rafmacalaba/datause-encoder-data huggingface dataset:
While severe education deprivation considers any child not deprived if he/she ever went to school, the findings suggest the need to also consider the rate of those children not being currently enrolled while of school going age. Being a milder form of educational deprivation, this is important though in the analysis of school enrollment and drop out ages in different demographic groups.

<!-- Start of picture text --> Figure 11: National Level Share of Children of School-going-age Not in School<br>All Children<br>a) b) c)<br>Aggregate Not in School at School Rural - Urban Not in School at North - South Not in School at<br>going Age Deprivation - GHS School going Age Deprivation - School going Age Deprivation -<br>80%70%60% 80%70%60% GHS and MICS 80%70%60% GHS and MICS<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Wave 1_R Wave 2_R Wave 1_N Wave 2_N<br>Wave 3_R MICS 2016/2017_R Wave 3_N MICS 2016/2017_N<br>Wave 1 Wave 2 Wave 4_R Wave 1_U Wave 4_N Wave 1_S<br>Wave 3 MICS 2016/2017 Wave 2_U Wave 3_U Wave 2_S Wave 3_S<br>Wave 4 MICS 2016/2017_U Wave 4_U MICS 2016/2017_S Wave 4_S<br>Girls only<br>d) e) f)<br>Aggregate Not in School at School Rural - Urban Not in School at North - South Not in School at<br>going Age Deprivation - School going Age Deprivation - School going Age Deprivation -<br>80%70% GHS and MICS 80%70% GHS and MICS 80%70% GHS and MICS<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Wave 1_R Wave 2_R Wave 1_N Wave 2_N<br>Wave 1Wave 3Wave 4 Wave 2MICS 2016/2017 Wave 3_RWave 4_RWave 2_UMICS 2016/2017_U MICS 2016/2017_RWave 1_UWave 3_UWave 4_U Wave 3_NWave 4_NWave 2_SMICS 2016/2017_S MICS 2016/2017_NWave 1_SWave 3_SWave 4_S<br>Boys only<br>g) h) i)<br>Aggregate Not in School at School Rural - Urban Not in School at North - South Not in School at<br>going Age Deprivation - GHS School going Age Deprivation - School going Age Deprivation -<br>80% GHS and MICS GHS and MICS<br>70% 80% 80%<br>60% 70% 70%<br>50% 60% 60%<br>40% 50% 50%<br>40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Wave 1_R Wave 2_R Wave 1_N Wave 2_N<br>Wave 1 Wave 2 Wave 3_R MICS 2016/2017_R Wave 3_N MICS 2016/2017_N<br>Wave 3 MICS 2016/2017 Wave 4_R Wave 1_U Wave 4_N Wave 1_S<br>Wave 2_U Wave 3_U Wave 2_S Wave 3_S<br>Wave 4 MICS 2016/2017_U Wave 4_U MICS 2016/2017_S Wave 4_S<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4 and MICS 2016/17.

Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

24
small and insignificant (columns 4-6). In the models explaining non-resource FDI, terrorism, political terror, and assassinations are not significantly different from 0. The effect of conflict (BRD) remains strongly negative and significant at the 0.1% or 1% level. The type of conflict moderator is positive and significant at the 5% level. Hence, nationwide political conflicts have a strong negative effect on nonresource FDI, whereas localized conflicts have a negligible effect on this type of FDI. **Table 3 Effect of Political Conflict, Terrorism, Political Terror and Assassinations on Greenfield FDI**

