rafmacalaba/data-use-mentions-extended huggingface dataset:
than what we would expect in a random network. We compute the modularity of the weighted adjacency matrix at the sector-county level. We find that modularity in the revised network with informal firms increases by about 46% suggesting that the revised network exhibits a more pronounced group structure.<sup>27</sup> To further characterize this group structure, we apply a community detection algorithm to the trade flows between counties as per the revised network. As illustrated by Figure C3, the group structure now correlates strongly with Kenya’s geography, i.e., geographically proximate counties are now more likely to be clustered in the same group.

Next, we explore for which group of counties we underestimate the overall number of links the most. To do so, we regress the change in county-level outlinks between baseline and revised network on various county characteristics, including the aggregate level of formality, population, Gross County Product, and market access (Table C3). The results reveal that the baseline network, which relies exclusively on formal sector data, most significantly underestimates connectivity in smaller counties and those with high market access. Notably, after controlling for these other characteristics, the relationship between a county’s aggregate formality share and the change in outlinks is no longer statistically significant.

Finally, we find that incorporating informal firms also reshapes inter-sector trade patterns (Figure 9). Sectors with substantial informal activity like other services, retail, and wholesale, now gain prominence as buyers in the network. Manufacturing, wholesale, and mining experience the largest relative gains in new links.

# **5.4 Simulating the Effect of Economic Shocks**

As a next step, we ask how the newly predicted network that accounts for informal firms compares to the previous network in terms of its role in propagating domestic and international shocks. How does the predicted impact of the shock depend on whether we account for informality? Are sectors and regions with more informality more or less vulnerable to shocks than the administrative data would suggest?
For the empirical analysis, we combine the satellite data on global human settlement with data from two rounds of population census to create panel data sets at the commune level. To estimate the effects of the CIV trade shock on spatial reallocation across Abidjan and non-Abidjan routes, we adopt a triple-difference strategy with location fixed effects. A difficult challenge in analyzing the effects of a large-scale trade cost shock is that, because of general equilibrium effects, it may be difficult, if not impossible, to find a comparison area that remains unaffected (Donaldson (2015)). This is especially challenging in developed countries where the economy is spatially well-integrated by extensive transport infrastructure. The fact that, in many developing countries, a significant part of the economy is effectively in an isolated autarkic equilibrium offers us a plausible strategy; we can use as comparison the part of the economy that remains autarkic both before and after the trade shock. The changes in employment structure and built-up density observed in these comparison areas cannot be due to the trade shock, and thus provide an estimate of the structural changes due to the underlying long-term technological and institutional factors.

The core of our empirical strategy is to compare the DID estimate of the effect of the CIV crisis on the integrated segment of the non-Abidjan route with the corresponding DID estimate for the integrated segment of the Abidjan route, with the respective autarkic areas as the comparison. It is important to appreciate that the triple-difference set-up is used for understanding the spatial reallocation, not for estimating the causal effects of the negative trade shock on a route. An important advantage of our focus on spatial and sectoral reallocation is that the intractable problem of double-counting one faces while estimating the causal effects is not relevant in our context.<sup>4</sup> Using data on a vector of relevant characteristics, we find that the Abidjan and non-Abidjan routes in both Mali and Burkina Faso do not differ significantly in the pre-war period, thus allaying the worry that the shifts in the spatial margins of integration and specialization would have
**Table 1:  Google Traffic colors and speeds (km/hr) in Dhaka**

|Color<br>Speed<br>(km/hr)|
|---|
|Green<br>40|
|Orange<br>15|
|Red<br>7|
|Purple<br>4|
|Source: Google Traffic|

Figure 3 provides a summary of traffic data for the .01-degree cell surrounding the CAMS-3 monitoring station. The figure displays hourly median and 1st-quartile traffic speeds. Several features of the display are evident. First, each day the average speed varies from about 15 km/hr to 37 km/hr. Second, a regular pattern recurs from Sunday to Thursday: Average traffic speed reaches maximum levels from midnight to 4 AM; falls rapidly to a minimum from 8 to 10 AM; rises to somewhat higher speeds from noon to 4PM; falls to another minimum from 8 to 11 PM; and then rises rapidly. Third, the pattern on Friday and Saturday is distinctly different: Average speed falls steadily from around 4 AM until 8-11 PM. Finally, the worst traffic conditions are somewhat more volatile on Friday and Saturday, with more rapid afternoon declines in the first quartile measure.

