rafmacalaba/data-use-mentions-extended huggingface dataset:
barriers to property rights and barriers to banking freedom - and the _Cost of Contract Enforcement_ indicator from the Doing Business database. While higher values of the _Governance Index_ indicate a more effective institutional environment, higher values of _Barriers to Economic Freedom_ and _Cost of Contract Enforcement_ indicate a less developed institutional framework. The correlations suggest a positive relationship between access to and use of banking services and better governance, contract enforcement and economic freedom. These correlations are confirmed for the _Governance Index_ by the regressions in Tables VI. The _Governance Index_ enters positively and significantly in all but the loan-income ratio regressions. The _Barriers to Economic Freedom_ indicator enters negatively and significantly (5% level) only in four of them. Finally, the cost of contract enforcement indicator is negative and significant in only three of the eight regressions. Overall, the Table VI regressions suggest a strong association of better institutional quality with banking sector outreach, but it is more difficult to disentangle the specific elements of the institutional framework that are associated with different dimensions of outreach.

Fourth, there is some indication that more effective credit information sharing and fewer restrictions on banks’ activities are associated with better access, while high entry barriers are associated with lower use of lending and deposit services (Table VII). Correlations and regression results suggest that in countries with more effective credit information sharing, banks have relatively more outlets, but do not necessarily extend more loans. The indicator on _Restrictions on Bank Activities_ only enters negatively and significantly in the branch penetration regressions, suggesting that banks are less likely to expand their branch network if they are restricted to their core business of deposit taking and lending. The indicator of _Entry into_ 17
_Table 6 Intention‐to‐treat effects of the original intervention on main psychological and behavioral outcomes_

||(1)<br>ITT|(2)<br>ITT|
|---|---|---|
||(no controls)|(controls)|
|_Main outcomes – Ps_|_ychological and beha_<br>|_vioral_<br>|
||‐0.009|0.002|
|Psychological wellbeing index|(0.845)|(0.965)|
||[0.845]|[0.965]|
|Risk index|‐0.043|‐0.052|
||(0.501)|(0.383)|
||[0.692]|[0.537]|
|Trust index|‐0.035|‐0.055|
||(0.482)|(0.274)|
||[0.692]|[0.480]|
|Crime and violence index|‐0.080|‐0.089*|
||(0.119)|(0.090)|
||[0.343]|[0.315]|
|Migration index|‐0.026|‐0.015|
||(0.593)|(0.767)|
||[0.692]|[0.896]|
|List experiment cattle index|0.172*|0.169**|
||(0.075)|(0.050)|
||[0.343]|[0.315]|
|List experiment argument index|‐0.135|‐0.132|
||(0.147)|(0.149)|
||[0.343]|[0.322]|
|Observations|1
network. The covered communes are the most populated ones, as 20% of the population has access to electricity and 15% of the population has access to the internet broadband. Recent improvements of the paved road network since 2014 as shown in Figure A18 in the Appendix show that the percentage of communes and population having access to a paved road has largely increased.

The Lake Chad area, which includes The Extreme North region in Cameroon, the regions of Kanem, Lac, Hadjer-Lamis, and Chari-Baguirmi in Chad, and the regions of Borno, Yobe and Adamawa in Nigeria as depicted on Figure A21 in the Appendix, is characterized by a limited access to infrastructure. Only 30% of the 80 locations - districts in Nigeria, communes in Cameroon and Chad - have a paved road, 16% access to electricity, and 10% access to Internet. Only half of the population has access to a paved road, 20% to electricity, and 30% to the Internet broadband.

> 15Global elevation (in meters) are from Shuttle Radar Topography Mission (SRTM) dataset (v4.1) at 500-meter resolution. See Jarvis et al. (2008).

> 16A district is classified as electrified when at least 50% of its population has access to electricity as monitored with night-time lights.

9
the problems posed by corruption and disorder. Again, there is a large difference from pioneers in other markets: the proportion of those in fragile states reporting the matter as a ‘major constraint’ is two or three times as high. Strikingly, however, one potential constraint which donors have strongly emphasized—education—appears not to be important for pioneers, both in fragile and non-fragile environments. To the extent that an expansion of pioneering is critical to the generation of productive employment, this suggests that the greater emphasis put on schooling, training, and technical advisory in comparison with that put on other sources of comparative advantage for enterprise development, may need to be reassessed.

**Figure 2. Pioneers face more severe constraints than other firms in fragile situations**

<!-- Start of picture text --> Inadequately educated<br>workforce<br>20<br>Water insufficiencies 15 Access to land<br>10<br>5<br>Access to finance 0 Transportation<br>-5<br>Crime, theft and<br>disorder Electricity<br>28.45<br>Corruption Political instability<br>Non-FCS FCS<br><!-- End of picture text --> _Source:_ Calculations based on the 2018 World Bank Enterprise Surveys. _Note:_ The chart reports coefficients on the linear probability that a pioneer identifies the category as a major constraint, conditional on sector fixed effects. Empty field markers indicate coefficients that are not significant at the 1 percent level.

