rafmacalaba/datause-encoder-data huggingface dataset:
9 as an additional mechanism to spur research in this area. There are smaller adverse repercussions from critiquing our own work.

# **_3.2. Cross-country Evidence_**

Thorsten Beck and Asli Demirguc-Kunt, and Ross Levine (2007) examine the relationship between financial development and the fraction of the population living on less than $1/day. For a cross-section of up to 68 developing economies, we use data on poverty averaged over the period 1980-2005. Thus, we use one observation per country. We average over this long time period to aggregate away any business cycle fluctuations or crises that might distort our assessment of theories that focus on the long-run relationship between the operation of the financial system and changes in the fraction of the population living below the poverty line. We look at both the relationship between finance and the level of poverty and the relationship between finance and the growth rate of poverty. The growth rate has statistical advantages because we can reduce the importance of country-specific factors. The growth rate also has conceptual advantages because then the analyses of poverty link directly with larger cross-country growth investigations. In any event, we find similar results when using the level or the growth rate of poverty. We simply present the regressions using the growth of poverty.

Figure 1 graphs the relationship between the growth rate of poverty and the level of financial development. The specification conditions on many country characteristics, including the initial level of poverty, initial income per capita, economic growth, population growth, the age distribution of the population, and measures of trade openness and inflation. Figure 1 provides the partial scatter plot, which is the two dimensional projection of the relationship between poverty growth and financial development, while conditioning on these other factors.
As is usually the case in this literature, earnings are defined as log of hourly wages, to take into account differences in intensive margin. Though significant, the difference in the intensive margin is smaller compared to other countries—women work on average eight hours per day, which makes them fulltime workers, while men work on average nine hours per day. Though this additional hour might not be significant in terms of daily productivity, it might be associated with a career path that involves more responsibility. Gender differences in pay can be observed for most of the groups defined by the covariates, as indicated in Tables A3a and A3b in the appendix.

The raw gender wage gap varies considerably along the earnings distribution. As opposed to what is observed for other high-income countries (Christofides, Polycarpou, and Vrachimis, 2013), the raw gap is larger in the center of the earnings distribution. The raw wage gap for men and women in the median is almost 35 percent, while the raw wage gap at the 10th and 90th percentiles is about 15 percent. In the next section, we analyze the possible factors determining the gender wage gap at each percentile applying the FFL decomposition methodology.

**Figure 3. Gender pay gap by percentile, 2011**

<!-- Start of picture text --> 0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>Percentile<br>Gender pay gap<br><!-- End of picture text --> _Source:_ Authors’ calculations using RLMS 1996, 2002, and 2011. _Notes:_ Percentage gender gap in earnings by percentile. Earnings measured as log-hourly wage.

# **4. Results**

The gender pay gap in the Russian Federation is one of the highest among high-income countries. Previous studies have found that most of the gender gap in pay remains unexplained when applying 11
diagonal matrix indexed over _i, j_ with diagonal elements equal to the value added shares of output **Z** . We then use **M** to provide a breakdown of the flow of value added across activities in the form of the matrix **V** .

Similar to the Leontief inverse matrix itself, the **V** matrix identifies the inputs of value added in each sector related to a unit of final demand. If we multiply **V** by the diagonal matrix **Y**<sup>**ˆ**</sup> whose non-zero elements are the vector of final outputs, the matrix yields a breakdown of economy-wide value added (the primary component of Gross National Product on a source basis). Similarly, if we multiply **V** by the diagonal matrix **X**<sup>**ˆ**</sup> whose non-zero elements are the national export vector, we can recover the value added content of exports **X** (both direct and indirect).

