rafmacalaba/data-use-mentions-extended huggingface dataset:
Next, we compare key variables in terms of quantiles that allow assessing distance to the frontier. For the variables in Figure 8 above, we compute Ghana’s quantile within the two respective benchmark groups, ‘aspirational peers’ and ‘comparator countries’ which have been identified according to four criteria that would make a country relatively similar to Ghana.<sup>19</sup> That means, if Ghana performs best on the respective variable (within the comparison group) it gets a value of (close to) 1 and if it performs worst, a value of (close to) 0. A value of 0.5 would accordingly indicate that 50 percent of countries do better, whereas the other 50 percent do worse.<sup>20</sup> This exercise can inform us where policy gaps are particularly large compared to comparator countries and thus helps to prioritize reforms as large gaps usually entail that advancement in this dimensions is not particularly costly and that the existence of the gap might be a particularly binding constraint for further economic development.

The results are depicted in Figure 9. As one can see, roads and institutional quality (proxied by the polity2 index) do not seem to be areas of urgent need for policy action: Ghana comes close to the best performers in those categories, even if compared to the aspirational peers with a much higher income level. Key gaps, however, exist concerning inflation management and aspects of infrastructure, but also regarding schooling, trade openness, and financial development. From Figure 9 it is obvious that Ghana is considerably lagging behind in terms of inflation management and telephone lines: for both variables, it is the worst performer within both peer groups. The same is true for schooling and mobile phones within the group of aspirational peers, which have a much higher income level though. But Ghana also does worse than over half of the countries within the group of comparably similar countries in those two variables (right panel). Finally, Ghana falls short of more
model that relates each size share on key moments of the firm size distribution. The idea is to get an empirical source to predict the share of micro, small, medium and large firms based on just a few moments of the firm size distribution, reducing notably the data requirements.

The strategy also helps with the harmonization of the resulting dataset. It is not uncommon that countries have their own size shares, corresponding to idiosyncratic interests. While that could be relevant for the country itself, it prevents comparability. Our approach defines one set of thresholds that is applicable to all countries. To further leverage information from the WBES, we define the same size thresholds at 5, 20 and 100 employees.

This also follows the suggestion in Li and Rama (2015) that correcting the biases arising from the exclusion or under-representation of micro- and small firms in enterprise surveys is necessary, using household and labor force surveys to re-calibrate the firm size distribution.

The first goal is to map the relationship between the shape of the distribution and each size proportion through these four moments. Ideally, we would observe several firm size distributions of different shapes that will inform a statistical model that connects size shares and the shape of the firm size distribution. However, we have only 5 countries with microdata that allow this: Burkina . Faso (2016), Cameroon (2009), Ghana (2014) and Rwanda (2014, 2017 and 2020) and Kenya (2017)<sup>8</sup> To increase the number of distributions, we can observe, for which we can derive key moments and firm size shares, we break each country’s microdata into age groups from 0 to 61 to create more distributions with different shapes. This leaves us with 62 firm size distributions by country, totaling 434 distributions from which we can extract size shares and information on the shape of the distribution.

We compute the four size proportions from each firm size distribution and the mean, p25, p50, p75, and p90.
distribution of consumption is closer to the 2018/19 NLSS distribution – we only apply this transformation to the 2018/19 GHS to exploit information for an overlapping year.

We find that assuming a lognormal distribution is a reasonable approximation for our data, in line with other literature on survey-to-survey imputations (Tamakatsu et al., 2021). Specifically, we compare the distribution of the imputed consumption vector resulting from our main model to the one obtained after applying zero-skewedness transformations and compare whether different moments of the distribution estimated with the latter are closer to the _true_ distribution from the 2018/19 NLSS. We find that the poverty headcount rate at the US$1.90 poverty line is 42.4 percent when using a zero-skewedness transformation and 42.6 percent when using a Box-Cox transformation. Both estimates confirm the robustness of the imputation exercise for 2018/19. However, the difference with the 2018/19 NLSS official poverty headcount rate (39.1 percent) is slightly higher than what we obtain with our preferred specification (41.9 percent), suggesting that assuming a lognormal distribution in the imputations is a good fit for our data. Looking at different moments of the distribution confirms this finding.