|||Dependen<br>Total FDI<br>|t Variable:<br>|Log greenfie<br>Reso<br>|ld FDI (in U<br>urce-related<br>|SD million<br>FDI<br>|s), LSDVC E<br>Non-Re<br>|stimation<br>source relat<br>|ed FDI<br>|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|BRDt-1(ln)|-0.133+|-0.125+|-0.112|0.037|-0.004|-0.036|-0.254***|-0.216**|-0.226**|
||(0.075)|(0.073)|(0.072)|(0.107)|(0.103)|(0.083)|(0.073)|(0.071)|(0.070)|
|BRD (ln)*<br>localizedt-1|0.034|0.039|0.034|-0.074|-0.053|0.058|0.202*|0.198*|0.202*|
||(0.096)|(0.096)|(0.096)|(0.138)|(0.137)|(0.113)|(0.082)|(0.094)|(0.094)|
|Terrorismt-1(ln)|0.074|||0.031|||0.098|||
||(0.072)|||(0.102)|||(0.070)|||
|Political terrort-1||0.186|||0.632**|||-0.006||
|||(0.166)|||(0.236)|||(0.139)||
|Assasinationst-1|||0.006|||0.035|||0.004|
||||(0.063)|||(0.090)|||(0.062)|
|Observations|707|707|707|707|707|707|707|707|707|
|Number of<br>countries|90|90|90|90|90|90|90|90|90|
|Economic<br>controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Note: Political conflict proxied by battle-related deaths. Bootstrapped standard errors are in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.10

# **Within-sector heterogeneity**

The analyses so far distinguish between non-resource and resource industries but there might be substantial within-sector heterogeneity that may affect our results. There are substantial differences between the manufacturing and service industries, for example, in terms of sunk costs and labor intensity. There might also be considerable heterogeneity in the natural resource category. Investments in the hydrocarbon industry (oil, gas and coal) might not be sensitive to political violence because they occur in remote locations (offshore). Recognizing these differences, we re-estimate the model using more disaggregated data on manufacturing and services FDI flows and hydrocarbon and nonhydrocarbon FDI flows.

28
SSA, larger enterprises are relatively more likely to have been started as large enterprises than to have grown into large enterprises (Soderbom and Teal (2004); Sleuwaegen and Goedhuys (2002)). While Tybout (2000) dispels misconceptions that developing countries exhibit less competition or turnover than industrialized countries, he acknowledges that adverse investment climate conditions can pose greater obstacles for small enterprises. Beck et al. (2005) do find evidence that small enterprises can be particularly hurt by limited property rights and access to finance.

Van Biesebroeck (2005) based on a rich enterprise-level panel data highlights differences in the evolution of the size and productivity between firms in nine African countries and other developed countries. He observes that while surviving small American firms reach industry size and productivity levels in a relative short period of time, large and productive African firms contribute disproportionately to aggregate productivity growth. This raises concerns that while employment growth clearly has benefits, that it is occurring at higher rates in small firms may imply that resources are not being allocated efficiently and that productivity growth will suffer as a result. Using a larger set of countries, the results here indicate that smaller enterprises’ lower contribution to overall productivity is of particular concern in SSA compared to other low income regions.

_b) Investment climate and enterprise performance in SSA_ A number of the authors cited above conjecture that the broader investment climate in which enterprises operate could play a critical role in explaining these patterns of enterprise dynamics, although few have the data available to test this directly. This is in line with a broader interest in looking at which dimensions of the investment climate matter for growth and improved performance. This is true within the media, where publications such as the Global Competitiveness Report and Doing Business are widely quoted, as well as in the academic literature, e.g. the importance of institutions in explaining longer run growth 7
(Table 7, Panels C and D).<sup>19</sup> Firms that use domestic capital market financing, be it equity or bond financing, start out larger and grow faster than non-issuing firms. For instance, the median firm issuing domestically is twice as large as non-issuers. In contrast, the median firm issuing abroad is 26 times as large. Despite being considerably larger, firms issuing in foreign markets also typically grow faster than non-issuing firms. Complementing and reinforcing the previous results, the differential growth of issuers in foreign markets relative to non-issuers also declines monotonically with firm size. Small, foreign issuers grow faster than small, domestic issuers. But at the 9<sup>th</sup> decile, the differences in growth between both equity and bond foreign issuers and non-issuers are typically no longer statistically significant.

