rafmacalaba/data-use-mentions-extended huggingface dataset:
sample, as well as younger respondents and respondents in households with multiple income sources. Summary statistics for the medium-run sample are reported in appendix table A4.

Figure 6 summarizes the one-year labor market indicators. Job loss over the full year (Figure 6A) ranged between 30% and 73%. In all countries except Ethiopia, the one-year rate of job loss is about ten percentage points higher than early-phase job loss. This indicates that while most job loss occurred in those first few months of the pandemic, it continued after July 2020. By February/March 2021, 14 to 23 percent of respondents who were working pre-COVID-19 were out of work (Figure 6B) and on average, pre-COVID-19 workers were jobless for around 2 months during the first year of the pandemic (Figure 6C).

**Figure 6: One-year labor market indicators**

<!-- Start of picture text --> Job loss up to Feb/Mar 2021 Average number of months<br>Jobless in Feb/Mar 2021 -<br>- conditional on working conditional on conditional without work up to<br>pre-COVID-19 Feb/March 2021 -<br>on working pre-COVID-19<br>conditional on working pre-<br>100% 100%<br>COVID-19<br>80% 71% 80% 3.0<br>60% 60% 1.8 1.9<br>2.0 1.7<br>1.6<br>37%<br>40% 34% 40%<br>27%<br>22% 19% 1.0<br>20% 20% 15% 14%<br>0% 0% 0.0<br><!-- End of picture text --> _Source_ : Job loss and months without work are based on HFPS R1-10 for Ethiopia and Nigeria, R1-8 for Malawi, R1-11 for Nigeria and R1-5 for Uganda. Joblessness is based on the latest of these
In the MENA context, geospatial data can be particularly useful in conflict affected countries to prioritize interventions aiming to provide basic services. For welfare measurement, geospatial data can help design surveys by providing population estimates and help create sampling frames in countries where this information is obsolete or the displaced population is large, such as in Iraq, Lebanon, Djibouti and others (Hoogeveen and Pape, 2020). Finally, geospatial data can improve modeling for consumption imputation for poverty maps (small area estimation).

# <u>Summary of relevance and feasibility of different methods</u>

To summarize the discussion of methods/data to fill HBS data gaps, table 6 ranks approaches from “1” to “3” indicating both relevance and feasibility in each country context. Lower numbers indicate the highest relevance and feasibility. We put “0” when the method is irrelevant or not feasible in a particular country.

Table 6. Ranking different methods and types of data for measuring well-being in the MENA countries

||measuring<br>pove<br>collecting|monetary<br>rty|measuring no|n-monetary poverty or<br>well-be<br>|use as auxiliary da<br>ing|ta to measure|
|---|---|---|---|---|---|---|
|||||Food|||
||||||||
||ouseo<br>budget<br>survey|Imputation|asse<br>index|security/subjective<br>well-being|mnsrave<br>data|er g<br>data|
|Algeria|1|0|2|2|3|3|
|Djibouti|1|1|0|2|3|3|
|Egypt, Arab Rep.|1|1|0|2|3|3|
|Iran, Islamic Rep.|1|2|2|
(b) For recipients of matching grants _Note:_ The adjusted confidence intervals are obtained from regressions that use (a) recipients of cash grants and the control group only and (b) the matching grant recipients and the control group only. _Source:_ Estimates based on Bagré Growth Pole Data Set.

