rafmacalaba/data-use-mentions-extended huggingface dataset:
in round parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level, referring to Romano-Wolf p-values. These are obtained from a step-down procedure to adjust for multiple hypothesis testing performing 1000 bootstrap replications as proposed by Romano and Wolf (2005). Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of sampled clients to village size. Amounts are in Indian Rupees (1 USD = Rs. 67.5). Data source: household survey. Dependent variables come from administrative data.

# **6.4 Non-sanitation investments and consumption**

We have established so far that sanitation loans were taken-up, and sanitation investments made by over half of the households that took a sanitation loan, but within the context of no change in overall household borrowing. We further provided some (weak) evidence that households seem to be switching away from non-MFI borrowing, both formal and informal towards borrowing from micro-finance lenders. The model indicates that an increase in sanitation loan take-up, and in sanitation investments, coupled with substitution out of other investments and other loans, and no change in overall borrowing would be consistent with the loan label mattering. To assess whether this is the case, we now consider intervention impacts on productive, non-sanitation investments.

Results are shown in Table 13. We start by looking at impacts on business ownership and closure. We consider impacts on the likelihood of the household owning any type of business (column 1), an agricultural business<sup>51</sup> (column 3) or whether it went through a business closure during the experiment. We do not detect any significant changes induced by the introduction of the sanitation loan product. We further check whether the intervention increased the likelihood of households making a large business investment and intervention impacts on reported profits. All estimated coefficients are insignificant, so that we conclude that, on average, sanitation loans were not used to make business
2002, which provided benefits through a combination of mandatory individual insurance accounts fully funded by individual contributions and solidarity funding. The first tier would minimize disincentive and moral hazard and the second tier, funded by general tax, would protect vulnerable population without impeding formal jobs (Duval and Loungani, 2019). Other developing countries, such as Brazil, Colombia, Jordan, and Mauritius followed the suit. In Chile, the evaluation of the program revealed that an extension of UB period led to a longer duration of unemployment for beneficiaries utilizing the solidarity fund. However, for those dependent on selffunded individual insurance, there was no change in the duration of unemployment, suggesting the absence of moral hazard in the latter group (Hartley et al. 2010; Sehnbruch et al. 2022).

The impact of UB scheme on productivity and re-employment wages is mixed and the analyses focus on developed countries. On one hand a longer UB duration raises reservation wage as it gives UB recipients more time to search for better jobs and to receive training, on the other hand it lowers wages offered by employers. Research by Soltes (2017) using data from 16 developed countries suggests that a one-indexpoint increase in the UB generosity index is associated with about a one-percentage-point increase in total factor productivity (TFP) growth. In the United States, longer UB duration encouraged workers to seek higher productivity jobs, particularly for those with lower education levels, and promoted job creation (Acemoglu and Shimer 2000; Centeno and Novo 2006). In Austria, there was a 0.5 percent increase in reemployment wage (Nekoei and Weber 2017), while in Germany there was a 0.8 percent reduction in 6
# Figure _1 :Median Ex-post Real World Interest Rates: 19 70s to 1990s_