While Figure 3 provides a wealth of evidence about traffic conditions around the CAMS-3 station, we should note that this represents only one cell in a grid that covers the Dhaka metro region. Within each of several hundred cells, Google Traffic permits the calculation of average speeds by day and hour. Such information was simply unavailable before the advent of Google Traffic, and it provides systematic evidence that can support a much finer-grained approach to traffic analysis and transport planning for the Dhaka region.

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decision was based on positive reports of P4P schemes in two Provinces that had been developed by a number of NGO’s (Kalk et al. 2005; Soeters et al. 2005 and 2006). The P4P scheme provides bonus payments to primary care facilities based on provision of various types of services and the quality of those services. P4P payments go directly to facilities and are used at each facility’s discretion. The overall amount of P4P payments is large in comparison to facilities’ budgets: a study of 68 facilities receiving P4P payments shows that P4P payments represent an increase in funding of 24.6% above the base budget. On average, 77 percent of P4P funds were used to compensate personnel resulting in an increase of 38 percent in staff compensation (Basinga et al 2011).

> 4  See Witter et al., (2012) for a recent systematic review of health care P4P in low and middle income countries

4
Figure 6: Temporary employment by age group. (Mid-1990s/Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s<br>Argentina Brazil Chile Mexico El Salvador<br>>64 years old 50‐64 40‐49 25‐39 15‐24<br><!-- End of picture text --> Source: Own calculations based on Household surveys.

In the case of temporary employment, there is evidence of increasing female participation in the five countries analyzed, with large variations in several cases. Indeed, in the five countries for which we identified temporary workers, while temporary employment was predominantly male in the 1990s, today women show a participation higher than 50% in all cases.

Figure 7: Temporary employment by age group. (Mid-1990s/Mid-2010s) employment by gender (Mid-1990s/Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s<br>Argentina Uruguay Brazil Chile Peru Bolivia Dominican Mexico El Salvador<br>Republic<br>Female Male<br><!-- End of picture text --> Source: Own calculations based on Household surveys Page 12 of 51
growth as an opportunity to graduate from social assistance programs. To the best of our knowledge, graduation from these kinds of programs has been highly neglected so far, possibly due to the lack of identified longitudinal data.

We start by analyzing the sociodemographic factors related to the program graduation and show that being male, being better educated, and living in urban areas are associated with a shorter time in BF. To isolate the effect of local employment growth on the probability of leaving BF, we combine several administratively identified datasets and follow families over a period of 84 months. By applying a shift-share IV approach and controlling for family- and time-fixed effects, we find that an increase in local employment has a positive and significant effect on 23
affect both the probability that the firm enters the program and the future outcome of the firm, based on economic theory and previous empirical findings (Caliendo 2006).

In this research, we utilized the age of the firm and dummies variables for the existence of R&D expenditures, export activities and production workers trained. Others dummies variables are the size of the firm (large, medium and small), the country income level (high/ upper-middle), and the existence of domestic owners. The industrial sector was divided into four categories based on the World Bank‘s Enterprise Surveys (ES). As the survey does not contain information related to the performance or productivity of firms, we calculated the technical efficiency of each firm using one output (sales) and five inputs (capital stock , labor cost, materials, energy cost and others costs).

We evaluated the effect of overdraft facility policy by observing various aspects of a firm‘s performance. The outcome variables considered are: technical efficiency, R&D expenditures, export activities and production workers trained. The definition of variables including firm characteristics and outcomes are explained in Table 3.

