rafmacalaba/data-use-mentions-extended huggingface dataset:
We also consider a widely used measure of political institutions. Since 1972, Freedom House has published an annual assessment of the state of freedom in all countries (and select territories), now known as “Freedom in the World.” Individual countries are evaluated based on a checklist of questions on political rights and civil liberties that are derived in large measure from the Universal Declaration of Human Rights. Each country is assigned a rating for political rights and a rating for civil liberties based on a scale of 1 to 7, with 1 representing the highest degree of freedom and 7, the lowest level of freedom. The combined average of each country’s political rights and civil liberties ratings determines an overall political freedom measure on a scale of 1-3, with 1 designating “free,” 2 “partly free,” and 3 “not free.” While it is useful to try to distinguish the influence of different types of institutions, there is also a tradeoff in that country coverage for each measure is not complete. Therefore, as a robustness check, we also use a single overall index of institutional quality from Kaufman, Kraay, and Zoido-Lobaton (1999). This measure averages different institutional quality indexes and has very wide country coverage.

Aside from institutional quality, we also include on the right-hand side the log of per capita GDP in 1990 (from Heston et al. 2002) because very poor countries may have limited supporting resources to make development projects succeed. Third, we consider the effect of the overall level of aid relative to GDP on project success rate (aid flows taken from OECD DAC 2003). The influence of total aid could go either way: there could be “increasing returns” in the sense that a lot of aid may create a better environment of supporting services and resources (e.g., an education project may be more likely to meet its objectives if there are also projects in transport, rural development, etc.). On the other hand, one could as easily
the 20<sup>th</sup> century, most social scientists moved away from this grounded, multi-method process of enquiry. Economics, in its quest toward greater scientific precision, became almost entirely focused on the analysis of quantitative data. Other less homogenous disciplines like sociology had subdisciplines that focused on either qualitative or quantitative methods, while anthropology split into (at least) two fields – cultural anthropology emphasizing participant observation and ethnography, and physical anthropology emphasizing the analysis of various types of quantitative data from archeological digs, genetic analysis, and experiments. Psychology moved from observational studies into an entirely experimental direction. These differences in method were a primary characteristic of how the social sciences distinguished themselves from one another.

However, even a cursory look at recent social science journals shows that things are changing. Disciplinary differences are increasingly getting bridged. Economics has taken a behavioral turn (Thaler, 2016), sociology and political science are getting increasingly interested in mixing qualitative and quantitative methods (Small 2011, Humphreys and Jacobs 2015), psychology is moving towards a mix of experimental, qualitative and survey methods (Paluck, 2010), and mixed methods are being actively used by development researchers and practitioners (e.g. Kanbur and Shaffer, 2007). We social scientists have expanded the questions we ask and seem to be moving towards a world where methods are subservient to questions. Consequently, we are increasingly mixing methods.

This paper provides a selective overview of mixing quantitative and qualitative methods – which is still a rarity in economics. I argue that qualitative methods offer a powerful set of tools that are of central concern to economists, and particularly for economists interested in questions of discrimination and development. They help reduce the distance between researchers and the subjects of their research and can help introduce an element of _reflexivity_ into economics. The paper does not attempt a full-fledged review of mixed methods, whether in economics or in other disciplines, but rather tries to illustrate various ways in which a judicious combination of qualitative and quantitative methods and
Some of the rationales for the Turkish export boom of the 2000s are the abandonment of the currency peg after 2001 which allowed the Turkish lira to devaluate (even though later in the 2000s the Turkish real exchanged rate appreciated), improved macroeconomic conditions more generally, improved ‘competitiveness’ of the Turkish economy captured in sustained increases in labor productivity while unit labor costs remained well below those of competitors, and the entry into force of the European Union-Turkey Customs Union which also brought strong competitive pressure domestically.<sup>4</sup> In this paper we take the rationales for the episode of strong export growth as given and we provide stylized facts on the micro dynamics behind this growth. Using very disaggregated exporter-level customs data from Turkey for the period 2002-2011 we characterize the firm-level dynamics in the export sector and we decompose export growth at the aggregate, sectoral, and destination market levels to examine the role of firm turnover, destination turnover, and product turnover.<sup>5</sup> Our key findings are as follows. First, short-run aggregate export growth in Turkey is dominated by growth in exports of continuing exporters, i.e., the firm intensive margin. For these continuing exporters, short-run growth is dominated by exports to their continuing destinations, i.e., the destination intensive margin, and for these continuing destinations, short-run growth is dominated by exports of continuing products, i.e., the product intensive margin. However, the dynamics across firms, destinations, and products play an important role as very high degrees of firm entry and exit into export markets and of churning along destination and HS 6-digit and 12digit product dimensions from year to year characterize both the export boom period and the financial crisis and subsequent recovery period.