<!-- Start of picture text --> Industrial countries Developing countries<br>Median real interest rates Nldian real interest rates<br>% per annum % per annum<br>-6-4-202468 -6-4-20246 8<br>75-77 75-77<br>80-82 _*Money Mkt 80-82 *-ADney Mkt<br>*TB *~~~~~~~~~~ITB<br>>! X 85-87 =_COfficial _TB 5 85-87 _ N fcafficial<br>90-92 r-IDeposit 90-92 _tDeposit<br>95-96 95-96<br>Quoted hntermediation Spreads<br>ledian for Industrial and Developing Countries<br>% per annum<br>0 2 4 6 8<br>75-77<br>80-82<br>X 85-87 *Industrial<br>C) *Developing<br>90-92<br>95-97 -<br><!-- End of picture text --> This annual data reveals some similarities and some contrasts between developing and industrial country interest rates, _Developing country real interest rates on an upward trend_ The real interest rates shown begin at predominantly negative levels, with even the third quartile generally negative or close to zero in the late 1970s. Developing country rates were, on average, even lower than those in industrial countries up to the mid-1980s; but thereafter developing country rates increased and passed out the industrial countries to end the period _5_
making it difficult to detect anything less than a 70% impact of the program, which is higher than prior estimates of PES impacts in the literature, and is unlikely to occur because of remaining natural forest losses due to events such as fires or hurricane damage. However, we did find that within areas at high risk of deforestation, the program statistically significantly reduced the loss of tree cover, by approximately 30 percent, with larger effects for those who have been in the program longer (38 percent). Although imprecise, this study’s estimates of the percentage change in loss rates due to PES are similar in magnitude to studies of the 2003-2010 cohorts, which found changes in the rate of expected forest loss of 20-50 percent (Alix-Garcia, Shapiro and Sims 2012, Alix-Garcia, Sims and Yanez-Pagans 2015, Sims and Alix-Garcia 2017). Unfortunately, given the limitations of the satellite data, we cannot draw conclusions about whether or not changes in targeting or more differentiated payments have increased the environmental impacts of the program compared to earlier cohorts.

With respect to socioeconomic outcomes, summary statistics show that the program continues to reach many marginalized and remote communities and is frequently used to support daily wages and community investments (see Appendix Figure A1). Analysis of the communal and household level impacts suggests the program has maintained socioeconomic indicators for program participants to be on par with non-participants. Estimated impacts were small and not statistically significant for indicators of average household assets, housing stock, food consumption and primary education. These results contrast somewhat with evidence from prior cohorts, which indicated small positive impacts of PES on household wealth (Alix-Garcia et al., 2015; Sims et al., 2017) and small positive effects on some educational opportunities and household and community-level assets for poor participant households compared to poor non-participant applicants (Alix-Garcia et al., 2015). In this

> 1 Applicants with scores within 25 points of the scoring cutoffs represent 97% of the possible applicants.  However, for the environmental analysis, we further restrict this
**Labor hours and other inputs** : Data on labor hours, material inputs, IT capital, non-IT capital by industry-country-year come from EU KLEMS (Timmer, van Moergastel, Stuivenwold, Ypma, O’Mahony, and Kangasniemi, 2007). These data were originally reported for 28 industries, which were mapped into the sectoral classification of the IFR data (see Table A1).

**Replaceability** : Following Graetz and Michaels (2018), we constructed an industry-level measure of replaceability using data from IFR on robot applications, the US census of occupational classifications and the distribution of hours across occupations and industries from the 1980 US Census. The IFR distinguishes between different applications of robots, including (among others) welding, processing, and assembling (International Federation of Robotics, 2012). We take the 2000 Census three-digit occupations and assign a replaceability value of one to the three-digit occupation if the name and/or description of at least one of the SOC five-digit occupations included in it contains at least one of these IFR application categories and zero otherwise. Note that we assume that services occupations (occupations with SOC codes _<_ 45000) are not replaceable. In addition, this procedure inadvertently identifies some occupations as potentially replaceable by robots that presumably are not, such as “agricultural inspectors”, “infantry”, and “airport cargo handling supervisors”. These are typically occupations that include tasks and/or titles such as “repair”, “inspect”, “drive”, “maintain”, “actor” etc. We remove such cases from the list of potentially replaceable occupations.

We then map the resulting replaceability measure into the 1990 Census occupational classification, which is available for the 1980 and 2000 censuses. If several 2000 occupations map into one 1990 occupation, then we assign the 1990 occupation a replaceability value of one if and only if at least one of the corresponding 2000 occupations has a value of one. To arrive at a measure of replaceability at the industry level,
|25.8|34.1|***|
|**Female knowledge of the land law**|||||||
|Possible to obtain certificates of customary ownership|32.2|33.8||33.6|28.1||
|Certificate can be converted into ownership upon payment|29.0|42.3|***|26.3|32.0|*|
|_Mailo_tenants are protected from eviction|27.8|39.4|***|14.8|57.4|***|
|_Mailo_owners can prohibit investments|21.8|60.6|***|9.6|41.4|***|
|Anybody occupying land for 12 years gets a certificate|31.0|43.7|***|25.2|42.2|***|
|Recognition of family land needs application to LC1|31.6|46.5|***|28.1|36.3|**|
|Index|28.9|44.4|***|22.9|39.6|***|
|Number of observations|970|71||643|256||

* significant at 10%; ** significant at 5%; *** significant at 1% for the t-statistic to test the equality of group means using customary tenure as a reference group. _Source:_ Own computation from 2004 WB/Makerere/FASID land tenure survey.