# 7. Discussion

The main strength of this analysis is that two very different approaches yield very similar estimates of a poverty trend for Nigeria in the period between 2009 and 2019. In this section, we discuss the implications of the main findings and discuss possible caveats of this analysis.

# _<u>7.1. Implications for Nigeria’s poverty trend</u>_

Figure _6_ combines the main results of the backcasting exercise and of the survey-to-survey imputations using all waves of GHS data. As noted in Section 5.2, the survey-to-survey imputations seem to overestimate poverty by around 2 percentage points when compared to the backcasted estimates and to the 2018/19 NLSS official poverty rate. Notwithstanding this difference in levels, the two methodologies yield a very similar poverty trend showing a decline in poverty
non-destructive fishing gear category involves some judgment about how these gears are actually used in practice. Most of these gears we classify as non-destructive have traditionally been used by local fishermen. While they may generate some negative environmental impacts if used carelessly or by too many fishermen, the gears by themselves do not generate substantial habitat destruction.

> 8 The FAO’s Fisheries Global Information System contains detailed fishing gear type fact sheets, which includes a description of the fishing gear, how it is used, and an assessment of the gear’s impact on the catch of juveniles and other species and the marine environment.

> 9 We should note that dynamite and poison fishing are likely underreported in our survey. We believe this is case for two reasons. First, households may be less likely to report they use these methods simply because these are known to illegal activities. Second, dynamite use is very seasonal. Marine park police report that use of dynamite fishing increases at specific times of the year—such as when school fees are due or during particular holidays, when households may be in need of extra cash. The timing of our survey and recall period of one month would therefore miss this type of sporadic use.

22
698|193|698|698|

**Notes:** This table reports the treatment effect of receiving employment or cash relative to the control group on future employment. The columns show OLS estimates of a regression of the outcome on a dummy for assignment to the work treatment, as well as the baseline measure of the dependent variable, camp and enumerator fixed effects, and sociodemographic controls as determined through a double-selection LASSO procedure Belloni, Chernozhukov, and Hansen (2014).

Outcomes collected during the six-week followup survey. ‘Day labor’ is a binary variable that equals 1 if the respondent reports engaging in agriculture or construction work in the prior month, and 0 otherwise. ‘Salaried’ work equals 1 if the respondent reports engaging in service or teaching work, and 0 otherwise. ‘Any work’ equals 1 if the respondent reports engaging in any work in the prior month, and 0 otherwise. ‘Daily wage’ is the wage in USD received if a respondent worked in the previous month. ‘Total expected’ is the total compensation expected in the coming month if one were to find work (USD). Standard errors are clustered at the block level.

Table A6: Mental health is correlated with family but not community value-ranking

||(1)<br>PHQ|
|---|---|
|Rank within family|0.052<br>(0.020)|
|Rank within|0.003|
|community|(0.019)|
|Observations|726|

**Notes:** This table reports the baseline relationship between ranking of oneself within one’s family and one’s community on PHQ score. The PHQ score is reversed such that lower PHQ reflects a poorer outcome, or higher likelihood of depression. Regression includes camp and enumerator fixed effects. Standard errors are clustered at the block level.

50
**The global crisis has already produced considerable spillover effects to bank credit conditions in the rest of the world** (Exhibits V.1 and V.2). Emerging markets began to experience significant tightening of bank credit conditions in 2010 reaching a trough in 2011Q4, particularly in emerging Europe. At that time, all emerging markets across the world indicated their credit standards had been severely affected by European financial turmoil (Exhibit V.3). Especially international funding conditions for emerging markets deteriorated excessively (Exhibit V.4). As discussed above, the ECB LTROs provided welcome relief and overall bank credit conditions, credit standards, and local and international funding conditions continued to tighten in 2012, but to a lesser degree. It also appears the ECB statement to “preserve the Euro whatever it takes” and the launch of the OMT program slowed the tightening pace in funding conditions for the first time since 2012Q2 and has recently slightly loosened. On the demand side, conditions have continued to be expansionary, but at an increasingly slower pace since 2010. Demand was stagnant during end-2011 and has been increasing slightly since 2012Q2 (Exhibit V.3).

