rafmacalaba/data-use-mentions-extended huggingface dataset:
has shown, for example, that most studies find a positive or non-significant effect of FD on hosts’ employment, wages and household well-being, a finding that disputes much of the popular perceptions on this question (Verme and Schuettler, 2021).

One of the important factors that has limited research on FDPs in the past was the chronic shortage of quality microdata, something that is quickly changing. Today, microdata on these populations can be found in two main publicly available repositories: The World Bank microdata library and the UNHCR microdata library recently established in collaboration with the World Bank. An analysis of these data repositories as of October 2022 shows that the WB microdata library has 576 data sets on refugees (454 dated after 2011) and 206 on Internally Displaced (142 dated after 2011), whereas the UNHCR microdata library has 274 data sets related to refugees (263 dated after 2011) and 34 data sets on IDPs (all dated after 2010).<sup>5</sup> This new research area is also generating significant innovations with the potential to expand research methods in the poverty measurement field. In the area of targeting based on means-tests, for example, a study has shown that Receiving Operations Characteristics (ROC) curves can be an effective decision making tool for humanitarian assistance programs (Verme and Gigliarano, 2019) while another study found that poverty differences in prediction methods for targeting purposes among refugees are attributable to few data fields suggesting that refugee homogeneity can make poverty predictions and targeting easier as compared to regular populations (Altindag et Al., 2021, Beltramo et al., 2019). The existence of the UNHCR refugee registration system, which can be regarded as a live census of refugees, has encouraged others to use cross-survey imputation techniques to estimate poverty among refugees even in the absence of income or consumption data (Dang and Verme, 2021, Beltramo et al., 2021). The mobile nature of refugees and IDPs also lends itself to experimenting with new methodologies to measure poverty with alternative methods
noted earlier are associated with greater flows of technological inputs in those sectors. In contrast, related-party trade in these industries is unresponsive to the other forms of IPAs. In those cases, the third-party trade effects seem more associated with locally generated capacity expansion in U.S. affiliates. One notable finding is that related-party exports in ICT are reduced in all three agreement types. This outcome suggests that affiliate production in electronics goods becomes less reliant on imported inputs from headquarters locations in the wake of joining such an agreement.

# **5. Concluding Remarks**

Our intention in this paper was to apply a treatment-control approach, used in other empirical trade contexts, to the World Bank’s Database on PTAs and to see if it unearthed systematic evidence that PTAs with enforceable IPRs chapters, and those with more specific provisions, have effects on trade in treated countries versus control countries. We note that the approach may not be the best way to identify such impacts because our treatment definitions may not sufficiently exclude other important impacts on trade arising from the agreements themselves or, indeed, from provisions not directly related to intellectual property protection. Nevertheless, it offers a complementary avenue for thinking about such effects.

Our approach is rigorous in its exclusion of intra-PTA trade and its use of extensive fixed effects. These controls, along with the difficulty of identifying the potential effects of actual IPRs provisions, make it challenging to identify systematic effects. Indeed, while we noted earlier that the aggregate sectoral trade analysis reveals some tentative conclusions, they are only weakly supported in the bilateral analysis. Nonetheless, it does seem that, at least as regards legally enforceable IPAs involving the US, the EU, or EFTA, they have detectable impacts on exports of pharmaceuticals and other goods, depending on the income group. Beyond that, we conclude that, as valuable as the World Bank Database on PTAs clearly is, it is difficult with our approach to use that information to detect trade impacts strictly within the context of intellectual-property provisions. Future refinements in
Pouliquen, 2022), there is some emerging evidence from Africa as well. Santoro, Amine and Magongo (2022) evaluate the effect of the Eswatini government mandate to adopt e-filing and e-payment services for all income taxpayers as a response to the pandemic, introduced in September 2020. Using extensive administrative data from the tax agency, the authors examine the impact of IT adoption by exploiting the partial take-up of the mandate. A first key finding is that, indeed, more than 8 months after the mandate came into force, only a minority (41 percent) of taxpayers moved to filing through the e-Tax system. In line with other research from Africa described in Box 4, qualitative evidence from interviews with the tax agency reveals that most small taxpayers lack the equipment, an adequate connection, and the knowledge to navigate e-Tax. These taxpayers, mostly from rural areas where internet coverage is poor, then resort to imperfect solutions such as bringing their manual returns to the revenue office or emailing them and asking staff to help enter them in e-Tax.

