rafmacalaba/datause-encoder-data huggingface dataset:
geographical digital divide between the urban population – where 70 percent of the households own at least one mobile phone – and the rural population – where only 25 percent of the households own a mobile phone (Figure 5).

**Figure 3: Share of urban population and mobile 3G coverage**

<!-- Start of picture text --> 100% Egypt Morocco Tunisia<br>Lesotho<br>Yemen Ghana SyriaCôte d'Ivoire<br>80% Kenya Cameroon Djibouti<br>Nigeria<br>Sao Tomé and Cabo Verde<br>Swaziland<br>60% Principe Congo<br>Sudan<br>Zambia<br>40%<br>Mauritania<br>20%<br>0%<br>0% 20% 40% 60% 80% 100%<br>Urban population (% of total population)<br>3G population coverage (%)<br><!-- End of picture text --> Source: GSMA (2018), World Bank World Development Indicators)

**Figure 4: Digital device ownership gap between the richest and poorest households by quintile**

<!-- Start of picture text --> NATIONAL 10%<br>AVERAGE 75%<br>Richest quintile 27% 89%<br>4th quintile 9% 86%<br>3rd quintile 3% 81%<br>2nd quintile 2% 70%<br>Poorest quintile 0% 41%<br>Computer Cellphone<br><!-- End of picture text --> Source: Calculations based on EDAM4

**Figure 5: Digital device ownership gap between urban and rural households**

<!-- Start of picture text --> NATIONAL 10%<br>AVERAGE 75%<br>13%<br>Djibouti City<br>89%<br>Urban (except 7%<br>capital) 70%<br>0%<br>Rural<br>25%<br>Computer Cellphone<br><!-- End of picture text --> Source: Calculations based on EDAM4 The lack of mobile phone infrastructure (and associated coverage) in rural areas – combined with less than optimal transport infrastructure and low penetration of telephone service– helps explain why a large share of the rural population remains offline. Only one third of the rural population is less than 10 minutes away from a location where a phone call can be made, to two-thirds of the urban population. Even worse, a third of the rural population is more than an hour away from such infrastructure, compared to less than 5 percent for the urban population (Figure 6). There are no official coverage maps from Djibouti Telecom, but crowd-sourced coverage maps provided by 6
# **Table 5: Double difference estimates of whether different types of non-road infrastructure building occurred during the** **<u>project period</u>**

|||**Total sampl**<br>|**e**<br>|**T**|**reatment sup**<br>|**port**<br>||**Common sup**<br>|**port**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**DD**|**PS kernel**<br>**matched**<br>**DD**|**PS**<br>**weighted**<br>**DD**|**DD**|**PS kernel**<br>**matched**<br>**DD**|**PS**<br>**weighted**<br>**DD**|**DD**|**PS kernel**<br>**matched**<br>**DD**|**PS**<br>**weighted**<br>**DD**|
|Schools|0.08|0.16|0.24|0.08|0.16|0.24|0.09|0.14|0.24|
||(0.06)|(0.10)|(0.16)|(0.06)|(0.11)|(0.16)|(0.06)|(0.09)|(0.16)|
|Health center|-0.02|0.12|0.18|-0.02|0.12|0.18|-0.05|0.07|0.14|
||(0.08)|(0.09)|(0.09)*|(0.09)|(0.11)|(0.09)*|(0.09)|(0.11)|(0.10)|
|Commune cultural<br>center|-0.06|-0.17|-0.14|-0.07|-0.17|-0.14|-0.06|-0.16|-0.14|
||(0.05)|(0.09)*|(0.10)|(0.05)|(0.11)*|(0.10)|(0.05)|(0.10)|(0.10)|
|Commune offices|0.03|0.09|0.14|0.03|0.09|0.14|0.02|0.05|0.12|
||(0.08)|(0.10)|(0.11)|(0.08)|(0.10)|(0.11)|(0.09)|(0.11)|(0.11)|
|Any construction|0.04|0.15|0.24|0.05|0.15|0.24|0.04|0.12|0.24|
||(0.04)|(0.09)*|(0.16)|(0.04)|(0.10)|(0.16)|(0.04)|(0.09)|(0.16)|

