rafmacalaba/datause-encoder-data huggingface dataset:
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- Fleming, Marcus J. 1962. “Domestic Financial Policies Under Fixed and Floating Exchange Rates.” _IMF Staff Papers_ 9 (3): 369-79.

- Gal í , Jordi, and Tommaso Montacelli (2005). "Monetary Policy and Exchange Rate Volatility in a Small Open Economy." _Review of Economic Studies_ 72(3): 707-34.

- Gali, Jordi. 2015. _Monetary Policy, Inflation, and the Business Cycle – An Introduction to the New Keynesian Framework and Its Applications,_ second edition. Princeton University Press, Princeton, New Jersey.

- Gallaway, Michael P., Christine A. McDaniel, and Sandra A. Rivera. 2003. "Short-Run and Long-Run Industry-Level Estimates of US Armington Elasticities." _The North American Journal of Economics and Finance_ 14: 49-63.

- Gust, Christopher, Sylvain Leduc, and Nathan Sheets. 2009. "The Adjustment of Global External Balances: Does Partial Exchange-Rate Pass-Through to Trade Prices Matter?" _Journal of International Economics_ 79: 173-85.

- Hanson, Kenneth, Sherman Robinson, and Stephen Tokarick. 1993. "U.S. Adjustment in the 1990's: A CGE Analysis of Alternative Trade Strategies." _International Economic Journal_ 7: 27-49.

- Head, Keith, and Thierry Mayer. 2014. “Gravity Equations: Workhorse, Toolkit, and Cookbook.” In Elhanan Helpman, Kenneth Rogoff, and Gita Gopinath, eds., _Handbook of International Economics_ , Vol. 4, 131-95. Amsterdam: Elsevier.

- Hertel, Thomas, David Hummels, Maros Ivanic, and Roman Keeney. 2007. "How Confident Can We Be of CGE-based Assessments of Free Trade Agreements?" _Economic Modelling_ 24: 611-635.

- Hillberry, Russell and David Hummels. 2013. "Trade Elasticity Parameters for a Computable General Equilibrium Model." In Peter B. Dixon and Dale W. Jorgenson, eds. _Handbook of Computable General Equilibrium Modeling, Vol. 1B._ Oxford: North-Holland, Elsevier, pp. 1213-70.

- Ho, Mun S., and Dale W. Jorgenson. 1998. "Modeling Trade Policies and U.S. Growth: Some Methodological Issues." In N. Benjamin, ed., _The Economic Implications of Liberalizing APEC Tariff and Nontariff Barriers to Trade._ Washington, D.C: U.S. International Trade Commission (Publication 3101).

- Justiniano, Alejandro, and Bruce Preston. 2010. "Can Structural Small Open Economy Models Account for the Influence of Foreign Disturbances?" _Journal of International Economics_ 81: 61-74.

- Khan, Moshin S. 1974. "Import and Export Demand in Developing Countries. _IMF Staff Papers_ 21: 67893.

- Kohli, Ulrich R. 1978. "A Gross National Product Function and Derived Demand for Imports and Supply of Exports. _Canadian Journal of Economics_ 11(2): 167-82.

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3 employs twice as many workers as an average new plant. Hsieh and Klenow find that this pattern holds across many industries and for formal and informal establishments alike. The authors conclude that this pattern reflects lower investments by Indian and Mexican plants in process-efficiency, quality, and market access, due to distortions that impede allocation of resources toward firms with higher productivity and higher growth potential.

Various Africa-specific papers also shed some light on the issue of firm growth. Harrison et al. (2013) find that when they control for geography, political competition and the business environment, formal African firms lead in productivity levels and growth. They conclude that Africa’s disadvantages arise from its weak business environment, including the lack of infrastructure and finance. Sandefur (2010) uses a panel of Ghanaian firms, spanning 17 years, to model firm dynamics. In contrast with the results for analysis of American and European firms, he finds that “entry of new firms and selection on observable characteristics, rather than within-firm growth,” dominates industrial evolution in Ghana. Teal (1999) also looks at firm-level data for Ghana. He finds that the removal of high levels of protection combined with substantial real devaluations have changed the environment in which Ghanaian manufacturing firms operated in the 1990s. Teal finds that the rate of job creation in Ghana's manufacturing sector is highest in medium-sized firms and that small firms do not grow more rapidly than larger firms. Hallward-Driemeier et al. (2010) find that African firms face enormous uncertainty with regard to the policy environment. As such, they argue that firms face deals rather than rules—that policies are enacted in a firm-specific manner even if in theory, all firms in a country are subject to the same policies. Consistency in policy implementation improves with stronger institutions and better governance.

