, service and commercial sector fewer than 100 employees).

- In India, SMEs are defined in the Micro, Small and Medium Enterprises Development Act, 2006, and the definition for enterprises engaged in manufacture, production, processing, or preservation of goods is investment in plant and machinery in their business of less than Re 100 million ($1.6 million).

> 3 See Edinburgh Group, “Growing the Global Economy through SMEs”; Enterprise and Industry Publications, European Commission, “The New SME Definition”; Reserve Bank of India, “Micro, Small and Medium Enterprises.”

8
observations based on their intensity. In practice, this approach amounts to removing all observations at locations that are distant from homogenous bright cores. The third approach also involves removing all observations from locations with background noise, but the way these locations are identified is different. For 2015, an annual composite of stable VIIRS nightlight was released (Elvidge et al. 2017). Comparing the raw data with the stable annual composite helps identify locations with background noise, which can then be removed from all VIIRS monthly data.

We find that the differences between the cleaned data produced using the three cleaning procedures outlined above are minor when the outputs are aggregated at the district level. We prefer the second method because it gives us the flexibility to define the background mask and hence have full control over the cleaning procedure. Throughout the paper we present results using data cleaned by the second method. However, the results are robust across data obtained from all three procedures.<sup>2</sup> Our final data processing step is to construct an annual series of nightlight data from 1992 to the present, through linking the annual DMSP-OLS data and monthly VIIRS data at the district level. This is, however, easily done by taking advantage of the overlap between the two data sources in 2013.

Most other variables used in this study – GDP in constant local currency units and in constant USD, value added in agriculture, manufacturing, and services, income per capita PPP adjusted and rural population – are obtained from the World Development Indicators (World Bank 2018). For the applications in Section VII, we use district level socio-economic variables from the South Asia Spatial Database (Li et al. 2015) and conflict data from the Global Terrorism Database (START 2017).

> 2 The data from all three cleaning procedures is available upon request.

6
|**Cocaine**||**Opiates (Heroin, Mo**|**rphine, and Opium)**|
|---|---|---|---|
|||**in heroin e**<br>|**quivalents**<br>|
|Colombia|32%|Iran|29%|
|United States|28%|Pakistan|21%|
|Spain|6%|Turkey|12%|
|Venezuela|5%|China|9%|
|Mexico|5%|Afghanistan|4%|
|Peru|2%|Tajkistan|4%|
|Netherlands|2%|Russian Federation|3%|
|Netherlands Antilles|2%|Other countries|18%|
|Boliva|2%|||
|Other countries|16%|||
|**Total (% of estimated**<br>**consumption)**|356 tons (61)|**Total**|120 tons (32)|

Source: 2006 UNODC World Drug Report, p. 70-72, 91.

# **Table 5: Highest Ranking Countries for Seizures of Cocaine and Opiates in 2004 (by Percent of World Total)**

## **2. POSSIBLE EXPLANATIONS FOR THE PATTERN OF NATIONAL INVOLVEMENT IN THE DRUG TRADE**

The concentration of coca and opium production in these few developing countries is an important fact for policymakers. It creates the sense, probably illusory, that success is just around the corner because only two or three countries need to exit the industry.

The concentration is a paradox for three reasons. First, many nations are capable of producing each drug. Historically, substantial opium production has been recorded in China, Iran, and Macedonia, for example, none of which now produces.<sup>15</sup> Australia, France, and Spain have entered the legal opiate market in recent times, obtaining production quotas from the International Narcotics Control Board under an international treaty agreement for that market (INCB, 2002). Coca has been grown commercially in Java (while under Dutch rule) and Taiwan (while under 9
Table 7: Comparison of Forecast Errors across Regions

|Ri|5 year for|ecast horizon|10 year f|orecast horizon|
|---|---|---|---|---|
|egon|sMAPE|MdRAE|sMAPE|MdRAE|
|AFR|0.09|0.44|0.11|0.63|
|EAP|0.05|0.54|0.05|0.53|
|ECA|0.06|0.45|0.07|0.49|
|LAC|0.05|0.52|0.08|0.62|
|MENA|0.05|0.42|0.08|0.52|
|SAR|0.02|0.25|0.05|0.41|

the 5- and 10-year forecast horizons. All in all, the best performing method is highly accurate with average sMAPE varying between 2 and 9 percent over the 5-year forecasting horizon and between 5 and 11 percent over the 10-year forecasting horizon, respectively. Consistent with the results from the previous section, the best performing method is also more accurate than the Näıve model, with average MdRAE varying between 0.25 and 0.54 over the 5-year forecasting horizon, and between 0.41 and 0.63 over the 10-year forecasting horizon, respectively.