Notes: (i) The impact variable is a dummy variable for whether the commune did construction on the various types of infrastructure. (ii) Standard errors clustered at the district level are given in parentheses; (iii) ** and * indicate significance levels of 5 and 10%. (iv)The total sample has 194 observations (96 comparison and 98 project communes); treatment support has 191 observations (93 comparison and 98 project communes); common support has 185 observations (93 comparison and 92 project communes).

28
**Table 9: Regression of log(earnings) on human** **_and_ physical capital**

||All|1|2|Cluster'<br>3|4|_5_|6|
|---|---|---|---|---|---|---|---|
|Education|0.03<br>(2.80)|0.07<br>(4.37)|0.05<br>(1.49)|0.03<br>(1.67)|-0.08<br>(-1.06)|0.05<br>(0.96)|-0.08**<br>(-1.94)|
|Experience|0.02<br>(3.51)|0.01<br>(1.62)|0.01<br>(0.23)|0.03<br>(3.34)|0.01<br>(0.23)|0.06*<br>(4.25)|-0.002<br>(-0.074)|
|Experience<sup>2 </sup>(104)|-3.07<br>(-5.11)|1.71**<br>(1.66)|-0.22**<br>(-0.079)|-7.78<br>(-5.03)|-2.05<br>(-0.47)|-8.72<br>(-4.86)|-1.64**<br>(-0.67)|
|Ed*exp (10')|-6.95<br>(-2.62)|-30.00*<br>(-5.43)|-5.30<br>(-0.36)|-6.45<br>(-0.75)|18.52<br>(0.31)|-17.11<br>(-1.49)|22.84**<br>(2.21)|
|Capital (10-5)|1.48<br>(11.46)|1.15<br>(7.36)|0.77**<br>(4.02)|2.03<br>(3.68)|1.38<br>(5.08)|3.1<br>(6.85)|4.05**<br>_(5.55)_|
|Log(hours)|0.29<br>(15.84)|-0.11**<br>(-5.43)|0.19<br>(3.03)|0.27<br>(9.48)|0.06*<br>(0.51)|0.38**<br>(8.95)|0.39**<br>(8.49)|
|Intercept|5.08<br>(44.11)|7.42**<br>(34.28)|5.68<br>(12.46)|5.06<br>(27.48)|6.53<br>(6.92)|4.08**<br>(12.92)|5.41<br>(10.95)|
|dummy for cluster 1|0.86**<br>(29.70)|||||||
|dummy for cluster 2|0.27**<br>(6.45)|||||||
|dummy for cluster 4|0.29**<br>(5.47)|||||||
|dummy for cluster 5|0.094**<br>(2.84)|||||||
|dummy for cluster 6|0.24**<br>(7.07)|||||||
|R<sup>2</sup>adjusted<br>F value|0.33<br>201.2**|0.14<br>18.57**|0.10<br>6.79**|0.09<br>24.45**|0.13<br>5.27**|0.15<br>33.3**|0.14<br>21.9**|
|joint test of coefficients<br>number of observations|10.24**<br>4426|127.12**<br>640|11.74**<br>309|1409|6.39**<br>174|3.93**<br>1092|11.48**<br>802|

Numbers in parentheses are t-statistics. 'The asterisks on coefficients show significant differences from the coefficients of the base case, cluster 3. *=Statistically significant at the 10-percent level, * 5-percent level.