The **G** matrix and the **H** matrix give us the set of linkages, both direct and indirect, between value added across sectors. We provide an example for Italy in Tables 2 and 3 below. These are based on GTAP data, aggregated to 10 sectors as indicated in the tables. Starting with Table 2, we can see that 41.76 percent of value added was located in various commercial or market service activities (including for example financial services and ICT services). This follows from reading the bottom row of column S9, which provides the value added in this sector, broken down into the various sectors where these activities fed, ultimately, into final demand. At the same time, much of this actually served as intermediate inputs, so that in terms of final demand for commercial services, this accounted for 33.2 percent of value added (the last column in Table 2). Basically, on a cost basis, almost 10 percent of value added of the economy as a whole involves commercial services used as intermediate inputs by other sectors (the difference between the column and row totals for S9 in the table). If we focus on other machinery, in terms of sales for final consumption (again the last column in the Table) roughly 10 percent of total value added goes into output in this sector. However, only 6.56 percent of economy wide value added (or about two-thirds of the total) involves value added in the sector itself. The rest involved inputs from other sectors (again including market or commercial services, which account for roughly 26.5 percent of total value added costs in the other machinery sector).

We next turn to the export structure of Italy on a value added basis. From Table 3, market services account for 37.2 percent of Italy’s exports, in terms of the activity content of trade (last row of S9). What we mean by this is that for every e100 of value added contained in exports in 2007, e37.2 originated in the market services sector. However, if we focus on the value added contained in exports on a sector basis (for example how much value added was embodied in steel exports), we see that manufacturing is where Italy’s exports are concentrated. For example, 8
SOLOMON ISLANDS

- 2. Projections NRR-1 by 2040

|_YVo_|Pqpulatfon|DGP.<br>ratio|<br>Pwied|Smith<br>rate|C|Cm|TFR|INoe|go<br>Feml|in|xlO00|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|<br>324|98.2|1990-94<br>|3.54|42.3|6.9|6.13|64.3|66.4|44<br>|63<br>|
|1995|<br>386|94.0|1995-9|3.40|39.9|6.0|5.62|66.1|68.7|35<br>|48<br>|
|2000|<br>458|93.2|2000-04|3.21|37.2|5.1|5.11|67.8|71.0|29<br>|37<br>|
|200S|<br>537|90.7|2005-09|2.95|34.2|4.7|4.60<br>|69.0<br>|72.4|24<br>|32<br>|
|2010|<br>623|84.0|.010-14|2.71|31.4|4.3|4.09|70.2<br>|73.8<br>|20<br>|26<br>|
|2015|<br>713|76.9|2014-19|2.45<br>|28.5<br>|4.0<br>|3.58<br>|71.4<br>|75.3<br>|16<br>|21<br>|
|2020|<br>806|69.9|2020-24|2.16|25.4|3.7|3.07|72.6|76.8|12|16<br>|
|2025|<br>896|63.6|2025-49|1.52|18.9|4.0|2.28|75.6|80.3|6|8|
|2050|<br>1312|47.3|2050-74|<br>0.81|14.2|6.2|2.07|78.8|84.1|4|5|
|2075|<br>1608|57.5|2075-99|0.30|12.4|9.5|2.06<br>|80.5|86.4<br>|3<br>|4<br>|
|2100|<br>1733|70.2|2100-24|0.06|11.9|11.1|2.06|61.8|U.2<br>|2<br>|3<br>|
|2125<br>2150|<br>1769<br> <br>1734|72.5<br>73.6|<br>2125-49|0.03|11.8|11.4|2.06|82.2|88.9|2|3|

3. Age structure (percent): 1990 and 2025 <!-- Start of picture text --> 75 +<br>70.74<br>85-69<br>80.84<br>55.59<br>50-54<br>EFM 45.49<br>40-44<br>35-39<br>30.34<br>] §RR EmA ~~~~25-29 1U<br>I R2422 g7Ul ~~~20-24 R5R E<br>15-19<br>10-14<br>5-9<br>0-4<br>10 d 6 4 2 0 2 4 6 8 10 10 a 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> 4. Comparison of Country with Region and Income Group, 1985-90 <!-- Start of picture text --> TFR eo Growth rate<br>Income group Country Region Income group Country Region Income group<br><!-- End of picture text --> <!-- Start of picture text --> Country "egion Income group<br><!-- End of picture text --> 99
- (6) An **_unshared improved sanitation facility_** , an indicator of **_improved sanitation,_** is one that hygienically separates human excreta from human contact and is not shared with any other household.<sup>7</sup>

- (7) **_Sewerage_** implies that an improved sanitation facility is connected to a sewer system.

