rafmacalaba/datause-encoder-data huggingface dataset:
Reforms also had no impact on the electricity prices of South Asian countries like Bangladesh and Pakistan where electricity prices are not cost-reflective and politically determined (Bhattacharya, 2007). Electricity prices continue to be below the cost recovery levels giving rise to high commercial losses among the transition countries such as Georgia, Moldova, Ukraine and the Commonwealth of Independent States (CIS) since reforms started in the early 1990s (Nepal and Jamasb, 2012a). The difficulty of the socially vulnerable consumers to absorb further price increases (low affordability) has often prevented pursuing tariff reforms in many transition countries (Fankhauser and Tepic, 2007). In Turkey, the introduction of a tariff system reflecting costs differently affected the production and consumer prices of electricity. The effect on consumer prices was slightly lesser than for producer prices (Akkemik, 2011).

**Table 4: Reforms and Electricity Prices**

|**_Study_**|**_Approach_**|**_Region_**|**_Data_**|**_Method_**|**_Policy impacts_**<br>**_and relevance_**|
|---|---|---|---|---|---|
|Nagayama<br>(2007)|econometric|global|panel data: 83<br>countries (26<br>developed);<br>1985-2002|ordinary<br>least squares,<br>fixed effects,<br>random<br>effects|neither unbundling<br>nor introduction of a<br>wholesale pool<br>market on their own<br>necessarily reduces<br>the electricity prices;<br>unbundling may<br>work to reduce prices<br>when coexistent with<br>an independent<br>regulator|
|Nagayama<br>(2009)|econometric|global|panel data: 78<br>developing,<br>developed and<br>transition<br>countries;<br>1985-2003|ordered<br>response,<br>fixed effects,<br>random<br>effects|higher electricity<br>price drive<br>liberalisation;<br>liberalisation models<br>does not necessarily<br>reduce electricity<br>price|
|Erdogdu<br>(2011)|econometric|global|panel data: 63<br>developed and<br>developing<br>countries,<br>1982-2009|fixed effects,<br>random<br>effects|no uniform pattern<br>for the impact of<br>reforms process as a<br>whole on price-cost<br>margins and cross-<br>subsidylevels;|

in these countries do not cover the full costs of electricity supply. Countries like Angola, Malawi, South Africa, Zambia and Zimbabwe have maintained highly subsidized low prices below the cost levels (Eberhard et al. 2011).

17
# I. Introduction

In 1911, Joseph Schumpeter argued that the service. provided by financial intermediaries - mobilizing savings, evaluating projects, managing risk, monitoring managers, and facilitating transactions - are essential for technological innovation and economic development. Empirical work by Goldsmith (19691 and McKinnon [19733 _llustrates the close ties between financial and economic development for a few countries.<sup>1</sup> But, numerous influential economists believe that finance is a relatively unimportant factor in economic development. Notably, Robinscn (1952] contends that financial development simply follows economic growth. More recently, Lucas (19881 terms the relationship between financial and economic development "over-stressed." In this paper, we study whether higher levels of financial development are positively associated with economic development using data on over 80 countries over the 1960-1989 period. Specifically, we investigate whether higher levels of financial development are significantly and robustly correlated with faster current and future rates of economic growth, physical capital accumulation, and economic efficiency improvements.

To examine whether Schumpeter was right, we must define "financial development" empirically. We construct four indicators of financial development that are designed to measure the services provid3d by financial intermediaries. First, we compute the traditional measure of financial depth, which equals the overall size of the formal financial intermediary system, i.e., the ratio of liquid liabilities to GDP. Second, we distinguish among financial institutions conducting intermediation. Due to data limitations, this means examining the importance of deposit banks relative to the central bank in allocating domestic credit. Banks are likely to offer better risk management and investment information services than central banks. Third, we examine where the financial system distributes assets using two measures: (a) credit issued to nonfinancial private firms divided by total credit (excluding 1 Also, see Gertler and Rose (19921, King and Levine (1992a,b), DeGregorio and Guidotti (19923, and the World Bank (19893. For microeconomic evidence, see Schiantarelli, et.al. (1992]. On theoretical linkages, see Greenwood and Jovanovic [1990], Bencivenga and Smith [19913, Levine 11991, 19923, Saint-Paul [19923, and Roubini and Salai-i-Martin [1991, 1992].
stolen by rebel groups. Another reason is that arms imports may lead to political repression. Blanton (1999) has indeed shown that arms imports are linked to human right violations in developing countries. A third reason is that arms purchases by governments may crowd out more development-friendly state spending. One example here is Fan et al. (2018), who showed that across countries military expenditures do crowd out health expenditures.