# **_The black box of missed opportunity_**

Ultimately the fact that there are not enough large, formal firms in fragile states, with good access to markets, know-how, and financing, as well as with strong management, is reflected in the constraints that pioneers face, and in a missed opportunity in fragile markets.

11
space by offering a higher payment to ride in the public space. This was flagged in the app to make the price difference salient (Figure 1b).<sup>24</sup> Participants proceeded through a common fixed sequence of rides with varying price differentials ranging from $0 to $0.20, as graphed in Figure 2 and detailed in Table A2.<sup>25</sup> To limit framing in these first phase rides, our exit survey does not include questions on potential experiences of harassment during these rides.

# **5.2 Reduced-Form Results**

We use data from the crowdsourcing app to estimate the effect of assigning an opportunity cost to ride in the reserved space on riders’ demand for the reserved space. We then present results about key demand shifters (presence of men and risk perception). Lastly, Section 8 investigates and rules out journey planning, avoiding other crimes, sorting of perpetrators and congestion as alternative demand mechanisms.

We estimate a simple difference-in-differences specification as follows, where _di,t_ indicates that rider _i_ was assigned a positive opportunity cost to ride in the reserved space at period _t_ : In all ride-level specifications, we weigh observations by _N_ <u>1</u> _i_<sup>,theinverseofthenumberof</sup> rides taken by the individual rider throughout the revealed preference exercise; this accounts for variation in participation frequency, which was not fully controlled by the app. Crucially in interpreting these results, we show that reporting through the app is uncorrelated to treatment assignment; hence, attrition is unlikely to bias our estimates across phases of the study (Appendix D). Zero opportunity cost rides are the omitted category. We include individual rider fixed effects _γi_ , such that the effect of the opportunity cost is identified from within-rider variation across rides; as commuters have fairly rigid commuting habits,

> 24Note that, if anything, adding salience on the price difference in this case provides a conservative measure of willingness to pay for the reserved
captures the costs of trust in exporters, and if the PowerSeller mechanism were to be effective, then we would expect a smaller distance coefficient for transactions undertaken by PowerSellers. As predicted, we find that distance affects non-PowerSellers significantly more. We test for the statistical significance of the difference on the distance coefficient of PowerSellers by appending the PowerSeller and non-PowerSeller data and interacting each of the trade cost

> 15We also run the same specification for sales by non-business exporters (e.g. consumers) and perhaps surprisingly found a similar distance elasticity as for B2C flows of around -0.5.

> 16See eBay’s website for more details here: http://pages.ebay.com/sellerinformation/sellingresources/powerseller.html

> 17See Cabral and Horta ̧csu (2010) for a recent analysis of the export consequences of seller reputation on eBay

12
ββ + ββ CC + ββ XX + εε on activity =1,2,3 corresponding to market work, unpaid work, and other activities, respectively, and , =0,1,2, and are as defined TT ii aa above. The model is estimated for each of the three activities captured in the time-use survey with an θθ CC θθ XX εε Ordinary Least Squares (OLS) estimator.<sup>14</sup> As mentioned above, the time-use model in (2) must meet restrictions to capture the interdependence of activities = 1, 2, 3 performed in any given day. Any variation in the amount of time spent on one activity due to a change in an exogenous variable must be compensated by a corresponding aa change in the time spent on other activities. Hence, the sum of the intercepts of the three equations must equal the total number of hours in a day or 24 hours. In addition, the sum of the coefficients for each of

> 13 Activities such as weaving/sewing/textile care are considered unpaid work because, in the time-use module, the paid employments are captured using the category “‘work as employed.’” As many women from low-income households work in the garment industry, the activity weaving/sewing/textile care may also capture paid labor market work rather than unpaid work. Nevertheless, in the sample studied, women reported spending 0.1 hours (6.5 minutes) on average per day on weaving/sewing/textile, suggesting that these activities referred to unpaid work rather than employment in the garment sector. 14 There are general arguments on whether to use Tobit or OLS to explain individuals’ time allocations using time diaries. We use the OLS approach because time-use specialists suggest it is more appropriate for analyzing time diary data (Stewart 2009). The reported zeroes in any activity might be just measurement error rather than non-participation. Foster and Kalenkoski (2013) find that Tobit estimations are sensitive to censoring window. Nevertheless
Figure 1.11 Average inventory levels (weighted average), 2005-2010 <!-- Start of picture text --> 15.0%<br>10.0%<br>5.0%<br>0.0%<br>2005 2006 2007 2008 2009 2010<br>Private SOEs<br><!-- End of picture text --> Source: Authors estimates based on the NBRB dataset.