Next, we examine the characteristics and performance of issuing firms by differentiating the nature of their home securities markets. The developed countries are classified according to their financial structure—whether they have bank- or market-based financial systems. For emerging economies, we do not have enough variation to split this group by bank- and market-based financial systems. Following Demirguc-Kunt and Levine (2001), we construct a measure of financial structure based on relative size. The developed countries with an average ratio of total banking claims on the private sector to equity market capitalization above the sample median are classified as bank-based economies—16 developed countries in the sample are classified as such. The other 15 developed economies are classified as having market-based financial systems.<sup>20</sup> Tables 8 and 9 show quantile regressions for equity and bond issuers splitting developed countries (also divided in bank-based and market-based financial systems) and emerging countries.

> 19 The results reported here are quantitatively and qualitatively robust to separately analyzing the patterns for domestic equity issuers, foreign equity issuers, domestic bond issuers, and foreign bond issuers.

> 20 See Appendix Table 1 for the list of countries in each of these categories. To classify countries, we use data between 2000 and 2003, but these measures tend to be fairly stable over time. Moreover, several papers show that measures of financial structure based on size are very similar to measures based on activity, such as the ratio of bank credit to value traded in equity markets. Indeed, Beck and Levine (2002) show that the correlations between these two measures are over 0.65 and significant at the 1% level. Also see Levine (2002), Demirguc-Kunt and Maksimovic (2002), Ergungor (2004), and Demirguc-Kunt et al. (2013).

23
## FIGURE 3

# CREDIT CONTSTR,.iNED COUNTRIES: PRIVATE NET TRANSFERS AND ACTUAL DEBT

<!-- Start of picture text --> 10 BOLIVIA<br>0.01 -<br>0 - 0oiu;<br>-0.01 - 0 NIGERIA<br>v -0.02-<br>z -0.03 - URUGAY YUGUSLAVIA OBRAZI<br>-0.04-3 JAMAICA<br>of<br>n6 -0.05 - 0 COTE DHVOIHIUPPINES 0 MEXICO<br>( -0.06 -<br>of ~~~~~~~OVENEZU%AARENTINA<br>I'- -0.07-<br>z O ECUADOR<br>, -0.08 -<br>0 COSTA RICA<br>> -0.09 -<br>-0. 1<br>0 CHILE<br>-0.12 - O CHILE<br>0 20 40 60 80<br>(Thousands)<br>COMMERCIAL+ST+PNG DEBT 1982 (SB)<br>0 1:MOROCCO 2: PERU<br><!-- End of picture text --> Correlation Coefficient: -.12 (-.34 excluding Brazil and Mexico) Statistically Significant at 5% Level
**Figure 1. Minimum-to-median wage ratio, Kosovo and selected OECD countries a. Kosovo, 2012-2018**

<!-- Start of picture text --> 0.6<br>0.52 0.51<br>0.49<br>0.5 0.45<br>0.44 0.42 0.40<br>0.45 0.44 0.43<br>0.4 0.42 0.40<br>0.42 0.38<br>0.3<br>0.2<br>0.1<br>0.0<br>2012 2013 2014 2015 2016 2017 2018<br>Age 15-34 Age 35-64<br>Minimum-to-median wage ratio<br><!-- End of picture text --> _Source: Own estimates based on 2012-2018 Kosovo Labor Force Survey._

# **b. Selected OECD countries, 2018**

<!-- Start of picture text --> Spain 0.41<br>Czech Republic 0.42<br>Estonia 0.43<br>Germany 0.46<br>Belgium 0.46<br>Netherlands 0.47<br>Ireland 0.48<br>Greece 0.48<br>Slovak Republic 0.49<br>Latvia 0.50<br>Lithuania 0.51<br>Canada 0.51<br>Hungary 0.52<br>Poland 0.53<br>Luxembourg 0.54<br>United Kingdom 0.54<br>Romania 0.58<br>Slovenia 0.59<br>Portugal 0.61<br>France 0.62<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7<br>Minimum-to-median wage ratio<br><!-- End of picture text --> _Source: OECD statistics for full time workers. Note: the median wage is defined as follows: 50 percent of workers receive a wage below the minimum wage._ 5
Table 5: Estimates of global poverty changes as a result of three SSM simulations (average changes with the corresponding standard deviation in parentheses where available)