<!-- Start of picture text --> V.1 As the Euro Area crisis deepened, emerging V.2 The intensification of the Euro Crisis in<br>market credit conditions deteriorated significantly. 2011Q4 was strongly felt in all emerging markets<br>Conversely, they improved after ECB intervention across the world<br>Emerging Market Overall Credit Conditions 2011Q4 Impact of Euro Crisis on Credit Standards<br>Diffusion index, below 50 indicates tightening relative to Diffusion index, below 50 indicates tightening relative<br>previous quarter to previous quarter<br>70 60<br>65<br>60 50<br>55<br>40<br>50<br>45<br>30<br>40<br>35 20<br>10<br>GLOBAL AFME LATAM<br>0
# **Table 6:  Competition, Innovation, and Regulation and Family Management Practices**

|**Model**||**OLS w**|**ith Country**|**FE (Cross-sect**|**ion)**||
|---|---|---|---|---|---|---|
|**Outcome Variable**||**O**|**verall Mana**|**gement Score**|||
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|All Family Management x No or One Competitor<br>(domestic)|-0.100**<br>(0.042)||||||
|Family Management share x No or One Competitor<br>(domestic)||-0.119**|||||
|||(0.047)|||||
|All Family Management x Introduced Process<br>Innovation|||0.047**<br>(0.022)||||
|Family Management share x Introduced Process<br>Innovation||||0.056**|||
|||||(0.023)|||
|All Family Management x Labor Regulations<br>Major/Severe Obstacle|||||0.092***<br>(0.032)||
|Family Management share x Labor Regulations<br>Major/Severe Obstacle||||||0.125***<br>(0.033)|
|All management is
***|_0.779_|-0.860|_0.626_|-3.732***|_0.873_|-1.756**|_0.725_|
|R2 (pseudo)|0.134||0.183||0.148||0.251||
|Observations|1613||2137||1565||2137||

_Note_ : Standard errors in parentheses; the coefficients on state dummies are not reported * significant at 10%; ** significant at 5%; *** significant at 1% _Source_ : NFHS I and II.

21
the main meals the household ate each day, and additional information is recorded if any components of these meals were not captured in the diaries. This is important because the 2012 State of Food Insecurity report incorporated, for the first time, tentative estimates of food losses, which led to a significant revision of some of the world hunger numbers (FAO, WFP, and IFAD 2012). Our diaries explicitly account for any food that has been used for seed, fed to animals, or thrown away. The recall modules do this implicitly by asking about the food consumed, which eliminates the counting of seeds and animal feed as consumption, but may not eliminate food scraps and leftovers that are fed to animals.

The survey was administered on paper. To minimize data entry errors, all questionnaires were entered twice, and discrepancies were adjudicated. Because nonstandard units are common in Tanzania, the experiment collected conversion factors during a community price survey conducted by the field supervisors in each sample community. Supervisors used a food weighing scale to obtain a metric value of food-specific nonstandard unit combinations. Median district-level metric conversion rates were used to convert nonmetric units into kilograms or liters. If district-level conversion rates were not available, the sample median was used. In a handful of cases where neither was available, measurements at the survey’s headquarters were taken after the fieldwork was done. Further details on the experiment implementation, including the relative costs to field each module, are described in Beegle et al. (2012).

> 4 The survey teams were small, extensively trained on all modules, and well supervised. They stayed in the field for the entire 12-month study period to ensure that well-trained survey teams consistently applied the modules across all districts and also to abstract away from seasonal concerns that might have interacted with specific survey designs. 5 There is almost no item nonresponse in the consumption section of the recall modules, that is, all respondents answered virtually all questions on all consumption items, including a response of no, or zero,
40 coordinating tax reforms: can lost tariffs be recouped?