Second, despite a dominance of the firm intensive margin in accounting for the Turkish long-run aggregate export growth over the whole 2002-2008 period, net exporter entry plays a more substantive role for long-run growth than it does for short-run growth. Similarly, the churning across destination
# **1 Introduction**

The COVID-19 pandemic has caused severe disruptions to the global economy, with significant effects on livelihoods particularly in developing and emerging economies. To stem the spread of the virus and save lives, governments around the world imposed restrictions on movement of people. Notable among these restrictions were stay-at-home (SAH) orders or lockdowns and social distancing. As a consequence, many (non-essential) employment activities also transitioned to home-based-work (HBW). For work environments, where home-based-work was not possible, workers often worked in shifts to reduce the capacity of people at the work place at any given point in time. In the developing world, where majority of people work in the informal sector, these restrictions and SAH orders were less strict due to the economic and logistic constraints. Nonetheless, overall, the restrictions and associated impact on the global economy have implications on energy demand. For instance, the transition to HBW is likely to increase residential energy consumption while decreasing non-residential consumption, especially given the important role of energy in the modern economy. At the same time, pandemic relief measures such as electricity subsidies implemented in several countries are likely to have intended and unintended consequences on electricity consumption (Saltane et al., 2022; Berkouwer et al., 2022).

In this paper, we examine the electricity demand response of households and firms to the COVID pandemic in Africa using unique administrative data on billing records from Ghana and Rwanda. The choice of these countries is motivated by two unique features: (i) Ghana has one of the highest electrification rates (86%) in Africa, while access rate in Rwanda remain relatively low (47%) but rising;<sup>1</sup> and (ii) Ghana offered electricity subsidies to customers during the pandemic while Rwanda did not. Thus, data from these countries will provide comprehensive evidence of the effect of the pandemic and associated policy measures on households and firms in the context of developing economies. Specifically, we seek to address two
0.4922)||(0.3402)||

Notes: The variables relate to the questionnaires administered to schools and students in the rotated booklet, marked with _(r)_ . For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix. The variable means of Dev7 and Vietnam are statistically different at the 95% significance level. Figures in parenthesis represent standard deviations.

The overall student-teacher ratio is not much different for Vietnam and Dev7 and stands at roughly 20 students per teacher. However, there are more specialized mathematics teachers per student in Vietnam, as shown by the values for SMRATIO (121 in Vietnam compared to 188 for Dev7). There is a higher percentage of certified teachers in Vietnam and higher 13
to choose a point of economic significance. To do so, we use data from the Brazilian Institute of Geography and Statistics, IBGE ( _Instituto Brasileiro de Geografia e Estatistica_ ) on the registration of selected locations, which offers georeferenced data on cities. A location is considered a city if it has a city hall. It consists of the urban area of the headquarters district and is bounded by the urban perimeter by municipal law. This dataset contains a total of 5,565 cities. If a microregion has several cities, we choose the one with the largest population based on the IBGE Census data. If a microregion has no urban areas, we likewise select the rural center with the largest population. Figure 4a shows a zoomed-in view of a specific section of the observed network with the locations of the centroids. Figure 4b shows the discretized network with shifted centroids for the same section.<sup>14</sup>

> mates in Couture et al. (2018) imply _γ_ = 0.1, which is the one used by Fajgelbaum and Schaal (2020). We choose _γ_ = 0.47, which is in the middle of the interval [0.1, 0.84].

> 14To compute to which region each centroid belongs to, we use the function `inpoly2` that substantially reduces computation times (Engwirda, 2021).

19
(ii,iv,v),I|**39.7**<br>nsititute(vi),|**50.9**<br>Freedom H|**8.0**<br>ouse(vii),WD|**1.5**<br>I(viii)|**4.2**|**4.5**|**0.1**|

IaDB Public Policy Attributes (PPA) Database (iii), Open Bugdet Indicators (vi) _Notes:_ Unemployment for 2007-2008, as available. Freedom House Scores presented as 1-100, with 100 free (based on original 1-4 scale) * Net oil & gas exporters, ** Significant oil and gas exports. Global averages are unweighted.

12
process, by 10 percent in January 2014 and another 10 percent in May 2014. The government introduced a lifeline electricity tariff for households consuming less than 100 kilowatt hours (kWh) per month in 2014. Also in January 2014 the government established a new automatic price formula for gasoline to align domestic prices to international prices over time, but without a clear calendar.

