rafmacalaba/data-use-mentions-extended huggingface dataset:
How well do these models predict who returns and who seeks out restitution? We report receiver operating (ROC) curves for out-of-sample (test set) predictions in Figure A.6. The area under curve metric is .68 for the decision to return and .7 for restitution-seeking, which represents an improvement of 36% and 40% over guessing at random, respectively. In overall terms, these improvements are modest, which suggests other, unknown factors may be more predictive of these outcomes.

# 6 POLICY AND PROGRAM IMPLICATIONS

Displacement is a deeply traumatizing experience that affects millions around the world. The prospectofresettling,restituting,andrebuildingcommunitiesdestroyedbydisplacementalso represents a daunting challenge for societies in conflict and global humanitarian efforts. Understanding how displacement shapes victim’s trajectories is thus crucial for crafting informed policy-makingandadvocacy. Inthisproject, weadvanceourunderstandingoftheeffectsofdisplacement using original data from rural households with a high incidence of displacement in Colombia. The results have numerous implications for both the academic literature and policymakers.

First, the results provide strong evidence that there can be substantial heterogeneity of experience within the broad umbrella term of “wartime victimization”. Even after adjusting for other forms of wartime victimization, the experience of displacement appears to both undermine and mobilize victims in distinct ways that may set them apart from other conflict victims. Disaggregating wartime victimization may thus be one way of working towards resolving what areattimescontradictoryfindingsintheliteratureontheeffectsofvictimization. Withrespect to policy, this potentially means designing interventions that take into account the disproportionate economic burden faced by IDPs (Arias et al., 2014), or that leverages IDPs apparently higher capacity for collective action in ‘bottom-up’ peacebuilding efforts (Autesserre, 2021).

Second,thestudyunderscoresthatthereissubstantialvariationamongIDPsinwhatcomes after displacement. Explaining and addressing this variation is likely crucial for the long-term well-being of IDPs. With respect to return, ongoing security concerns originating from postconflictinstability(Premetal., 2020)anddifficultyrecoveringpropertyappearasmajorimpediments. Interventiondesignshouldthustakeintoaccountthefraughtsecuritysituationpresent in many post-conflict settings. In terms of restitution-seeking and the recovery of property, a key insight is that few victims formally pursue restitution, and that many in our sample claim to lack access to representation
expression when _Te ≤ t ≤ T_ is

> With only post-intervention follow-up data, we cannot test if _βP_ and _βT_ changed due to delays. We thus estimate _βD_ assuming that _βP_ and _βT_ are fixed. With pre-intervention data, one could estimate _βD_ directly and allow for different values of _βP_ and _βT_ depending on whether households experienced delays or not.

> 10We do not include the 28 municipality fixed effects in equation 2 because the nonlinear routine in STATA did not converge for either SWB measure when included. Appendix Table A6 replicates Table 1 without the municipality fixed effects and finds were similar results.

10
relatively well developed banking sectors. While the instrumental variables approaches focus on between-country differences, the RZ approach captures within-country, between-industry differences in labor growth rates. That both approaches yield similar results provides support for the plausibility of our findings. Our confidence regarding the beneficial role of banks in poor countries is further boosted by our finding that a more developed banking system is associated with higher investment rates, more employee training, and larger firm sizes _only_ in poor countries, which suggest that banks spur both physical and human capital investment besides boosting job growth in such countries.

The WBES also contains a substantial amount of information on firm characteristics (size, industry, ownership structure, legal status) that enable us to further pinpoint the types of firms that benefit from a relatively well developed banking system in countries at early stages of development. Although banks appear to be the financial institutions best suited to serve firms in low income countries based on our results, it does not appear that they disproportionately serve the small-scale firms that are said to characterize the early stages of economic development. In low-income countries, labor growth increases with the size of the banking sector primarily for relatively large, capital-intensive firms.