Table 3 The definition of variables including firm characteristics and outcomes.

|**Variables**||**Definition**|
|---|---|---|
|Production<br>Function<br>(output and|Sales|Used as the measure of output for the production function estimation. For all<br>countries, sales figures in local currency are converted into USD using PPP<br>conversion factor to the official exchange rate ratio<br>|
|inputs)|Capital<br>Stock|Book value of all fixed assets<sup>1</sup>. For all countries, capital stock figures in local<br>currency are converted into USD using PPP conversion factor to the official<br>exchange rate ratio.<br>|
||Labor cost|
series of Ordinary Least Square (OLS) models to study the relationship between the level of development (proxied by Gross Domestic Product, GDP, per capita) and the incidence of the three types of payments.

Ideally, to study transitions between stages of development (in this case of payment systems), we would rely on panel data. By following countries over time, we would be able to ascertain for sure whether poor economies have in fact leapfrogged from 16<sup>th</sup> century bank-centered payment systems to 21<sup>st</sup> century digital payments. Unfortunately, the available data do not cover enough countries over sufficiently long periods of time to enable this type of research.<sup>4</sup> However, with rich cross-sectional data covering hundreds of countries, we can estimate two key parameters that can be interpreted as suggesting evidence of leapfrogging.

> 2 Digital (or electronic) payments are transfers of value that are executed and/or received using digital (or electronic) devices and channels to transmit the instructions. They include payments that are initiated by mobile phone or computer. Card payments are cashless digital payments but distinct from payments made through the internet.

> 3 Global Findex database: <u>https://globalfindex.worldbank.org/.</u>

> 4 The Findex data on ownership of a mobile money account, for example, cover data in the 21st century, and most countries do not have more than one observation over time. For the 2014 and 2017 waves of the survey, fewer than 70 countries have data for both years.

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for the same reasons, most countries are competitive for import substitution. Given both greater productivity and improved infrastructure, the expansion of regional markets could eventually provide the basis for competition in export markets (World Bank, 2009).

Within the next few decades, the majority of Sub-Saharan Africa‘s people will be living in urban centers and will depend on a diminishing minority of the population to produce food. Ongoing demographic change means that countries in eastern and southern Africa regions are increasingly dependent on imports of staple foods. Net maize exports in East and Southern Africa as regions display downward trends, with substantial variability, over the past few decades (FAOSTAT). Net exports are negligible in West and Central Africa and show no trend. All countries in Southern Africa have a negative trend in net maize exports. In East Africa, there is a negative trend in net maize exports for 2 of 6 countries (Kenya and Rwanda). Kenya and Zimbabwe, net exporters of maize in the 1970s and 1980s, are now chronic importers. Malawi has also been a net maize importer in four of the past six years. The reduction of maize production subsidies in South Africa has also reduced the exportable surplus in that country, although it remains a reliable exporter.

Urban populations are growing at over 4 percent per year in Sub-Saharan Africa compared to less than 1 percent among rural populations. To feed urban populations, especially in coastal cities, maize imports would have been much larger but for rising imports of wheat and rice. For example, in the urban areas of East and southern Africa shown in Table 3, wheat and rice (much of which is imported) are more important than maize in consumption. The consumption shares of wheat and rice in urban areas are growing rapidly even in areas where maize has long been the primary staple crop, reflecting the overall decline in maize self5
their use after the pandemic.

<!-- Start of picture text --> (a) By BPS round (b) By income groups<br>60 100<br>47<br>80<br>41<br>40<br>60<br>28<br>40<br>20<br>20<br>0 0<br>Developing countries High income<br>1 2 3<br>Survey Round Started Increased Use but didn't increase<br>Cumulative predicted probability<br>Prob. of starting or increasing use of digital<br><!-- End of picture text --> Note: (a) Result from 24 countries with three rounds of data. Aggregate indicator might differ from figure 2 which includes all available data. (b) BPS countries includes all 4 categories. For WBES data, previous rounds are used to identify firms that used digital platforms but not increased use.

