rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 10. ECA: Investment decomposition, 2010-15**

_After the global financial crisis, public investment growth slowed or turned negative across the region. In Central Europe, the slowdown in investment was driven mainly by weak manufacturing sector investment_ .

# **A. Contributions to investment growth**

# **B. Contribution to investment in Central Europe**

<!-- Start of picture text --> Percentage points Public Private Billion EUR Others Real estate<br>15 200 ICT Construction<br>10 Manufacturing Agriculture<br>5 150<br>0<br>100<br>-5<br>-10<br>50<br>Commodity Commodity Russia 0<br>exporters excl. importers<br>Russia<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Sources: Haver Analytics, International Monetary Fund, World Bank. A. Investment growth rates are growth rates of subgroup aggregated gross fixed capital formation in constant 2010 U.S. dollars.

B. EU4 (Bulgaria, Hungary, Poland, Romania). Sectorial allocation of investment is not available for other countries.

In general, in _commodity-importing EMDEs_ in the region _,_ investment financing became difficult to obtain from domestic banking sectors that were still healing from the crisis and pre-crisis credit booms (Hungary, Moldova, Serbia). The 2012-13 debt crisis and subsequent weak growth prospects in the Euro Area weighed on investor sentiment. Weak trade growth and lower capital inflows reduced prospects for strong investment returns and increased financing costs. Net capital inflows exceeded 10 percent of GDP before the crisis but have been negative since 2013 in the Central and Southeastern Europe. Large foreign currency-denominated debt amplified the damage to the banking sector (EBRD 2015a). In some countries, this was compounded by policy uncertainty and lack of public investment (Figure 10). The recovery in investment in commodityimporting economies has been gradual since 2013, despite support from accommodative monetary and fiscal policies in some countries, and sharply lower oil prices that lifted business confidence and real incomes.

In _commodity-exporting EMDEs_ , the global financial crisis-related fiscal stimulus supported double-digit investment growth in 2010. Investment growth remained robust until 2013, but slowed sharply once oil prices started sliding in 2014. Since mid-2014, investment has contracted year-on-year in every quarter, weighed down by the following factors: the unfolding conflict in Ukraine, intermittent border tensions in the Caucasus, international sanctions that heavily restricted access to finance in Russia, a severe terms-of-trade shock that hit energy exporters (Azerbaijan, Kazakhstan, Russia), and contracting public sector investment. Neighboring countries suffered from spillover effects, including weaker trade, remittances, and FDI (World Bank 2016c).

21
**_<mark>Caveats.</mark>_** <mark>Our proposed Nexus measure only captures an association between financial sector</mark> and sovereign health. It is silent on the direction of causality and does not disentangle potential channels of transmission (including domestic and global channels).

- 3.4. Reverse stress test: Haircuts on sovereign securities to breach minimum bank capital

Severe sovereign distress is often accompanied by haircuts. To gauge banks’ risk exposure to sovereign haircuts, we calculate for each banking system in our sample the required haircut to government securities that breach the 4.5% CET1 capital buffer, assuming other sources of capital are unavailable.<sup>10</sup> This is a strict approach since we assume only the highest quality capital (CET1) is accessible. We calculate this haircut as follows: Haircut� �∑������CET1 ratio�,� �4.5%�∗RWA�,���∑���� Holdings Government Sec�,� (2) where subscripts _c_ and _i_ represent country _c_ and bank _i,_ respectively, and _N_ the total number of banks in country _c_ . _CET1 ratioi,c_ , _RWAI,c_ , and _Holdings Government Seci,c_ are the Common Equity Tier 1 ratio, the Risk Weighted Assets, and the total amount of Holdings of Government Securities reported by bank _i_ in country _c_ . 4.5% is the minimum CET1 ratio required in Basel III.

