rafmacalaba/data-use-mentions-extended huggingface dataset:
-17-

# VI. EMPIRICAL ANALYSIS

42. In this section, we apply the above model to evaluate fiscal performance in three countries: Zambia, India and Argentina. These countries have different income levels and are located in three different continents. We compare the derived fiscal indicators with conventional fiscal indicators and show that the proposed indicators are both richer in a normative and positive sense to conventional indicators. The three countries are selected on the basis of formal fiscal analyses which were based on the above model of solvency and sustainability or equivalent and whose results have been presented in World Bank economic reportsl<sup>4</sup> . These countries also cover a range of different stages of economic development, as well as fiscal problems commonly seen in developing countries. In all cases the empirical results have been discussed with Government officials so that no policy surprises are expected. Before reviewing the empirical results, issues concerning data and interpretation are summarized below.

# **Data Problems and Issues**

43. **Problems in estimating public sector activities.** Ultimately, any model is as good as the data it applies to and this model is no exception. As discussed earlier, a correct measurement of fiscal activities is a necessary condition for good fiscal analysis. A country's fiscal problems can be easily hidden if a comprehensive measure of fiscal activities is not available. But comprehensive information on accounts of the non-financial public sector - other than that of the central government-such as local government, extra-budgetary funds, state-owned enterprises, and government guarantees are often hard to get and if available, are often not up-to-date. The losses of state-owned enterprises (SOEs) often created a huge implicit liability to the government. In order to finance its operating losses, SOEs usually rely on issuing short-term debt with credit on inventory and future sales of output. Ultimately, these losses will be borne by the public sector, either because the debt will be taken over by the Government or because public assets will be depleted at the time of
Overall, we find that well-being rankings are highly correlated but accounting for non-monetary dimension may imply substantial re-rankings, especially when giving an intermediate weight to mortality.

# **Section 5.2: Quantifying budget transfers across states**

Quantifying re-rankings is a binary method to probe changes in well-being comparisons. Indeed, a ranking between two states is either reversed or not. The limitation of this method is that, when the ranking is not reversed, the changes in the relative well-being levels of the two states being compared is not accounted for. The method used in this subsection accounts for such changes.

The next method is based on a hypothetical social policy. Assume that the Nigerian government has a budget for social protection policies. Further assume that this budget is distributed across states proportionally to needs as captured by a well-being indicator. In the case of poverty, this budget could be distributed proportionally to the number of poor individuals in each state. When contrasting monetary poverty with multidimensional poverty, our method reports the fraction of the total budget that must be redistributed across states when using the latter instead of the former. Consider the following simplified example with two states, where state A has 0 monetary poor and 1 multidimensionally poor individual while state B has 1 monetary poor and 3 multidimensionally poor individuals. Under monetary poverty, state B receives 100 percent of the budget, while under multidimensional poverty state B receives 75 percent of the budget and state A 25 percent. The fraction of the total budget that must be transferred is thus 25 percent, from state B to state A. We use a straightforward adaptation of this method that accounts for the fact that poverty _decreases_ with well-being while PALE _increases_ with well-being, which we describe in detail in Appendix 5.

Table 6 shows the fraction of the total budget that must be transferred when comparing two of the following list of indicators: monetary poverty ( ), multidimensional poverty (H) and PALE. The fraction transferred corresponds to 6 percent of the budget when moving from monetary poverty to PALE
is a further reduction in the number of observations because the Lipscomb et al.’s (2013) instrument is not available for the years 1975 and 1985.

The results using OLS and IV are presented in Table 5. We see that when we move from the OLS to the IV regressions, estimated values for electricity increase, although there is some reduction in the significance of the estimated coefficients. This reduction is in part due to the lower number of available observations. Nevertheless, we prefer the IV ones as they should be less biased. Coefficients for electricity remain significant in the manufacturing and services sectors. Capital and labor are highly significant in all cases. The Table also says that the estimated coefficients are compatible with the model assumption of decreasing returns over capital and labor. In sum, we choose ( _βa, βm, βs_ ) equal to (0.045, 0.230, 0.108).