The rest of the paper is organized as follows. Section II describes the enterprise survey and financial structure data in greater detail and presents summary statistics. Section III describes the variables that we use as instruments for our indicators of financial development in firm growth regressions. It also shows how those variables are related to each other and to our indicators of financial development. Section IV explains both our estimation approach based on instrumental variables and that based on the RZ methodology, and presents our main regression results. Section V examines the types of firms that are most affected by financial structure in high- and low-income countries. Section VI examines whether other firm characteristics and performance measures are related to financial development, so as to better understand potential mechanisms by which banking sector development fosters firm growth in low-income countries. Section VII concludes.

6
distribution.

**Setting and adjusting the minimum wage presents a complex challenge requiring a balanced approach.** While the process involves political deliberation and consultation with social partners, evidencebased decisions are crucial. Minimum wages should be established at levels that meet workers' and their families' needs, but also consider potential labor market and poverty impacts. Striking the right balance is essential: setting wages too low offers limited protection against low pay and poverty, while setting them too high can result in poor compliance and adverse employment effects. Regular inflation-adjusted changes provide predictability, but countries with hyperinflation may require more frequent updates. When well-designed, minimum wage policies can be part of a broader strategy to alleviate poverty through increased labor income, especially when the minimum wage approaches or matches living wages.

**When setting minimum wage levels, assessing adequacy and living wages is an important factor. In this paper, we construct estimates of real living wages for a representative poor Romanian household, relying on EC estimates of the consumption basket.** For effective comparisons, our estimates of living wage are derived from absolute estimates of essential needs rather than relative percentages of median income.

47
products. For example, Denmark’s 1976 population census relied on a population register, a business register, and a set of tax registers (Jensen 1983). By contrast, administrative data in lowercapacity countries are just beginning to become available for statistics.

High-capacity NSSs use a variety of big data applications to official statistics.<sup>10</sup> With more sophisticated production processes that deliver a wider array of statistics, staff in high-capacity NSSs are highly specialized. For example, dedicated teams develop supply-use tables, the foundation for rebasing GDP estimates. In contrast, lower-capacity NSSs produce supply-use tables less frequently, and the work is done by nondedicated teams. Unlike lower-capacity NSSs, higher-capacity NSSs also have staff devoted to developing analytical and innovative products.

These and other differences suggest that technical assistance works best when high-capacity advisors are familiar with the circumstances in low-capacity environments. The IMF Regional Technical Advisory Centers provide a range of statistical support to developing countries. In 2016, they delivered 563 technical assistance (TA) products and 120 training events (IMF 2016). These centers are typically staffed by statisticians with long experience in high-performing NSSs, who coordinate efforts and sensitize foreign experts to local circumstances. Relocating these statisticians to immerse them in the working circumstances in their countries likely results in better advice and more sustainable outcomes. Leading bilateral providers of TA are adopting a similar approach. The presence of in-country strategic advisors has helped improve donor coordination on NSSs and sensitize experts with little experience in low-capacity environments (UK Foreign, Commonwealth, and Development Office 2020).

The gap between the cluster of high-performing NSSs and the rest of the world shown in Figures 1 and 2 also suggests that expanding South–South knowledge exchange could improve the impact of capacity building. Cooperation among developing countries is not new. The Statistical, Economic, and Social Research and Training Center for Islamic Countries (SESRIC) and AFRISTAT are examples of capacity-building programs for member states. Broadening South– South support to improve official statistics remains relevant in the face of
Policy Research Working Paper 10680

# **Abstract**

Surveys frequently rely on annual recall to capture individuals’ labor activities over the preceding year. This paper uses a panel of rural households in Malawi for a survey experiment to test the effect of a long, annual recall window on reported labor supply relative to a set of quarterly interviews. The paper documents large losses in reported labor participation using the long recall window with reductions of over 20 percent of reported activities and months worked and a 2.5 times greater incidence of reported unemployment relative to the shorter window. These losses are greater for activities further in the past and especially for individuals whose labor supply is reported by other family members, reaching up to 50 percent for some outcomes. The profile of households’ primary respondents, predominantly male and older, and differential effects by age further suggest that long recall may cause meaningful biases in the resulting data for women and younger household members.