# **Geo-spatial covariates and population data**

In addition to the NWSS’s outcome data on the indicators of interest, a second category of data used for analysis was a suite of geo-spatial covariates that may be correlated with the indicators of interest, and thus partially explain observed spatial variation, allowing for more accurate predictions across each map. Geo-spatial covariates are gridded spatial data: each grid cell (or pixel) contains the value of a particular property. An initial set of spatial covariates were identified as potentially useful predictors of water and sanitation access levels, based on previous attempts to predict poverty in Nigeria (Gething & Molini 2015). This set of covariates is presented in Figure 2 and consists of (1) a vegetation index, (2) aridity, (3) land-surface temperature, (4) brightness of nighttime lights, and (5) estimated travel time to the nearest functioning water source. The spatial covariates may be described as follows:

- (1) _Vegetation index_ (Figure 2a). NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS, http://modis.gsfc.nasa.gov/) generates high-resolution satellite imagery on various measures of environmental conditions. This includes the Enhanced Vegetation Index (EVI), which measures reflectance in the green and red parts of the visible spectrum to provide a relative measure of the density of photosynthesizing vegetation in each pixel. These data were preprocessed to provide average values for the year 2015 in each 1x1 kilometer (km) pixel.

- (2) _Aridity_ (Figure 2b). The Consultative Group for International Agricultural Research (CGIAR) Consortium maintains high-resolution global raster climate data related to evapotranspiration processes and a rainfall deficit for potential vegetative growth. These are based on data from the WorldClim project (Hijman et al. 2005), and ultimately from

> 7 The global SDG indicator for sanitation, “percentage of population using safely managed sanitation services,” implies the use of an improved sanitation facility that is not shared with other households, and where excreta are safely disposed on site or transported and treated offsite. Unfortunately, at the time the FMWR commissioned data collection for the National Water and Sanitation Survey (NWSS), this indicator had not yet been defined, so data about excreta disposal or treatment were not collected.

5
depend on the validity of different auxiliary assumptions. Hence, they may yield (and in practice have yielded) different conclusions regarding fiscal sustainability. This section describes the various empirical approaches and attempts to explain and reconcile the various findings.

The tests are classified into two groups. The first involves tests derived from the NPG condition using the real interest rate as the discount rate. The second group of tests is based on the transversality condition using marginal rate of substitution as discount factor.

# Table I **Recent Studies of Fiscal Policy Sustainability**

|Study|Country/<br>Sample|Data Requirements|Auxiliarv<br>Assumptions|Test<br>Methods|Conclusions|
|---|---|---|---|---|---|
|Flavin-<br>Hamilton<br>(1986)|U.S.<br>1960-84|real primary surplus plus<br>seigniorage,<br>real stock of debt (at market<br>value)||Test stationary ol<br>fiscal deficit and<br>debt.|Both are<br>stationary,<br>implying<br>sustainable fiscal<br>policy.|
|Wilcox<br>(1989)|U.S.<br>1960-84|Market value of gov't debt;<br>ex-post real return on gov't<br>debt.|Debt follows<br>general ARIMA<br>process|Test whether<br>_discounted_debt<br>series is stationary<br>with mean zero.|Weak evidence<br>that discounted<br>debt is stationary,<br>yet its mean is<br>nonzero. Hence,<br>fiscal policy is nol<br>sustainable.|
|Trehan and<br>Walsh (1988)|U.S.<br>1890-1986|real government spending,<br>real interest payments,<br>real revenue, seigniorage|constant real<br>interest rate<br>G. T may be<br>stationary or<br>nonstationary|Test whether<br>fiscal deficit<br>inclusive of real<br>interest payments<br>is stationary.|Reject<br>nonstationarity,<br>implying fiscal<br>policy is<br>sustainable.|
|Trehan and<br>Walsh (1991)|U.S.<br>1960-84|Flavin-Hamilton dataset|Real return on<br>gov't debt is<br>strictly positive,<br>but need not be<br>constant.|Test for<br>stationarity of ()<br>real deficit<br>inclusive of real<br>interest or (2)<br>first-difference of<br>debt.|First difference ot'<br>stock of debt is<br>stationary,<br>implying fiscal<br>policy is<br>sustainable.|

21
enterprise reform, _American Economic Review_ 88, 422-427.