We follow a similar methodology to estimate the parameters _θi_ and _ψ_ . Equations (11), (13), (18) and (25) obtain Employing GVA per capita ( _Y pcjt_ ) as a proxy for _Amt_ , we can then use this expression to derive the following regression to estimate the effect of capital-electricity infrastructure on the average number of employees in sector _i_ at time _t_ in municipality _j_ (<sup> ̄</sup> _ljt_ ): The number of occupied people per establishment at the municipal level is available from IPEA for the years 1970, 1975, 1980, 1985 and 1995. These reflect information obtained in the agricultural census and in different surveys in manufacturing and services. These data sources include formal as well as informal employment. Employing the Brazilian municipality panel for those years, we again obtain estimates using OLS and IV results for each sector. In the IV estimation, we lose only 1975 and 1985 because the 1996 values of the instrument are assigned to year 1995.

36
are presented in Table 1. Column (1) estimates a sparse specification; column (2) adds household fixed effects; and column (3) adds both household and month-year effects.

Violence clearly escalated more following displacement in origin districts than for nondisplaced households following their most recent survey. Column (1) demonstrates there was an average increase of 1.58 more monthly fatalities in origin districts after displaced households fled than following the most recent survey of non-displaced households in their districts of residence. The overall increase for displaced households is large, and represents 40 percent of the monthly fatalities prior to the most recent survey of nondisplaced households in their district of residence. Furthermore, the increase is more than double the increase in the district of residence of non-displaced households following their most recent survey (1.37 monthly fatalities).

Importantly, this difference persists when accounting for regional, household, and time-specific factors. In the most complete specification estimated in column (3), the relative increase in fatalities following displacement remained nearly constant at 1.44 more monthly fatalities. However, the increase in fatalities in the district of residence for non-displaced households reduces in magnitude and becomes indistinguishable from zero when these additional factors are included.

Furthermore, we examine how violence was changing in the districts to which households fled (migration districts). Columns (4)-(6) of Table 1 re-estimate the baseline specification but use the district of migration for households that became displaced outside of their origin district.<sup>15</sup> We find that displaced households tended to flee to districts where violence was increasing similarly to the rest of the country (in contrast to escalation in their origin districts), further illustrating that violence was important to displacement decisions. In all specifications, the sign of the estimate for migration districts is the opposite of origin districts, the magnitude is significantly smaller, and the estimates are all statistically indistinguishable from zero. Importantly, given the sign and precision of the estimate, we can rule out
schemes. However, it allows to show the extent to which existing unemployment benefits and other related policies would have been able to cushion the shock as well as the extent the new schemes provided additional support.

_Table 1: Overview of simulated scenarios_

|**Scenario**|**Policy changes**|**Input dataset**|**EUROMOD**|
|---|---|---|---|
|A COVID|All COVID and non-<br>COVID related policy<br>changes|Adjusted for COVID-related<br>labor market changes|LMA Add-on|
|B COVID with<br>monetary<br>compensation<br>schemes|All non-COVID related<br>policy changes plus<br>newly introduced<br>monetary<br>compensation schemes|Adjusted for COVID-related<br>labor market changes|New extension to<br>switch non-<br>compensation<br>scheme related<br>policychanges off|
|C COVID<br>without<br>COVID-<br>related policy<br>changes|Non-COVID related<br>policy changes only|Adjusted for COVID-related<br>labor market changes. Employees<br>and self-employed selected to<br>transition to the monetary<br>compensation scheme are re-<br>transitioned to unemployment|New Add-on based<br>on LMA Add-on<br>for re-transition to<br>unemployment|
|D No COVID|Non-COVID related<br>policy changes only|No adjustment for COVID-<br>related labor market changes|New extension to<br>switch all COVID-<br>related policy<br>changes off|

Source: Author’s elaboration 7
revenues lost via piracy are significant, they are almost impossible to pin down.<sup>27</sup> And it must be remembered that many artists and creative businesses function in informal structures. While this informality is normally invisible in official statistics, it does not mean that it is illegal altogether (or, for that matter, that informality does not contribute to economic welfare).