Figure 3: Likelihood of starting or increasing the use of digital technology

# **4. The digital divide**

Even before COVID-19 hit there was growing evidence of a digital divide in firms (see Bach et al. (2013)). Results show differences in digital adoption across firm sector, size or manager characteristics that over time have persisted. Different channels such as access to finance, access to human capital, or differences in management practices have been hypothesized as mechanisms that might prevent this gap from closing. When lock-downs and customers’ behavior changed due to the pandemic, many of those channels were exacerbated. A report from the European Commission shows that about a third of jobs in the EU could be done remotely, and that the COVID-19 shock expanded remote work to younger workers in "blue-collar" occupations. Nevertheless, its expansion and persistence depends on improving firm digital capabilities. Vo et al. (2022) show that firms with higher digital infrastructure before COVID-19 were able to perform better during the crisis
sometimes used in oil‐exporting countries (for example in the Islamic Republic of Iran), with all households at least in principle able to access the compensation. Targeted cash transfers, on the other hand, remain one of the most preferred safety net tools available for energy subsidy reform. The experience of Indonesia (see Box 1) illustrates that targeted cash transfers can reduce the population’s opposition to subsidy reform, and help assist those most in need. In Pakistan, the largest cash transfer program is the Benazir Income Support Programme (BISP), a targeted program which reaches over 5 million households. The monthly payment to beneficiaries has been increased by 50% since 2013, likely offsetting the bulk of the adverse impact of the 2013 subsidy reform (Walker et al., 2014).

# **III. Distributional Incidence of Electricity Subsidies**

<mark>We begin by examining the incidence of subsidies using household survey data, updating findings from an earlier survey presented in Walker et al. (2014). Figure 1 shows how the current residential tariff structure concentrates subsidies on households with low to moderate monthly electricity consumption. However, the incidence of these subsidies depends on the patterns of consumption by welfare level.</mark> <mark>To examine this relationship, we use data from the 2013‐14 Pakistan Survey of Living Standards (PSLM) to construct total monthly household consumption per capita for each household (a measure of welfare). Figure 2 shows the breakdown of monthly electricity consumption by slab for each decile of household per capita expenditure. While there is a clear difference in consumption across deciles, the graph shows that the vast majority of households except in the top decile consume less than 300 kWh/month, and therefore continue to benefit from subsidies even after the 2013 reforms. However, since the total subsidy provided to households with moderate consumption (between around 150 and 300 kWh/month) is greater than that for households below 150 kWh/month, the bulk of subsidies
capital stock are lower than those in this paper and grow less rapidly – indicating a difference in the estimate of the capital stock in the housing sector. By 2014, the capital stock in this paper is estimated to be 5% higher than the estimates in the Penn World Tables.

# **_International comparisons of capital-output ratios_**

The extent to which there has possibly been over-investment in China can be judged domestically by the rates of return, but cross-country comparisons of capital-output ratios can also provide evidence to judge the level of Chinese investment. The OECD publishes data on the non-residential capital stock covering 28 of its member countries for the past two decades and data for somewhat fewer countries for the previous decade. The OECD data show that the average non-residential capital-output ratio for its members was around 2.6 for the five years ending 2016. However, this estimate is based on a capital-services definition of the capital stock rather than the balance sheet estimate definition used in this paper. While the comparable capital-output ratio for China has steadily increased over the 20 years prior to 2016, as infrastructure investment rose, its level is nonetheless slightly below that in the OECD area at 2.3 (Figure 14).

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Portugal, Martins (2011) finds that workers who move from a domestic to a foreign firm receive a considerable pay increase. However, few studies trace the same individuals in developing countries,<sup>36</sup> perhaps due to data constraints.