We find that the most vulnerable banking systems in MENA and SAR would require historically plausible haircuts of less than 20% to erode CET1 capital buffers ‐‐ these banking systems are thus particularly vulnerable to sovereign debt restructurings (Figure 6B). According to IMF (2012), in practice, the range of debt relief as a result from sovereign debt restructurings has varied from an estimated 5% net present value reduction in Dominican Republic (2005) to nearly 90% in Iraq

> 10 Our sample covers 474 commercial banks in 33 EMDEs that reported CET1 ratios, risk‐weighted assets, and holdings of government securities. Information is as of 2016, except for Georgia, Jordan, Eswatini (2015), and Cambodia, Kazakhstan, Kenya, Senegal, Tanzania (2014). The source of the information is Fitch.

24
**Table 1: Did you return (1) or are you still forcibly replaced?**

**-** **<u>Table: Returned (Y/N) Probit Baseline (August 2014)</u>**

||(1)|(2)|(3)|
|---|---|---|---|
||All|Head&Spouse|IDP only|
|Female (d)|0.041|0.027|0.008|
||(0.064)|(0.027)|(0.058)|
|Age|-0.001|0.001|0.000|
||(0.002)|(0.001)|(0.002)|
|Higher Education (d)|-0.146<sup>**</sup>|-0.034<sup>*</sup>|-0.232<sup>***</sup>|
||(0.069)|(0.017)|(0.072)|
|Married (d)|-0.031|0.002|-0.057|
||(0.069)|(0.032)|(0.059)|
|Songhai ethnicity (d)|0.131<sup>**</sup>|0.020|-0.101<sup>*</sup>|
||(0.065)|(0.027)|(0.059)|
|Kidal region of origin (d)|<br>0.552<sup>***</sup>|<br>0.250|<br>0.239<sup>***</sup>|
||(0.077)|(0.164)|(0.048)|
|HH size (August 2014)|<br>-0.034<sup>***</sup>|<br>-0.008<sup>**</sup>|<br>-0.039<sup>***</sup>|
||(0.009)|(0.004)|(0.008)|
|Member tribe dead in crisis (d)|-0.243<sup>***</sup>|-0.020|-0.222<sup>***</sup>|
||(0.062)|(0.022)|(0.079)|
|HH members dead in crisis (d)|-0.172|-0.030<sup>**</sup>|-0.290<sup>*</sup>|
||(0.105)|(0.015)|(0.151)|
|HH members behind (d)|0.141<sup>**</sup>|0.020|0.054|
||(0.067)|(0.032)|(0.057)|
|HH head left behind (d)|0.186|-0.034<sup>**</sup>|0.113|
||(0.135)|(0.015)|(0.080)|
|HH spouse left behind (d)|0.461<sup>***</sup>|0.307|0.221<sup>***</sup>|
||(0.112)|(0.301)|(0.042)|
|Northern Mali safe (d)|<br>-0.047|<br>-0.034<sup>**</sup>|<br>-0.069|
||(0.077)|(0.017)|(0.078)|
|Safe at home (d)|-0.016|-0.037|0.010|
||(0.072)|(0.033)|(0.064)|
|Police issues (d)|0.327<sup>***</sup>|0.116<sup>**</sup>|0.174<sup>***</sup>|
||(0.065)|(0.054)|(0.053)|
|>1 transfers before settling (d)|<br>0.270<sup>**</sup>|<br>0.108|<br>0.158<sup>**</sup>|
||(0.126)|(0.110)|(0.065)|
|Asset index above median (d)|0.463<sup>***</sup>|0.280<sup>***</sup>|0.249<sup>***</sup>|
||(0.054)|(0.075)|(0.074)|
|Have received assistance (d)|-0.367<sup>***</sup>|-0.164<sup>**</sup>|-0.111<sup>**</sup>|
||(0.064)|(0.078)|(0.057)|
|Work during displacement (d)|<br>-0.304<sup>***</sup>|<br>-0.032<sup>*</sup>|<br>-0.439<sup>***</sup>|
||(0.062)|(0.019)|(0.083)|
|Observations|470|233|306|
|Pseudo R^2|0.50863|0.54966|0.38677|

Marginal effects; Standard errors in parentheses Robust SE.