An ideal tax system raises revenue to fund socially sanctioned government expenditures in ways that are administratively and politically feasible while promoting equity and efficiency (Burgess & Stern 1993). As countries become richer, they move towards this ideal by relying more on direct sources of taxation on personal income. Because of capacity constraints and high enforcement costs, however, developing countries focus on narrow tax bases that not only distort incentives and resource allocation, but also raise revenue that is inadequate to fund development needs.<sup>39</sup> This paper contributes to the debate on ways to reform trade practices without adversely affecting the fiscal base of poor countries. Cross-country evidence from 35 low-income countries also shows that as countries move away from trade-based taxes, they manage to partially offset trade-based revenue losses with domestic sources of taxes. That the presence of VAT is not more strongly associated with revenue recovery is a surprise because this is not what the simulations suggest. This can only be resolved if we acknowledge the great heterogeneity in VAT regimes across countries, with efficacy dependent on factors beyond the ad valorem rate, such as the size of the informal sector, product coverage of the value-added tax, systems of tax refund, and the capacity of tax administrations.

Using actual data on import value, tariffs, and up to ten domestic taxes on more than 400,000 import transactions from Nepal, I develop scenarios in which tariffs can be cut with the least impact on total revenue. This requires eliminating widespread exemptions and an effective implementation of domestic taxes such as VAT and excise spanning a widened base. The degree of informality and the hardto-tax sectors complicate tax replacement, but a measured approach to reducing tariffs and expanding consumption taxes could minimize adjustment costs. In the

> 39 Consider a hypothetical scenario where a typical poor country raises taxes of around 12 percent of GDP. It raises another 3 percent of GDP in non-tax revenue. Add to it a fiscal deficit of 5 percent of GDP.
Russian context, to the life and liberty of owners. To test this hypothesis, the paper draws on a measure that captures private “raiders” attacks on firms – hostile, often violent takeovers of firms by outsiders aided and abetted by law enforcement agencies.<sup>2</sup> Arguably, the greater the number of raider attacks for a given region, the greater the uncertainty and the less likely investment.

In this paper, we compare these two important aspects of regulation using data from the Russian Federation in order to contribute to the literature and to advise policy making more broadly. Empirically, our paper focuses on micro-level firm investment behavior and how it is shaped (or not) by burdensome informal practices associated with regulation and with uncertainty in these informal practices. Specifically we focus on corruption and violent, state-abetted “raider” attacks on firms. We note that our approach is in contrast to the more macrolevel focus of much of the existing literature, as well as its focus on formal, written regulation.

Our paper also differs empirically from many contributions in the literature on regulation and investment in that our focus is on a single country. Focusing on a single country allows us to dismiss a number of potentially confounding omitted variables – historical legacies, culture, formal legal environment, etc. – that bedevil crossnational studies. It also allows us to deal with the more subtle problems stemming from the fact that respondents in different countries may perceive corruption differently for cultural or historic reasons or may simply have a different scale in mind when answering questions about it (Treisman 2007).

To examine the relationship between regulation and investment, our primary data source is the Russian Firms in a Global Economy Survey (RuFIGE), which we combine with data on corruption from the 2011/12 EBRDWorld Bank Business Environment and Enterprise Performance Survey for Russian regions(RR BEEPS) and

> 2 This indicator is calculated using a publicly accessible database of complaints to the NGO “Business Against Corruption” from entrepreneurs claiming to have faced unlawful
to place a lower weight on development objectives, relative to other donors, and a higher weight on “private” objectives related to commercial interests.

Descriptive statistics for these variables are provided in table 1 for the main sample of 1959 observations for the year 2007. Standard errors were adjusted for donor clustering of observations. Note that all observations where the donor is a multilateral aid agency are excluded from the sample, because they exhibit no variation in the dependent variable. All but the EC have no tied aid, and all of its aid is classified as partially tied<sup>6</sup> . Several small non-DAC donors are also dropped from the analysis, because they do not report aid tying status. The sample of donors thus includes 22 DAC members (all but the EC). All recipients of aid in the OECD’s Creditor Reporting System (CRS) are included, with the exception of a small number of countries that were already classified as high income by the World Bank.

