rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Error components and system GMM regressions for growth volatility on (independently) openness and product diversification<sup>_†_</sup>

|**Indicator**|**Threshold**|**Joint signifcance**|**CI**|**Share**|
|---|---|---|---|---|
|Herfndahl|0.154|9.03<sup>_∗∗∗_</sup>|[0_._012_,_0_._271]|0.797|
|5 product|0.481|6.23<sup>_∗∗_</sup>|[0_._244_,_0_._710]|0.563|
|10 product|0.575|4.93<sup>_∗_</sup>|[0_._289_,_0_._905]|0.469|
|PC product|-0.516|3.42|[_−_2_._761_,_11_._602]|0.672|

_†_ Notes: _χ_ 2 values calculated from Wald tests of joint significance of coefficients of the openness and interaction terms.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at the 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at the 1 percent level. Confidence interval reports 95 percent interval calculated from standard error of threshold level of diversification. Share reports number of countries in final period distribution falling below threshold.

<!-- Start of picture text --> Total effect of<br>25<br>openness on<br>volatility20<br>15<br>10<br>5<br>0<br>-5 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00<br>-10 Export concentration<br>-15<br>-20<br>-25<br>Source: Authors' calculations<br><!-- End of picture text --> Figure 2: Effect of diversification on growth volatility, after moderating for effect on openness, based on the 5 product index. The threshold diversification value of 0.48 has a 90 percent confidence band that includes fully positive values, along with parts of the distribution significantly above and below zero. 56 percent of countries in the final five year period fall under this critical threshold level of diversification, indicating that increased openness will decrease their growth volatility.

19
On the other hand, the increases in electricity production and consumption during the 2000s are unremarkable, compared to gas import and consumption. Between 2003 and 2008, electricity production and consumption increased by only 10 and 20 percent, respectively (Table 4). In contrast, as shown above, gas import and consumption more than doubled during the same period. Residential electricity consumption increased by only 30 percent, compared to a six-fold increase in residential gas consumption. The increase in gas consumption may have been driven by rapid gasification in the country as gas-based heating remains less costly than electricitybased heating. On the other hand, electricity production and consumption are more responsive to shocks than gas import and consumption.

**<mark>Table 4. Electricity production and consumption in Armenia</mark>** _<mark>(in million Kilowatt hours (KWh))</mark>_

|||||||||**(2008/**<br>|
|---|---|---|---|---|---|---|---|---|
||**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2003)**|
|Production|5,501|6,033|6,317|5,941|5,898|6,116|5,671|1.1|
|Total Consumption<br>|3,654|3,961|4,180|4,309|4,806|4,785|4,379|1.3|
|_of which:_|||||||||
|Residential|1,345|1,437|1,498|1,531|1,715|1,712|1,554|1.3|
|Budget Institutions|184|200|198|203|254|249|211|1.4|
|Industry|795|911|1,020|1,039|1,244|1,147|1,009|1.4|
|Transport|119|119|113|115|124|127|119|1.1|
|Irrigation<br>|223|261|229|227|92|112|124|0.5|
|Water supply and|||||||||
|sewage|255|207|193|178|181|183|164|0.7|
|Other|733|827|930|1,017|1,197|1,255|1,199|1.7|
|Total Consumption|||||||||
|(percent,Production)|66|66|66|73|81|78|77|1.2|

_Source:_ Publications of and contacts with Public Services Regulatory Commission, Republic of Armenia In Armenia, residential use of energy accounts for about 29 percent of total final energy use. As shown in the next section, space heating accounts for the largest share of residential energy expenditures. Heating energy is critical in Armenia to avoid freezing of the population during the cold winter months. As a result, energy expenditures are a significant portion of household total 7
A common problem in equation (1) is that of endogeneity of number of madrasas admitting females. Presence/emergence of co-educational or girls-only madrasas could be driven by cultural factors: regions where there are less cultural restrictions on female mobility are more likely to see emergence/presence of “liberal” religious schools that also encourage education of girls. We address the concern over identification of religious school effect using sub-district fixed-effects. This requires sufficient sub-district level variation in norms and/or a dataset with a large number of sub-districts. In the year 2003, there were 486 sub-districts (upazilas) in Bangladesh and our dataset contained all secondary schools in the country. Within the context of Bangladesh, socio-economic differences are usually across regions and districts - therefore, we can study growth in enrollment at the union level controlling for unobservables in a fixed-effect framework.