Asset index above median is computed using information about asset ownership in August 2014. Source: LDPS 2014-15 13
WPS3825

# International Benchmarking of Lesotho’s Infrastructure Performance

## Željko Bogetić

Abstract: This note provides a preliminary benchmarking of infrastructure performance in Lesotho in four major sectors––electricity, water and sanitation, information and communication technology, and road transportation––against the relevant group of comparator countries using a new World Bank international data base with objective and perception-based indicators of infrastructure performance from over 200 countries. The results of the benchmarking are revealing of several major, comparative deficiencies in infrastructure performance in Lesotho: (i) extremely low access to electricity and its affordability; (ii) poor coverage, quality, and the cost of local (non-cellular) telephony; and (iii) poor quality of roads. Infrastructure service delivery in electricity, telephony, and roads is well below what would be expected, on average, for a country in Lesotho’s income group. In these sectors, Lesotho also compares unfavorably with many other geographical country groups. Unless addressed, such infrastructure shortfalls are likely to adversely affect the welfare of Lesotho’s poor, and the cost competitiveness and growth prospects of a range of economic sectors (e.g., tourism, trade) that depend critically on a stable and competitive supply of basic infrastructure service. They could also affect the speed and quality of Lesotho’s regional economic integration within the SACU sub-region with attendant consequences for the longterm growth of regional trade and real output. By contrast, Lesotho’s performance is solid in the access to improved water and sanitation, in the aggregate and in both rural and urban areas. Finally, this benchmarking, combined with more in-depth, sector analyses, could provide policymakers in Lesotho a useful guide to the areas of infrastructure performance requiring attention.

## JEL classification: H54, H, R, O, O57

World Bank Policy Research Working Paper 3825, January 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Authors: Bogetić is a lead economist at the World Bank, Africa Region (AFTP1), Washington D.C. (Email: zbogetic@worldbank.org). Atsede Aemro-Selassie provided excellent research assistance. Helpful comments from Jane Walker, Andrew Macoun and Gylfi Palsson are gratefully acknowledged.

1
**Table E5: Relationship between food security measures and weight-for-age z-scores (Bangladesh): Limited Controls Version**

|VARIABLES|(1)|(2)<br>Weight-for|(3)<br>-age z-scores|(4)|
|---|---|---|---|---|
|HFIAS Score|-0.162***||||
||(0.009)||||
|Child DDS||-0.244***|||
|||(0.010)|||
|FCS|||0.166***<br>(0.010)||
|MDDS||||0.098***<br>(0.011)|
|Rural|-0.298***|-0.452***|-0.254***|-0.296***|
||(0.039)|(0.039)|(0.040)|(0.042)|
|Rajshahi|0.034|0.078**|0.044|0.043|
||(0.034)|(0.034)|(0.034)|(0.036)|
|Khulna|0.217***|0.273***|0.179***|0.221***|
||(0.042)|(0.041)|(0.042)|(0.045)|
|Barisal|0.065|0.061|0.021|0.031|
||(0.042)|(0.042)|(0.042)|(0.046)|
|Dhaka|0.130***|0.190***|0.079***|0.143***|
||(0.030)|(0.030)|(0.031)|(0.033)|
|Sylhet|-0.088**|-0.134***|-0.110***|-0.103***|
||(0.037)|(0.036)|(0.037)|(0.040)|
|Chittagong|0.110***|0.099***|0.110***|0.103***|
||(0.033)|(0.032)|(0.033)|(0.035)|
|Season 2|-0.070***|-0.034|-0.105***|-0.085***|
||(0.023)|(0.023)|(0.023)|(0.025)|
|Season 3|-0.019|-0.024|-0.009|0.006|
||(0.023)|(0.023)|(0.023)|(0.025)|
|Constant|-1.451***|-1.338***|-1.463***|-1.462***|
||(0.047)|(0.047)|(0.047)|(0.050)|
|Observations|11,556|11,556|11,556|10,144|
|R-squared|0.044|0.075|0.043|0.027|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 HFIAS Score is household food insecurity access scale, child DDC is the dietary diversity score constructed for each child, FCS is the food consumption score and MDDS is the mother’s dietary diversity score.