# _6.6 Spillovers_

Given that the treatment status varies within each of the nine municipalities and given that many of the treated firms operate within the same sector, it is worth exploring possible spillover effects. These are interesting for two reasons. First, it is important to check whether firms in the control group are affected by beneficiary firms which would reduce their suitability as a control group. Second, it is worth exploring whether the gains of beneficiaries depend on the competition they face (McKenzie 2017). In our case, especially, the slightly negative treatment effects on profits and turnover that we find for recipients of cash grants despite increased investment could, for instance, be due to the competition they face with other treated firms. Competition might make it difficult to increase sales especially in a context where the market size is relatively small and offers little possibilities to expand, for example, through exportation. Table 7 shows the effects of the number of treated firms (with cash) alternatively within 500 m, 2 km, and 5 km for recipients of cash grants, recipients of matching grants, and control firms.<sup>10</sup> These results suggest that overall, there are only very small, if any, spillover effects. The only remarkable pattern that comes out of these estimations is that the negative treatment effect on profits decreases slightly with the number of cash beneficiaries within a radius of 5 km. Yet, again, overall, we see little spillovers so far, neither negative nor positive. This issue will be further explored as more rounds of data become available. The survey includes various variables that measure in one way or another the competition firms face.

> 10 On average there
are obviously forced to do something with their time even if remuneration is equal or below subsistence. The World Bank poverty assessment for Tajikistan, based on a 1999 household survey, found an unemployment rate of 3% and a poverty rate of 98%. It is obvious that very poor people cannot afford to be unemployed, particularly in rural areas which are typically distant from employment exchanges and where some form of under occupation is always possible.

An overview of the main labor market indicators (table 1.5) shows that the Labor Force Participation Rate and the Employment Rate - on average - have decreased during the recession and during the growth period. The exceptions to these trends are Azerbaijan and Moldova which managed to reverse the negative path during the growth period. Unemployment rates remained generally low in all countries with significant increases prevalent during the growth period.

The sharp deterioration of wages and employment also contributed to a sharp rise in _poverty_ while the wild privatization process of the early 1990s and the asymmetric economic performance of different sectors of the economy throughout the 1990s contributed to a surge in _inequality_ . Atkinson and Micklewright (1992) found that poverty was already higher in Central Asia and in the Trans-Caucasus during the Soviet period. Within a few years from the desegregation of the Soviet Union, poverty increased in all countries by two to three folds. Poverty also continued to increase during the first couple of years of the growth period and is only starting from 1999 that downward trends have been observed in almost all the CIS-7. Inequality also followed a similar pattern with 11
If (4) is the preferred model, there are two routes to estimation. One obvious choice is to form a likelihood from (4) and estimate the and the (. ) directly. Alternatively, one could cc proceed in three steps. First, maximize the likelihood (3) and estimate ff(∙| ∙) ππ and the . Then, cc use Bayes theorem to estimate firm-specific class membership probabilities for each firm, ff(∙| ∙) ππ . ccii Finally, use regression techniques to estimate the functions , using as data the ππ� cc and . ππ (. ), cc= 1, . . . CC ccii ii ππ� There is a large literature, both theoretical and applied, reflecting on the choice between the ZZ two routes to estimation. From theory, there are procedures to obtain consistent estimates of and (. ) using the 3-step process (Vermunt 2010, Bakk et al. 2013, Bakk et al. 2014). The applied ff(∙| ∙) cc literature suggests that using the 3-step process is advisable unless one has great confidence in the ππ specification of (4), especially understanding which to include and exclude (Nylund-Gibson ii and Masyn 2016).<sup>52</sup> For that reason, we use the 3-step process because many of the relevant ZZ 's ii remain unmeasured or unknown when using cross-country data. This is the case because of the ZZ inherent difficulty of collecting cross-country data on transaction-related activities and because the discriminating-alignment research program—identifying the 's—is still a work in progress. ii Our decision to use the 3-step procedure also rests on our objective of understanding the ZZ

nature of the classes. A prime contribution of this paper is the characterization of the most common governance structures (step 1). By estimating the classes in general—independently of the correlates of class membership—we can focus on this goal and provide readers with results unencumbered by any more ambitious objectives. Moreover, by
TTB is imposed, and the timing of when TTBs are imposed and removed.<sup>38</sup> Following Trefler (1993) and Goldberg and Maggi (1999), among others, we construct import coverage ratios to track TTB use over time. These coverage ratios measure the stock of accumulated bilateral TTBs imposed by each importer against individual exporters

> 36We exclude two industries from the raw WIOD data: (1) Mining and Quarrying, which contains no final end use products, and (2) Coke, Refined Petroleum and Nuclear Fuel, which contains only one final end use HS 6-digit category.