In this paper, we build on the work of Hsieh and Klenow (2012). Using data from 41,000 firms across 119 countries, we examine the relationship between size and age at the firm level, with a special focus on African firms.<sup>3</sup> We find that African firms, at any age level, tend to be systematically smaller than firms in other regions of the world. We look at the impact of business environment measures as well as certain types of firm characteristics. These include access to as finance, infrastructure, legal rights, 3 “African” refers to Sub-Saharan Africa only, throughout this paper.
Sánchez-Martín, Escribano and de Arce, 2014. “How regional integration and transnational energy networks have boosted FDI in Turkey” framework and a series of positive externalities from which not only EU companies but also investors from other countries expect to benefit.

By sector, following the beginning of the EU accession negotiations, the largest foreign investments in Turkey have taken place in financial intermediation (47% of total FDI inflows in 20052008), transport, storage and communications (21%), and wholesale and retail trade (6%). This gives us the intuition that services and market oriented FDI dominates the recent inflow upsurge, which is in line with the findings in Esiyok (2011).

**Figure 5. Evolution of FDI flows to Turkey by sector over the last decade**

|**Average FDI flows by sector, % over total**|**2002-2004**|**2005-2008**|**2009-2013**|
|---|---|---|---|
|Primary sector (including mining)|3.0%|1.2%|2.4%|
|Secondary sector|29.2%|17.2%|24.7%|
|Electricity, gas and water supply|8.6%|2.7%|24.7%|
|Construction|0.6%|1.5%|5.1%|
|Transport and communications|18.0%|21.1%|4.6%|
|Financial intermediation|15.6%|47.7%|26.3%|
|Other services|25.1%|8.5%|12.2%|
|Yearly average FDI flows in the period (US$ million)|905|11863|9921|

Source: authors’ calculations based on Central Bank of Turkey and Ministry of the Treasury data In 2009-2013 it is possible to observe a different sector distribution pattern, as FDI directed towards manufacturing in Turkey has recovered some of its relative weight, whereas financial intermediation and transport and communications have contracted sharply, and foreign investment in Turkish utilities and energy have surged, becoming the principal activity attracting foreign capital nowadays.

At this point, one may wonder which part of the huge amounts of foreign funds invested in Turkish financial intermediation and communications have been attracted by the privatization process. If the weight of privatization operations was large, that would mean that the rise on FDI may be a transient phenomenon after which Turkey will return to inflow levels of 0.5% to 1% of GDP. Most of the papers on privatization in Turkey are from the early 2000s (Karatas, 2001; Simga-Mugan and Yüce, 2003), which does not allow us to clarify the effects of the acceleration of reforms after 2005. Thus, we gather recent data and reports from the Turkish Privatization Administration and focus on operations involving FDI. According to the official statistics, except for 2001, FDI through privatization seems to represent just a small proportion of total inflows. For example, if we look at the top five FDI inflows in 2008, only the acquisition of _TekelSigara_ by a British company is a privatization operation.

17
# **6.4 Prediction 5: Heterogeneity by Effectiveness of the DPEP**

We now turn to the fifth prediction of the model, which states that the effect of the DPEP on women’s likelihood of being elected should be stronger in districts where the program was most effectively implemented (i.e., in districts where the DPEP led to greater improvements in the local economy and educational performance).

## **A. Night Luminosity**

In Table 5, we first estimate the impact of the DPEP on district economic growth as measured by district-year nightlight luminosity during post-DPEP years (1994-2013). We estimate this effect separately for districts that had above- versus below-median economic activity prior to the DPEP, proxied by the luminosity in 1994. We select 1994 because it is the first year for which the nightlights data is available and it is also the first year of DPEP implementation making any program effects unlikely. Columns (1) and (2) of Table 5 show the estimated program impacts on total light per square kilometer aggregated to the district level, whereas columns (3) and (4) repeat the analysis for the log of aggregate light per square kilometer in each district. The first stage is only statistically significant for belowmedian districts (columns (2) and (4)), with the DPEP eligibility criterion impacting the probability of treatment by 39.2 and 41.1 p.p., respectively—likely because the majority of treated districts belong to the below-median luminosity sub-sample. Both columns (2) and (4) show that the DPEP significantly increased economic growth in districts with belowmedian economic activity prior to the DPEP, with treatment increasing total light by 2.99 and total log light by 1.61, on average. These effects are significant and constitute an increase in district economic activity of 63.3-87.5 percent due to the program. Hence, the DPEP appears to have had a large impact on local economic activity in treated districts that were underperforming at the start of the DPEP.