47
_Table 7: Determinants of applicant’s CV being clicked on_

|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|
|---|---|---|---|---|---|---|
|Female<br>0.0286***|0.0230*|0.0234**|0.0259**|0.0285**|0.0309**|0.0313**|
|(0.0100)|(0.0118)|(0.0117)|(0.0116)|(0.0122)|(0.0121)|(0.0136)|
|Accounting|||-0.1244***||-0.1216***|-0.1212***|
||||(0.0189)||(0.0184)|(0.0185)|
|Marketing|||<br>-0.0636***||<br>-0.0742***|<br>-0.0739***|
||||(0.0202)||(0.0194)|(0.0195)|
|IT|||<br>0.2084***||<br>0.1515***|<br>0.1504***|
||||(0.0279)||(0.0324)|(0.0325)|
|Istanbul Asia||||||<br>0.0096<br>(0.0183)|
|Ankara||||||0.0108|
|||||||(0.0220)|
|Total application size<br>(100)|||||||
|Female * Istanbul Asia||||||-0.0152|
|||||||(0.0155)|
|Female * Ankara||||||<br>-0.0078<br>(0.0191)|
|Female * Acct|||-0.0015||||
||||(0.0085)||||
|Female * Mrkt|||<br>-0.0189*<br>(0.0100)||||
|Female * IT|||-0.0250*||||
||||(0.0149)||||
|Female * Total applicatio|n size|||-0.0011*<br>(0.0006)|-0.0011*<br>(0.0007)||
|Vacancy and CV sectors|match|0.2004***||<br>0.1945***|<br>0.0470**|0.0474**|
|||(0.0200)||(0.0201)|(0.0225)|(0.0225)|
|Experience (in months)|0.0008|<br>0.0009|0.0005|<br>0.0008|<br>0.0005|<br>0.0005|
||(0.0011)|(0.0011)|(0.0010)|(0.0010)|(0.0010)|(0.0010)|
|Age<br>-0.0008|<br>-0.0067|<br>-0.0071|<br>-0.0037|<br>-0.0083|<br>-0.0050|<br>-0.0046|
|(0.0101)|(0.0130)|(0.0128)|(0.0127)|(0.0128)|(0.0126)|(0.0127)|
|<br>Objective beauty|<br>0.0310|<br>0.0309|<br>0.0194|<br>0.0324*|<br>0.0218|<br>0.0222|
||(0.0193)|(0.0191)|(0.0189)|(0.0191)|(0.0189)|(0.0190)|
|Subjective beauty|-0.0007|-0.0057|-0.0033|-0.0052|-0.0026|-0.0022|
||(0.0087)|(0.0086)|(0.0084)|(0.0085)|(0.0084)|(0.0084)|
|Total application size (100|<br>)|||<br>-0.0030***|<br>-0.0024***|<br>-0.0029***|
|||||(0.0010)|(0.0008)|(0.0009)|
|Constant<br>0.3290|0.4207|0.3996|0.3985|<br>0.4489|<br>0.4424|<br>0.4302|
|(0.2714)|(0.3173)|(0.3127)|(0.3093)|(0.3112)|(0.3080)|(0.3100)|
|N.obs.<br>10748|10748|10748|10748|10748|10748|10748|
|R-<br>0.001|0.001|0.025|0.047|0.030|0.051|0.051|
|squared|||||||

**Notes:** Models 1 to 7 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses.  Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant’s CV is clicked on. Variable _Female_ takes on the value 1 if applicant is female, 0 otherwise. Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Variables _Accounting_ , _Marketing_ and _IT_ denote the occupational clusters of vacancies (and so of applicants), and the baseline category is sales occupations. Variable _Total application size_ denotes the total number of applications for the vacancy. Symbols ∗∗∗, ∗∗, and ∗indicate significance at the 1%, 5% and 10% level, respectively.