The government also introduced other social reforms with the potential to mitigate the impact of subsidy reforms, although not designed specifically for that purpose. It launched a new social housing program (which was never really implemented), increased income tax deductions for the poorest households, and committed to creating a unified registry of beneficiaries of social 16
From a practical point of view, it is typically not feasible to collect the type of detailed expenditure data we use here to construct the poverty variable for the purpose of program targeting. Instead, programs often use proxy means measures based on easily observables baseline characteristics to obtain a proxy measure of poverty that can then be used for targeting. In Table 7 we show heterogeneity by poverty using the official proxy means formula developed in Nicaragua (see above). Overall, while the results point in the same direction as those obtained with actual per capita expenditures, they are generally not significant.<sup>13</sup>

# _Endogenous Aspirations_

Heterogeneity in outcomes hence appears related to characteristics that are harder to target on. This could be either because they were unobserved at baseline, because they were not revealed by stated baseline demand, or because they can be affected by the intervention itself. Recall that stated demand for the productive investment intervention was lower among the poor (Table 2). Low baseline demand for productive interventions does not necessarily imply that expected returns to productive interventions are low. Indeed, such low demand could also be driven by low aspirations. If aspirations change once households start participating in program activities, preferences for the productive interventions may in turn evolve, which can lead to higher returns. Such shifts in preferences due to the intervention can make baseline demand information ineffective for targeting.

To shed suggestive light on this hypothesis we rely on the data collected in 2006, during the implementation of both the training and the business grant interventions. When the data was collected, households eligible for training had selected and enrolled in the courses, but the courses had not yet started. Households eligible for the business grant had received the grant 2 to 3 months prior to data collection. Some had started to make the investments, but in many cases it was too early to have income from these investments. As such, by analyzing impacts of the intervention on households’ perspectives on the future at that moment, we arguably
level of overall access remains significantly behind—by about a

> 3 For example, the database reports the latest information available between 1997 and 2002, corresponding to arithmetic averages of available data.  In some cases, more recent within country information may be available that is not yet reflected in the database. Also, efforts to weight the raw data by population could help enhance the quality of the cross-country comparisons.

> 4 With significant new investments being planned by ESKOM over the coming years, prices are likely to increase.

> 5 The database observation for South Africa is from 2002. More recent country data suggests an access rate of 70% (UNDP/WorldBank 2005), but the overall conclusion remains the same.

4
3 representative of all developing countries? Are there, for example, some moreadvanced developing countries that have ‘overshot’ and adopted the potentially equally wasteful pro-agricultural policy bias of high-income economies? And how are those high-income countries’ somewhat-reformed policies and preferential access agreements now affecting developing country farmers?

To answer all but the last of those questions requires extending the time series of estimates of distortions in the Krueger/Schiff/Valdes sample and expanding theirs and the Jensen/Robinson/Tarp sample to a wider range of countries. That is the focus of a new research project getting under way at the World Bank.

As a prelude to that new project, though, it is possible to answer the question in the title of the present paper using a new database for 2001 at least. Specifically, the present paper addresses two questions. First, what would be the consequences for agricultural value added (net farm incomes) if all countries were to remove their trade distortions simultaneously (as in an ideal WTO round), as distinct from just reducing their own distortions? While no-one anticipates such a radical reform, the analysis can serve as a benchmark to suggest what is at stake in the WTO’s current round of multilateral trade negotiations (Anderson and Martin 2005, 2006). The second question addressed below is: for a selection of large developing countries and Sub-Saharan Africa, what contribution would their own unilateral reform make to the impact on net farm incomes of global reform?<sup>1</sup>

> 1 The Krueger, Schiff and Valdes (1988) and Jensen, Robinson and Tarp (2002) studies focused on effects of just own-country policies, the first using partial equilibrium and the second using national general equilibrium models. On the relationship between those two methodologies, and for reasons as to why the latter is superior in principle, see Bautista, Robinson, Wobst and Tarp (2001).
- Rest of the world (RoW) savings are taken from national SAM, and imbalances in the account RoW are treated as savings. The total amount is equal to €52.5 billion and are reported with the negative sign in the regional SAM as representing a negative investment.

# **3.8 Debt**

The debit account is the sum of households’ debt, enterprises’ debt, central Government debt, and RoW debt.

In the specific, each element is calculated as follows:

- Households’ debt (€24.8 billion) is redistributed on interest shares (the same process seen in Equation 7). We assume debt payment and borrowing from households are distributed with the same shares;

- Enterprises’ debt (€234 billion) is calculated as

where _EntInterests_<sup>_macroSam_</sup> are the interest on debt payed by enterprise from national macro SAM. This value is regionalized by using regional enterprises’ savings as share;

- Government debts (€414.3 billion) from national SAM;

- Rest of the world debts (€146.4 billion) from national SAM.