Third, there is also the ethical dimension. In the creative or cultural economy, cultural tourism as one example can be rife with ironies and ethical dilemmas.<sup>28</sup> While this area has potential to contribute to development, the concerns of cultural exploitation and uneven prosperity can undercut its promise. Other sectors, from finance to housing, are not immune from ethical traps. But given the political and social sensitivity of culture, measuring cultural or creative tourism cannot be seen merely as a way to legitimize this sector. The accounting here is unlikely to winnow out the pitfalls that may cause cultural and social impairment.<sup>29</sup> Fourth, on satellite accounts:<sup>30</sup> In the cultural sector, satellite accounts are expanding. In the first-ever groundbreaking partnership in the United States, the Bureau of Economic Analysis teamed up with the National Endowment for the Arts to develop an “Arts and Cultural Production Satellite Account” (ACPSA). This new account would “identify and calculate the arts and culture sector’s contributions” to GDP.<sup>31</sup> <mark>But inasmuch as political debates continue to focus on economic growth, accounts labeled ‘satellite’ might have a hard time becoming influential.</mark><sup>32</sup>

> 24 Robinson 2013, p. 36.

> 25 Kishtainy and others 2012, p. 52.

> 26 See Plumer, 2012.

> 27 Ibid; see also Kabanda 2016b, p. 5.

> 28 See Kaul 2009, p. 6
# **Introduction**

Minimum wage systems exist in about 90 percent of the International Labour Organization’s (ILO) 193 member states (Benassi, 2011). These minimum wage regulations aim to protect low-paid workers and typically take one of two broad forms: A single base minimum wage rate for a country or region, or a more complex system of multiple minimum wage rates by sector and/or occupation. In Mauritius, minimum wages have been legislated in the latter form, with specified wage rates for varying occupations across 30 industry-specific remuneration orders. There has, however, been little work on the nature and impact of these stipulated wage rates in the country. In this paper, we thus consider the effects of these minimum wages on employment and hours worked in Mauritius.

The standard textbook theoretical model of minimum wages predicts that in a competitive equilibrium, there is a negative relationship between minimum wages and employment. Early studies on the effect of the minimum wage on employment, dating as far back as the work of Stigler (1946), predicted that in a competitive labor market, the relationship between minimum wages and employment is a negative one. A growing number of later studies however suggested that the relationship between the minimum wage and employment is not necessarily always negative (Katz and Krueger, 1992; Card, 1992 a, b).

Based on the work of Neumark and Wascher (2007) and 17 more recent studies focusing on low- and middle-income (LMI) countries, DPRU (2016) presents the employment effects of minimum wage increases across various developed and developing countries derived from a review of 115 studies. The results include aggregate impacts for all workers coupled with the employment impacts for specific demographic groups, regions and sectors. Overall, employment elasticities in the studies reviewed a range from 2.17 (Katz and Krueger, 1992) to

-4.6 (Abowd, Kramarz, and Margolis, 1999). The mean and median of all the cumulative elasticities are -0.22 and
4|10.0|na|
|Total Private|17.5|38.1|49.1|66.1|73.8|
|as%ofGDP|21%|30%|34%|39%|41%|
|Bank (% of total)|23%|34%|57%|63%|57%|
|Short-Tenn (% of GDP)|12%|18%|20%|24%|21%|
|Short-Term**(%**of total)|58%|60%|59%|62%|_51%_|

