rafmacalaba/data-use-mentions-extended huggingface dataset:
17

## **4.3 Heterogeneity of effects**

This section explores heterogeneity in the adoption of green technologies in response to the 2022 energy crisis. We first explore the results by technology, as shown in Figure 4(a). This repeats the specification in Equation 1 but keeps job postings from specific technology groups only as the outcome variable. The results show that the differential increase in total green technology postings by more-exposed establishments after February 2022 was driven by generation technologies. This includes those related to energy generation, including renewable energy and applications of renewable energy, energy storage, heatpumps, biofuels, and decentralized energy systems.

We next explore the results by country and job skill group among the generation technologies driving our overall results. Figure 4(b) shows results from regressions run separately for each country in our sample, exploiting only the variation in firm energy intensity within countries. The effects with the greatest order of magnitude were for the Netherlands, Switzerland, Italy, Germany and the UK. In contrast, other countries and notably the United States, did not see significant effects for more energy-intensive establishments, likely reflecting their lower reliance on imported fossil fuels and therefore exposure to the energy price shock. Finally, in a similar specification as in Equation 1, we explore results by skill level. Figure 4(c) plots coefficient estimates when the dependent variable is the IHS-transformed number of generation technologyrelated job openings for occupations at each skill level. The rise in labor demand for these technologies after February 2022 was concentrated almost solely in high-skilled jobs, while lowand medium- skilled jobs saw no statistically significant effect.

# **5. Conclusion**

This paper constructs new measures of the diffusion of a comprehensive range of green technologies using online job postings data from 16 advanced economies from 2014 to 2022. It shows that growth in these job openings rapidly accelerated in 2022, and explores the role of the 2022 global energy crisis in driving this accelerated diffusion. It shows that establishments with greater exposure to the
# Figure 12: LAC: Investment needs

# A. Annual infrastructure investment needs

<!-- Start of picture text --> Percent of GDP<br>5<br>4<br>3<br>2<br>1<br>0<br>Transport Electricity Water Flood Irrigation<br> supply protection<br>and<br>sanitation<br><!-- End of picture text -->

# C. Fixed investment, and health and education spending

<!-- Start of picture text --> Percent of GDP<br>EMDEs excl. LAC<br>30<br>LAC<br>20<br>10<br>0<br>Capital Investment Health and education<br>expenditures<br><!-- End of picture text --> E. Selected health and education indicators <!-- Start of picture text --> Frequency EMDE region range<br>80 AEs<br>EMDEs<br>60<br>40<br>20<br>0<br>Pupil-teacher Physicians Safe sanitation<br>ratio, primary (per 10,000 (percent of<br>(ratio) people) population)<br><!-- End of picture text --> B. Projected urban population share in 2050 <!-- Start of picture text --> Percent of population<br>90<br>60<br>30<br>0<br>LAC ECA MNA EAP SSA SAR<br><!-- End of picture text --> D. Broadband and mobile connectivity <!-- Start of picture text --> Per 100 people Index<br>40 Broadband connections 90<br>Mobile connectivity (RHS)<br>30<br>60<br>20<br>30<br>10<br>0 0<br>LAC
where _j_ indexes locations (or cities). To simplify notation we will treat both _x_ and _g_ , as scalars, although we actually estimate coefficients of two indicators of business environment and three variables of economic geography. To these we add three error components of ln _AitR_ , two of which we assume to be time invariant. Let one of these,<sup>θ</sup> _i_<sup>,</sup> summarize the effect of unobservable establishment characteristics on productivity. Let the second, μ _j_ , be the effect of unobservable location characteristics on the same. The third, ε _it_ , is a zero mean, iid, random shocks uncorrelated with the other two and the indicators of geography and business environment. Assume also that the relationship between ln _A R_ and all of its determinants is linear such that where _i_ = 1,.., _N_ , _j_ = 1,..., _J_ ; _t_ = 1,..., _T_ ; α , β , γ are constants, and _z_ is a set of observed firm or establishment characteristics. We observe ln _A R_ and z annually for the three year period 2001-2003 so that _T_ = .3 We also observe _xijt_ only for the last of these three years, while our observation of _g ijt_ are obtained from sources other than the World Bank survey and relate to the 1990s. However, we assume that _x_ has been constant for each firm over the three-year period of interest and that _g_ would not have changed significantly since the late 1990s. In other words, we assume _xijt_ = _xij_ and _g ijt_ = _g ij_ so that we can re-write (3) as Assuming further that both _g ij_ and _zijt_ are exogenous to ln _A_<sup>ˆ</sup> _ijtR_ , (4) can be estimated as a random effects model consistently by GLS provided that<sup>_x_</sup> _ij_<sup>is also uncorrelated withθ</sup> _i_ and μ _j_ , or
The results display large variation within sectors. The estimated elasticity in the digital sector are large and significant for micro-output (estimated on the basis of firm- or household-level observations), between 0.05 and 0.06, but about three times smaller for macro output. Note that this is consistent with the positive and significant coefficient found for firm-level data in Table A.9 to A.11. For micro- and macro-elasticities energy, are between 0.04 and 0.05 and but from zero comparable, significant, indistinguishable when looking at labor market outcomes. Finally, for transport, elasticities are systematically significant, being the largest for trade (0.9), followed by labor outcomes (0.16), and micro-output (0.08), and smaller for macro-output (0.03), and null for population. It thus appears that across sectors, elasticities are larger when based on studies covering micro-, and labor market data than more aggregated data.