For our purpose, we create a dataset aggregated at the union level. Appendix Table 1 provides a summary of key variables. The period of 1999-2003 has seen exceptional growth in total female enrollment in secondary schools. At the union level, the average growth rate is 21%. But looking at enrollment in grade 10, this figure is as high as 31%<sup>12</sup> .

To examine the effect of BRAC schools on female enrollment growth in secondary grades in madrasas, we expand equation (1) by additionally including total number of BRAC schools in the union as a regressor on the RHS and estimate this equation at the madrasa level. The equation, when re-written, appears as follows: Gti = α 0 + α 2 X t-4,i + α 3 Δ Xit + α 4Wti+ α 5(# of BRAC schools in the union)i+ δ t + ε ti (2) where, Gti = growth rate of female enrollment in secondary madrasas during 1999-2003 δ t = Districts fixed-effects υ it= iid random error t = time index i = union index Equation (2) is estimated using establishment (i.e. madrasa or school) level data. The number of BRAC schools in the union in equation (2) is treated as endogenous. Madrasas that are located in poorer areas are likely to have experienced higher growth in female enrollment because of the FSP intervention. Yet, these regions would have a greater concentration of BRAC schools. Therefore, presence of BRAC schools could merely proxy for local poverty and hence endogenous. We correct for this endogeneity problem in an instrumental variable framework where we use data on the number of BRAC branches and the number of BRAC female members as excluded instruments.

> 12A similar pattern is observed form gross enrollment statistics. According to BANBEIS, gross female enrollment rate at secondary level in school (total # of females enrolled in secondary school/total # of 11-15 years old in the population) has increased from 42.5% to 48.4% between 1999 and 2003.

9
# **5. Estimating the Gains from Capacity Building in South Asia**

The previous section outlined a number of specific areas for reform and modernization in South Asia to facilitate trade. In this section, we draw on Wilson, Mann Otsuki’s (WMO) (2005) indicators for 75 countries in the trade facilitation to evaluate: port efficiency, customs environment, regulatory environment and service-sector infrastructure. ‘Port efficiency’ in this analysis is designed to measures the quality of infrastructure of maritime and air ports. ‘Customs environment’ is designed to measure direct customs costs as well as administrative transparency of customs and border crossings. ‘Regulatory Environment’ is designed to measure the economy’s approach to regulations. ‘Service-sector Infrastructures’ indicates the extent to which an economy has the necessary domestic infrastructure (such as telecommunications, financial intermediaries, and logistics firms) and networked information to improve efficiency and transform activities to enhance economic activity.

While these indicators cover a range of performance in trade and transport facilitation, _they do not cover all areas discussed in the previous sections_ . Data availability for cross country analysis is lacking in a number of areas, for example, direct use of information technology in customs and associated costs and benefits of such improvement. New datasets and cross-country indicators on trade facilitation, such as those planned for the World Bank “Doing Business 2006” report, among others, should help expand understanding of the link between trade and logistics costs.

The analysis here and indicators draw on three sources— _Global Competitiveness Report_ , _World Competitiveness Yearbook_ , and a dataset compiled in Kaufmann, Kraay and Zoido-Lobaton (1999). Each indicator is constructed as a simple average of two inputs as presented in the Appendix.<sup>25</sup> We have expanded the WMO data set for this analysis by including Pakistan given the availability of data in the most recent version of _Global Competitiveness Report 2003/2004_ for port efficiency and customs. Trade facilitation indicators are available for Bangladesh, India, Pakistan and Sri Lanka among countries in South Asia, but not for Afghanistan, Bhutan, Maldives and Nepal. The first country group represents the South Asia region well, however, as these countries represent approximately 98.6 percent of the region’s GDP.