> elections across Indian states. We compare elections results in districts that are exposed to a female leader at the village level for one or more years versus those that have no exposure. We find little evidence that exposure to women’s leadership at the local village level leads to differential effects of the DPEP on women winning state parliament elections. These results are available upon request.

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53

# Table 14: Crime and Inequality using OLS

## Property Crimes

## Violent Crimes

||Residential Burglary|VehicleTheft|AggravatedAssault|Rape|
|---|---|---|---|---|
|Ineqaliy<br>I PoiceStaion1.77<br>InequalityinPoliceStation|(o^0<br>1)**|1.03<br>(0.17)**|0.97<br>(0.08)**|0.76<br>(0.09)**|
|Constant|-3.37<br>(0.14)**|-6.78<br>(0.21)**|-3.96<br>(0.09)**|-5.95<br>(0.11)**|
|ProvinceDummies|YES|YES|YES|YES|
|Adjusted-R<sup>2</sup>|0.28|0.25|0.22|0.10|
|Observations|1066|1066|1066|1066|

Standard errors in parentheses. * significant at 5%; ** significant at 1%. Dependent variable is the log per capita crime rate. All explanatory variables are in logs. Crime rates equal to zero were replaced with the smallest value for that crime.

Table 15: Crime, Inequality and Mean Expenditure using OLS

||Propert|yCrimes|ViolentCri|mes|
|---|---|---|---|---|
||Residential<br>Burglary|Vehicle Theft|AggravatedAssault|Rape|
|**In**eqalit<br> olic Staion1.08<br>InequalityInPoliceStation|(0.I1)**|-0.23<br>(0.15)|0.93<br>(0.08)**|0.61<br>(0.10)**|
|MeanExpenditureInPolice<br>Station|1.05<br>(0.06)**|1.92<br>(0.08)**|0.06<br>(0.04)|0.23<br>(0.05)**|
|Constant|-11.05<br>(0.44)**|-20.77<br>(0.63)**|-4.39<br>(0.34)**|-7.66<br>(0.41)*|
|ProvinceDummies|YES|YES|YES|YES|
|Adjusted-R<sup>2</sup>|0.45|0.50|0.22|0.12|
|Observations|1066|1066|1066|1066|

Standard errors in parentheses. * signiificant at 5%; ** significant at 1%. Dependent variable is the log per capita crime rate. All explanatory variables are in logs. Crime rates equal to zero were replaced with the smallest value for that crime.
# **Brain Drain, Gain and Waste**

One of the most intensely debated issues in the migration literature involves the departure of highly skilled workers from poorer countries to wealthier ones. Often referred to as ‘brain drain’, loss of human capital in low-income countries that already suffer from relative skill shortages can have important negative development and growth implications. The main concern is that the positive externalities generated by high-skilled workers will disappear with their migration. The issue takes on an even more emotional dimension since the education of these migrants is funded through limited public resources and their move to high-income OECD countries implies a subsidy from the poor to the rich. Despite the extensive and old analytical literature (Bhagwati, 1979; Bhagwati and Hanson, 2009), empirical analysis has been relatively lacking. The first breakthrough was the construction of the bilateral migration databases for OECD destinations based on census data (Docquier and Marfouk, 2004, 2006; Dumont and Lemaître, 2004). These databases focused on migrant workers in the labor force, aged 15–65 generally, and disaggregated them by three education levels – primary, secondary and tertiary – since this was the most uniformly available human capital variable that could be used as a proxy for human capital and skill levels. Initially for the OECD countries, these data sets highlighted, for the first time, the extent of global skilled migration patterns.

There had been several extensions, starting with the gender dimension (Docquier et al., 2009, and Dumont et al., 2007). A more important issue is the age of migration, 34
**<u>Table 5: Statistics for the Study Area Drawn from Satellite Imagery</u>**