24
# **3. Data**

The road network data in the Ethiopia road database were collected from different sources. Federal Road Network data were obtained from the Ethiopian Roads Authority, the main agency in the Road Sector Development Program (RSDP) and Growth and Transformation Plan (GTP); and district- and woreda-level road data were collected from the Regional Road Authorities. The collected information includes years of new construction, upgrading, and rehabilitation, as well as the volume and distribution of the network in the country. It is important to note that the database includes rural roads that were motorable at the time of the national road inventory carried out in 2015−16. Rural roads in some parts of Oromia and Amhara regions were not covered due to missing bridges, as well as Somali region for security reasons.

In analyzing household-level impacts of the recent rural road development, this study primarily relies on the ESS. The ESS, part of the World Bank supported Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), started as the Ethiopian Rural Socioeconomic Survey (ERSS) in 2012. A sample of about 3,470 households were interviewed in rural areas and re-interviewed in 2014 and 2016. Among the rural communities covered by the ESS, this study focuses on the communities that received rural roads between 2010 and 2014 (i.e., treated group) and the other communities that did not receive rural roads between 2010 and 2016 (i.e., control group). This results in 204 communities, including 59 communities in Amhara, 46 communities in Oromia, 37 communities in SNNPR, 26 communities in Tigray, and 36 communities in the other regions.

The recipient indicator used for the econometric analysis in this study is a binary indicator about whether a rural road passes by within the community’s boundary regardless of the road length.<sup>11</sup> How to define households and areas affected by the rural road development is methodologically important. Given the nature of rural roads, this
|0.645|
|||||Nigeria|0.643|0.666|0.741|
|Middle East & North A|frica|||Rwanda|0.546|0.550|0.522|
|Egypt, Arab Rep.|0.523|0.581|0.588|Senegal|0.588|0.584|0.588|
|Jordan|0.474|0.468|0.551|Sierra Leone|n.a|0.517|0.515|
|||||South Africa|0.521|0.383|0.501|
|South Asia||||Tanzania|0.559|0.563|0.484|
|Afghanistan|n.a|0.523|0.591|Togo|0.446|0.570|0.519|
|Bangladesh|0.673|0.614|0.609|Uganda|0.648|0.666|0.552|
|India|0.658|0.631|0.631|Zambia|0.664|0.681|0.620|
|Nepal|0.638|0.618|0.588|Zimbabwe|0.569|0.609|0.625|
|Pakistan|0.595|0.633|0.679|||||

_Notes:_ This table presents the sibling correlation estimates for each of the 53 developing countries in the Demographic and Health Surveys (DHS) dis-aggregated by different decades of birth cohorts (the 1970s, 1980s, and 1990s).

36
# PPR Working Paper Series

|Title<br>WPS105 Securing International<br>Market Access|Author<br>Richard H. Snape|Date<br>October 1988|Contact<br>J. Sweene,<br>31021|
|---|---|---|---|
|WPS1O6 Energy Issues In the Developing<br>World|Mohan Munasinghe<br>Robert J. Saunders|||
|WPS107 A Review of World Bank Lending for<br>Electric Power|Mohan Munasinghe<br>Joseph Gilling<br>Melody Mason|||
|WPS108 Some Considerations<br>in Collecting<br>Data<br>on Household Energy Consumption|L. Leitmann|||
|WPS109 Improving<br>Power System Efficiency in<br>the Developing<br>Countries Through<br>Performance<br>Contracting|Philip Yates|||
|WPS11O Impact of Lower Oil Prices on<br>Renewable Energy Technologies|E. Terrado<br>M. Mendis<br>K. Fitzgerald|||
|WPS111 Recent World Bank Activities in Energy|!ndustry and Energy<br>Department|||
|WPS112 A Visual Overview of the World Oil<br>Markets|Kay McKeough<br>Jose Escay<br>Sompheap Sem|||
|WPS113 Public Sector Pay and Employment<br>Policy Issues in Bank Lending: An<br>Interim Review of Experience|Barbara Nunberg|||
|WPS114 Africa Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswor<br>31091|
|WPS115 Asia Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswor<br>31091|
|WPS116 Latin America and the
Each of these tests makes one or more assumptions that can be questioned. However, taken as a whole, they cast considerable doubt on the claims about the impoverishing effects of rising landlessness found in the literature and policy debates. Rising landlessness appears instead to be a positive factor in poverty reduction. This is less clear in the south, however.