The four South Asian countries exhibit a low performance in port efficiency relative to the sample average (Figure 9). In customs environment all the South Asian countries except Pakistan score lower than the global average. Pakistan is slightly above the sample average. In regulatory environment, Sri Lanka has a relatively high score, which falls just

> 25 Each input is normalized by the score of the highest ranked country such that the highest score for each input is one.

19
-37 -

Premature Mortality. The Framingham study<sup>7</sup> ' can be used to estime the change in mortaity due to the change in diastolic blood pressure. Controlling for serum cholesterol levels and smoldng, the association can be estimated by: change in Pr(MORT) = (1 + exp (5.3 - 0.035(DBP,)))' (1 + exp (5.3 - 0.035(DBP 2)D' where PR(MORT) = the 12 year probability of death from all-causes and the standard error of the coefficient of 0.035 is .007.

For this analysis, it may be necessary to establish a relationship between changes in lead emitted into the air and subsequent concntations of lead in the blood of adults. To detemine this relationship, we relied on many of the studies that relate air concentrations to the subsequent change in blood lead through many different media and expos routes. Results are obtained from both observational smtdies in the field and studies in mental exposure chambers. The laiter are particularly useful in providing1ex estimates of the blood lead/air lead slope since they may involve longitUdinl eamination of idividuals eosed to relatively small changes in air lead under controlled conditions. A review of many of tbesc studies is supplied by EPA.' Considering both the experimeal and observational information, we assume a blood lead/air lead slope of 2.0 for adults Since quantitative relationships have been established between air lead and blood lead, and between blood lead and health effects, we can use the above infonnation to directly estimate health effects related to changes in ambient lead.

Por adults, the effects of ambient lead on blood pressure can be expressed as: change in H = (1 + exp (2.744 - 0.793 (In 2PbAJ))Y' -

- (1 + exp (2.744 0.793 (In 2PbAJ))I

where:
period (all firms that are state-owned or have sales of more than 5 million yuan are selected into the sample); the variation of numbers of enterprises across years may be driven by changes in ownership classification or by increases (or reductions) in sales volume in relation to the 5 million yuan threshold. However, the data show that 5 million yuan is not a strict rule. Among non-SOEs, about 6 percent of the firms report annual sales of less than 5 million yuan in 1998; this number rises to 8 percent by 1999 and falls after 2003. In 2007, only 1 percent of non-SOEs have annual sales below 5 million yuan. In terms of the full sample, the percent of firms with sales less than 5 million yuan stays at the same level for 1998 and 1999 and starts falling in 2000. In 2007, around 2 percent of the sample consists of firms with annual sales less than 5 million yuan.

The original dataset includes 2,226,104 observations and contains identifiers that can be used to track firms over time. Since the study focuses on manufacturing firms, we eliminate non-manufacturing observations. The sample size is further reduced by deleting missing values, as well as observations with negative or zero values for output, number of employees, capital, and the inputs, leaving a sample size of 1,842,786. Due to incompleteness of information on official output price indices, three sectors are dropped from the sample<sup>11</sup> . Thus, our final regression sample size is 1,545,626.

The dataset contains information on output, fixed assets, total workforce, total wages, intermediate input costs, foreign investment, Hong Kong-Taiwan-Macau investment, sales revenue, and export sales. These are the key variables from which we obtain measures of firm-level foreign asset shares and the FDI spillover variable, which are discussed in detail in the next section. In this paper, to test the impact of FDI spillovers on domestic firm productivity, we use the criterion of zero foreign ownership to distinguish domestic firms and foreign owned firms, that is, domestic firms are those with zero foreign capital in their total assets. In the dataset,

> 11 They are the following sectors: processing food from agricultural products; printing, reproduction of recording media; and general purpose machinery. 9
# **Getting Real? The Uneven Burden of Inflation across Households in Turkey**