This paper is a product of the Development Impact Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sherskowitz@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank
039|-.16|.255|

|**Emerging Markets**||||||
|---|---|---|---|---|---|
|Variable|Obs|Mean|Std. Dev.|Min|Max|
|GDP Growth|1740|.038|.049|-.548|.368|
|Compensation to Employees|922|.044|.097|-1.234|.748|
|Growth||||||
|Goods and Services Growth|861|.04|.172|-.914|1.411|

Source: Authors’ calculations based on World Economic Outlook database 2019.

We can observe that, regarding GDP growth, while emerging markets show a higher average rate for the period considered, they also exhibit greater dispersion. This observation is consistent with
specifications include controls for other NTMs and import tariffs applied on plant’s outputs and inputs, plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

23
driven institutional change is contingent on the tailoring of components to fit national and systemic characteristics. Even when an administrative culture has become obsolete or dysfunctional it remains necessary to understand its institutional roots to secure durable improvements. Equally important, administrative capacity limitations and other institutional constraints must be taken into account when designing funding systems (Schiavo-Campo 1999).

Kusek and Rist (2002) propose adjusting the complexity and ambition of a results-based funding system based on a ‘readiness assessment’. Performing such an assessment gives reforming agencies an understanding of what institutional capacity they do or do not have and what resource can be drawn upon to initiative reform. A ‘readiness assessment’ can also help to clarify where within a government reforms might begin, under the auspices of who and what demand exists for the use of collected information.

# **Information systems**

As governments begin to address the challenges of results-based funding, they face the need to document performance with valid and reliable information. Allocating resources based on institutional performance presupposes credible and timely national and institutional statistics on a wide range of indicators. Without a good information system no basis exists for evaluating institutional performance. When reliable data are lacking, the priority must be to build a data and monitoring capability before considering the introduction of results-based funding (Schiavo-Campo 1999).

- 7 -
examines the changes in induced demand resulting from trade through supply chain linkages and finds an even more pronounced impact on labor force participation. Furthermore, Acemoglu et al. (2016) demonstrate that inter-industry linkages amplify the employment effects of trade shocks, doubling the size of the impact within manufacturing and producing a similarly substantial employment effect outside manufacturing. This emerging literature emphasizes the importance of indirect impacts from trade shocks but has yet to address the effects of export shocks, particularly in developing countries.

Few studies have exclusively focused on the impact of trade on service jobs. For example, Eliasson, Hansson, and Lindvert (2012) utilize data from Sweden in the mid-1900s and find that increased competition from low-wage countries abroad has a considerable impact on the creation of skilled jobs and the displacement of less skilled jobs in the tradable sector. In a more comparable setting, Mitra (2011) investigates a similar question based on India but only identifies negligible results regarding the direct and indirect effects of exports and imports on employment. These findings hold true for both formal and informal services.

This study extends this scant literature by studying the impact of export expansion on local labor market outcomes using a supply chain perspective, in a lower-middle income country such as Egypt. To the best of our knowledge, there is only one study in the literature that directly does so, by Goutam et al. (2018), who look at the impact of export-induced demand, mapped through input-output linkages, in Bangladesh on four types of employment: formal, casual, unpaid, and self-employment. They find a larger impact of trade when accounting for spillover or indirect effects. We differ from this work by focusing on not just employment but a range of labor market outcomes.

It is important to highlight that this work is an extension of Robertson, Vergara, Kokas and Acevedo (2021) who use the Bartik (1991) estimates to analyze if higher exports generated better local labor market outcomes in Egypt between 2009 and 2017. Their results suggest that that trade does
yields an unbalanced pseudo-panel at our unit of analysis: country years. Notably, the pseudo-panel imbalance is not systematically related to country trade policy or labor market outcomes and is instead a function of pre-determined survey schedules. As surveys are nationally representative, we use expansion weights in our calculation of country-level aggregates.