Next, results by level of country development are of specific interest for drawing policy implications. Theoretically, it could be that in contexts where there is greater scarcity of capital, the marginal product of infrastructure investment is higher, and that decreasing returns kick in and the marginal product decreases as countries accumulate larger stocks of capital. However, there could be threshold or network effects, so that returns are very different across countries at different levels of development or of coverage. Finally, marginal returns may be high in case of last-mile connections only.

In Figure 4, we represent sector-level (panel A) and sector-outcome results (panel B) including rigorous LASSO-selected controls, splitting the sample between developed and developing countries.<sup>17</sup> The results are somewhat surprising, as elasticities are higher for developed countries in 2 out of 4 sub-samples, namely digital and energy studies, and only larger for developing countries in the case of transport and cross-sectoral studies. This may argue for the existence of network effects, whereby
|Sierra Leone|1|2|1|2||
|---|---|---|---|---|---|
|South Africa|1|1|4|2|1|

# **2.2.2. Transport Questions in Community Questionnaires**

There are many transport questions of interest in the community questionnaires, especially for Ethiopia, Malawi, and Uganda in the standard LSMS collection, and Ghana and Kenya with LSMStagged surveys, where a group of questions on road and public transport are included. For instance, the road questions ask the surface type of the main road accessing the community, the distance to the nearest paved road, whether the main road is passable by vehicles throughout the year and, if not, in which months it is passable. The questions on public transport mainly ask whether public transport passes by the community and, if yes, the main means of public transport, the distance to the nearest public transport station, frequency of public transport stops in the community, as well as the cost of total fare to go to the nearest urban center from the community by public transport.

It is worth mentioning that there are a set of questions on the needs and changes regarding road construction and public transport improvement in the community. The community questionnaires of Ethiopia, Malawi, Niger, Nigeria, and Uganda contain the similar questions on whether the community members approached the leaders to express, discuss or find ways to address the needs for construction and maintenance of roads, bridges, and public transportations during the past five years, as well as the amount of funding raised and results. Some of these countries also have a standalone module regarding the changes in the community, asking respondents whether the access to transportation became better or worse compared to five years ago and to describe the relevant events or programs happened in the community during the past five years. An example of the module of community needs, actions,
all levels of development. The matching results offer robust evidence that food insecurity determines both vertical and horizontal dimensions of trust.

However, while matching methods ensure findings are free of observable bias, it is still possible for unobserved factors to introduce hidden bias. Hidden bias may be caused by unobservable variables that cannot be controlled for in the matching process that simultaneously affect trust and the probability of being food insecure. To explore possible unobserved mechanisms, we examine whether measures of poor health and subject well-being mediate the results discussed above (Table C2, Appendix C). _Health problems_ is a binary variable that equals one if the respondent has “ _any health problems that prevent you from doing any of the things people your age normally can do_ .” We also use GWP’s _Life Evaluation Index_ , which is a categorical measure of respondents’ subjective well-being. The life evaluation index has two items where respondents were asked to place their perceived life status on two ladders that ranged from 0 to 10 corresponding to the worst and best possible life. The first ladder refers to the present and the second ladder refers to the future (i.e., about five years from the present time). The responses were categorized as _Thriving_ if they scored 7 or higher on current life score and 8 or higher on future life score; _Suffering_ if they scored 4 or lower on current and future life scores; and _Struggling_ otherwise (Gallup, 2016).

Overall, Table C2 shows that while experiencing a health problem and poor subjective wellbeing are significant negative factors determining both dimensions of trust, the effects of food insecurity are substantively unchanged from previous results. The coefficient for health problems is statistically insignificant in the total sample but is associated with an increase in trust in national institutions for low-income countries, and a decrease in trust in national institutions in high-income countries. Experiencing a health problem is also negatively associated with trust in friends in family for each income sample. Struggling and suffering, compared to thriving, are strongly negatively associated with both dimensions
and productivity gains in sub-Saharan African countries. The effect of electrification tends to be larger than that of roads. When supplied simultaneously, these two types of infrastructure reinforce each other, resulting in greater efficiency. Indeed, they are highly complementary in the sense that supplying one significantly raises the marginal effect of the other. This complementarity is robust across the two outcomes we have analyzed so far, and across the two data sources. We next examine heterogeneity in the effects of infrastructure on the type of employment and occupational sectors.