> 37Roughly 40 percent of the HS 6-digit codes in the raw data are classified as final goods, which corresponds to the value share of final goods in world trade.

> 38The data cover all countries in Table 1, except for Russia. In our analysis of TTBs, we exclude China and Taiwan because nearly all of their TTBs are imposed on intermediate inputs.

22
|
|Ln asset|-0.076***|-0.103***|
||(0.007)|(0.008)|
|Firm age|0.012***|0.009***|
||(0.002)|(0.002)|
|Female||-0.020<br>(0.018)|
|Year of schooling||0.023***<br>(0.003)|
|Party member||0.032*<br>(0.017)|
|Membership in Congress or||0.160***|
|Consultative Conference||(0.021)|
|Prov, Sector, year|Yes|Yes|
|Observations|12,324|10,431|
|R-squared|0.063|0.090|

_Data sources: National Surveys of Privately Owned Enterprises in China_ _Data sources: National Surveys of Privately Owned Enterprises in China._ Notes: Standard errors are clustered at provincial level and reported in parentheses. Significance levels 0.1, 0.05 and 0.01 are noted by *, **, and ***, respectively.

44
475*|0.535***|0.526***|
||(0.195)|(0.248)|(0.158)|(0.161)|
|Observations|236542|236542|236542|230155|
|P-value F-test|0.107|0.171|0.002|0.002|
|Mean Violence|0.203|0.203|0.203|0.203|
|Mean ln(U<sup>f</sup>)|0.019|0.019|0.019|0.021|
|Mean ln(U<sup>m</sup>)|0.001|0.001|0.001|0.003|
|Year FE||Y|Y|Y|
|Country FE|||Y|Y|
|X controls||||Y|

# _Source:_

Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. The dependent variables, controls and standard error adjustments are as in Table 1. Relative to Table 1, the sample is now restricted to only those countries for which two or more years of data on women are available.
# **Collateralized Borrowing:**

# **Insights from The World Bank Enterprise Surveys**

Yuting Fan, Ha Nguyen and Rong Qian<sup>1</sup> The World Bank _Keywords_ : Financing Constraints, Collateralized Borrowing, Access to Finance _JEL classification_ : D21, G20, O12

> 1 Yuting Fan and Ha Nguyen are at the MENA Chief Economist’s Office and Rong Qian is at the Macroeconomics and Fiscal Management Global Practice in the World Bank. We are grateful to Anton Korinek, Aart Kraay, Justin Yifu Lin, Luis Serven, Carlos Vegh, Choon Wang, Juan Baron for comments and feedback. We thank Hannah Westman for her editorial help. The findings, interpretations and conclusions of this paper do not necessarily represent the views of the World Bank -All errors are our own. Emails: yfan3@worldbank.org, <u>rqian@worldbank.org</u> and <u>hanguyen@worldbank.org.</u>
, marital status, education, whether or not the individual is a head of household, and whether or not the individual is a migrant (defined as living in a state that is different from the state where the person was born). The reason for using ENE data in this paper is that it is high frequency data with broad geographic coverage that allows me to identify informal business owners and to track them over time. These features are essential for the impact analysis performed in later sections of this paper.

> 4 After 2004-IV, the ENE was changed to a new survey, the ENOE, and some of the questions used to define the variables in this paper where modified, limiting comparability across the two surveys. 5 Bruhn (2008) includes a detailed description of how I classified individuals into different occupation groups.

4
seek to maintain interest margins, or negatively affect profitability, which could eventually weaken the transmission of monetary policy.