It follows from Table 7 that the forecast accuracy is the highest for the countries of the South Asia region over both 5- and 10-year forecasting horizons and across both types of error accuracy measures. The forecast accuracy is the lowest for the Sub-Saharan Africa region based on the sMAPE criterion over both 5- and 10- year forecasting horizons, with other regions having broadly comparable forecast errors. The forecast accuracy based on the MdRAE criterion is the lowest for the East Asia and Pacific and the Latin America regions over the 5-year forecasting horizon, and for the Sub-Saharan Africa and the Latin America regions over the 10-year forecasting horizon.

To frther elucidate the observed differences in the forecast accuracy across regions, this study also reports the average sMAPE and MdRAE measures of forecast accuracy for individual countries, grouped across regions over the 5- and 10-year forecast horizons (see Appendix Table A3.2). For most countries, both sMAPE and MdRAE errors are small, which indicates that the best performing method is both highly accurate and yields considerable improvements over the Näıve model. However, the forecasting accuracy is greatly diminished for countries that have recently undertaken major investments (Ethiopia, Cameroon, Myanmar) or disinvestments (Lithuania) in electricity generation assets; coun14
An advantage of time use data is that they provide information on inputs and therefore reduce the number of assumptions required for estimating an empirical model. If output data only are missing, testing the sensitivity of results to alternative assumptions becomes more feasible. This is an important gain because opposing views on some of the most contentious issues in tax policy and design of welfare programs can be traced to conflicting assumptions on the productivity of domestic time. Examples are given in the following separate discussions of heterogeneity with respect to female labor supply and with respect to household saving.

# _Female labor supply heterogeneity_

Developed economies (and now increasingly, transition economies) are characterized by a high degree of heterogeneity in female labor supply. In a significant proportion of households with young children, one parent, typically the mother, specializes in domestic work and home child care, while in an equally significant proportion of households she specializes in market work, buying in domestic services and child care. As we demonstrate in Apps and Rees (1999a), specialization in domestic production may result either from higher productivity (as envisaged for example in Becker’s discussion of this subject), or, alternatively, from lower productivity in household work. High productivity in domestic production implies a low implicit price, and therefore a higher demand for domestic output and domestic labor supply. At the same time however the higher the productivity, the less time required to produce a given domestic output and the more time therefore available for market labor supply. The net effect will depend on the price elasticity of demand for domestic output in relation to domestic productivity. The main implication of this is that we cannot infer from observed market labor supply that a woman has higher or lower productivity in domestic production.

In the absence of output data, the assumption chosen will determine prices, and in turn the relative levels of welfare of household types distinguished according to female labor supply. If, for example, it is assumed that married women who specialize in work at home have relatively lower domestic productivity, then, for given net wage rates, their households will have a lower standard of living. The utility maximization problem in (20) 29
After substituting (A.5) and (A.6) to (A.7), one gets: where Σ = _Etεt_ +1 _ε_<sup>_′_</sup> _t_ +1<sup>.</sup> Hence, the steady state Periphery bond holdings of the Core investor could be written as:

# **Acknowledgements**

We would like to thank Anton Korinek, Luis Serv ́en, Jose Scheinkman and participants of MATSEG 2014 for helpful discussions and feedback. Ersal-Kiziler thanks University of Wisconsin - Whitewater for research support. This paper reflects the author’s views and not necessarily those of the World Bank, its Executive Directors or the countries they represent. All errors are our own.

# **References**

- Acharya, V.V., Steffen, S., 2015. The greatest carry trade ever? understanding eurozone bank risks. Journal of Financial Economics 115, 215–236.

- Bengui, J., Nguyen, H., 2011. Consumption baskets and currency choice in international borrowing. Policy Research Working Paper Series. The World Bank.

- Bernanke, B.S., Gertler, M., Gilchrist, S., 1999. The financial accelerator in a quantitative business cycle framework. Handbook of macroeconomics 1, 1341–1393.

- Brutti, F., Saur, P.U., 2014. Repatriation of Debt in the Euro Crisis: Evidence for the Secondary Market Theory. Technical Report.

- Chen, R., Milesi-Ferretti, G.M., Tressel, T., 2013. External imbalances in the euro area. Economic Policy 28.

- Chugh, S.K., 2006. Optimal Fiscal and Monetary Policy with Sticky Wages and Sticky Prices. Review of Economic Dynamics 9, 683–714.

- Coeurdacier, N., Gourinchas, P.O., 2011. When Bonds Matter: Home Bias in Goods and Assets. NBER Working Papers 17560. National Bureau of Economic Research, Inc.

- Coeurdacier, N., Martin, P., 2009. The geography of asset trade and the euro: Insiders and outsiders. Journal of the Japanese and International Economies 23, 90–113.