# **5.2 Skilled versus unskilled employment and occupational sectors**

We analyze the separate and joint effects of roads and electricity on skilled and unskilled employment and on different occupational sectors. The results are presented in Table 5 in the Appendix. Access to road and electricity networks increases employment in skilled occupations and decreases employment in unskilled occupations, leading to a structural transformation that consists of a transition from to occupations that entail higher skills.

Expanding the connections to the two networks leads to a significant employment increase in skilled and high-skilled occupations, as evidenced by the findings obtained by both data sources, the DHS sample (columns 4 and 5) and the LSMS sample (Column 9). Being 10 km closer to an electricity grid in locations along a main road increases skilled employment by 10 percentage points (Column 4) in the DHS sample, and high-skilled employment by around 7 percentage points in both the LSMS and DHS samples (columns 5 and 9). Similarly, being 10 km closer to a main road in locations along the electricity grid increases skilled employment by 19 percentage points (Column 4) and high-skilled employment by 10 percentage point in the DHS sample (Column 5), and by 21 percentage points in the LSMS sample (Column 9).

The joint effect of the two types of infrastructure is positive and highly significant, showing that they play complementary roles in the creation of skilled jobs. The marginal impact of getting closer to a main road decreases as distance to the grid
that help exporting firms self-select into global engagement and digital technologies that enable WfH.

Using four waves of Business Pulse Surveys (BPS) data for 61 countries during the COVID-19 pandemic, this paper presents novel findings on digitalization-enabled remote work as a source of resilience for firms. Our results suggest the following. _First,_ firms in sectors with greater amenability to remote work experienced a smaller adverse impact of the pandemic in countries with better digital infrastructure. _Second,_ these effects were larger, but not statistically significantly larger, for globally engaged firms vis-á-vis the domestically oriented firms. _Third,_ while the benefits of remote work in countries with better digital infrastructure accrue to both exporting and non-exporting firms, exporters in the manufacturing sector benefit more, thereby reflecting a premium to exporting, per se. These results are not observed in the services sectors, which in our sample primarily comprise firms in non-knowledge intensive services. _Fourth,_ the effects of the amenability to work remotely in countries with better digital infrastructure do not dissipate over time.

Our paper contributes to the literature on shocks and economic resilience. This spans the role of technology use, trade policy, and access to finance across previous crises. On technology, firms with higher levels of technological readiness performed better during the pandemic (Bai et al., 2020; Cirera et al., 2022; Muzi et al., 2022). Similarly, technologically diversified firms, measured by patents in different technologies, are less affected by natural disasters (Hsu et al., 2018). On trade policy, Cali et al. (2023) show that non-tariff barriers affected the ability of Indonesian exporters to respond to foreign demand shocks driven by a depreciation of the Chinese yuan. On finance, the 3
MENA countries that do not have state banks may not find it necessary to create new ones, because they have been addressing their policy objectives through alternative and probably more effective policy interventions. For example, Lebanon does not have state banks but has achieved a relatively high share of SME lending by MENA standards through the use of partial credit guarantee schemes.<sup>16</sup> Note that these schemes have also played a counter-cyclical role in many countries within and outside MENA, and this is one of the arguments that have been put forward to justify the presence of state banks.<sup>17</sup> Lastly, we note that credit guarantee schemes may also be a preferable form of policy intervention because they provide an easier exit mechanism.

Foreign banks in MENA remain generally small but would probably expand faster and contribute to more competitive and efficient financial systems if they had access to more and better credit information. As noted above and in other studies, addressing the weakness on credit reporting systems should remain one of the key items in the financial development agenda of MENA countries. This would entail both upgrading public credit registries and, especially, introducing private credit bureaus able to expand coverage and improve the depth of credit information.<sup>18</sup> Listed banks have performed better than non-listed banks, and this may be due to the stricter governance standards and disclosure requirements imposed on these banks. Introducing listing obligations for all licensed banks may be one option to improve the performance of familyowned and non-listed banks, but the same outcome may be achieved by the bank regulator by simply imposing and enforcing the higher governance standards and disclosure requirements on all banks, listed and non-listed.<sup>19</sup> In this regard, an interesting question is whether the listing of public banks could contribute to improvements in their performance. Unfortunately, the listing of MENA public banks is very recent and the sample too small to enable statistical testing, but