||<br>**Total**|**Urban**|**Rural**|**Source**|
|---|---|---|---|---|
|**Parcels**<br>|||||
|Mean parcel area (m<sup>2</sup>)<br>|544.04|524.27|699.00|LAIS|
|Mean parcel area (m<sup>2</sup>)<br>|419.56|399.15|565.11|LAIS|
|Property price (US$/m<sup>2</sup>)|52.06|55.96|21.55|LAIS|
|Distance to CBD (km)|6.01|5.56|9.55||
|Distance to tarred road (m)|475.48|391.76|1131.60|RF|
|Distance to primary school (m)|704.48|612.95|1421.83|SF|
|Mean building volume (m<sup>3</sup>)|475.00|486.13|387.77|SI|
|Median building volume (m<sup>3</sup>)|295.73|303.78|242.86|SI|
|No. of parcels|15545|13786|1759|LAIS|
|**Villages**|||||
|Share of area under buildings|0.18|0.20|0.05|SI|
|Mean building height (m)|3.66|3.70|3.42|SI|
|SD building height|1.56|1.60|1.27|SI|
|Number of villages|740|648|92||
|**Cells**|||||
|Size (km<sup>2</sup>)|||||
|Residential area (m<sup>2</sup>)|829361|840286|791580|LAIS|
|No. of residents employed in local establishments|1,111|1,329|360|EC|
|No. of cells|107|83|24||
|**Sector level**|||||
|No. of sectors|32|22|10||
|Size (km<sup>2</sup>)|||||

_Source_ : Data from LAIS, NISR establishment census (EC), NISR population and housing census (PC), NISR school shapefile (SF), RTDA road network shapefile (RS), and stereo satellite imagery (SI). _Note:_ The observations include parcels sold several times in 2013–16. After dropping duplicates the regression analysis was based on 12,592 unique parcels from urban areas.

19
16 We decompose our inequality measure to examine inequalities within and between racial groups in relation to crime.<sup>11</sup> Generalized Entropy inequality measures can be additively decomposed into a between and within-group component along the following lines:

# l0 = _[gi_ log( )] + _EIg,_

wherej refers to sub-groups, gj refers to the population share of groupj and **Ij** refers to inequality in group j. The between-group component of inequality is captured by the first term to the right of the equality sign. It can be interpreted as measuring what would be the level of inequality in the population if everyone within the group had the same (the group- average) consumption level _,uj._ The second term on the right reflects what would be the overall inequality level if there were no differences in mean consumption across groups but each group had its actual within-group inequality _Ii*_

## _Other explanatory variables_

We test the sensitivity of the results by introducing additional control variables that may be associated with crime levels. Population density may be an important Rama (2001), 38% of the unemployed stayed out of a job for more than three years in 1999. In any event, data on wages is not available for police station jurisdictions in South Africa. _"_ In South Africa, the census allows for five "population groups": African, White, Colored, Asian/Indian, and "other". Due to data availability limitations, we collapse these into three separate categories - African, White, and other - which we refer to throughout the paper as "racial groups."
# Box 1: Sovereign spreads are inversely related to sovereign ratings

The logarithm of spreads can be modeled as a function of sovereign ratings, a dummy that takes the value 1 if a country is considered investment grade, debt issue size and maturity (Cantor and Packer 1996, Eichengreen and Mody 1998, Kamin and von Kleist 1999): _Log(Spread) =_ α _+ β1(Investment grade dummy) + β2(Sovereign rating) + β3(Log(Issue size)) + β4(Maturity) + error_ (2) This model seems to work well in explaining the launch spreads of emerging market sovereign bonds issued during 2003-2005. All the coefficients have the expected sign and, except for issue size, are statistically significant. The adjusted R<sup>2</sup> ranges from 0.74 in 2003 to 0.88 in 2005 (box table 1)

# **Box figure 1: Relation between sovereign ratings and launch spreads**

<!-- Start of picture text --> 900 Interest spread, basis points<br>800 2003<br>700<br>600<br>500<br>400<br>Investment grade 2005<br>300<br>200<br>100<br>0<br>A+ A A- BBB+ BBB BBB- BB+ BB BB- B+ B B- CCC+<br><!-- End of picture text --> $100 million sovereign bond issue with a 7 years tenor. _Sources:_ Bondware, S&P, and authors’ calculations.

|**Box table 1: Regression results: relationship betw**|**een launch spread and so**|**vereign rating**|
|---|---|---|
|Dependent variable:Log (LaunchSpread)|2003|2005|
|Investment grade dummy|-0.69**|-0.76*|
||(0.33)|(0.38)|
|S&P sovereign rating (numeric equiv.)|0.16***<br>(0.05)|0.23***<br>(0.05)|
|Log(Issue size)|0.04|0.05|
||(0.13)|(0.08)|
|Maturity (yrs)|0.02*|0.03***|
||(0.01)|(0.01)|
|Number of observations|37|42|
|AdjustedR-squared|0.74|0.88|

_* significant at 10%; ** significant at 5%; *** significant at 1%. White robust standard errors are reported below coefficient estimates._ 4
2 over time into changes due to growth and inequality (Datt and Ravallion, 1992; Ravallion and Sen, 1996; Wodon, 1995; Essama-Nssah, 1997). This is potentially more interesting, but it does rarely provide sufficient evidence for generalization since only a few observations are typically available using these decompositions (N-1 observations at the country level for N surveys).