In terms of impacts, the evidence from Eswatini is rather positive, as e-Tax uptake significantly improved filing behavior, as e-Tax registered taxpayers are almost 60 percent less likely to file nil, and they declare 32 percent more turnover and 50 percent more taxable income. However, surprisingly, final tax liability remains unchanged due to corresponding increases in expenses and deductions. For payment behavior, the authors find a strong impact of e-Tax registration on the probability to pay conditional on filing, as well as on the final tax amount paid (70 percent and 64 percent increases, respectively).<sup>8</sup> In terms of mechanisms, the study suggests that technology is facilitating compliance through a salience or reminder effect and helping taxpayers to track their records and fully report their expenses and deductions.

As a cautionary note to these positive impacts of technology on income taxes, in Rwanda, Santoro et al. (2022) do not find a positive relationship between adoption of E-tax or M
the number of children below 15 varies from 0 (for nearly 44% of the cells) to 8, with an average of 1.36. Nearly half of the cells are those of a mother and her children. Women also tend to have under their responsibility grand-children, younger unmarried siblings or nephews and nieces.

# Consumption

Contrary to traditional consumption surveys that only aim at estimating the level of household consumption, PSF is designed to approximate, as much as possible, individual access 8
<br>5<br>20%<br>15% 4<br>10% 3<br>5% 2<br>0%<br>1<br>0<br>Source : Authors’ calculation using the World Bank database on Source : Authors’ calculation using the Global Financial Inclusion<br>housing finance launched by Badev et al., 2014: “Housing finance database (FINDEX) launched by Demirguc-Kunt, Asli and Leora<br>across countries: New data and analysis,” WPS6756. Klapper (2012), “Measuring financial inclusion: The global Findex<br>Database,” World Bank Policy Research Working Paper 6025.<br>Note : Percentage of adult population with an outstanding loan to<br>purchase a home. Unlike the mortgage depth indicator, the penetration<br>index refers to any provider of housing loans, including regulated<br>financial institutions, microfinance institutions, and other formal<br>sources.<br>6.56.3 6.3 6.1<br>30.8% 20.0% 5.7 5.4 5.4<br>4.3<br>3.6<br>2.8<br>2.3<br>2.5% 2.3% 2.0% 1.2% 1.2% 1.0% 0.5% 0.4% 0.4% 0.2% 0.1% 0.1% 0.1% 0.0% 0.0% 2.1 1.81.8 1.7 1.2 1.2 1.1 1.1 1.0 0.80.7 0.7 0.6 0.60.6<br>0.3 0.3 0.20.1 0.1<br>South Africa Namibia Kenya Botswana Senegal Rwanda Algeria
2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2021 2021 2021 2021 2021 2021<br>Ethiopia Malawi Nigeria Uganda<br>index<br>Government response stringency<br><!-- End of picture text --> _Source_ : Oxford COVID-19 Government Response Tracker (2021)

> 4 The COVID-19 Government Response Stringency Index is calculated by the Oxford COVID-19 Government Response Tracker (OxCGRT), which collects publicly available information on 20 indicators of policy responses that governments have taken to respond to the pandemic, such as school closures and travel restrictions. It includes information on 180 countries. See more here: <u>https://www.bsg.ox.ac.uk/research/researchprojects/covid-19-government-response-tracker</u> 5 The nine indicators included are the following: (1) School closings, (2) Workplace closings, (3) Cancelling public events, (4) Restrictions on gatherings, (5) Closing of public transport, (6) Stay at home requirements, (7) Restrictions on internal movement, (8) International travel controls, and (9) Presence of public information campaigns. More details about the methodology can be found here: <u>https://www.bsg.ox.ac.uk/sites/default/files/2020-12/BSG-WP-2020-032-v10.pdf</u>

4
four or five hours) as reported across the other three arms of the study is similar to the mean hours per activity in the recall NPS survey, further supporting this interpretation.

> 15 Of the 3,707 individuals ages 10 and older in the 854 households in our study, 821 reported no agricultural work and are excluded from the analysis.

> 16 As we show in Section 4.2, this is consistent with the fact that hours worked per day are more regular and less variable relative to weeks and days per week worked.

11
single region, but are concentrated in two or more adjacent regions; and we check how these cases influence the results of the analysis.

32 Data for this variable come from national statistical yearbooks and the variable is coded for the year for which we have household survey data on income or the closest year, in a few cases before the year income is measured. We treat the 0-1 index as a continuous variable. The results presented later are robust to the use of Tobit regression, which accounts for the truncation of the variable at 1 (see supplement).

18
- Dabalen, Andrew and Saumik Paul. 2012. “Estimating the Effects of Conflict on Education in Cˆote d’Ivoire.” <u>Journal</u> of Development Studies 50.

- Diwakar, Vidya. 2015. “The Effect of Armed Conflict on Education: Evidence from Iraq.” The <u>Journal</u> of Development Studies 51(12):1702–1718.

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- Falck, Oliver, Stephan Heblich and Susanne Link. 2011. “The Evils of Forced Migration: Do Integration Policies Alleviate Migrants’ Economic Situations?” Discussion Paper, IZA (5829).