18
# **2 Motivating facts**

In this section I look at some motivating empirical evidence for the connections between external debt before the 2008-2009 crisis, the declines in stock prices, capital outflows, and the declines of GDP during the crisis. The set of countries is emerging markets with quarterly data. External debt data are from the Joint External Debt Hub-JEDS. The proxy for capital outflows is the decline in countries’ external debt position. Data for stock indices are from Thomson Reuters Datastream, and data for GDP are from the International Financial Statistics.

<!-- Start of picture text --> BelarusArmenia<br>Croatia<br>Moldova<br>IndiaColombiaIndonesiaEl SalvadorSlovak RepublicJordan<br>BrazilEgyptThailandPeruEcuadorUruguay Kazakhstan<br>Costa RicaChile ArgentinaIsraelUkraineTunisia<br>Mexico MalaysiaTurkey Hungary<br>South Korea<br>Russia Bulgaria<br>Czech RepublicPoland Lithuania<br>Estonia<br>Slovenia Latvia<br>0 50 100 150<br>Gross external debt (Q1/08) as % of GDP<br>5<br>0<br>−5<br>−10<br>Changes in external debt (Q1/09−Q3/08), % of GDP −15<br><!-- End of picture text --> Figure 2: External debt pre-crisis and capital outflows Crisis data indicate significant negative impacts of external debt pre-crisis on capital outflows and output during the crisis. However, the relationship between capital outflows and declines in stock prices is less significant. Figure 2 shows a positive relationship between external debt in quarter I, 2008 and the decline in external debt position between quarter III of 2008- the time Lehman Brothers collapsed- and quarter I of 2009, for a set of 36 emerging countries. External debt and changes in external debt are measured as percentages of 2007 GDP. Negative numbers indicate shrinkages of external debt (i.e. capital repatriation). It is statistically significant that countries with higher levels of external debt pre-crisis (i.e. high _φ_ ) see larger declines in external debt level (the t-statistic is -4.03).

When it comes to changes in stock indices, Figure 3 indicates a positive relationship between financial outflows and the declines in stock indices for 30 emerging countries. However, it is not statistically significant (the t-statistic is 1.44).

6
beyond transitional dynamics). If the model exhibits a balanced-growth path, the growth rate of each sector _y_ ˆ _i_ and of the economy will be exclusively determined by the growth rates of productivity in all the different sectors of the economy (the<sup>_g_</sup> _i_<sup>s). Characterizing</sup> the balanced growth path of the model is beyond the scope of this paper; nevertheless, equation (8) is valid both during transitional dynamics and in balanced growth.

Our assumption that poverty changes are only a function of the growth rate of real ˆ labor income corresponds to assuming that _h_<sup>ˆ</sup> =ψ ( ω ) , where _h_<sup>ˆ</sup> is the growth rate of poverty. In the empirical section of the paper, we will estimate the parameters of the ˆ ˆ linearized version of this relation _h_ = γ 0 +γ 1 ω as our benchmark case, but we will also consider some non-linear relations in our robustness analysis.

# **III. Empirical Evidence**

Our empirical analysis consists of two related sections. In the first, we address the connection between the pattern of growth and poverty alleviation by disaggregating growth into its sectoral components and examining their corresponding effects on poverty. This is the traditional approach, and, thus, it allows us to place our analysis in the context of the received literature. The second empirical section modifies the sectoral analysis by introducing labor intensity as the source of the differential impact of sectoral growth on poverty reduction. This approach is derived from the theoretical model and, thus, establishes the link between theory and empirics in the paper.

## **Data and sample**

Our sample consists of a cross-section of developing countries with comparable measures of poverty changes, disaggregated value-added growth rates at 3- and 6-sector levels, and unskilled employment at the same levels of disaggregation. In practice, our dataset is the result of combining the Kraay (2005) database on poverty spells,<sup>3</sup> World

> 3 The Kraay database results from processing income distribution data for a large number of developing countries.  In turn, its source is the collection of household survey data estimated from primary sources and made comparable across countries by Martin Ravallion and Shaohua Chen at the World Bank.  For details, see Kraay (2005).