Javier E. Baez<sup>a</sup> Osman Kaan Inan<sup>b</sup> Metin Nebiler<sup>c</sup> _World Bank World Bank World Bank_ Keywords: income distribution, inequality, poverty, inflation, Turkey, labor income JEL Classification Codes: E24, E31, I3, O50 ______________________

> * The authors wish to thank Pinar Yasar, David Knight, Erdem Atas, Cigdem Celik, Evren Ceritoğlu, Müşerref Küçükbayrak and Özlem Sevinç for thoughtful comments on earlier versions of this work. a jbaez@worldbank.org

> b <u>okaaninan@gmail.com</u>

> c <u>mnebiler@worldbank.org</u>
|Feng et al. [17]|United States|1997-2013|2, 3, 5, 13, 14|
|---|---|---|---|
|Xie et al. [45]|China|1995-2013|2, 5, 6, 15, 16|
|Mousavi et al. [18]|Iran, Islamic Rep.|2003-2014|2, 3, 4, 5, 6|
|Henriques and Borowiecki|Europe, North|1980-2011|1, 2, 3, 5, 6|
|[46]|America and Japan|||
|Shahiduzzaman and Layton|United States|1973-2014|1, 2, 3, 5, 6|
|[47]||||
|Chen et al. [48]|Macao|2000-2011|2, 4, 5, 8|

**_Notes:_ a)** 1. Income effect, 2. Energy mix effect, 3. Population effect, 4. Economic structure effect, 5. Energy intensity effect, 6. Emission coefficient effect, 7. Economic activity effect, 8. GDP, 9. Electricity generation efficiency effect, 10. Thermal power structure effect, 11. Electricity structure effect, 12. Electricity intensity effect, 13. Consumption pattern effect, 14. Production structure effect, 15. Industrial activity effect, and 16. Industrial scale effect; and **b)** EU denotes European Union and US denotes United States.

29
_II.2 How have the poor fared over the past years? A look at income levels_ We have seen in the previous sub-section that Latin America’s income inequality is high. However, what are the recent trends in terms of the evolution of income inequality? To address this issue Figure 2 plots the average growth rate in the incomes of the poor and the average growth rate for the population as a whole. Panel A focuses on the extreme poor (i.e. those with income levels below US$1<sup>7</sup> per person per day) and panel B on the moderate poor (i.e. those with income levels below US$2 per person per day). To construct these figures we have relied on household surveys for 18 countries. The first year of the spell falls typically in the early 1990s (the average initial year for the 18 countries is 1992), and the last year in early 2000s (the average final year for the 18 countries is 2002).

The figures also plot the regression slope. If the slope relating these two variables were equal to 1, it would indicate that on average the income of the poor has been increasing at the same pace as the income for the average individual. If on the other hand, the slope is smaller (larger) than 1, it would indicate that the income of the poor is increasing slower (faster) than the income of the average individual and hence that inequality is increasing (declining).

**<u>Figure 2. Growth in the incomes of the poor vs growth</u>**

<!-- Start of picture text --> $1 Poverty line $2 Poverty line<br>10 8<br>y = 0.736x - 0.2239 y = 0.7886x - 0.3432 6<br>5 4<br>2<br>0<br>0<br>-15 -10 -5 0 5 10<br>-15 -10 -5 -2 0 5 10<br>-5<br>-4<br>-10 -6<br>-8<br>-15 -10<br>Growth Growth<br>Growth income of the poor Growth income of the poor<br><!-- End of picture text --> Source: Authors’ calculations Inspection of these two figures suggests that during the 1990s the Latin American poor have been benefiting from growth less than the average individual. Panel A in figure 2 has an associated slope of .74; the slope in Panel B is slightly higher (.78) but it still indicates that the incomes of the poor have increased less than proportionally than those of the non poor.<sup>8</sup> There are two ways to look at this data. On the one hand, it is difficult to defend that the Latin American poor have not benefited from growth during the 1990s. Indeed, save a couple of exceptions the income of the poor has increased when average

> 7 All international poverty lines are expressed in US$ adjusted for purchasing power parity differences. 8 It is likely that these results are affected by the presence of important outliers. Yet, re-estimation of the slopes using robust estimates to outliers tends to lower the slopes even further to between .5-.6 in the case of the US$1 a day and to .6-.7 in the case of the US$2 a day.