|Simulation|Net change in<br>poverty (%)|Net change in<br>poverty (millions)|Gross increase<br>inpoverty (%)|Gross increase in<br>poverty (millions)|
|---|---|---|---|---|
|Q-SSM|0.46(0.5)|25.5(27.4)|0.54(0.51)|29.9(28.3)|
|Q-SSMwithdutyredistribution|0.42(0.5)|23.2(27.4)|0.52(0.5)|28.4(27.6)|
|HypotheticalQ-SSM|0.35(1.78)|19.3(98.1)|N/A|N/A|

19
# **(1)** Q **model of investment** (table 1)

The empirical results are based on a panel of 603 manufacturing firms from the Standard and Poor's Compustat database for the 1972-1990 period. The Q ratio has been computed following the methodology outlined in Salinger and Summers (1983).

It is probably useful to begin the discussion of the results with a baseline model of the Q theory. Table 1 presents the estimates in levels, first difference, and estimates using oneperiod lag of Q as an instrument.'<sup>4</sup> These estimates appear to be consistent with the previous findings in the literature. For instance, most of the existing studies estimate the coefficient on Q to be between 0.003 and 0.010 and the estimate here is well within the range.'<sup>5</sup> These estimates also imply highly convex adjustment costs and very slow adjustment."<sup>6</sup> The specification is also run in first differences, following the practice in the literature regarding concerns about serial correlation. There has also been a suggestion in the literature that the Q ratio may in fact be endogenous and correlated with the error term. The instrumental variable approach, using a one-period lag of Q as the instrument is meant to address this issue."<sup>7</sup> In general, the relationship is specified as Yit = **io** + fXitX1<sup>t + cai + vt + eit</sup> where a; is the individual firm effect and v, is the year effect. The standard approach for sweeping out fixed effects, by transforming variables to deviations from their firm-specific means, has been used in this paper. These estimates are also referred to as the "withingroup" estimate in the literature. See Hsiao (1986) for a more detailed discussion of this approach.

" Salinger and Summers (1983) report estimates of 0.004 to 0.006; Fazzari et al. (1988) of 0.004; Hayashi and Inoue (1991) of 0.004; Hoshi and Kashyap (1987) of 0.009; and Blundell et al. (1992) of _0.005._

**16** It may be recalled that the adjustment cost parameter is the reciprocal of the estimated Q coefficient.

**7** See Schaller (1990) for instance.

16
<!-- Start of picture text --> 8<br>ong<br>Q<br>&<br>DB<br>A \. J<br>2000 2005 2010 2015 2020<br>year<br>——— Treated —---~- Synthetic control<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>8<br>o8 /“<br>2 Ve < a<br>& y as<br>OD\ea \,<br>2000 2005 2010 2015 2020<br>year<br>——— Treated —---~- Synthetic control<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>8<br>on8 SH"A \<br>D \,. / \ \ il U.<br>& W/V(" 1<br>. w/Vv<br>2000 2005 2010 2015 2020<br>year<br>——— Treated -—-—-—-—- Synthetic control<br><!-- End of picture text --> <!-- Start of picture text --> 8g82 Va/~in > aw Zf- \ | a-f A<br>2000 2005 2010 2015 2020<br>year<br>——— Treated —---~- Synthetic control<br><!-- End of picture text -->

<!-- Start of picture text --> 2<br>&<br>g<br>g&<br>2<br>2000 2005 2010 2015 2020<br>year<br>——— Treated -—-—-—-—- Synthetic control<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>&<br>g<br>g&<br>2<br>2000 2005 2010 2015 2020<br>year<br>——— Treated -—-—-—-—- Synthetic control<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>&<br>gg<br>88g<br>2 2000=e2005 2010 2015 2020<br>year<br>——— Treated —---~- Synthetic control<br><!-- End of picture text -->
Source: Authors’ analysis based on data described in the text. The dependent variable is the natural logarithm of hourly NO2 at ITO. Control variables are the same as used in table 4, with the addition of season fixed effects and interactions between the discontinuity dummies and the time polynomial. Std. errors are clustered at one week. * indicates significantly different from zero at 10 percent level, ** at 5 percent level, and *** at 1 percent level.