# **8. Conclusions**

The prospect of renewed class differentiation — in particular the reemergence of a rural proletariat — has fuelled much debate about the wisdom of liberalizing agricultural land markets in the transition economies of East Asia. Vietnam has taken this step, but neighboring China has not. On theoretical grounds, we have argued that one should not be surprised to find a higher incidence of landlessness among the poor after breaking up the collectives and introducing a market in land-use rights. Many farmers will no doubt benefit from the new opportunities to use their wealth in other ways. But there could also be losers from such a reform. Welfare losses can occur for those who were previously landless, who receive lower wages than without the reform, and for those farmers who find that other benefits provided by the collectives are retrenched once their role in land allocation is removed. It is an empirical question whether the process of rising landlessness in the wake of such a reform is poverty reducing on balance.

Survey data for Vietnam — spanning a decade after legal reforms to introduce markets in land-use rights — confirm the expected rise in the landlessness rate among the poor. Even so, the post-reform landlessness rate tends to be higher for the rural non-poor in Vietnam as a whole. By and large, it is not the currently poor who took up the new opportunities for selling (or buying) land and acquiring land titles, but the relatively well off. Access to formal credit appears to have improved overall (and displaced informal credit), though more markedly for better off 34
the frequency with which they use computers at work—never, a couple of times a week, or more than three times per week—followed by a description of the computer-related tasks required in their job. These computer tasks are used to define binary variables indicating different levels of complexity of computer use on the job. Four levels of complexity have been defined: level 1 includes using _browser-based tasks_ (for example, use of email and Internet); level 2 involves use of _basic Microsoft Office functions_ like the word processor, presentations, and graphics; level 3 involves use of _basic programming tasks_ (involving spreadsheets and databases); and level 4 captures _advanced programming tasks_ (for instance, designing websites, using computer-aided software, programming software, and/or managing networks).

Noncognitive skills measures in the STEP surveys come from self-reported responses to various items measuring personality traits and behaviors. The personality traits include the Big Five traits—openness, conscientiousness, extraversion, agreeableness, and emotional stability (which is the obverse of neuroticism)—grit, and behaviors such as decision-making.<sup>12</sup> Items measuring these traits and behaviors are rated on a four-point Likert scale from “Almost never” to “Almost always _._ ” The STEP household surveys include extensive modules on education and employment history that provide information on educational and labor market outcomes. Further, the household roster of the survey gathers information on several individual and household characteristics used in this study. These include information on the number of children in the household, the marital status of the respondent, and retrospective information on the economic status of the household when the respondent was 15 years of age. The Appendix includes descriptive statistics on some of these key variables.

# **_Analytic Sample_**

To address the research questions posed in this study, the analytic sample for each country was limited to wage workers and self-employed adults between ages 25 and 64 who were in full-time employment. The self-employed group constitutes a large proportion of the sample
not possible to obtain meaningful estimates when they are included separately. We therefore use the overall STRI. For the regressions using aggregate services trade data, we use PCA to produce a weighted average of STRIs across all sectors. We then also use selected sectoral STRIs where they correspond closely to sectoral definitions in the TiVA trade data.

The original STRI produced by the OECD only captured MFN policies in services (Geloso Grosso et al, 2015). Subsequently, however, the organization has produced measures of preferential policies for countries within the European Economic Area (EEA), essentially EU members, but not for other countries. We therefore merge these two data points into one STRI policy data index to take account of the special rules that govern services trade within the EEA, but include an RTA dummy as well (see below) to take account of the fact that coverage of preferential policies is not complete.