The recent experience of Mozambique provides an illustrative anecdote on the link between newfound wealth and arms imports, and its potential consequences. After discovering – enormous amounts of natural gas off its coast in 2009 estimated at the time to be – worth around 50 times its GDP in net present value terms the country experienced an unprecedented growth spurt and a foreign investment boom (Toews and Vezina, 2017). It ended sharply five years later when a debt crisis hit. It turned out the government had borrowed too much, and illegally, to purchase military ships. This transaction worth hundreds of millions of dollars was made possible by loans worth around 2 billion dollars, or 12.5% of Mozambique’s GDP. It inadvertently revealed cracks in the political system and that was enough to derail the entire economy (see Hill and Nhamire (2018)). Other cases of oil-for-arms episodes include Sudan’s oil for guns deal with China (Herbst, 2008), Azerbaijan wasting the wealth from its post-2010 energy boom on vast amount of weapons (Altstadt and Menon, 2016), or the 1990s Angolagate scandal, which saw Angola’s oil revenues exchanged against weapons from ex-Soviet countries (Economist, 2008). Figure 1 shows that, in those four countries, arms imports did surge after giant discoveries, in 2009-2011 in Mozambique, in 2003 in Sudan, in 2010 in Azerbaijan, and in 1996-1998 in Angola.

To examine the relationship between oil and gas discoveries and arms imports I combine data on oil and gas discoveries from Horn (2011), updated by the World Bank, with arms imports data from the Stockholm International Peace Research Institute (SIPRI), as well as from the United Nations (COMTRADE). I estimate a panel covering 120 developing countries over 1990-2017, i.e. the post-Cold War years which also include the latest oil price boom that peaked in 2011 (Figure 4). I find that in the five years following a giant oil 4
The inundation effect of storm surges has been assessed using the two‐dimensional Bay of Bengal Model recently updated and upgraded under the Comprehensive Disaster Management Program of Bangladesh (UK DEFRA 2007). The model is based on Mike 21 hydrodynamic modeling system, and its domain covers the coastal region of Bangladesh up to Chandpur and the Bay of Bengal up to 16o latitude. A detailed description of the model has been provided in Appendix 1.

For approximating a condition of cyclones in Bangladesh without climate change, cyclone tracks of all major 19 historical cyclones making landfall in Bangladesh during 1960 and 2009 (For details, see Appendix 2) have been used with corresponding observed wind and pressure fields. For simulating the storm surge and associated flooding, the Bay of Bengal model was then applied to generate the extent and depth of inundation without climate change. Resulting inundation map is based on the maximum level of inundation at every grid points of the model.

For approximating cyclones in a changing climate by 2050, potential cyclone tracks have been selected simulating 19 major cyclones making landfall in Bangladesh during 1960‐2009, and the affected coastal regions. From the historical cyclone simulations, it has been observed that the Sunderban coast, the southwestern coast (Sunderban to Patuakhali), the Bhola and Noakhali coast in the Meghna Estuary and eastern coast (Shitakunda to Bashkhali) are covered by 1974, 1988, 1991 and 2007 cyclone tracks. In order to include the Sandwip coast and the part of the Noakhali and Chittagong coasts at the central region of the Meghna Estuary, an artificial track has been generated. Figure 1 shows the five cyclone tracks considered for the determination of inundation zones due to climate change induced storm surges (see Figure 1).

In order to determine potential future inundation zones in a changing climate by 2050, the storm surge model was run for the above mentioned five cyclone tracks covering the whole coastal area, incorporating a sea level rise of 27cm, 10% increase in wind speed, and landfall of the cyclones during high tide. Once again, inundation maps for 2050 with climate change have been generated based on the simulation results taking into potential intensification of cyclone induced inundation (See Figure 2).