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- Giustozzi, Antonio. 2010. “Nation-Building Is Not for All: The Politics of Education in Afghanistan.” Afghanistan Analysts Network Report .

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Martins, P. M. G. 2019. “Structural Change Rediscovered: The Role of Human and Physical Capital.” World Bank Research and Policy Briefs, July 2019, Washington, DC.

- Micco, A., and C. Pagés. 2006 “The Economic Effects of Employment Protection: Evidence from International Industry-Level Data.” Discussion Paper No. 2433, IZA, Bonn.

- Nicoletti, G., and S. Scarpetta. 2005. “Product Market Reforms and Employment in OECD Countries.” Economics Department Working Paper No. 472, OECD Publishing, Paris.

- Penn World Table, version 9.0:  Feenstra, R. C., R. Inklaar, and M.P. Timmer 2015. “The Next Generation of the Penn World Table.” American Economic Review 105 (10): 3150‒82.

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Table 2 _Number of_ _<u>poor people across environmental risk categories at district‐level</u>_

|Risk<br>category|Air<br>pollution|Tree cover<br>loss|Land<br>degradation|Slope|Rainfall<br>Variability|Temperature<br>Variability|Flood<br>hazards|Drought<br>hazards|
|---|---|---|---|---|---|---|---|---|
|Low|23,393|19,792|18,206|21,302|22,117|21,688|30,318|20,172|
|Medium|24,874|25,046|27,650|21,858|28,054|27,455|21,769|24,281|
|High|25,741|28,967|28,713|30,632|24,106|25,124|21,704|29,349|
|ANOVA|||||||||
|F|1.08|16.99|28.63|22.27|7.09|6.56|19.82|16.94|
|Prob>F|0.3405|0.0000|0.0000|0.0000|0.0009|0.0015|0.0000|0.0000|

Notes: Table shows the number of poor people across the three environmental risks categories as calculated for Figure A.1 and statistics from a one‐way analysis‐of‐variance (ANOVA).

_Source: Authors’ calculation based on data from Brauer et al., 2015, Hansen et al., 2013, Vu et al,. 2014, Harmonized World Soil Database, Climate Research Unit, Bangalore et al., 2016,
Column (4) of Table 5 shows that sole proprietorships have worse management practices, which is in line with the results of Lemos and Scur (2012, 2018) who find that family‐owned firms have significantly lower management scores. Column (5) shows that establishments that offer formal training to their workers, unsurprisingly, have better management practices in place. This echoes the findings of Bloom and Van Reenen (2010) who show that firms that use human capital more intensively (as proxied by more educated workers) are better managed. Interestingly, the significance of the relationship between sole proprietorship and management practices disappears once other characteristics are accounted for, but the relationship between formal training and management is more robust to the inclusion of other controls (column (7)). Finally, column (6) suggests that establishments who recruited their manager from outside the establishment (as opposed to those who promoted internally) have better management practices—a relationship that also holds after controlling for other establishment characteristics (column (7)).

# 4.2 Management practices and productivity

Across countries in both the developing and developed world, studies have shown that there are positive and significant returns to improved management practices. Furthermore, research by Bloom et al. (2013) has shown particularly strong positive impacts in some developing‐world contexts. Our results based on seven countries in Latin America confirm that there is indeed a significant relationship between improved management practices and productivity.

To examine the relationship between management practices and productivity, we estimate the model described in equation (1) on the establishments surveyed; the results of this regression using labor productivity as the outcome variable are presented in table 6 below. Column (1) shows that there is a positive and significant link between an establishment’s _average_ management score and its sales per worker. It also illustrates which other establishment characteristics are, _ceteris paribus_ , associated with higher labor productivity; namely size (bigger establishments have higher labor productivity), foreign ownership, exporting status,
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B4.3. SOSD: Social Sustainability Index B4.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.95*** 1*** 0.98***<br>H<br>Kendall tau-b 0.83*** 1*** 0.94***<br>Spearman 0.93*** 0.97*** 0.65*<br>A<br>Kendall tau-b 0.83*** 0.89*** 0.5*<br>Spearman 0.98*** 0.98*** 0.98***<br>M0<br>Kendall tau-b 0.94*** 0.94*** 0.94***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.1<br>1<br>.8 0<br>.6<br>-.1<br>CCDF<br>.4 Difference<br>.2 -.2<br>0<br>-.3<br>.
0.01]|0.35|(0.48)|1,604|
|Adults hunger last week (=1)|0.17***|(0.06)|[0.01]|0.38|(0.49)|1,610|
|Respondent hunger last week (Days)|0.65***|(0.22)|[0.01]|0.91|(1.46)|1,604|
|Adult hunger last week (Days)|0.55***|(0.21)|[0.01]|0.79|(1.36)|1,444|

**_Notes_** :

- The table shows the regression results on pre-specified physical health outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The outcomes that require definitions are: Disability is defined using the Washington Group Short Set on Functioning and equals 1 if the respondent is classified as disabled and 0 otherwise. Disability 3 is the recommended cutoff and as we moce from Disabiloty 1 to Disability 4, disability level becomes more extreme. Respondent (and adult) hunger equals 1 if the respondent went to bed hungry on at least one day in the last week and 0 otherwise.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age).