Several of our aggregated variables warrant more detailed explanation. We calculate employment rates for children under 14 and children 14–17 as the share of the surveyed age group reporting employment. We separately calculate under 14 and 14–17 employment for several interrelated reasons. First, we are concerned that survey respondents might misreport employment by young children due to social, religious, cultural, or even legal prohibitions, since 14 is frequently the legislated minimum age for employment and hence misreporting or mismeasurement of employment rates for 14–17 year olds is far less likely.<sup>9</sup> Second, our theoretical model explicitly predicts different responses in younger child labor markets and older child labor markets. By leveraging our rich survey microdata we are able to separately calculate child employment by age ranges and test our theoretical hypotheses. Finally, 14 years is commonly the age that differentiates primary and secondary schooling. Our differentiated child employment rates will roughly correspond to primaryand secondary-school-aged children.

For each country at each date, we calculate the relative income of households with children as the median household per capita income weighted by the share of the household members that are children, divided by the median household per capita income for the whole sample. We compute two related versions of this measure. In the first, we consider households with _any_ children (i.e. under 18 years of age) and weight household observations by the share of the household members under 18. In the second measure we only consider households that have children in both of our age ranges (younger than 14 and 14–17) to better capture intra-household child labor substitution effects predicted by our theoretical model. Both measures will approximate income inequality impacts on households that contain
Size|678|678|753|753|753|753|753|753|753|508|
|P-value: all treatments zero|0.057|0.011|0.002|0.004|0.494|0.136|0.023|0.013|0.004|0.004|
|P-value: all treatments equal|0.036|0.020|0.004|0.038|0.480|0.233|0.095|0.029|0.018|0.002|

Notes: Regressions control for stratification batch fixed effects. Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels. **Investment index** is the average of dummies for taking new loan financing, new equity financing, and making a big investment (100,000 Naira or more); **Innovation index** is the proportion of 17 innovative activities being done by firms; **Website, Facebook, Twitter,** and **Instagram Effectiveness** are averages of 12 ratings on each of these forms of social media; **Global Quality** and **Global Quantity** are single question ratings; and **Overall Social Media Effectiveness** is an average of these 50 ratings of functional and aesthetic quality. The Unconditional estimates code firms with no social media as 0, conditional estimates are for firms with some social media or website presence The investment index and innovation index were measured in round 2 only; Social media quality measured in October 2019.

42
Haven, CT. Goldstein, M. and C. Udry. 2008. "The Profits of Power: Land Rights and Agricultural Investment in Ghana." _Journal of Political Economy_ , **_116_** (6), 980-1022.

Gossage, S. J. 1997. _Land Use on the Tobacco Estates of Malawi: Report of the Land Use Survey of Tobacco Estates in Malawi, 1996._ Government of Malawi and Overseas Development Institute, Lilongwe.

Harttgen, K., S. Klasen and R. Rischke. 2016. "Analyzing Nutritional Impacts of Price and Income Related Shocks in Malawi: Simulating Household Entitlements to Food." _Food Policy_ , **_60_** , 31-43.

Holden, S., R. Kaarhus and R. Lunduka. 2006. "Land Policy Reform: The Role of Land Markets and Women’s Land Rights in Malawi." _Noragric Report No. 36_ , Norwegian University of Life Sciences, Aas, Norway.

Holden, S. T., K. Deininger and H. Ghebru. 2011. "Tenure Insecurity, Gender, Low-Cost Land Certification and Land Rental Market Participation in Ethiopia." _Journal of Development Studies_ , **_47_** (1), 31-47.

Hyder, A., J. R. Behrman and H.-P. Kohler. 2015. "Negative Economic Shocks and Child Schooling: Evidence from Rural Malawi." _Development Southern Africa_ , **_32_** (4), 458-476.