> 24 Since scientific evidence to date is pointing towards an increase in frequency of intense cyclones in Bay of Bengal, probability of a potential landfall during high tide will also increase.
<!-- Start of picture text --> Chart 1. Global Long-Tein Real Interest Rates<br>(In percent)<br>8 8<br>66<br>4 4<br>2 2<br>o 0<br>-2 -2<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993<br>Sounma IMiAf kW Econonic Outlook, May 1995<br>Note GOP wighted avew of ten-year (or nosiest maturity) government bond rte forth. United<br>Stes. Japan, Germany, the United Kingdom, Canada, Begium, the Ndtherands, and Switzer-<br>land minus long memory Inflaion estimate<br><!-- End of picture text --> <!-- Start of picture text --> Chart 2. Net Long-Term Capital Flws to Developing Countrks, 1989-94<br>(in bilions of US$)<br>US$ MElons<br>200<br>Private Flows<br>150<br>100 Official k<br>50<br>0<br>1989 1990 1991 1992 1993 1994<br>Souma World Bnk (19958)<br><!-- End of picture text --> 4
characteristics. To capture this idea, let individuals derive utility V from total consumption expenditures X and from public amenities A: where j denotes the individual and k the location. To keep the presentation simple, other factors such as prices, product variety, etc, are ignored for now. We introduce them later. We ignore savings, so that income equals consumption.

Individuals prefer the location k where their utility Vjk is highest. Whether they can relocate or not depends on the functioning of the labor market. We fi rst discuss the case in which workers locate freely and costlessly. We then examine the case where workers are immobile or move at a cost.

# 2.1. The cost of isolation

Assume for a moment that A is the same across locations. If individuals can move at no cost, arbitrage implies that utility — and hence income — are equalized across locations. To generate di ff erent levels of income and utility across locations, let us follow Roy (1951), as modi fi ed by Dahl (2002), and assume that workers di ff er in ability ε j so that some individuals have higher marginal productivity. In a competitive labor market with free movement of labor, workers are paid their marginal product. We thus have Xjk = Xk( ε j) with ∂ Xk( ε )/ ∂ε > 0.

Now assume that, for technological reasons, jobs that require a high ability are located in or near cities, i.e., that ∂<sup>2</sup> Xk( ε )/ ∂ε∂ dk < 0.<sup>2</sup> With these assumptions, average welfare is higher

> 2 This is not an unreasonable assumption. Fafchamps & Shilpi (2005), for instance, have shown that there are larger fi rms and more job specialization in and around cities. We also know from the work of Jacoby (2000) that, in Nepal, land located far from markets has a lower value and yields a lower income. This is undoubtedly due to lower average prices for agricultural output and less emphasis on commercial farming, an issue that is revisited by Fafchamps & Shilpi (2003). All these factors probably generates higher returns to education and to entrepreneurial ability in urban centers.

3
<!-- Start of picture text --> Box gure 14 The World Bank's long-term projections of average GDP growth for the decade of the 1980s<br>compared with outcomes<br>tpercent<br>High-case projecbon Lotw-case projection<br>Actual GBY' growth Actual GOP growth<br>F & : =<br>fa fa<br>i bi<br>a | 5<br>44<br>34<br>zL2<br>1Ht}<br>0 oO 1 2 7 4 3 a ? B a 0 I 2 a. 4 5 4 i &<br>Mrojecked GOP growth Projected COV growth<br>asia excluding China Lailn America #) Sul-Saharan Atrica<br>] All developing countries excluding China SB orc Bl Silexparters<br>aT<br>ate: Years refer tu the edition af World Deveiounent Rexoet In which the peajertian was published, Uf auteemer matched projections<br>exactly, the lata woald fall alang the diagwnal line, Points below the line indivate aptimistle projechons; poants abe, pessimistic ors<br>Ret more information on the prejecthana and autores, ser the technical mete at the end of the main text.<br>dure: orld Bank data.<br><!-- End of picture text -->
a strong regional presence and less “sophisticated” markets (less stringent quality or packaging requirements).<sup>14</sup>

## **Table 2.3   Shares of all formal sector employment 1994-2000**

## **(percent of all formal sector workers)**

|**Firms**|**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|
|---|---|---|---|---|---|---|---|
|Micro|81.91|81.53|81.59|81.04|81.48|82.57|82.51|
|Small|15.05|15.33|15.26|15.62|15.36|14.48|14.51|
|Medium|2.11|2.17|2.17|2.28|2.17|2.03|2.04|
|Large|0.93|0.97|0.98|1.06|1.00|0.93|0.94|
|**Total**|**100**|**100**|**100**|**100**|**100**|**100**|**100**|
|**Micro, Small &**<br>**Medium Total **|**99.07**|**99.03**|**99.02**|**98.94**|**99.01**|**99.08**|**99.06**|