An important caveat for the data set assembled as described above is that it mainly includes developed countries and a small number of mostly large developing countries. Consistent trade and policy data are not available more broadly, due to consistent difficulties in reporting services trade data disaggregated by sector and partner country in many developing countries, as well as the continuing lack of a global database on services trade restrictiveness. We therefore use the broadest sample possible, but in interpreting results, this limitation in terms of sample breadth should be kept in mind.

# **_TFIS measures_**

We can then move to consider the various indicators relevant to TFiS, as per the discussion in the previous section. To measure regulatory heterogeneity, we use the **OECD’s index of regulatory heterogeneity** , based on differences in answers to the regulatory questionnaire used to produce the STRI (see Nordås, 2016). As for the STRI itself, as explained above, we use PCA to produce a weighted average of sectoral measures in aggregate regressions, otherwise we use sectoral data where there is a close 16
For the 4,434 source-host pairs in our sample, all pairs have air distance, 2,715 pairs have sea routes, and 2,366 pairs are connected in Google Maps Distance Matrix.

We find that unit transportation costs vary significantly across regions. One measure for unit transportation cost is average driving distance per hour within each region, which depends on the quality of road as well as congestion.

Using the driving distance and driving time data from Google, we calculated the average driving distance per hour for each WBG region using source-host pairs located within the same region. To see how BRI countries compare with these regions, we report the average driving distance for BRI and non-BRI in Table 3 and Figure 11. We find that North America has the highest driving speed, around 103 km per hour, followed by the Middle East & North Africa (89 km per hour) and Europe & Central Asia (88 km per hour). South Asia has the lowest driving speed among all the regions with an average of 41 km per hour. BRI countries fall behind North America, MENA and ECA regions with an average of 85 km per hour.<sup>12</sup> Table 3: Driving speed across regions

|Region|DrivingSpeed(kmper hour)|
|---|---|
|East Asia & Pacific|57.62335|
|Europe & Central Asia|88.189293|
|Latin America & Caribbean|70.214012|
|Middle East & North Africa|89.411758|
|North America|103.63007|
|South Asia|41.549953|
|Sub-Saharan Africa|69.450729|
|Non BRI|77.015732|
|BRI|85.389648|

> 12 For some country pairs in the East Asia & Pacific region, the drive route involves a ferry. For example, driving from Brunei to Singapore requires a ferry from Sampit to Surabaya.

17
matter for a global sample of economies beyond those included in our sample. Regarding some of the differences, the sales decline gap is only 6 percentage points in MENA and about 11 percentage points in the other regions; government support significantly narrows the gap in MENA at some of the quantiles but widens it in all the other regions; a shortage of skilled workers widens the gap in SSA and MENA at some of the quantiles and narrows it in ECA and LAC. Several other differences are discussed in detail below. Thus, an eclectic approach is required that incorporates the common findings in this study and the broader literature while appropriately accounting for the prevailing regional or local conditions.

We contribute to the literature in several ways. First, we use cross-country comparable survey data on formal SMEs and large firms in 19 developing countries. Most previous studies on the impact of COVID-19 or other crises on small vs. large firms are restricted to rich countries or a handful of developing countries (see Section 2). Second, we identify several factors that contribute to the sales decline gap between SMEs and large firms. An estimate of their contributions is provided. Third, we use the mean and quantile decomposition methodology to decompose the contribution of the individual factors to the sales decline gap into “endowment” and “structural” effects. To the best of our knowledge, ours is the first such study. Fourth, we shed light on how the size of the gap and the contributions of the individual factors to the gap via the “endowment” and “structural” effects vary along the sales decline distribution. We are not aware of any previous work that does so. Fifth, we explore heterogeneities between geographical regions in the size and nature of the sales decline gap. This is important for assessing the relevance of regional factors. Sixth, the breadth of the surveys we use enables specific policy recommendations regarding the importance of salient firm factors, such as initial labor productivity, input supply 6