> 16 Rocha, Farazi, Khouri, and
**Table 1. Social Sustainability Indicators in the Social Sustainability Global Dataset**

|**Component**|**Indicators**|**Obs**|**Source**|
|---|---|---|---|
|<br>**Social Inclusion**||||
|**Access to markets**|Labor force participation rate|113|GMD|
||Unemployment rate<br>|109<br>|GMD<br>|
||Percentage of people that work and are self-employed|79|GMD|
||Percentage of people that work and have a contract|36|GMD|
||Share of population that owns a bank account|105|FINDEX|
|**Access to basic**<br>**services**|Percentage of households that have access to water|135|GMD|
||Percentage of households that have access to sanitation|137|GMD|
||Percentage of households that have access to electricity|104|GMD|
||Percentage of households that have access to internet|50|GMD|
||Share of population that uses the internet|42|AF, AB, ASB|
||Share of population that uses the internet as a source of information|73|WVS|
|**Access to human**<br>**capital services**|Percentage of people that are attending primary school/people of school age|114|GMD|
||Percentage of people that completed primary education|107|GMD|
||Percentage of people that are attending secondary school/people of school age|114|GMD|
||Percentage of people with health insurance|28|GMD|
||Percentage of people that have social security|30|GMD|
|**Access to political**<br>**and civic spaces**|Percentage of
examine the effect of household wealth and student academic ability on the type of school attended, we use the junior secondary school sample to estimate a multinomial logit regression of school type attendance. The dependent variable is an indicator of whether the student attended public school, private secular school, private Muslimaffiliated school, private Madrassah, or a private non-Muslim religious school.<sup>9</sup> There are few students attending private non-Muslim religious schools; these schools include Christian and Catholic private schools that are considered to be of comparable quality to public schools.<sup>10</sup> Therefore, we focus on the determinants of attendance at private secular, Muslim-affiliated private schools, and Madrassahs. The entire set of household and student characteristics listed in the data section are included as control variables, but we report only the results from indicators of household wealth, the student’s prior academic achievement and parental education. Table 1 reports the marginal effects of each variable on the probability of attending a particular type of school, and whether the variable was statistically significant in the multinomial logit model. For dummy variables, the marginal effect is the sample average of the difference in the predicted probabilities when the dummy variable is set to one or zero. For the test score and consumption variables, we report the average of each observation’s marginal effects of the variable and its square. For these variables, the significance of the variable and its square were tested jointly.

> 8 The coefficients on the province dummies are as high as one standard deviation. These substantial provincial differences, which we do not attempt to explain, may be the topic of future research.

> 9 In this analysis, public Madrassahs are grouped with public secular schools for brevity. Only 7% of public school students attend public Madrassahs.

> 10  Three percent of the full sample and ten percent of the private-school attendees attend non-Muslim religious schools.   Of these schools, about 60 percent of this category are Catholic schools, while the remaining 40 percent are
Figure 3: Simulation, using national distribution of books <!-- Start of picture text --> National<br>Santiago del Estero City of Buenos Aires<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>segregation segregation segregation<br>mean gap U - L meanmean gap U - Lgap U - L<br>14<br>15 14<br>12<br>12<br>10 10<br>8 10<br>Gains in test scores Gains in test score 5 Gains in test score<br>6 8<br>4<br>6<br>0<br><!-- End of picture text --> Note: The vertical lines indicate the degree of segregation found in each case.

the gap than for the average test scores. In other words, the equity aspect is significantly more sensitive to changes in the allocation of students on the basis of socioeconomic status than the mean is. Both these results are consistent with the pattern observed across localities in the introduction. Indeed, peer effects alone can account for, at least the sign of the relationship observed in the date. The variation across schools (with different human, social and physical resources and management) most likely works in the same direction, strengthening the results. Still, even disregarding the (certainly sizeable) differences across schools, there is a trade-off between gap and mean test scores that is entirely due to the effects that peers have on other pupils.

# **7 Conclusions and policy implications**

This paper has argued that the standard, linear-in-means approach to estimating peer-group effects does not fully account for the consequences of economic segregation. After relaxing two assumptions of the linear-in-means model, I find that wealthier pupils are more sensitive to peer effects. Second, there is evidence that peer effects are non-monotonic, such that
**<u>Table A3: Knowledge of Legal procedures for Registration of Land Transactions Post LTR</u>**

||**Region**<br>**Total **|**South **|**West**|**North **|**East**|
|---|---|---|---|---|---|
|Land markets— Male|0.07|0.06|0.03|0.14|0.07|
|Land markets — Female|0.04|0.03|0.02|0.05|0.05|
|Inheritance: Register spouse if CPR male|0.68|0.69|0.62|0.75|0.69|
|Inheritance: Register spouse if CPR female|0.70|0.69|0.67|0.74|0.72|
|Purchase: Register spouse if CPR male|0.46|0.44|0.55|0.35|0.48|
|Purchase: Register spouse if CPR female|0.65|0.64|0.61|0.70|0.66|
|Number of observations|3351|1069|814|678|788|

_Source:_ Own computation from 2015 round of Rwanda Land Tenure Regularization Survey _Note_ : CPR refers to community of property marriage regime.

29