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- Lin, Chen, Ping Lin, Hong Zou. 2012. Does property rights protection affect corporate risk management strategy? Intra- and cross-country evidence. _J. of Corporate Finance_ 18, 311-30.

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# _Trade liberalization_

We use the Sachs and Warner (1995) data on the date of trade liberalizations, as updated by Wacziarg and Welch (2008), to determine the relationship between liberalization and export surges. Of our 92 episodes that take place in 82 countries, they have information on openness for 65 countries.

Sachs and Warner (1995) and Wacziarg and Welch (2008) construct a dummy variable for openness based on five specific trade-related policies. A country is classified as closed if it displayed at least one of the following characteristics: (a) an average tariff rates of 40 percent of more (TAR); (b) nontariff barriers covering 40 percent or more of trade (NTB); (c) a black market exchange rate that is depreciated by 20 percent or more relative to the official exchange rate (BMP); (d) A state monopoly on major exports (XMB); and (e) a socialist economic system (as defined by Kornai, 1992) (SOC).

Using these criteria, they classify the date of openness as the first year when none of these policies is in place. We compare the dates of the export surges with the dates of the liberalizations. Table 2 reports the results. We find that surges are more likely in open economies (36 percent of observations), or within five years of liberalizations (40 percent of observations). In 7 percent of the observations, the liberalization year and the episode year are identical, and they are within one year of each other in an additional 4 percent of observations.

While this is suggestive that openness is important for surges, a concern is that by the end of the period most countries are open, so perhaps liberalization in the surge countries simply reflects global trends. Table 3 compares openness between the 11
# **Risky Business**

**Political Instability and Greenfield Foreign Direct Investment in the Arab World**<sup>∗</sup>

Martijn Burger,<sup>♦</sup> Elena Ianchovichina,<sup>♣</sup> and Bob Rijkers<sup>♠</sup>

**Key words** : Foreign direct investment, Greenfield FDI, political instability, natural resources, tradables, non-tradables, Dutch disease.

**JEL codes** : F21, F23, P48, O13, O16

**Sector Board** : EPOL

> ∗ We would like to thank the IMF’s Middle East and Central Asia Department and, in particular, Alberto Behar, Harald Finger, and Gohar Abajan for sharing their high frequency data with us. We also benefitted from useful feedback from Shantayanan Devarajan, Aart Kraay, Beata Javorcik, Philip Keefer, Nada Choueiri, Kevin Carey, Aaditya Mattoo, Mary Hallward-Driemeier, Moshin Khan, and seminar participants at the World Bank and the International Monetary Fund. The research was supported by funding through the World Bank’s Knowledge for Change Program. The views expressed in this paper are entirely those of the authors; they do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> ♦ Martijn Burger is assistant professor at the Department of Applied Economics, Erasmus University, Rotterdam, Tinbergen Institute and Erasmus Research Institute of Management (ERIM), P.O. Box 1738, 3000 DR Rotterdam, the Netherlands, Tel: +31 (0) 10 4089579, Fax: +31 (0)10 4089141. E-mail: mburger@ese.eur.nl. URL: http://www.mjburger.net.

> ♣ Elena Ianchovichina is lead economist at the Chief Economist Office, Middle East and North Africa Region, the World Bank, 1818 H Street NW, Washington, DC 20433, USA, Tel: +1 202 458 8910, E-mail: eianchovichina@worldbank.org.