31
less vulnerable to liquidity constraints, fared much better during the crisis than comparable domestically owned firms.<sup>2</sup> Discriminating between the competing predictions of the cleansing and scarring paradigms requires firm-level analysis on the impact of crises on resource allocation, as it requires one to analyze the link between productivity, exit and firm growth. Although such analysis is scant, there are a few studies that shed light on the debate. Liu and Tybout (1996) compare the performance of continuing plants and exiting plants in Chile and Colombia from 1980-1985 but find no evidence for systematic covariance of the efficiency gap between these two groups of firms over the business cycle in either country, even though Chile suffered a recession in 1982. Exit rates of Chilean firms only increased modestly during the recession. Casacuberta and Gandelsman (2009) examine the impact of the 2002 banking crises in Uruguay on resource allocation. Even during the crisis, productivity was negatively correlated with exit, although the evidence suggests the crisis may have attenuated the link between productivity and exit somewhat. Nishimura, et al. (2005) use a cohort analysis comparing productivity of entrants and survivors to examine the impact of the Japanese recession on productivity growth and find that the 1996/97 banking crisis induced the exit of some relatively efficient firms among the youngest cohorts. Similarly, Eslava et al. (2010) demonstrate that Colombian firms dependent on external credit were more likely to exit during the 1998-2001 recessions, even if they were highly productive. On the other hand, using manufacturing data from Russia, Brown and Earle (2002) show that the recession induced by the transition from communism to a market based economy coincided with an improvement in the reallocative process.

In short, theoretical models yield competing predictions regarding the impact of crises on resource allocation and the empirical evidence on the impact of crises on firm dynamics is limited and ambiguous.

> 2 Similarly Oh et al. (2009) find that the Korean credit guarantee scheme for SMEs implemented in response to the Asian crisis stifled the creative destruction process by enabling relatively inefficient firms to survive and maintain their size. By contrast, Borensztein and Lee (2002) find evidence that in response to the East Asian crisis Korean banks reallocated credit from conglomerate (chaebol) firms to relatively more efficient firms.

6
PHN 1991 Sector Review 26 4.14 Finally, with the exception of<sup>the notable</sup> efforts of EDI, the country-based orientation of<sup>Bantk</sup> assistance tends to constrain opportunities<sup>for the</sup> exchange of experience and idea _among_<sup>developing</sup> countries, a significant potential stimulus<sup>to learning</sup> and development. The Populadon<sup>and Human</sup> Resorrces Operations Division of the<sup>East Africa</sup> Department (AF2PH) has initiated<sup>such activities</sup> during FY91 through specilly designed<sup>sector work,</sup> which permits a number of countries in<sup>the region to</sup> analyze, compare, and improve their<sup>capacities In</sup> policy analysis, management and implementation. 'he Safe Motherhood initiative<sup>in francophone</sup> Africa has recently establisied a network<sup>of experts</sup> from the region to promote needed improvements<sup>in</sup> the region at the policy and program leve.<sup>These</sup> initiatives should be monitored, and the<sup>successful</sup> ones should eventually be replicated in<sup>other depart-</sup> ments and regions. Other ways of supporting<sup>com-</sup> munication, learning, and collaboration<sup>among</sup> developing countries should also be explored.

# _5. t0e Recmme_daqMns_

4.15 The subsector classification of PHR<sup>projects</sup> needs to be revisited to accommodate the<sup>changing</sup> composition of the PHN project portfolio<sup>-especial-</sup> ly its marked increase in social sector<sup>projects that</sup> encompass PHN, educadon and employment<sup>and</sup> often other sectors as well.