Souce: BOT

**Figure** 5. **The Share of Foreign Liabilities and Net Foreign Liabilities of Commercial Banks**

<!-- Start of picture text --> 25% 1200<br>F _For.Liab./TotalLiab. I<br>Net For. Liab.<br>1000<br>20%I<br>800<br>15%19 1<br>600 .<br>I a 0<br>:~~~~ 10% ~~~~~~~400<br>200<br>0% 0<br>1990 1991 1992 1993 1994 1995 1996<br>Year<br>Source: Bank of Thailand, Quarterly Bulletin.<br><!-- End of picture text -->

- 35 The data also illustrate that measures taken in 1995-96 by the government to curb short-term capital inflows (via BIBFs) seem to have had some effects (See Section V.2). In 1996, short-term, in particular bank, debt declined both in absolute terms and in relative terms, and as noted above, firms increased their direct external borrowing. However, according to World Bank DRS data, the average maturity of private debt continued to decline in 1996.
Policy Research Working Paper 9364

# **Abstract**

Recent research on productivity spillovers from affiliates of multinational corporations in developing and emerging economies finds that backward linkages from affiliates of foreign-owned firms to local suppliers constitute the main channel transmitting productivity spillovers. This finding has important policy implications, given that host economy governments often spend considerable resources on attracting multinational corporation investments and promoting their impact on technological development and economic growth. This paper conducts a new and comprehensive survey of recent empirical studies that focus on the drivers and impacts of backward linkages between multinational corporation affiliates and their local suppliers. The literature survey reveals that several characteristics of multinational corporation affiliates and domestic firms, host economy conditions, and various mediating factors influence the level of use of local suppliers, the nature and degree of technology dissemination, and the materialization of productivity spillovers among domestic firms. These findings are used to identify the main areas where policy making can be effective. The paper discusses various types of soft or light-handed industrial policies that host economy governments can design and implement to foster the extent of linkages between multinational corporations and local suppliers, facilitate technology dissemination, and enhance productivity spillovers among domestic firms.

This paper is a product of the Equitable Growth, Finance and Institutions Practice Group. 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 wdouw@ifc.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
directly (e.g., through the provision of geological surveys) or indirectly (e.g., through the allocation of property rights).

As a result of these economic features, out of the 15 disaggregated mining and quarrying sectors, 10 are classified as partially contestable (Table 5). Of the 10 contestable sectors, eight are deemed to be partially contestable due to sunk costs: mining of hard coal (510), mining of lignite (520), extraction of crude petroleum (610), extraction of natural gas (620), mining of iron ores (710), mining of uranium and thorium ores (721), mining of other non-ferrous metal ores (729), and mining of chemical and fertilizer minerals (891) (Table 6). Mining of uranium and thorium ores is also associated with negative externalities (Sorrentino 2020). The two remaining partially contestable sectors do not appear to have high entry barriers, but they are associated with possible market inefficiencies due to uncertainty; these include support activities for petroleum and natural gas extraction (910) and support activities for other mining and quarrying (990).

Five disaggregated sectors are considered competitive (Table 5). These sectors are quarrying of ornamental and building stone, limestone, gypsum, chalk, and slate (811), operation of gravel and sand pits and mining of clays and kaolin (812), extraction of peat (892), extraction of salt (893), and other mining and quarrying (899), which includes the mining and quarrying of different materials. The classification for each disaggregated sector in this industry is provided in Appendix 2, Table A.2.

_Table 5_ **_. SOE taxonomy in mining and quarrying_**

|_Dist_<br>**Competitive**|**Mining and Quarrying Indus**<br>_ribution of disaggregated secto_<br>**Natural Monopoly**|**try (B)**<br>_rs
<!-- Start of picture text --> 2500<br>2000<br>1500<br>1000<br>500<br>0<br>Hydro Thermal Total<br>m 2010Energy HHI =m 2015 Energy HHI<br><!-- End of picture text --> <!-- Start of picture text --> 2500<br>2000<br>1500<br>1000<br>500<br>0<br>Hydro Thermal Total<br>TM2010Capacity HHI ~=TM 2015 Capacity HHI<br><!-- End of picture text -->
less adaptable to rising tariffs, they suffer a disproportionately larger welfare loss. Although this issue has received attention in the literature, few studies have addressed it in a systematic way.<sup>3</sup> The model also indicates a smaller aggregate price elasticity of demand than a model ignoring the heterogeneity in households’ price elasticities, suggesting that a homogeneous estimator will both neglect the variance and underestimate the aggregate impact on the population.

