rafmacalaba/data-use-mentions-extended huggingface dataset:
2 USD/day in 2011 PPP or 759.4 MDL per month per capita) International - below the international line (5.5 USD/day in 2011 PPP or 1305.2 MDL per month per capita) Income deciles were defined on the basis of disposable income without the transfer or pension Source: authors’ calculations using HBS-2017 data.

Labor pensions, while non-conditional on income by construction, are moderately efficient in reducing inequality and poverty at the lower lines but become very inefficient in reducing the poverty at the higher line. Since many of the retirees rely on pensions as the only or major source of income, labor pensions, despite high cost, generate large marginal effects. Hence, the cost of reducing the extreme poverty rate and the poverty gap at the national line as well as inequality through labor pensions is relatively low. Pensions are also quite well targeted despite the absence of the targeting mechanism in the design: 65 percent of labor pensions go to bottom 40 percent of the distribution (defined before pensions), and only 11 percent goes to the top income quintile (Table 15).

The targeted social assistance to poor families benefits are the most efficient. Of all the types of direct transfers, poverty benefits have the lowest cost of fighting poverty and inequality (Table 15). This type of intervention has also the best targeting results – up to 40 percent of the program budget goes to bottom decile and almost 80 percent goes to the bottom 40 percent of the distribution, while only one percent goes to the top quintile (Table 15). Social pensions and child and family benefits are also relatively well targeted, but mostly focused on the bottom 40 percent of the distribution, while bottom decile receives twice lower share of the budget than social assistance to poor families (Table 15).

25
is correlated with ability (Griliches and Mason, 1972; Spence, 1973), however, and there is no more plausible proxy available in the WVS.

Measuring morality, defined as an aversion to earning a living through appropriative as opposed to productive work, is even more difficult. There are several survey questions in the WVS that are potentially relevant. In selecting our primary indicator of morality from among them, our first criterion was that the question pertain directly to work or employment choices. Our second criterion was that it must bear on the willingness to sacrifice income (or job-related leisure) to do work that may be regarded as more moral. No WVS question fully meets these two criteria, but the question that comes closest inquires about particular job characteristics that would be “most important” to the respondent if he/she “were looking for a job.” Specifically, we construct a “morality” dummy coded 1 for respondents who place the highest value on “doing an important job which gives you a sense of accomplishment,” and coded 0 for those who place greater value on high income, job security or likable co-workers.<sup>10</sup> The results we report below rely primarily on this morality proxy. However, in a subsequent section we present results for several alternative morality proxies from the WVS.

Following the theory, the empirical investigation is mainly concerned with identifying whether or not it matters for economic performance if workers with more ability are also more “moral.” Therefore, the key independent variable in our analysis is the _correlation_ of morality and ability among workers _within_ each country.<sup>11</sup> In computing the correlation and in generating country-level aggregates for morality and ability, we drop all respondents not in the labor force

> 10 See the Data Appendix for the complete survey question. If we measure morality more expansively as the share of respondents selecting “doing an important job which gives you a sense of accomplishment” as either their first or second priority
country’s imports from RoW relative to imports from the Spokes themselves, ∆ _impj,srow −_ ∆ _impj,ss_ . In our data set, imports from RoW retain the by-product’s bilateral dimension, i.e. imports to Estonia at the HS6 level from each of the non-participating countries. Meanwhile, imports to each Spoke from the other Spokes lose the bilateral dimension and are aggregated at the HS6 level, i.e. total imports into Estonia of a particular HS6 line from all the other 6 Spokes. Tables 8 and 9 in Appendix A provide a complete list of countries included in our analysis.

We combine trade data with tariff-level data from UNCTAD Trade Analysis Information System (TRAINS), which provides HS-based tariff line level (HS 6-digit). When tariff data are missing, we use the nearest data point available favoring earlier years when possible (both for pre- and post-PECS). Our tariff measure captures by how much Spoke countries lowered tariffs on imports among themselves compared to tariffs on products from non-participating countries. We compute the tariff change using both preferential tariffs and most favored nation (MFN) tariffs. Specifically, we use MFN tariff every time that we have a missing preferential tariff. Table 2 presents descriptive statistics on tariffs applied by Spoke countries with respect to other Spokes and RoW countries. Notice that the change in tariff between Spoke countries and RoW, ∆ _τj,srow_ , keeps a bilateral dimension (at the six-digit level of the HS), while the change in tariff among Spokes, ∆ _τj,ss_ , is constructed as a simple average of the HS6 tariff applied by each Spoke country to all the other Spoke countries.