**_How have key financial variables evolved since the introduction of NIRP?_** Key financial variables have moved as implied by the standard transmission channels of monetary policy. For example, policy rate cuts to negative levels have been generally reflected in corresponding declines in money market rates and short-term government bond yields. The fall in bank wholesale funding costs has helped lower lending rates to varying degrees across countries. The decline in rates on new loans has been particularly notable in the euro area, although other policy initiatives make the specific contribution of NIRP difficult to isolate. The pass-through in other countries has been more limited. Inflation expectations have continued to decline in most NIRP economies. Currencies of countries implementing NIRP have on average depreciated since the corresponding policy decisions, with the exception of the appreciation of the Japanese yen and Swiss franc.

An event study indicates that money market rates and sovereign bond yields fall on the day of NIRP announcements, whereas inflation expectations do not change much and currency movements vary across countries. For the euro area, a comparison of the responses of key financial variables to announcements of NIRP, QE, and conventional interest rate cuts shows that the responses following NIRP are directionally consistent to those of conventional cuts, although somewhat smaller. Overall, despite its potential drawbacks, NIRP appear to have helped deliver additional monetary stimulus in a challenging macroeconomic environment.

**_How could NIRP affect financial stability?_** NIRP could pose risks to financial stability, particularly if rates go substantially below zero or if NIRP are employed for a protracted period of time. Adverse outcomes could include the erosion of profitability of banks and other financial intermediaries, and excessive risk taking. Available data so far provides inconclusive evidence of significant impact of NIRP on bank profitability. While some bank surveys indicate a perception among survey participants that NIRP are having an adverse impact on banks’ net income, bank lending margins remain within post-crisis ranges
<mark>Anomalies: A long-run Test for Rural Tanzania.</mark> _<mark>Ecological Economics</mark>_ <mark>, 164, 106365.</mark> **<mark>[</mark> 4]** Di Falco, S., Veronesi, M., & Yesuf, M. (2011). Does adaptation to climate change provide food security? A micro‐perspective from Ethiopia. _American Journal of Agricultural Economics, 93.3_ , 829-846. **[1]** Di Falco, S., Kis, A. B., & Viarengo M. (2022). Cumulative Climate Shocks and Migratory Flows: Evidence from Sub-Saharan Africa. IZA Discussion Paper No. 15084. **[1]** <mark>Dillon, A., Mueller, V., & Salau, S. (2011). Migratory responses to agricultural risk in northern Nigeria.</mark> _<mark>American Journal of Agricultural Economics</mark>_ <mark>,</mark> _<mark>93</mark>_ <mark>(4), 1048-1061.</mark> **[1]** <mark>Dinku, T. (2019). Challenges with availability and quality of climate data in Africa. In</mark> _<mark>Extreme hydrology and climate variability</mark>_ <mark>(pp. 71-80). Elsevier.</mark> **[3]** <mark>Feng, S., Krueger, A. B., & Oppenheimer, M. (2010). Linkages among climate change, crop yields and Mexico–US cross-border migration.</mark> _<mark>Proceedings of the National Academy of Sciences</mark>_ <mark>,</mark> _<mark>107</mark>_ <mark>
extent that distribution changes, growth may not trickle down automatically to the poor (see Heltberg (2002), and Dagdeviren, van der Hoeven, and Weeks (2002)).

An alternative approach is to relate directly the poverty rate, estimated for some base period, to the rate of change of the growth rate of consumption per capita derived from the model, using an estimated partial elasticity. This approach is attractive for countries where a representative and reliable household survey is not available (as is the case in several low-income countries), and only a point estimate of poverty can be relied on. Another advantage of this procedure is that changes in income distribution can be captured indirectly; by varying the partial growth elasticity within a “plausible” range around (minus) unity, non-neutral changes in growth rates can be accounted for. Although somewhat _ad hoc_ , we also use this approach in the simulation results reported below.

A complete list of the model’s equations is provided in Appendix A, whereas a list of endogenous and exogenous variables, as well as parameter values, is provided in Appendices B and C. The structure of the model is summarized in Figure 1, under the assumption that the government fiscal deficit is financed by domestic and foreign borrowing.