Although our aid share measure varies across donors within each recipient, the number of donors does not. We therefore test the model’s prediction that more donors will be associated with higher tied aid using a cross section of 133 recipient countries for 2007. Specifically, we estimate the following equation: where _σi_ represents the share of aid recipient _i_ receives that is tied, _Ni_ is the number of donors present in recipient country _i_ , _LDCi_ is a dummy indicating whether the recipient country is classified as a Least Developed Country, and _Xi_ is a vector of recipient-specific controls. Summary statistics of these variables are found in table 2. Standard errors robust to possible heteroskedasticity are reported in all tests.

Data on aid tying shares were computed from the AidData portal, a database which provides development financing data at the project level<sup>7</sup> . AidData uses the CRS as a main source. The CRS reports for each project the total commitment amount, the amount of tied aid, the amount of partially
in the flood prone Upazilas members of the households with access to credit or safety net are more likely to be in the same occupation as compared with households in flood prone Upazilas with no access to credit or safety net (Table 3, columns 1 and 2). Thus, access to credit or safety net is helpful to some extent to the households in the flood prone Upazilas in reducing the need to diversify into different occupation.

On the other hand, access to market may completely obliterate the households’ need to diversify into different occupations if they live in flood prone Upazilas (Table 3, column 3). The interaction between access to market and flood prone Upazilas is positive and significant and almost as big in magnitude as the coefficient of the flood prone Upazila by itself. This implies the strong positive effects of the interaction between market and flood completely cancels out the negative effects of flood on need to diversify into different occupations.

The effects of the policy action variables are similar when we consider local rainfall variability in the nonflood prone Upazilas. We have already established that local rainfall variability is only important to the households who live non-flood prone Upazilas. Thus, the analysis of policy interaction with local rainfall variability is only relevant for households in non-flood prone Upazilas.

14
|0.386|
|||[0.071]||[63.127]||[0.014]||[0.268]|
|Climate|No|Yes|No|Yes|No|Yes|No|Yes|
|Fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Conflict dummy|No|Yes|No|Yes|No|Yes|No|Yes|
|_N_|24856|22764|24957|22864|20315|18378|20315|18378|
|R-squared|0.074|0.076|0.969|0.969|0.795|0.791|0.951|0.950|
|Dep. Variable [mean]|0.029|0.029|288.423|283.912|0.254|0.247|4.003|3.626|

Notes: (1) The table shows the regression results across districts in Africa during 2002–2007. (2) The results show the local economic impacts of China's WTO accession in the presence of mineral deposits in Africa. (3) The standard errors, clustered at the country level, are in brackets; * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01. (4) Growth, Spatial Gini, and Sen Index are measured using lights data as described in the data section. (5) Population density is measured as population per square kilometer. (6) Prices are reported in 1998 constant US dollars. (7) Fixed effects include time-fixed effects, district-fixed effects, and country-year-fixed effects. (8) Climate is measured using the standard deviation of rainfall distribution at the district level. Source: Authors’ estimations.

Table 5b: The local economic impacts of active mining activities.

|_Dependent variables:_|_Per capit_|_a
contribution of 2006 and 2010 legal reforms on gender outcomes.

# **3.1 Data**

To obtain a complete set of textual and spatial data before and after the intervention, we digitized paper records from registry books for all transactions registered before LAA migrated to a functional digital platform. This provides data on the number of parcels for which rights were registered for the first time, the gender of registered rights holders, and registered sales and mortgage transactions, with attendant details for all of Maseru from 1981 through 2019.

While these data lack information on parcel attributes or the socio-economic characteristics of owners other than gender, they cover the universe of registered transactions, thus avoiding the potentially large errors, sampling and non-sampling, that would be likely in household surveys because land transfers are rare and information on land rights may not be shared with all household members. The data also include the actual 8