**But there is a longer answer to this question which is important to discuss in view of the current situation of the economy.** SOEs were then (and now) under enormous pressure to minimize the negative social impact of the crisis. In the absence of an independent social safety net, SOEs perform the role of social insurers of last resort providing de facto income transfers to their employees during a recession.  While this is understandable it is also extremely costly and, in the jargon of economists, suboptimal.  It is understandable because many of the poor performing enterprises are located in monocompany towns where they are the single largest employer. Cutting labor in these towns was bound to have a very high cost in terms of unemployment.  Even relatively successful companies such as MAZ were pressured to take over other poorly managed enterprises, effectively bailing them out.  At the same time, making SOEs perform this function is extremely costly and sub-optimal.  What sense does it make to continue producing when there is no demand?  A far better alternative would be to pay directly to the employees and save in the raw materials and intermediate products used in production.  It is also sub-optimal because the firm affected by a negative shock is the insurer, which clearly does not allow spreading risks in a larger society pool.

**This does not mean that SOEs remained immobile to market changes: in fact they reduced number of hours of work, cut bonuses etc. and imposed wage cuts about three times larger than private enterprises** (see Figure 1.12).  It just
# **IV.** Verifying Free-Floating Regimes

We now turn to the verification of free-floating regimes. The concept of verifiability has a different meaning under floating regimes. Under these regimes, governments do not make a commitment to a nominal anchor. There are no exchange rate rules to be verified, except that the exchange rate is floating or that the government is not intervening in the market. Applying the methodology used for exchange rate bands, market participants can check if the exchange rate is uncorrelated with major exchange rates.

**A** rejection of no correlation is not a rejection of a free-floating regime. Using observed exchange rate data, it is generally difficult to fail to reject that weights are equal to zero, either because governments intervene or because exchange rates co-move. Rejecting zero weights is not necessarily a sign of intervention. However, failing to reject zero weights is a clear sign of no intervention (or no pegging to other currencies). We rejected zero weights in the case of the Swiss franc above. Now, we move to the case of emerging markets.

Before proceeding with the estimations, note that there are other alternative ways of verifying free-floating regimes. Market participants can essentially check every month whether the central bank has intervened **by** seeing whether its reserve holdings have changed. Also, banks usually know who is participating in the market, so they can tell the difference between a system where the central bank never intervenes and where it intervenes occasionally. These methods require some type of additional information,

**_30_**
# **B.5 Tanzania**

Three waves of the LSMS-ISA data from Tanzania are included in our analysis. The first wave includes 3,265 households and the sample is representative for the nation, and provides reliable estimates of key socioeconomic variables for mainland rural areas, Dar es Salaam, other mainland urban areas, and Zanzibar (TNBS, 2011). In Wave 2, all original households were targeted for revisit (TNBS, 2012). For those household members still residing in their original location, they were simply re-interviewed. For adults who had relocated, these individuals were tracked and reinterviewed in their new location with their new households. As a result of this, the sample size for the second round expanded to 3,924 households. Wave 3 adhered to the same tracking protocol as Wave 2, resulting in a final sample size of 5,015 households (TNBS, 2015).

# **B.6 Uganda**

The LSMS-ISA from Uganda includes five waves, of which we use three in this analysis. Wave 1 (2009/10) includes approximately 3,200 households that were previously interviewed by the Uganda National Household Survey (UNHS) in 2005/06 (UBOS, 2014a). The sample was designed to be representative at the national-, urban/rural- and regional-level. For subsequent waves, the Wave 1 sample was followed, including tracking of shifted and split-off households, for two additional rounds: 2010/11 and 2011/12 (UBOS, 2014b; UBOS, 2016). Each round includes nearly 3,000 households.

107
3 Arnold et al. also concluded that the rapid rise in charcoal production—which requires large quantities of wood to manufacture—warranted further investigation, especially where urban demand for charcoal was rising against the backdrop of the global population migration from rural to urban areas. The statistics from the Food and Agriculture Organization<sup>3</sup> show that global charcoal production doubled from 26 million tonnes in 1980 to 54 million tonnes in 2020. During the intervening four decades, Africa’s share increased steadily, from 44 percent in the first decade (1980–1990) to 53 percent in the second decade (1990–2000), 57 percent in the third decade (2000–2010), and 63 percent in the decade ending in 2020. With wood fuel representing more than 90 percent of all wood harvested, Africa lost 3.94 million hectares per year of forest area between 2010 and 2020, the highest net loss of any region, followed by 2.60 million hectares per year in South America. Africa is the only region of the world where the total forest area decreased steadily in three successive decades from 1990 to 2020 (FAO and UNEP 2020).