4.16 Last years Annual Sector Review<sup>recommend-</sup> ed that PHRHN intensify its effort<sup>to improve</sup> processes and systems for the collection<sup>and analysis</sup> of information on the Bank's PHN work<sup>portfolio.</sup> During FY91, a PHR sectoral information<sup>feature</sup> was set up on All-in-One that provides basic<sup>infor-</sup> mation on PHN and education lending<sup>and super-</sup> vision portfolios, drawn from the MIS.<sup>PHRHN is</sup> now embarldng on an information needs<sup>assessment,</sup> including a review of existing Information<sup>systems</sup> with a view to improving the availability<sup>and quality</sup> of essential information.
||Table 5: A|id & bilateral|OIL – 2<sup>nd</sup>sta|ge||
|---|---|---|---|---|---|
|)<br>|1<br>=<br>(<br>_X_<br>_bilateral_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|_Ln(Fatalities)_|0.008|0.026|0.024|0.019|0.019|
||(0.016)|(0.017)|(0.018)|(0.017)|(0.016)|
|_Ln(Affected)_|0.032***|0.024***|0.023***|0.010|0.014**|
||(0.005)|(0.006)|(0.007)|(0.007)|(0.007)|
|_Ln(GDP p.c.)_|-0.053|-0.119**|-0.125**|-0.138***|-0.122**|
||(0.042)|(0.048)|(0.060)|(0.054)|(0.055)|
|_Ln(Population)_|-0.103***|-0.100***|-0.100***|-0.096***|-0.099***|
||(0.026)|(0.027)|(0.027)|(0.028)|(0.028)|
|_Trade (% of GDP)_|-0.001|-0.002|-0.002|-0.001|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|_Distance_|-0.070***|-0.082***|-0.082***|-0.082***|-0.079***|
||(0.009)|(0.010)|(0.010)|(0.010)|(0.010)|
|_Affinity index_|0.679**|0.470|0.467|0.546*|0.563*|
||(0.296)|(0.317)|(0.318)|(0.319)|(0.319)|
|_Common religion_|-0.028|0.003|0.002|-0.045|-0.048|
||(0.073)|(0.084)|(0.085)|(0.084)|(0.084)|
|_Common language_|0.137|-0.067|-0.067|-0.019|-0.023|
||(0.088)|(0.124)|(0.124)|(0.122)|(0.129)|
|_Former colony_|-0.091|-0.166*|-0.166*|-0.151|-0.177*|
||(0.090)|(0.099)|(0.099)|(0.100)|(0.100)|
|_Ln(GDP p.c.) donor_|-0.188***|-0.149***|-0.152***|-0.162***|-0.161***|
||(0.052)|(0.051)|(0.051)|(0.051)|(0.050)|
|_Fuel exports (% of_|0.002**|0.009***|0.008***|0.006***|-0.003|
|_merchandise exports)_|(0.001)|(0.001)|(0.003)|(0.001)|(0.004)|
|_Regulatory quality_||0.504***|0.522***|||
|||(0.091)|(0.122)|||
|_Fuel exports_×|||-0.001|||
|_Regulatory quality_|||(0.004)|||
|_Corruption control_||||0.387***|0.477***|
|||||(0.104)|(0.110)|
|_Fuel exports_×|||||-0.014**|
|_Corruption control_|||||(0.006)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|ρ|0.717***|0.793***|0.788***|0.773***|0.776***|
||(0.120)|(0.123)|(0.123)|(0.121)|(0.119)|
|Constant|1.308|1.731**|1.813*|2.197**|2.077**|
||(0.800)|(0.843)|(0.943)|(0.909)|(0.896)|
|Loglikelihood|-7258.304|-6054.057|-6053.534|-6056.025|6032.289|
|N|3158|2632|2632|2632|2632|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is bilateral, a dummy that switches to 1 if the aid flow was bilateral. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

19
variables that are highly correlated with each other. While the share of female-headed households is balanced between the two treatment arms, it is significantly higher in the control group.

# **3.2. Estimation Strategy**

The experimental study design gives us a reliable source of identification. To estimate intention-to-treat effects of the program in each treatment arm on schooling outcomes, we employ a simple reduced form linear probability model of the following type:<sup>31</sup> where _Yi_ a schooling outcome (enrollment, attendance, or a test score), _TiC_ and _TiU_ are binary indicators for CCT and UCT, respectively, and Xi is a vector of baseline characteristics. The standard errors _εi_ are clustered at the EA level which accounts both for the design effect of our EAlevel treatment and for the heteroskedasticity inherent in the linear probability model.