8
Table 1 shows the average farm size (self-cultivated own and rented-in) for the whole sample as well as for each province. The second column shows the average land size in the sample of farmers who had non-negative crop incomes in both years. It is clear in the table that farm size is smaller in the Java provinces than outside Java. The average farm size is less than 0.5 ha in the Java provinces (Column 2), in contrast to non-Java where the average farm size is above 1 ha.<sup>6</sup> If we use the sample of farmers who had non-negative crop incomes in both sample years, the average size increases marginally, except for North Sulawesi in our sample. In the analysis below, I check whether small/marginal and larger farmers behave differently by using 0.6 ha as the threshold size (i.e., many of the farmers in our Java sample fall below 0.6 ha due to skewed landholding distributions).

# **3.3 Labor Markets and Wages**

Village-level wages for both agricultural and non-agricultural works are estimated from individuallevel employment data in 2007 and 2010. Both rounds used the identical module to record job type, wage rate, number of days worked, duration, contract type, etc. for each individual employment experience in the past year. Individual frequently had several employment spells in the previous year and this method was applied to both agricultural and non-agricultural employment.

The data processing followed a few steps. First, we computed the daily wage for agriculture and the monthly wage for non-agricultural work at the individual level. Second, the distribution of the householdlevel average wages was obtained. Third, we compared the average wages at the village level – both agriculture and non-agriculture – between 2007 and 2010. The analysis uses growth rate of the villageaverage wages by sector. Yamauchi (2011) used these household-level wage data to show that road infrastructure projects increased non-agricultural wages by connecting workers to employment opportunities outside the village (also increasing speed of transportation).

> 6 In addition to the observed difference in farm size, it is possible that soil quality significantly differs between Java and non-Java islands.

9
economic effect is small is—as we show—FDI comes to the more productive firms in the first place and hence once this effect is controlled for (which is only done by a few papers so far in the literature) further improvements in productivity are small.

**Table 4: Foreign ownership and firm productivity—Fully Owned Subsidiaries**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||∆ln(TFP)|∆<sup>2</sup>ln(TFP)|∆<sup>3</sup>ln(TFP)|∆<sup>4</sup>ln(TFP)|∆<sup>4</sup>ln(TFP)|
|∆ln(FO)|0.000<br>(0.003)|||||
|∆<sup>2</sup>ln(FO)||0.001<br>(0.003)||||
|∆<sup>3</sup>ln(FO)<br>∆<sup>4</sup>ln(FO)|||0.001<br>(0.003)|0.023***|0.022***|
|||||(0.003)|(0.003)|
|Observations|3008|2889|2765|1823|1055|
|Year Fixed Effects|Yes|yes|yes|yes|yes|
|Sector Fixed effect|Yes|yes|yes|yes|yes|
|Sector-Year Fixed effect|No|no|no|no|yes|

_Source:_ authors’ calculations based in the Orbis dataset for Turkey _Notes:_ The regressions are estimated by GLS. TFP is total factor productivity, computed using the WooldridgeLevinsohn-Petrin methodology (WLP). FO is transformed as (FO _/_ 100) + 1. FO is the share of foreign-owned equity. ∆<sup>_k_</sup> indicates the change between year _t_ and year _t_ − _k_ where _k_ = 1 _,...,_ 4. Standard errors clustered at the firm level are in parenthesis. *** , **, *, denote significance at 1%, 5%, and 10% levels.

All these regressions are estimated by feasible Generalized Least Squares (GLS). There is a large difference in the variance of the error terms across firms, so GLS is more efficient. We do this in two steps: first OLS estimation, and then we use residuals from the OLS estimation to calculate firm-specific standard errors which we then use to weight observations in the second step.

In spite of the firm level heterogeneity, sectors are not that different in terms of delivering this result. All our regressions in Table 1 and Table 3 have sector fixed effects and the last columns have sector-year fixed effects (both at 2 and 4 digit sectors). It is clear that the results of last two columns in both tables are very similar. To test the sectoral heterogeneity hypothesis further, we interact FDI 9