Jaffee, S. M. 2003. "Malawi’ S Tobacco Sector: Standing on One Strong Leg Is Better Than on None ", _Africa Region Working Paper Series No. 55_ World Bank, East & Southern Africa Agricultural Division, Washington DC. Jayne, T. S., et al. 2015. "Africa’s Changing Farmland Ownership: Causes and Consequences ", _Paper Presented at the joint AAEA & WAEA Annual meeting_ , San Francisco, CA.

26
|21.11|-2.85|
|m_obese|RUS_2003_RLMS_v01_M; RUS_2003_WHS_v01_M|RUS_2003_RLMS_v01_M|0.13|0.02|
||RUS_2011_ISSP_v01_M;<br>RUS_2011_WBHHS_v01_M;<br>||||
|m_obese|RUS_2011_RLMS_v01_M|RUS_2011_RLMS_v01_M|0.16|0.02|
|w_mam_2y|GRC_2003_EUBM_v01_M; GRC_2003_WHS_v01_M|GRC_2003_EUBM_v01_M|0.38|0.06|
|w_mam_2y|HKG_2014_PHS_v01_M_pub|HKG_2014_PHS_v01_M|0.38|0.20|
|w_mam_2y|HUN_2003_OECD_HS;HUN_2003_WHS_v01_M|HUN_2003_OECD_HS|0.60|0.06|
||MEX_2006_National Health and Nutrition<br>||||
|w_mam_2y|Survey_v01_M; MEX_2006_OECD_HS|MEX_2006_NHNS_v01_M|0.38|0.08|
|<br>wmam2y|<br>PRT2003WHSv01M; PRT2003EUBMv01M|PRT2003EUBMv01M|0.69|0.09|
|__|____ ____<br>RUS_2011_WBHHS_v01_M;|____|||
|w_mam_2y|RUS_2011_RHS_v01_M_pub|RUS_2011_WBHHS_v01_M|0.13|-0.16|
|w_obese|NPL_2003_STEPS_v01_FS;NPL_2003_WHS_v01_M|NPL_2003_WHS_v01_M|0.02|-0.04|
|w_obese|RUS_2003_RLMS_v01_M; RUS_2003_WHS_v01_M|RUS_2003_RLMS_v01_M|0.30|0.05|
||RUS_2011_ISSP_v01_M;<br>||||
||RUS_2011_WBHHS_v01_M;||||
|w_obese|<br>RUS_2011_RLMS_v01_M|RUS_2011_RLMS_v01_M|0.30|0.06|
15-64. 95 percent confidence intervals are in brackets._

**Figure 13. Simulated reduction in earning inequality as a result of minimum wage increase**

<!-- Start of picture text --> 2.47 2.56<br>2.15<br>1.75 1.72<br>1.53<br>5/1 decile 9/1 decile<br>Actual<br>Simulated, long-run. With 0 earnings those who loose a job<br>Simulated, long-run. Only who remain employed<br><!-- End of picture text --> _Source: Own estimates using 2018 Kosovo LFS._

**Although overall disemployment effects are small, unemployment among the young (aged 15-24) is simulated to increase by 1.1 percentage points in the long run; unskilled workers are especially affected; and there are more women than men among job losers.** Figure 14 Panel a depicts the effect of the policy on unemployment by demographic characteristics. The youth unemployment (aged 15-24) is simulated to increase by 0.7 percentage point in the short run, and 1.1 percentage points in the long run.  The unemployment rate among those with primary school or less is expected to increase up to 0.8 percentage point. There are potentially more women than men among job losers.

**When looking at the impact on earnings, the minimum wage increase is effective in increasing earnings among young workers (aged 15-24) in the short run but has little effect on those aged 25-64. The net increase in earnings is slightly higher for women than for men, decreasing the gender gap in**

23