In all single difference impact regressions on schooling outcomes, we include baseline values of the following variables as controls: a household asset index, highest grade attended, a dummy variable for having started sexual activity, and dummy variables for age. These variables were chosen because they are strongly predictive of schooling outcomes and, as a result, improve the precision of the impact estimates. We also include indicators for the strata used to perform block randomization – Zomba Town, within sixteen kilometers of the town, and beyond sixteen kilometers (Bruhn and McKenzie 2008). Age- and stratum-specific sampling weights are used to make the results representative of the target population in the study area.

To estimate program impacts on marriage and fertility, we use a panel regression with individual fixed-effects:

> 31 The self-reported enrollment regressions could be analyzed using a panel regression with individual fixed-effects. However, since the enrollment data from the school surveys are only available in Rounds 2 & 3, and achievement tests were only conducted in Round 3, we analyze all schooling outcomes using single difference regressions for consistency. The self-reported enrollment findings are qualitatively the same when we use panel regressions.

18
-46-

# 4. Sector of Employment

Most of the country data sets, except data for the Dominican Republic (1989), provide information on the sector of employment for each individual. Each sample was split into "public" and "private" sub-samples using the following rule: PUBLIC Public sector employees.

- PRIVATE Private sector employees, self-employed individuals, employers and family workers.

Of course we had to adapt the above general rule depending on the availability of information in the respective country surveys:

- Argentina 1980, 1989

- PUBLIC Industry of natural gas and petroleum, manufacturing industry related to petroleum and minerals, electricity, gas and water supply industry, water and aerial transportation industry, communication industry, social services, public and defense administration, and drainage services.

- PRIVATE Manufacturing industry not related to petroleum refining, rubber, mineral, machinery and equipments, construction industry, commercial and services industries, ground transporation industry, finance and real estate, cleaning and laundry services.

- Bolivia 1986, 1989 PUBLIC Public administration and public employees or mixed.

PRIVATE Employees of private sector and cooperative.

- Brazil 1989 PUBLIC Social services and public administration.

# PRIVATE Self-employed.

# Mexico **1984**

- PUBLIC Industries of oil, electricity and natural gas, public administration, education and research.

- PRIVATE Industries of food processing, textile, furniture, printing, chemical industry, mineral, metal and machinery industry, manufacturing and construction industries, wholesales and retail, hotel industry, transportation industry, real estate and services.
# **Does Energy Efficiency Promote Economic Growth? Evidence from a Multi-Country and Multi-Sectoral Panel Data Set**

Ashish Rajbhandari<sup>_∗_</sup> Fan Zhang<sup>_†_</sup> Stata World Bank

**Keywords:** Energy efficiency, energy intensity, economic growth, panel cointegration, Granger causality, vector autoregressions

**JEL Classification:** C32, C33, O13, Q43

> _∗_ Contact: arajbhandari@stata.com

> _†_ Contact: World Bank, 1818 H Street NW, Washington, DC, USA. fzhang1@worldbank.org We thank Uwe Deichmann for help conceptualize the research question. We thank Vivien Foster, Yadviga Viktorivna Semikolenova, Govinda Timilsina, and Maria Vagliasindi for helpful comments on earlier drafts. Financial support from the Trade and Competitiveness Multi-Donor Trust Fund is greatly appreciated.
correlation with the middle-income pattern for the 1980s and a negative correlation with the middle income pattern of skill-upgrading for the 1970s. Thus, in contrast to middle-income countries, India’s participation in an international pattern of skill-biased technological change seems to be limited to technologies embodied in capital or somehow related to increased output.

**Table 10: Estimated Wage-bill Share Equations – State and Industry Effects State  Manufacturing  1990-98 using three digit industries for 22 states.**

|**Dependent variable:**<br>Change in non-manual<br>wage-bill share||**1990-98**||
|---|---|---|---|
|d Log capital/value added|**0.0343**|**0.0328**|**0.0324**|
||(.0067)|(.0078)|(.0067)|
|d Log value added|**0.0283**|**0.0268**|**0.0184**|
||(.0084)|(.0092)|(.0069)|
|Constant|-0.0005|-0.0003|0.0006|
||(.0011)|(.0012)|(.0009)|
|28 industry effects||X||
|20 state effects|||X|
|**R**<sup>**2**</sup>|0.10|0.16|0.27|
|**Observations**|495|495|495|

**Note:** Figures are annualized changes for three-digit manufacturing industries in twenty two state groups. Heteroskedasticity-consistent standard errors are reported in parentheses. Coefficients significant at the 5% level are in bold face. Regressions estimated by weighted least squares with wage-bill shares averaged over the period as weights. See equation (4) in text.