- In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%),
de jure’ rules is observed in Russian regions. Only in 4 out of 26 regions covered by the data on the number of days needed to get a construction permit did _de facto_ measures exceed the _de jure_ number of days. The same situation occurs for electrical connections. Graphs A1 and A2 in the Annex provide the comparison of _de jure_ and _de facto_ number of days required to get construction permits and electricity connection in Russian regions to illustrate this. Consequently, we do not believe that the comparison of Doing Business and BEEPS Russian Regions data reflects regulatory uncertainty in the way we theorize it in this paper, because formal rules and regulations do not appear to be being violated by authorities. If anything, these data suggest firms may be more likely to invest due to uncertainty having upside risks. This, of course, is not to say that Russian firms do not face negative uncertainty linked to formal regulation. Nonetheless, in absence of new data sources, we cannot measure the extent of negative uncertainty linked to formal regulations.

To measure the level of corruption, we make use of the two variables that draw on the same question about the percentage of annual sales spent on bribe payments taken from questions on the RR BEEPS 2012 survey.<sup>10</sup> The first, “bribe incidence” is the share of regional firms (for each region) that reported paying some percentage and those answering “don’t know” and measures how common bribery is in the region.<sup>11</sup> The second measure is the regional mean value of bribes paid by firms reporting that they paid bribes, “bribe tax”, which measures

> 10 The RR BEEPS question read as follows: _“It is said that establishments are sometimes required to make gifts or informal payments to public officials to” get things done” with regard to customs, taxes, licenses, regulations, services, etc. On average, what percentage of total annual sales, or estimated total annual value
universal, flat transfer (column 4). Finally, under a proportional loss shock, using an indicator of which households suffered a shock (columns 7-10) does not deliver any additional impacts as all households are affected: when income ranks do not change because of a shock, the best responses continue to be to support households according to their pre-shock poverty status.<sup>22</sup> _Table 8 : Moldova – Proportional loss shock_

|**Targeting →**||||**Pr**|**oportiona**<br>**Hous**<br>**Eligibi**|**l Loss Sho**<br>**ehold**<br>**lity ↓**|**ck**||||
|---|---|---|---|---|---|---|---|---|---|---|
|||**Means**|**Means**||||||**PMT Ex-**|**PMT Post**|
|**Budget ↓**|**Original**<br>**Beneficiaries**|**Testing**<br>**(Ex-**<br>**Ante**<br>**Poor)**|**Testing**<br>**(Post**<br>**Shock**<br>**Poor)**|**Universal**|**PMT Ex-**<br>**ante**|**PMT**<br>**Post**<br>**Shock**|**Perfect**<br>**Loss**<br
Our survey data provide additional information which can help us interpret the results in Tables 5 through 8. The fact that subsidies have a temporary effect and that there is little evidence that the increase in profits is greater than the subsidy amount suggests that labor markets work without major frictions for the micro enterprises in our sample. This picture is consistent with the view that urban informal labor markets working reasonably well. One of the advantages of experiments is that they create shocks to opportunities that allow researchers to observe how actors respond. The survey data provide details that help us understand the aggregate responses we observe in Tables 5-8.

We begin by looking at responses as they relate to key aspects of the heterogeneity examined in Table 8.

- _Learning about type and managerial ability_ : Did owners learn about their types, for example, did they learn about their ability to manage workers? Among those with a worker hired under the program who left, we asked which among several reasons the worker left. One option was that the owner had come to realize he was not able to properly manage the worker. Across several rounds and more than 30 such cases, this reason was only ever given once. There is a somewhat larger sample of owners who were eligible for the subsidy but never hired a worker. In October 2009, we asked these owners whether any of 12 reasons for not hiring were important in their case. Only 13.3 percent of the owners said concern about their ability to manage an employee was a reason for not hiring. The far more common responses for not hiring related to a lack of demand for labor, including that the additional employee would not be profitable (43.4 percent), that the enterprise does not require an additional employee (26.5 percent, and that the subsidy is not large enough (13.3 percent). Together, these data suggest that learning about managerial ability is not a central issue among these firms; the lack of hiring appears mainly to reflect a lack of demand