- De Santis, R.A., Grard, B., 2009. International portfolio reallocation: Diversification benefits and European monetary union. European Economic Review 53, 1010–1027.

- Devereux, M.B., Sutherland, A., 2010. Country portfolio dynamics. Journal of Economic Dynamics and Control 34, 1325–1342.

20
restrict the number of observations only to countries with a non-negative level of private investment in T&D.

**Table 5**

# **Key Determinants of PPP Investment in Transmission and Distribution**

|**Two Step (1)**|**Two Step (2)**|**(3)**|
|---|---|---|
|**SECTORALGOVERNA**<br>|**NCE VARIABLE**<br>|**S**|
|**Introduction of a regulatory**<br>**agency**_REG_<br>_First step inred_<br>0.427***<br>(0.121)|0.419***<br>(0.129)||
|**ECONOMYWIDEGOVER**<br> <br>*|**NANCE VARIA**<br>|**BLES**|
|**Control of Corruption**_wgi_cc_<br>_First step inred_<br>0.419<br>(0.244)|0.107<br>(0.116)||
|**Degree of democracy****_Polity2_**<br>_First step inred_<br>0.037***<br>(0.012)|0.034***<br>(0.013)||
|<br>**LONG RUN ENVIRONMENTAL S**|<br>**USTAINABILIT**<br>|**Y FACTORS**|
|**PPP in Renewables**<br>_First step inred_|0.943***<br>(0.138)||
|**Constant**<br>-1.696***<br>(0.111)|-2.041***<br>(0.133)||
|<br>**MACROECONOMI**<br>|<br>**C CONTROL**<br>||
|**GDP**<br>_ln_GDP_<br>1.068***<br>(0.241)|1.032***<br> (0.221)|0.897***<br>(0.262)|
|**Population**<br>_ln_Pop_<br>-0.874***<br>(0.257)|-0.860***<br>(0.228)|-0.697**<br> (0.118)|
|**Constant**<br>-1.696***<br>(0.111)|-6.298**<br>(3.207)|`-`6.201**<br>(2.992)|
|**N**<br>1185|1185|91|
|**Censored N**<br>1094|1094||
|**Wald χ**<sup>**2**</sup><br>22.59***|22.50***|14.33***|
|**Within R**<sup>**2**</sup>||0.58|
|**Between R**<sup>**2**</sup>||17.57|
|**Overall R**<sup>**2**</sup>||26.35|

_Note:_ *, **, *** indicate significance of the coefficient respectively at 10%, 5% and 1% confidence level.

**Some interesting results are worth highlighting. We will first focus on the different factors that affect entry and the level of investment in T&D.**

**First, sectoral and economy wide governance are crucial in attracting the entry of private investors, but they do not affect the extent of investment in the presence of substantial sharing of risks between the public and private sector.** The sectoral economy wide governance

15
> - 19 -

percent of US antidumping and countervailing duty cases, Japan of only 6 percent. Among developing countries, imports from Brazil generate a disproportionately high number of cases, and imports from Taiwan, Hong Kong and Singapore disproportionately low numbers. Table 4 shows that Korea's experience is representative of the central tendency: Korea supplies 4.2 percent of US imports and was hit by 4.7 percent of US cases.

Table 5 Countervailing Duty and Antidumping Outcomes Compared, 1980-89

|**Coanb**<br>or**wow**|**Antdwm nga**<br>**adwva**<br>**of**tota<br>runbU|**Re6n**<br>**wcett**<br>Antdumprs|**ct"R**<br>**OLiatm**<br>**m**Of**t0 **<br>**Counteva**|**. a**a<br>**cm**<br>**ilg**<br>**rot**|**Nopiod**<br>**_dSi_**<br>**_A&N4umpw_**|expat**rs**<br>**ofMSfltN**<br>**_g_**<br>**Countera**|**aumt a**<br>**Oi**<br>**ui**<br>**Both**|
|---|---|---|---|---|---|---|---|
|All countries|50|72|67|70|63|66|64|
|Developed countries|49|69|61|65|65|82|74|
|Developing countries|46|73|77|75|55|46|49|
|Korea|54|77|100|86|82|86|84|
|Eastem European countries|87|91|60|87|77|100|78|

Source: Finger and Murray 1990

* Almost half the cases (348 of 774) have been superseded by negotiated export restraints. Thus virtually all of the import restrictions the United States has put in place are GATT-legal or better -- "or better" in the sense that the exporter preferred the negotiated restraint to the by-the-books action that was just around the administrative corner. Negotiated restraints have superseded nearly three-fourths of cases against Korea.