5
The motivation for this study, and the steps in our analysis, can be grouped around five claims. Let us begin with two widely-heard claims: <u>Claim 1:</u> _The majority of the developing world’s population lives in rural areas, but the urban population share is rising and will soon exceed the rural share._ <u>Claim 2:</u> _The incidence of absolute poverty is higher in rural areas of developing countries._ Support for Claim 1 has mainly come from the useful compilations of demographic data and population forecasts done by the UN Secretariat’s Population Division, in its regular report, _World Urbanization Prospects_ (WUP). The “urban” versus “rural” split of the population is largely based on national statistical sources. In the latter, an “urban area” is typically (though not universally) defined by a non-agricultural production base and a minimum population size. However, there are many differences between countries in the definitions used in practice; for example, the minimum population size can vary from two to five thousand. Some of the measured growth in the urban population stems from changes in the definition of an “urban area;” Goldstein (1990) describes how this happened in China during the 1980s. The distinction between “urban” and “rural” areas is also becoming blurred; urban areas are heterogeneous, with a gradation from “mega-cities” to towns. While very few people (ourselves included) question the validity of Claim 1, there is in fact a cloud of doubt about definitions and magnitudes.<sup>1</sup> The foundations for Claim 2 are no more secure. Almost all of our prevailing knowledge concerning the urban-rural poverty profile has come from country-specific poverty studies, using local poverty lines and measures. The World Bank’s country-specific _Poverty Assessments_ are examples of this type of evidence; compilations of the national (urban and rural) poverty measures can be found in the Bank’s _World Development Indicators_ (WDI; this is an annual publication; the latest issue is World Bank, 2006). Drawing on evidence from this type of data, Ravallion (2002) estimates that 68% of the developing world’s poor live in rural areas.

> 1 The Panel on Urban Population Dynamics (2003) makes recommendations on how to address the problem of differening definitions of “urban”, but the implementation of their recommendations is not feasible with the survey data used for poverty measurement currently available in most countries.

3
according to the market capitalization ratio, it has below average turnover. On the other hand, Thailand has an average market capitalization ratio but has a "top five" turnover ratio. Argentina has the most volatile market, which is ten times more volatile than that of the United States. In terms of international integration as measured by the APT indicator, Venezuela appears the least integrated, with a value 2.5 times that of the United States. The emerging markets have almost twice the amount of mispricing as the average of the U.S., the U.K., and Japan, as measured by both the APT and ICAPM measures of integration.

Table 2 presents the correlations and corresponding p-values of the six stock market development indicators and the two conglomerate indexes. The market capitalization ratio is positively related to the total value traded ratio and the indexes, and negatively related to volatility and the ICAPM and IAPM measures of market integration. The total value traded ratio is significantly correlated with turnover, with a correlation coefficient at 0.79. Also, using different measures of integration, we confirm Harvey's (1995) finding that volatility is strongly and positively correlated with international integration suggesting that less integrated markets are more volatile.

# _C. Policy Event Dates_

To evaluate what happens to stock market size, liquidity, international integration, and volatility after countries change international capital controls, we need to identify dates on which countries changed their policies. Selecting the one or two key dates when a country importantly changed policies toward international capital flows is both arduous and, ultimately, less systematic than we would like. We reviewed the International Monetary Fund's inanalxhangeRions the International Finance Corporation's Emerging Markets Fact Book, and various World Bank country reports from 1980-1993. Based on this information, we selected one, and in the case of Korea two, period (s) when the country liberalized restrictions on international capital flows or the repatriation of dividends. Based on our review of the above IMF/World Bank documents, we tried to choose "important" policy 10