Household use of wood fuels has far-reaching effects beyond cooking and heating. An estimated 880 million people worldwide spend part of their time collecting fuelwood or producing charcoal, many of them women. More than 40 million people, or 1.2 percent of the global workforce, are engaged in commercial fuelwood and charcoal activities (FAO and UNEP 2020). To the extent that those collecting fuelwood for own consumption can free up time, clean household energy provides an additional benefit. But to the extent that charcoal production and firewood gathering for sale is bringing income to 40 million people, the loss of revenue would initially present a socioeconomic problem. Such activities could even provide social stability. A study collecting primary data in 2011 in Sierra Leone found that 63 of 64 charcoal-producing villages visited during the data collection had not been involved in charcoal production prior
falling under the category [0-10] percent decentralization; Argentina, Colombia and Germany are more decentralized with the central government and local governments sharing the fiscal responsibly for construction activities. Moving further to the right, in South Africa, financing of construction works is with local governments at [70-90] percent decentralization. Finally, the USA is the most decentralized of all countries. Counties and townships and state governments are almost 100 percent responsible for financing of construction activities in their jurisdictions.

_14_ Quality of roads is used as a proxy for preference costs and quality is further interpreted as paved roads. The expenditure data is separated into three activities: construction, maintenance and administration. It was assumed that, the three activities would contribute differently to preference costs and that the actual effect is latent and hence unobservable. Furthermore, preference costs are assumed to be related to user savings due to better quality roads. Quality of roads has been transformed into costs using a latent variable model specification where the indicator of quality is related to the three expenditure categories to get the separate effect of the three activities on preference costs. For more detail on the ability to relate indicators of quality to user costs using a latent variable model specification see Ben-Akiva and Gopinath (1995) The analysis was performed on a panel data set. The years for which data is available may not be totally representative of the current situation in these countries.

13
could still be notable differences between the two comparison groups at baseline, which we address in a second step, by using an IPW method. The IPW allows to balance the treated and control samples on a set of covariates. A balance check table is presented in Appendix 2 and shows that the inverse probability reweighting improves the covariate balance in most cases. One notable exception is the household dependency ratio which remains different and slightly higher after matching. However, overidentification tests conducted after each estimation confirm that the IPW successfully rebalanced the covariates in each estimation. We therefore consider the matching to be satisfactory. However, we only control for characteristics that are observed in the data and there could be other omitted variables driving the short- and medium-term impact that is not captured in the surveys.

Furthermore, the timing of the ECT implementation and the two rounds of CIMS surveys create both opportunities and challenges for our analysis. While it allows us to estimate impact on beneficiaries who received the ECT before and after the first round of data collection, the long-time lag between the end of the ECT implementation (August 2020) and the start of Round 2 of the CIMS survey (November 2020) presents difficulties in interpreting the results for beneficiaries who received the transfers after the conclusion of the first round. Descriptive data also shows that compared to June-August 2020, the economic recovery was well on-track by November-December 2020 and could introduce sources of bias in the findings, especially on Round 2 beneficiaries.

Finally, while the CIMS data provided a timely opportunity to track the outcomes of ECT beneficiaries, the survey itself had limitations because of the way it was designed. In particular, part of the questionnaire was administered to the full set of respondents available in the sample, but other sections 25
year on average). Secondly, the drought that affected the country for the better part of 2016 and 2017 negatively affected the consumption of rural households, and rural poverty went up.<sup>2</sup> Poverty in urban areas has remained unchanged since 2009, at a level of around 9 percent, which shows that the purchasing power of urban households has been stagnant for the last decade.

Going forward it seems that higher economic growth will not be enough for poverty reduction as the responsiveness of poverty reduction to growth has declined: poverty-growth elasticity<sup>3</sup> fell from -1.24 for 2000 to 2016 to -0.5 for the period 2009 to 2016 (see Figure 1b). The implication is that even if economic growth recovers over the medium term, further poverty reduction will require a stronger focus on inclusive and equalizing policies.

Consumption growth between 2012 and 2016 brought disproportionate increases for the middle class and rich households, contributing to a more uneven distribution of resources. Inequality, as measured by the Gini index, increased from 0.40 in 2012 to 0.43 in 2016 (see Figure 1c). This represents a shift away from the broad-based economic progress observed during the period between 2009 and 2012, when inequality declined. Overall, the country has not been able to make significant progress in reducing consumption inequality for over a decade. While inequality reduction is often pursued for its own sake, it has also been shown that elevated inequality slows economic growth and has negative effects on sociopolitical stability (Berg and Ostry, 2011; Ostry, Berg and Tsangarides 2014).

> 2 This is linked to the fact that many of the rural poor depend on their agricultural production for their own subsistence. 3 The elasticity of the poverty headcount rate under the international poverty line to GDP per capita growth.

4