* Cases against developing countries more often come to restrictive outcomes than cases against developed countries -- three-fourths versus twothirds. Against Korea, 31 of 36 cases (84 percent) ended with a restrictive
model is _c_ = _β_ 0 + _β_ 1 _x_ + _u_ where _c_ are unit costs, _β_ 0 is a constant, _β_ 1 is our coefficient of interest, and _u_ is an error term. Consider a project with the same level of benefits in two countries. Let _x_ be corruption. Assume that corruption increases costs so that _β_ 1 _>_ 0, and that one country has a high level of corruption, while the other country has a low level of corruption. While the project is undertaken in the low corruption country, it might fail to generate a high enough NPV in the high corruption country. We therefore miss projects with high _x_ and high _u_ . Thus, _x_ and _u_ will be negatively correlated in the truncated sample and the OLS estimate of _β_ 1 will be downward biased (towards zero), _underestimating_ the effect of corruption on unit costs.<sup>26</sup> Thus, our estimates can be viewed as conservative. If the benefits of a project are a function of the individuals affected by the improved road, and congestion costs are important, we would expect the benefit of transport infrastructure to be higher in densely populated areas, so that projects are more likely to be selected even if costs are higher than in an otherwise equivalent context. Unfortunately, we do not have information on projects that have not been carried out. We are therefore limited to controlling for population density to account for selection on observables.

# **IV Empirical Results and Discussion**

We start by presenting the main results from equation (3), including our measures of conflict, and then turn to corruption. Given the correlation between conflict and corruption, we initially examine these variables separately. We then test the robustness of our results by including country fixed effects, employing alternative functional forms and dependent variables, and different ways of dealing with missing data. As a next step, we discuss sources of omitted variable bias and include variables that proxy for the business environment and government capacity in contracting. Finally, we provide additional evidence for some of the geographical and scale variables from a subset of 941 road work activities which we

> 26 Similarly, assume that _x_ is a measure of flatness of the terrain, so that higher values correspond to flatter terrain, and lower values to mountainous terrain. Since it is cheaper to build a road on flat terrain, _β_ 1 _<_ 0. Consider again a project yielding the same level of benefits in a flat and in a mountainous country. Following the logic above, a project yielding the same benefits is more likely to be in our sample in flat terrain (high _x_ ) and we will tend to miss out on projects in mountainous areas, so that _x_ and _u_ will be positively correlated in the truncated sample and the OLS estimate of _β_ 1 will be upward biased, i.e. again towards zero. In this case, we will underestimate the cost-reducing effect of flat terrain.

16
- 43 -

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expressed in Acemoglu and Johnson (2005) (AJ (2005) hereafter), who unbundle institutions into two types: contracting institutions and property rights institutions. Contracting institutions refer to those that “enable private contracts between citizens”, while property rights institutions refer to those that “protect citizens against expropriation by the government and powerful elites.” They argue that government officials, and to a lesser extent, economic elites, can use force or other resources to back up their demands and therefore pose real dangers. In contrast, when contracting institutions increase the costs of doing business in some way, contracting parties may use alternative ways of transacting. For example, instead of using bank finance, firms may resort to reputation-based financing such as trade credit. While AJ (2005) make an invaluable theoretical contribution to the literature, their empirical tests are not without problems. They use legal origin variables as instruments for contracting institutions, and settler mortality as the instrument for property rights institutions. But they may be invalid instruments (Glaeser et al. 2004; Morck and Yeung, 2011; Bazzi and Clemens, 2013) if they are correlated with other determinants of development such as human capital, infrastructure, and other aspects of institutions. Without convincingly valid instruments, the potential for reverse causality remains (Glaeser et al. 2004).

In this paper, we follow AJ (2005) by investigating how contracting institutions and property rights institutions affect a key development channel: external finance. We differ from the literature in relying on firm-level micro data. An advantage of our empirical design is that there is less potential for reverse causality. While it is plausible that the level of development (as measured by GDP per capita, for example) would affect institutions, it is much less likely that firm-level access to external finance would affect institutions, especially in the short run. This research design thus improves the credibility of the estimates of the impact of institutions. We choose external finance as our dependent variable because it is well-established that access to external finance is a key channel through which development occurs. For instance, cross-country empirical studies have shown that financial development contributes to higher growth rates (e.g. King and Levine, 1998; Demirguc-Kunt and Maksimovic, 1998; Clague et al., 1999; Levine, Loayza and Beck, 2000). Rajan and Zingales (1998) show that industries that are more dependent on external finance grow more rapidly in countries with more developed financial systems. Within-country studies find that this relationship holds across regions within Italy, and show at a micro level that the rate of new firm creation is higher in more financially-developed regions (Guisi, Sapienza and Zingales, 2004).

4