**Table 9. Growth Rate in Registered Private Vehicles**

||Cars a|nd Jeeps|Two-|wheelers|
|---|---|---|---|---|
||Delhi|Mumbai|Delhi|Mumbai|
|1997–2002 (a)|59.4|25.0|25.5|44.5|
|2002–2007 (b)|43.5|31.8|43.4|66.7|
|City difference (b) - (a)|-15.9|6.9|17.9|22.2|
|Delhi difference - Mumbai<br>difference|-22.7|-4.3|—|—|

Source: Various issues of Road Transport Yearbook, published by the Transport Research Wing, The Ministry of Road Transport and Highways.

Each figure in the table is a growth rate over a five-year period expressed in percent terms.

49
This hybrid translog function approximates the ordinary translog function when θ approximates zero. The authors chose θ = 0.01 so (17) is virtually identical to ln t. For this estimation, the authors used time series data of 18 annual observations (1966-1983) of the Port of Ashdod (Israel). Applying the iterative technique modified by Zellner, a system of equations was estimated. These equations consisted of the translog total cost function, the factor cost share equation (Shephard’s lemma) and the appropriate parametric restrictions so cost function meets the conditions of symmetry and homogeneity in factor prices.<sup>16</sup> .

_Martínez Budría (1996)_ analyzed the provision of port infrastructure services managed by the Spanish Port Committees that evolved into the present port authorities and, at the same time, to analyze the differences between them. It assumes that the technology used by all port authorities is the same and that it can be analyzed through a model with an error structure including a fixed time effect and a specific individual effect. The fixed time effect is common to all firms although it varies along periods and it reflects the technical change during the observation period. On the other hand, the specific individual effect enables the analysis of the reasons for cost differences. He estimated the cost function estimation with a panel of data of 135 observations from 27 ports of general interest, covering a five-year period (1985-1989). The functional form specification selected by the author is consistent with the following Cobb-Douglas function: where: _CTit_ = Total costs of port i during period t, _Qit_ = Activity of port i during period t, _wit_ = Labor input price in port i during period t, _mit_ = Intermediate inputs price in port i during period t, _rit_ = Capital input price in port i during period t, _di_ = Specific individual effect of firm i, _Di_ = Dummy variable for firm i, _dt_ = Time effect of year t, _Dt_ = Dummy variable representing period t, ε _it_ = Error term, and, _A_ , _c1_ , _c2_ , _c3_ , parameters to be estimated.

16 Linear homogeneity in factor prices is achieved by placing the following restriction: and symmetry is ensured with the following restriction: γ _ij_ = γ _ji_ 15
# **References**

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A9
the opposition). In addition, the survey collected information on cooking fuel and electricity consumption patterns, which allow us to identify potential “losers” from the reform. Finally, the survey collected a variety of individual and household characteristics that are used as controls (see table A.7 for details).

Surveys between May 2011 and August 2012 collected only a subset of the questions asked in the January 2011 survey. These included questions about the level of satisfaction with the subsidy reform implemented, whether an individual received the benefit, political views, cooking fuel and electricity consumption patterns and household characteristics. The September 2013 survey expanded on this subset by including questions about the ex-post level of information (whether individuals know the electricity threshold that qualifies for the subsidy, or how often it is distributed), and questions about the mechanism through which the benefit was received (whether it was received through the electricity bill or electronic card or withdraw in cash or not). These two sets of variables are used as part of the robustness checks, to verify if, even years after the implementation, there are misconceptions about the benefit, and whether issues such as salience play a significant role (table 2 provides descriptive statistics on the satisfaction rates across surveys).

13