One remarkable feature of skill-upgrading in India is the different experiences across states in the 1990s. Even among the states with large manufacturing sectors, the growth in wagebill share of skilled workers ranges from a high of 0.89 percentage points a year in Gujarat to a low of –0.07 percentage points per year in West Bengal (as reported in Table 4). Table 10 reports that this heterogeneity in skill upgrading across states remains evident when covariates are 28
Table C.6: Regional Technology Sophistication and Productivity

||(1)<br>|(2)<br>Avg. EXT|(3)|(4)<br>A|(5)<br>vg. MOS|(6)<br>T|(7)|(8)<br>Avg. GAP|(9)<br>|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|ABF|GBF|SSBF|ABF|GBF|SSBF|ABF|GBF|SSBF|
|Regional Productivity|0.39***|0.41***|0.38***|0.25***|0.27***|0.21***|0.14***|0.15***|0.17***|
||(0.03)|(0.04)|(0.04)|(0.03)|(0.03)|(0.02)|(0.01)|(0.02)|(0.03)|
|Observations|16|16|16|16|16|16|16|16|16|
|R-squared|0.93|0.89|0.86|0.86|0.82|0.85|0.82|0.67|0.72|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The observation is subregion within each country. The firm-level average of technology in each region is regressed on regional productivity, which is the log of value-added per worker in each region controlling for disaggregated sector dummies.

Table C.7: Regional Cross-firm Variance in Technology Sophistication and Regional Productivity

||(1)<br>V|(2)<br>ar(EXT)|(3)|(4)<br>|(5)<br>Var(MOST)|(6)<br>|(7)<br>|(8)<br>Var(GAP)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|ABF|GBF|SSBF|ABF|GBF|SSBF|ABF|GBF|SSBF|
|Regional Productivity|0.07***|0.06**|0.02|0.13***|0.14***|0.04***|0.04***|0.07***|0.03**|
||(0.02)|(0.02)|(0.01)|(0.02)|(0.03)|(0.01)|(0.01)|(0.01)|(0.01)|
|Observations|16|16|16|16|16|16|16|16|16|
|R-squared|0.46|0.29|0.10|0.68|0.66|0.34|0.28|0.56|0.24|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The observation is subregion within each country. The cross-firm variance of technology in each region is regressed on regional productivity, which is the log of value-added per worker in each region controlling for disaggregated sector dummies.

Table C.8: Regional Average Within-Firm Variance of Technology Sophistication on Regional Productivity

||(1)<br>Avg. of|(2)<br>within Va|(3)<br>r(EXT)|(4)<br>Avg. of|(5)<br>within Va|(6)<br>r(MOST)|(7)<br>Avg. of|(8)<br>within V|(9)<br>ar(GAP)|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|ABF|GBF|SSBF|ABF|GBF|SSBF|ABF|GBF|SSBF|
|Regional Productivity|0.07***|0.06**|0.15***|0.04***|0.05**|0.05***|0.03***|0.03**|0.14***|
||(0.02)|(0.02)|(0.02)|(0.01)|(0.02)|(0.01)|(0.01)|(0.01)|(0.02)|
|Observations|16|16|16|16|16|16|16|16|16|
|R-squared|0.51|0.39|0.72|0.44|0.37|0.60|0.49|0.28|0.71|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The observation is subregion within each country. The within-firm variance of technology in each region is regressed on regional productivity, which is the log of value-added per worker in each region controlling for disaggregated sector dummies.

76