# **III. AN APPLICATION TO ETHIOPIA**

To illustrate the functioning of the model developed in the previous section, we apply it to Ethiopia, a country with one of the lowest income per capita in the world. We begin with a brief review of trends in growth and poverty, foreign aid and its composition, and public investment in Ethiopia. Next, we report econometric estimates of some of the behavioral equations of the model and describe some features of the calibration procedure (such as the estimation of the capital stock variables), as well as the household survey that we use. We then discuss the assumptions underlying trend-based projections for the period 2003-15, as a prelude to the policy experiments that we conduct in the next section.

25
response efforts and a rapid recovery (that is, the countercyclical role of fiscal policy for economic resilience), but fiscal policy is also crucial in pre-disaster planning, as it can incentivize investments in long-term resilience, transfer risks, and provide the fiscal space for debt-financed crisis response. Next, we highlight the link between climate risks and fiscal policy empirically, aiming to quantify the impact of climate shocks on output given existing fiscal policies. Using data from the EM-DAT database for the number of people killed or affected by natural disasters, our analysis indicates that economies with constrained fiscal space, proxied by high government debt, procyclical policies, and running persistent deficits, experience more pronounced negative effects from natural disasters, indicating a reduced ability to respond effectively.

We then use the World Bank's macro-fiscal model (MFMod) to study the effects of natural disasters on economic resilience and fiscal resilience. MFMod is one of the World Bank’s main forecasting tools and provides the basis for tailored country analysis. Its primary purpose is to generate countryspecific forecasts of the main macroeconomic and fiscal variables and to assess the potential effects of policy changes. Importantly, the MFMod framework permits the evaluation of policy (and climate-related disaster) impacts alongside traditional economic indicators like economic growth, fiscal sustainability, price inflation, and stability of the external account. Hallegatte, Jooste, and McIsaac (2024) use MFMod to study macroeconomic consequence of natural disasters with a focus on monetary policy. Harnessing the detailed representation of the national accounts in MFMod, we focus on the fiscal aspects of natural disasters. Thus, we are combining macroeconomic modeling with the “financing gap” analysis of disaster research (Mechler et al. 2006).

We model the impact of a climate shock on a small open economy with strong reliance on tourism and vulnerability to climate events. We simulate the effect of a 5 and 10 percent destruction shock to the country’s existing capital stock, emulating the effects of a natural disaster. We explore several ways of funding reconstruction efforts (
In addition to analyses that examine evidence of diaspora connections within the oDesk data, we demonstrate how the broader Indian diaspora in a country—measured as migrants from India as a share of country population—systematically connects to a greater ethnic Indian use of oDesk and a larger share of contracts being sent to India from the country. This background connects studies that consider diasporas from a macro perspective (e.g., linking trade flows to diaspora shares by country) with studies that consider micro evidence (e.g., that international patent citations are more likely among inventors of the same ethnicity).

These results are quite striking. oDesk’s business model minimizes many frictions and barriers to outsourcing—for example, providing companies with knowledge of workers for hire overseas and their qualifications, providing infrastructure for monitoring and payments between companies and workers, and creating a labor market where workers build reputations that enable future work and higher wages. These frictions that oDesk minimizes, of course, are frictions that diaspora networks have historically been used to overcome. Our work suggests that diasporas continue to be important in an online world—if for no other reason than the taste-based preferences that shape contract placement. We view our results as a lower bound on the importance of diasporas in settings where frictions to interactions are larger.

At a higher level, the Indian diaspora likely played an important, but modest, role in India’s rapid development on oDesk. At several points, we provide descriptive evidence of the magnitudes of these interactions that place upper bounds on how large this role could have been. For example, ethnic Indians account for 3.9% of oDesk company users in the United States by contract volume, while 29% of outsourced contracts from the United States go to India. We likewise find that only 5.7% of workers in India who complete three or more jobs on oDesk had their initial contract with an overseas ethnic Indian employer. These magnitudes suggest that diasporas continue to use online platforms in an effective