> 9More details on tariffs data are provided below.

> 10Notice that we never include EFTA and Turkey in the Spokes importing group.

12
_associated with higher unadjusted (observed) prices. In other words, firms only export the most expensive goods to the most distant markets_ ”.

In quality competition models with constant mark-up, firms sell at a constant mark-up over marginal cost in all markets, so that fob prices do not vary across destinations. Nevertheless, the most productive (qualitative) firms will use more expensive (quality) inputs and will charge higher prices. As a consequence, quality competition models predict differences in fob prices across firms, but not across destinations.

Lastly, quality competition models with variable mark-ups build on the Melitz and Ottaviano (2008) model, to which quality differentiation is included. Kneller and Yu (2008) are an example of a quality competition model with variable mark-ups: they simply assume that firms with higher marginal costs produce higher quality. Mark-ups are heterogeneous, so that firms that produce higher quality products will charge higher prices both because they use more expensive inputs and because they can charge higher mark-ups. In these models, the fob price charged by a firm will differ across destinations, according to the degree of faced competition.

From this review of the theoretical framework, it is clear that the theoretical models predict different relationships between export prices and gravity variables at the firm level. This finding is also true at the product level (not reported in Table 1): Melitz and Ottaviano (2008) predict a negative relationship between prices and distance, while Baldwin and Harrigan (2011) and Johnson (2007) predict and show the opposite (that prices increase with distance). Regarding the wealth in destinations, the framework developed by Hallak (2006) predicts a positive relation between (aggregate) export prices and the GDP per capita of the trade partner, while Melitz and Ottaviano (2008) predict a negative relationship.

With regard to the empirical literature, Manova and Zhang (2012) is the main reference for this paper. The authors analyze a custom database on Chinese firms and establish stylized facts on the variation in export
and organizational capabilities to be important for firm productivity: (i) performance monitoring: collecting and analyzing information on daily activities of the firm for continuous improvement, (ii) target setting: using and stretching short and long run targets, tracking outcomes, and taking appropriate action, and (iii) performance incentives: rewarding high-performing employees, and retraining or moving underperformers and careful hiring. To collect information on these practices, two alternative strategies could be deployed: (i) Open-ended questions (e.g. world management survey, WMS)<sup>9</sup> and (ii) Closed-ended questions (e.g. Management and Organizational Practices Surveys, MOPS).<sup>10</sup>

# _2.2 Sample and MOPS survey process in Russia_

To collect data on managerial practices, we follow the MOPS framework which is comprised of 36 multiple choice questions about the establishment. The survey is split in three broad sections: management practices (16 questions), organization (13 questions) and background characteristics (7 questions). In response to a request from the Ministry of Economic Development, we modified the instrument to include questions that can help understand the reasons why Russian firms do not adopt structured management practices. The survey was conducted through telephonic interviews of company managers, who are senior

> 9 Designing these surveys takes some expertise in terms of selecting questions and response grids and hence intense training and monitoring of the interview team. The full questionnaire is available on <u>www.worldmanagementsurvey.com.</u> 10 The full questionnaire is available on http://bhs.econ.census.gov/bhs/mops/form.html

5
_Table 4: Impact of two deprivations statuses (extreme and moderate) on poverty rates, baseline scenario_

||**Others-**<br>**dimensions poor**<br>**(%)**|**National poverty headco**<br>**Omitted**<br>**poor**<br>**(%)**|**unts**<br>**Consistently**<br>**poor**<br>**(%)**|
|---|---|---|---|
|Extreme deprivation considered|27.9|11.4|16.5|
|All non-monetary deprivations are||||
|assumed moderate|16.2|6.7|9.5|

_Source:_ Authors’ estimates based on data from NLSS 2019 _Note_ : The table presents other-dimensions poverty rates in Nigeria i) under the baseline scenario, ie, when the two-thresholds cutoffs (extreme and moderate deprivation) are applied for each non-monetary dimension and ii) under a modification of the baseline deprivation mapping such that only a one-threshold cutoff is applied for each non-monetary deprivation (that is, only the moderate deprivation status is considered: any extreme non-monetary deprivation is converted into a moderate deprivation). The overall other-dimensions poverty is decomposed into omitted poor and consistently poor to account for individuals who are both monetary and other-dimensions poor.