Source: Caracterización de las Micro y Pequeñas Empresas. Based on data from CASEN Survey, 1990-2000 Figures do not include the self-employed, military forces, public service, household service, and undefined categories, which account for more than 42 % of total employment)

**Table 2.4   Shares of productive sector employment**

**(percent of productive sector employment)**

|**Employment**|**1990**|**1992**|**1994**|**1996**|**1998**|**2000**|
|---|---|---|---|---|---|---|
|Micro|33.97|33.60|30.26|38.95|30.85|33.18|
|Small|28.62|31.04|33.47|32.36|24.91|26.92|
|Medium|17.85|18.08|17.63|17.07|17.22|17.18|
|Large|19.56|17.28|18.64|11.61|27.02|22.71|
|**Total**|**100.00**|**100.00**|**100.00**|**100.00**|**100.00**|**100.00**|
|**Micro, Small &**<br>**Medium Total **|**80.44**|**82.72**|**81.36**|**88.38**|**72.98**|**77.28**|

Source: _Caracterización de las Micro y Pequeñas Empresas_ . Based on data from CASEN Survey, 1990-2000 Figures do not include the self-employed, military forces, public service, household service, and undefined categories, which account for more than 42 % of total employment)

# **Contribution to National Economy**

# **SALES**

The importance of SMEs in the national economy has been small and declining. Figures 2.1 and 2.2 show annual sales growth rates by size between 1994 and 2000. Figure 2.1 shows the annual sales growth rates for the periods 1997-95, 1995-96, 1996-97, 1997-98, and 1999-2000. Figure 2.2 compares the growth rates for the six-year period 1994 to 2000. As large firms have increased their dominance, SME sales have fallen. Micro and SME sales fell from around 27 percent of total sales in 1994 to just over 23 percent in 2000. In addition, the relative weight of large firms increased during the 1990’s in terms of absolute sales, sales per worker and sales per company.<sup>15</sup> SMEs may be less vibrant than micro businesses or large firms during economic growth spurts and appear more vulnerable to shocks. In 1996, the SME growth rate lagged micros and large firms during

> 14 This second indicator of SME sales to the interior refers to how sales are distributed in the various sectors of the economy. Measured by:  Sales by SME / Sales by all enterprises

> 15 Alarcón and Stumpo (2001).

10
Table 4. Leading causes of death in selected African countries from vital registration data <!-- Start of picture text --> South Africa Lagos Bamako<br>(black population) Nigeria Mali<br>15-64 years 15+ years 15-64 years<br>Rank % % Rate<br>1. External 30 Road accident 26.3 Liver disease 1.32<br>2. Circulatory 18 Circulatory 19.7 Cardiac insuf. 0.86<br>3. Neoplasms i4 Pneumonia 6.1 TB 0.70<br>4. Inf/parasitic 11 Dia/dysentery 5.2 Intest.inf. 0.62<br>5. Respiratory 10 Maternal 4.5 Malaria. 0.36<br>6. Inf/parasitic 3.4 Pneumonia 0.26<br>7. Neoplasms 3.3 Meningitis 0.16<br>8. Malaria 2.3 Dehydration 0.16<br>9. Anaemia 0.14<br>Ill-defined 20<br>Total deaths 99469 5219<br>Source: Botha & Bradshaw Ayeni (1980) Fargues & Nassour<br>(1985) " (1987)<br><!-- End of picture text --> Notes: **1** Death rate/1000/year in age group 15-64 years 35
This ‘first-stage’ estimation is then carried out using nationally representative survey data, which are usually stratified at the province, or regional, level for rural and urban areas separately, but always setting household weights respecting survey’s sample design.<sup>4</sup> This regression model of household income also allows for intra-cluster correlation in the regression residuals - failure to take account of correlation in the disturbances would result in underestimation of standard errors. Thus, the vector of disturbances, u, in (1) is decomposed as _u ch_ = η _c_ + ε _ch_ where η _c_ is a location component and ε _ch_ a household component. (see Elbers, Lanjouw and Lanjouw, 2002, 2003 for more details). To capture latent cluster-level effects, census mean variables and other aggregate level variables are included among the set of potential regressors. The ELL method also allows for heteroskedasticity in the household-specific part of the residual, limiting the number of explanatory variables to be cautious about overfitting. Finally, the estimated variance-covariance matrix is used to obtain GLS estimates of the first-stage parameters and their variance.