# **3.9 Rest of the World**

We consider the Rest of the World (RoW) to take into account the relationships between Italy and all the other countries. In this context, a detailed description of how imports and exports were treated seems to be essential for replications.

- Import of goods: data refer to 2016 and are taken from the ISTAT-ICE yearbook " _Commercio estero e attività internazionali delle imprese_ ", edition 2017. Data refers to imports by economic activity and region. Data on imports of goods describe different sectors linked to the sectors of our interest. For the electricity/water category, we had to split them so that we used additional 2 digit ICE-ISTAT data for the water and electricity sectors. Specifically, the data are not divided between regions for the electricity sector, but we have only a national figure (about €1.6
Electricity generation capacity additions that are either planned or are under construction between 2018 and 2035 are presented in Table 2. Of the total planned capacity additions, more than half will be natural gas based. Renewable sources of electricity (solar, wind and hydro) will share more than 10% of the total planned or committed capacity in the region. Note that the addition of new power plants also replaces retiring capacities.

**<u>Table 2. Planned/Under Construction Capacity by 2035 (MW)</u>**

|**Economy**|**GT**|**CC**|**ST**|**DG**|**Hydro**|**Nuclear**|**Coal**|**Wind**|**PV**|**CSP**|**IGCC**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Algeria|10,800|-|-|-|-|-|-|70|200|-|<br>-|**11,070**|
|Bahrain|4,125|-|-|-|-|-|-|1|105|-|<br>-|<br>**4,231**|
|Egypt, Arab<br>Rep.|2,430|7,940|10,550|-|32|2,400|1,950|160|179|-|<br>-|**25,641**|
|Iraq|15,000|2,500|13,000|-|
constraint|32|27|n.a.|18|
|_Source:_AICD Road Sector Datab<br>a. Total network includes the clas<br>b. Main network for most countrie|ase on 40 Sub-Saharan African countries.<br>sified and estimates of unclassified and urban netw<br>s is defined as result of adding the primary and sec|orks.<br>ondary networks.||||

c. Rural network is generally defined as the tertiary network and does not include the unclassified roads d. _Source:_ World Bank—IFC Enterprise Surveys on 32 Sub-Saharan African countries. AADT = average annual daily traffic; GIS = geographic information system. n.a. = Not applicable.

**Table 10. Quality of classified roads in Zimbabwe, 2010**

||Paved/s|ealed|Gra|vel|E|arth|To|tal|
|---|---|---|---|---|---|---|---|---|
||km|%|km|%|km|%|km|%|
|Very good|705|7|39|0|6|1|750|4|
|Good|1,512|15|298|4|90|12|1,900|10|
|Fair|2,720|28|706|9|56|7|3,482|19|
|Poor|4,857|50|6,670|86|612|80|12,141|66|
|Total|9,793|100|7,713|100|764|100|18,273|100|

_Source:_ 2010 Zimbabwe Road Condition Survey.

Based on the physical configuration and condition of Zimbabwe‘s infrastructure network, it is possible to estimate the resources that would be needed to clear
# **Does Mobile Money Use Increase Firms’ Investments? Evidence from Enterprise Surveys in Kenya, Uganda, and Tanzania**

# **1. Introduction**

Investment, or the acquisition of assets with the expectation of future returns, is one of the pillars of firm growth. This view has been widely accepted from earlier models of economic development. In the seminal Harrod-Domar model, for instance, the rate of growth of output is in tandem with the rate of growth of investment, assuming constant capital to output ratios (Harrod, 1993; Domar, 1946). That is, investment in fixed assets is directly associated with output growth. The role of investment as a driver for firm growth has been also at the core of a vast empirical literature. Several studies have highlighted how certain types of investments -including investments on IT, R&D, and innovation, can enhance firm productivity (see Syverson, 2011 for a review), increase exports (Liu and Lu, 2015 ), and promote overall economic growth and development (De Long and Summers, 1993).

The role of private investment for development is particularly important for Sub-Saharan African countries given the low rates of economic growth and high poverty rates in the region (Ouredraogo and Kouaman, 2014). Even for those economies where recent growth rates have picked up, initial low levels of development make the case for considering how to accelerate investment levels. Against this backdrop, several developing countries have witnessed a dramatic transformation in their financial systems due to the emergence of mobile-phone-based technology for the delivery of financial services (also known as mobile banking or mobile money). The dramatic rise of mobile money use has important implications for the private sector, potentially providing expansion opportunities. An important question that arises is whether the use of mobile money has any relationship with firm outcomes, specifically private investment.

The use of mobile phone financial services has rapidly increased during the past years spreading outside of the well-known and successful experience of M-Pesa in Kenya and besides