I estimate the demand model using disaggregated consumption data for a representative sample of 18,671 Turkish households from the 2008 and 2009 Household Budget Survey (HBS) of the Turkish Statistical Institute. The dataset provides rich detail in appliance ownership, dwelling

> 2 It should be noted that although electricity reform may result in price increases, it also provides opportunities that would not otherwise exist to improve quality and reliability of power supply, and to re-direct public resources more transparently to the poor.  Analyzing the broader impact of electricity pricing reform is beyond the scope of the paper.

> 3  Previous studies have noted that poor households devote a larger proportion of income to energy, and therefore, carry a disproportionally higher burden of rising energy prices.  However, few (if any) studies have estimated the differences in the welfare loss (measured by changes in consumers’ utility) between the poor and nonpoor.

2
reputational collateral can increase access to finance. Credit bureaus in about 65 percent of the 94 countries for which this variable is available share positive information (Table 1).

Figure 3 plots the two measures of depth of credit bureau information across country income groups. Both measures follow a similar pattern in that middle income countries have the greatest depth of information, followed by low income countries and then high income countries.

Finally, the _transparency index_ is a tally of affirmative responses to five questions. These questions ascertain whether borrowers are guaranteed access to their credit history data by law, whether they can inspect their data in practice, and whether there is a cost for inspecting one’s own credit information. The average of this index is about 2.5 (table 1). Middle income and high income countries have higher levels of credit bureau transparency on average than low income countries (figure 4).

# **3.2 Measures of competition and concentration**

As a measure of banking competition, we chose to use the log minimum capital requirement (in millions of USD) for banks in 2001 from the World Bank’s 2013 Global Financial Development Report (GFDR) database. We prefer this measure of bank entry regulation over measures of market power in banking, such as the Lerner index or the Boone indicator for two reasons. First, the variable is conceptually closest to the entry barriers that Pagano and Jappelli (1993) include in their theoretical model and also to the way in which Brown and Zehnder (2010) vary entry barriers in the lab setting (i.e. through transactions costs for entering a new market). Second, market power is a function of entry regulation as well as other factors, including the existence and depth of credit information, and is thus more likely to be endogenous to our outcome variables (see section 4 for a discussion about reverse causality).

We have data on the minimum capital requirement for 137 countries, ranging from zero to USD 15 billion. Close
are less competitive, they did not find any evidence that banking system concentration is negatively related to the _H_ -measure. In fact, CL found some evidence that more-concentrated banking systems are more competitive. Their _H_ -statistic estimates, for example suggest that some countries with highly concentrated banking markets, such as the Netherlands, have very competitive banking systems. They also found that systems with greater foreign bank entry and lack of restrictions on entry and activity have a higher competitiveness score. Thus, it can be seen that the degree of banking system concentration and degree of competition need not capture similar aspects.

# **3. DATA**

We use industry-specific and country-specific data from a variety of sources. Table 1 presents an overview of the variables used in our empirical analysis together with their sources. Most of the variables are self-explanatory and have been used by RZ or CG. For our dependent variable, growth in value added, we have data on value added from the UNIDO database (United Nations Database on Industrial Statistics). For most regressions, we use the growth in value added over the period 1980—1990 (as RZ do). We also have figures for growth in value added for the period 1980—1997, which we use as a robustness test. We always use the average rates of growth over the period under study.

As a measure of external financial dependence at the sectoral level, we use the data from RZ, who assume that the external financing ratio for each industry in the United States 15