The next stage is to combine the results of the first-stage regression model with census information, vector of characteristics X, to predict welfare measures and estimate prediction errors. These estimates can be generated via several routes as described in Elbers et al (2002), Pfeffermann and Tiller (2005) and Demombynes et al (2006). In brief, for each household in the census data disturbance terms, η<sup>~</sup> _cr_ and ε<sup>~</sup> _chr_ , are drawn from their corresponding sampling distributions estimated on the survey data. Each household in the census data then obtains an estimated

> expenditure, _y_ ˆ _chr_ , based on both predicted log expenditure, **x** _CENSUS_ ' _ch_ ⋅<sup>β</sup> ~ˆ _SURVEYr_ , and the disturbance

Finally, this simulated expenditure is used to calculate estimates of the welfare measures for each target population. The procedure is repeated _M_ times, drawing each time different set of

> 4 Within each region there are usually further levels of stratification, and also clustering. At the final level, a small number of households (a cluster) are typically randomly selected from a census enumeration area.

6
responsible for the local specialization value in most districts. Roughly three-quarters of districts have their specialization in a traditional industry, especially in the manufacturing sector. The computer and communication industries form the specialized industry for fewer than 20 districts over the sample period, which is a substantially smaller count than many of the larger traditional industries (average count across industries is about 24). Advanced industries like computer and related activities (NIC 72) and research and development (NIC 73) are rarely the source of a district’s specialization due to the extreme degree to which they are specialized in certain locations. We later analyze whether these pockets of concentration for modern services are especially linked to growth.

The bottom of Table 6 documents the correlation of these counts between adjacent surveys. While the patterns across the 2000 and 2005 surveys are quite stable, more substantial differences emerge at the industry level for the 2010 survey compared to earlier years. Ghani, Kerr and Tewari (2013) show this break with manufacturing alone. This is likely due to the change from the NIC-2004 industry codes in the 2005 data to the NIC-2008 industry codes in the 2010 data. The fact that the reduced correlation occurs in both sectors suggests that it is most likely a definitional issue rather than a true economic pattern. Most of the remaining analyses only employ the 2010 data to calculate growth rates at the district level and do not depend upon industry definitions, thus minimizing the importance of this issue.

# _Correlations to District Traits_

Table 7 documents multivariate analyses of the district traits that associate with specialization and diversity levels, as well as changes in these values over time. Outcome variables are indicated by column headers. These dependent variables are expressed in a tenpoint scale for the levels metrics, representing deciles of the raw index values, and in unit standard deviation for the change metrics. District traits are taken primarily from the 2001 Population Census, with some specific covariates about the sizes of the organized and unorganized sectors also calculated directly from the ASI and NSS. We transform non-logarithm explanatory variables to have unit standard deviation for interpretation. Estimations include state fixed effects, weight districts by their log 2001 population, and report robust standard errors.

16
For purposes of obtaining an indication of improved market integration during 1992, changes in the coefficient of variation between February and August provide the best measure. The Table 2 data indicate little of such integration for some commodities, while the coefficient of variation de-clined substantially for cheese, butter, vermicelli, vegetable oil, milk, sour cream, sugar, and cigarettes.

Table 4 provides similar comparisons for prices in collective farm markets. Here we see more of the. expected trend toward market integration, particularly for beef, but there is still a lack of convergence for some commodities.

# Causes of City-to-City Price Differences

In order to investigate the causes of persistent price differences between cities, we can attempt to explain statistically each city's deviation from the all-Russia ave.age at a given time by means of supply/demand variables. The Center for Economic Analysis and Forecasting provided data on:

* Income per worker in each month of 1992 in each province

* VWhether each province was a net producer or consumer of each commodity * The pricing zone in prior USSR administration (proxy for transport costs) * Presence or absence of provincial price regulation after January 1992

These data were used to estimate the following linear regression equation for each commodity: (1) Dt = 00 + lit + 02Ej + **i3Ij** + **4**<sup>**Rkt**+</sup><sup>_5Zi_+ eit</sup> where Dit is the difference between city i's price and the all-Russia price in week t, Yit is income in the province of city i in the month containing week t, EA = 1 if the province produced more of the commodity than it consumed (otherwise zero), Ii = **1** if the province consumed more of the commodity than it produced, and R, = 1 if the city maintained regulation in week t. Z1 is a vector of 3 dummy variables-Z, = 1 if the province is in zone 1, Z2 = **I** if in zone 2, Z, = **1** if in zone 3. The O's are parameters to be estimated and the q, are random errors assumed to be independently and identically 16