Two of the rare countries for which time series data have been available for analyzing the relationships between growth, inequality, and poverty over time are the United States and India. Yet, panel data techniques could be used for many other countries with only a few surveys provided one is willing to carry the analysis at the regional rather than national level. This is shown in this paper using five cross-sectional surveys from Bangladesh spanning the years 1983 to 1996. By constructing a regional panel of consumption, poverty and inequality measures for fourteen areas and the five survey years, we are able to analyze not only the impact of growth and inequality on poverty, but also the impact of growth on inequality. The results differ strikingly between urban and rural areas, and they can be used by policy makers to promote faster poverty reduction. Section 2 of the paper describes our method for estimating poverty lines and obtaining measures of consumption, poverty, and inequality in real rather than nominal terms. Section 3 shows the insights and limits of standard methods of analysis used for empirical work on single countries. Section 4 analyzes the relationships between growth, inequality, and poverty using a regional panel. By combining the panel estimates of section 4 with the output of a consistent macro-economic model, section _5_ gives simulations of the reduction in poverty which could be achieved under alternative sectoral growth patterns over the next ten years. A conclusion follows.

# **II Poverty lines and welfare measures**

# _I. 1 Regional poverty lines_

To analyze the relationship between growth, inequality, and poverty one needs first to obtain good measures of these variables. Poverty lines must be estimated for obtaining poverty measures. In a
the CEE and Baltic countries have made the most progress in establishing independent regulators, while many of the CIS countries have made the least.

Figure 4 disaggregates the infrastructure index along five sectoral dimensions—electric power, roads, railways, telecommunications, and water and wastewater—and assesses the cumulative reform progress in each of the ECA countries. On average for the region, progress has been most pronounced in the sectors of telecommunications and electrical power. Often this will reflect a mix of commercialization, deregulation and privatization of national telecom companies. The index shows little or no progress in utility and infrastructure reform in Turkmenistan, Tajikistan Belarus, Uzbekistan, and Kyrgyzstan, with most progress made in Hungary, Poland, the Czech Republic, Estonia and Romania.

**Figure 4: Infrastructure Reform, by country and Sector, 2004**

<!-- Start of picture text --> 20<br>16<br>12<br>8<br>4<br>0<br>power rail roads tele water<br>Turkmenistan Belarus Tajikistan Kyrgyzstan Uzbekistan Ukraine Azerbaijan FYROM SaM Albania Moldova Armenia BiH Georgia Kazakhstan Russia Lithuania Slovak Republic Croatia Latvia Bulgaria Slovenia Czech Republic Romania Estonia Poland Hungary<br><!-- End of picture text --> _Source:_ EBRD (2004). The scale ranges from 0 to 20, representing the cumulative progress for each country on the five indicators, which individually range from 1 to 4.3—see Annex 1.

In the telecommunications sector, fixed-line services are still quite underdeveloped in most economies. This has given rise to faster growth of, and stronger competition in, the mobile services sectors, especially in CEE and Baltic countries, followed by SEE. In the rest of the FSU, mobile penetration rates fall short of fixed line services. In many of the latter countries, independent telecom regulators have yet to be established. The incumbent fixed-line operator may oppose interconnection agreements; tariffs are frequently low and distorted, and crosssubsidies between different types of calls and customers continue to be prevalent. The least progress has been made in the rail, road and water sectors. Only some CEE countries, (e.g., Poland, Hungary, and Croatia) have introduced private sector participation through toll roads. Reforms in the railway sector are also at an early stage in terms of private sector participation, 9
Chapter 2 11

# Figure 1 Transport Ton-Km per $ of GDP (Road, Rail & Water)

( Area, 000 Km2 <!-- Start of picture text --> USSR (22,272) 359<br>Poland (305) 0.86<br>CSFR (125) 0.82<br>China (9,597) 0.78<br>Canada (2,305) 0.74<br>Bulgaria (111) 0.72<br>USA (9,167) 0.64<br>Hungary (92) 0.58<br>India (2,973) 0.51!<br>Yugoslavia (255) 0.48.<br>Spain (499) 0.37<br>Holland (34) 0.34<br>Sweden (412) 0.32<br>Belgium (30) 0.32<br>W. Germany (244) 0.28<br>U.K. (242) 0.26<br>Italy (294) 0.23<br>France (546) 0.22<br>Austria (83) 0.21<br>0 1 2 3 4 5<br>Tonne-Km per $ of GDP<br><!-- End of picture text --> GDP Is Purchasing Power Adjusted