> ♠ Bob Rijkers is economist in the  Trade and International Integration Unit of the  Development Economics Vice Presidency, the World Bank, 1818 H Street NW, Washington DC 20433, USA, Tel: +1 202 473 0426, E-mail: brijkers@worldbank.org.
**Table 12: robustness**

|||Lower|Kling-Liebm<br>Lower|an bounds<br>|||Lee b<br>|ounds<br>||
|---|---|---|---|---|---|---|---|---|---|
||ITT|<br>with 0.1<br>diff.|<br>with 0.25<br>diff.|Upper with<br>0.1 diff.|Upper with<br>0.25 diff.|Lower w/o<br>controls|Lower w/<br>controls|Upper w/o<br>controls|Upper w/<br>controls|
||0.933**|0.6863***|0.1758**|1.3670***|1.8775***|0.8807**|1.0071***|1.2106***|1.2849***|
|Age at marriage|(0.01)|(0.000)|(0.042)|(0.000)|(0.000)|(0.018)|(0.005)|(0.001)|(0.000)|
||176|583|583|583|583|131|131|130|130|
||0.457**|0.3740***|-0.1049|1.0126***|1.4916***|0.5496|0.5496|0.9122**|0.9122**|
|Age at first birth|(0.05)|(0.000)|(0.291)|(0.000)|(0.000)|(0.188)|(0.188)|(0.016)|-0.02|
||299|600|600|600|600|148|148|147|147|
|Fraction of domains in|0.036*|-0.0518***|-0.0906***|-0.0002|0.0385***|-0.0480**|-0.0466**|-0.0147|-0.0133|
|which respondent's<br>opinion is heard|(0.08)|(0.000)|(0.000)|(0.985)|(0.001)|(0.040)|(0.040)|(0.515)|(0.541)|
||437|883|883|883|883|426|426|425|425|
|Fraction of domains in<br>which respondent cares|0.041**|-0.0420***|-0.0727***|-0.0012|0.0295***|-0.0398**|-0.0387**|-0.0119|-0.0113|
|about making the<br>decision|(0.03)|(0.000)|(0.000)|(0.905)|(0.004)|(0.029)|(0.029)|(0.542)|(0.551)|
||437|883|883|883|883|426|426|425|425|
||0.066*|0.0071|-0.0680***|0.1073***|0.1824***|0.0534|0.0534|0.0895*|0.0895*|
|Prevalence of economic<br>IPV|(0.06)|(0.669)|(0.000)|(0.000)|(0.000)|(0.309)|(0.309)|(0.066)|(0.066)|
||437|665|665|665|665|212|212|211|211|
||0.080**|0.0099|-0.0575***|0.0996***|0.1670***|0.0520|0.0520|0.0884*|0.0884*|
|Incidence of economic<br>IPV|(0.02)|(0.529)|(0.001)|(0.000)|(0.000)|(0.290)|(0.290)|(0.050)|(0.050)|
||437|665|665|665|665|212|212|211|211|

35
labor market before exiting the market and destroying whatever little jobs they do create.

Indeed, this creates questions about the focus of public policies on encouraging entrepreneurship, without adequate focus on the needs of existing enterprises. Why are some firms, industries and cities able to grow employment, while others seem to lag behind? If a large share of employment growth comes from incumbent firms, i.e. firms that survive over a period of time, then it will be worthwhile understanding the factors that allow firms to enter, survive and grow over time in cities. The next section will analyze some of the factors that might predict job growth in Ethiopian cities over time, and provide clues for a more robust multivariate analysis in section 4.

# **II.C: What Are the Predictors of Jobs?**

**Does location matter to NJC?** The net jobs created by cities vary significantly over time and space – it turns out that one of the major factors that seem to affect these distributions is proximity to the capital city – Addis. Cities closer to the capital might benefit from demonstration effects, knowledge spillovers, market access, and forward and backward linkages.

**Figure 15: City-level NJC (2000-2011) and distance from Addis**

<!-- Start of picture text --> 50,000<br>45,000<br>40,000<br>35,000<br>30,000<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>100 and less 101‐200 201‐300 300 and more<br>Distance from Addis (kms)<br><!-- End of picture text --> Source: LMMIS, CSA Figure 16 shows that sector location choice varies by the level of capital intensity – capita intensive firms clearly seem to locate closer to Addis. Capital-intensive sectors usually require access to good infrastructure, and this could be one reason why certain sectors are more attractive to Addis. However, the average distance-from-Addis is increasing for both high and medium capital-intensive firms – following the earlier logic, this might imply that cities closer to Addis might also have improved their infrastructure capacity to accommodate such sectors.

16