This section examines how wages of _identical_ individuals would have changed as they “switch” between domestic and foreign sectors by exploiting a panel of individuals in the VHLSS 2002 and 2004.

I estimate a worker-specific fixed-effects model of the type used by Korenman and Neumark (1991) and Lemieux, MacLeod and Parent (2009). I start with equation (1) and introduce the time invariant individual specific characteristics _ηi_ which may be correlated with independent variables.

_Ln Wit =_ **_α Ownership_** _it +_ **_β X_** _it +_ **_γ ES_** _it+_ **_δ Location_** _i + ηi +_ εit (2) where _i_ and _t_ index individuals and years (2002 and 2004) respectively. _Wit_ is an individual’s wage; **_Ownership_** _it_ are a series of dummy variables reflecting ownership (foreign, state, private, and informal wage); **_X_** _it_ is a vector of individual characteristics; **_ES_** _it_ and **_Location_** _i_ are a set of industry and location dummy variables defined in equation (1) and _ηi_ is an unobservable timeinvariant individual characteristic of individual _i_ .

In order to remove the individual-specific effect _ηi_ , a first-difference is taken between the years 2002 and 2004, and then the parameters will be estimated via Ordinary Least Squares (OLS).

> 36 The results of some studies based on _firm-level_ panel data suggest that impacts of foreign ownership in labor markets may differ between developed and developing countries (Almeida, 2007). For instance, using the panel of Indonesian manufacturing data to control for unobserved plant level characteristics, Lipsey and Sjöholm (2006) find that acquisition of Indonesian plant leads to
25 both by economic contraction (mean income in the PNAD sample fell from R$332 in 1981 to R$316 in 1993) and by rising inequality, but rising inequality was the dominant force. The redistribution component was the larger force behind increasing poverty between 1981 and 1993, across all poverty lines and measures, and its power was greatest for the most bottom-sensitive poverty measures, the poverty gap and FGT(2).

With the advent of economic stability, both components changed sign. From 1993 to 2004, both growth and falling inequality contributed to declining poverty although, this time, the growth component dominated. Of the ten-point decline in poverty incidence, over six points are accounted for by growth, with redistribution responding for 3.5 points. The redistribution share does improve for FGT (1) and (2). Looking at the whole 1981-2004 period, there was a net decline of some seven percentage points in poverty incidence, five of which are attributable to growth, and two to redistribution.<sup>32</sup>

# **5) The Impact of Macroeconomic Performance.**

The dynamic decompositions in the previous section shed some light on the changes in Brazilian inequality and poverty over the last two and a half decades. In particular, a convergence in mean incomes between urban and rural areas; a reduction in average returns to education; and possibly a decline in inter-racial disparities seem to account for at least part of the decline in inequality during 1993-2004. As we learned from the decomposition by income sources in Section 3, larger volumes of better-targeted social assistance transfers may also have contributed.

But the picture was less clear for the increase in inequality during 1981-1993, when changes in the distribution of education appear to account for only a small part of the substantial overall increase in inequality, and the other decompositions fail to explain much.<sup>33</sup> Bearing in mind that the outstanding economic fact of the 1980s in Brazil was macroeconomic instability and, in particular
and impose collection restrictions, including on fodder, firewood and timber. Communities

> 3 National information available from the Ministry of Forest and Soil Conservation (MoFSC) Department of Forestry (DoF) Community Forestry Division website

> <u>http://dof.gov.np/dof_community_forest_division/community_forestry_dof.    Hill district information based on</u> author calculations and data from the Community Forests Database (August 2015) available at <u>http://dof.gov.np/image/data/Community_Forestry/Summary.pdf and the 2013 Nepal Statistical Yearbook (most</u> recent available), which is available at <u>http://cbs.gov.np/publications/statisticalyearbook_2013.  All accessed August 16,</u> 2016.

> 4 See <u>http://www.dfosalyan.gov.np/eng/images/pdf/database/database_of_cfugs.pdf for details.</u>

5