# **Section 4.3: Incidence of low well-being (quantity and quality of life)**

We focus in this subsection on the values taken by PALE, our well-being indicator that aggregates quantity and quality of life. Our objective is to get a sense of the impact of accounting for the direct impact of mortality when measuring low well-being as well as the impact of the value selected for the normative weight given to mortality. We first contrast PALE, which accounts for both quantity and
.010|0.022|0.048|0.023*|
||(0.015)|(0.026)|(0.031)|(0.012)|
|Mean in control|0.108|0.252|0.394|0.078|
|Adj. R-squared|0.368|0.306|0.255|0.145|
|Observations|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from ultra-poor households. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. In Panel A, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household received any transfers from any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In Panel B, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household gave any transfers to any ultra-poor, other poor, middle class or upper class households (within her community) respectively.

14
17% of all bank branches.<sup>9</sup> In addition to _Ej,[p,G,]_ and _Ej[Gjj_ (section 3.2), we include population density; more dense areas will presumably generate higher demand for GB loans per unit area, and may also entail a higher marginal cost to the bank.

Regressing GB density on these three variables we obtained the results in Table 6. The impact of the poverty-weighted marginal benefit on the spatial allocation of GB branches is highly significant (a t-ratio of 4.14, which is significant at the 0.1% level). The other two factors hypothesized to influence local demand for GB loans are also significant.

Are similar factors at work in determining the geographic distribution of other banks? The third column of Table 6 also gives the results obtained on regressing the density of other "non-Grameen" banks on the same three variables. While population density continues to have a positive impact on bank density, the other two variables have reversed their signs; as expected, higher values of the unweighted gain from sector switching attract other banks, consistently with our interpretation of the result for GB density in Table 6. However, the other banks are clearly attracted by gains to the non-poor, as indicated by the (significant) negative coefficient on poverty-weighted gain, controlling for the unweighted gain.

It is notable that, comparing the regressions in Table 6, the coefficients on povertyweighted gains and unweighted gains are roughly equal, but with opposite signs, for the two types of banks. The restrictions that the coefficients on the weighted and unweighted mean gain sum to zero is accepted for both regressions (a t-test gives a value of 0.615 for GB branches and 0.789 for other banks). Columns 2 and 4 of Table 6 give the regressions when these restrictions

**9** For fuller discussions of how Grameen Bank works see Hossain (1984), Khandker, Khalily, and Khan (1995),
# **_How well supported are the rules by public financial management?_**

# **Public financial management is reasonable in many respects.**<sup>14</sup> The quality of budgets,

forecasts, accounts, and statistics is hard to summarize, but in the region, there are generally

- Functioning budgets

- Medium-term fiscal projections

- Monthly or quarterly in-year budget-execution reports

- Accounts or statistics on fiscal outcomes at the end of the year

- Detailed data on traditionally defined debt and debt guarantees (that is, on the bonds the governments have issued and the loans they have contracted, along with the guarantees they have given of other entities’ bond and loans).

In many of the countries, the basic information is published in a timely and accessible manner. There are also some interesting innovations, such as the use of a fiscal council in Serbia and Republika Srpska and the use of external GDP forecasts in Albania.

# **But there are several problems with fiscal information** .

- _Coverage of public institutions_ . Statistics do not include data on all the public institutions that are part of general government according to standards such as ESA 2010 (EC 2013). For example, the recent IMF Fiscal Transparency Evaluation for North Macedonia estimates that statistics on general government should, but do not, include the activities of 12 state-owned enterprises with spending equal to about 3 percent of GDP (IMF 2018, pp. 13–14). Similarly, the Fiscal Transparency Evaluation for Albania estimated that there were about 27 entities that should have been included in data on general government, but were not (IMF 2015, p. 12).

- _Coverage of fiscal stocks and flows_ . None of the central governments publishes a set of audited accrual-based financial statements according to standards like International Public Sector Accounting Standards or a comprehensive set of statistics like those required by the IMF’s _Government Finance Statistics Manuals_ of 2001 and 2014. None can yet produce reliable estimates of the debt and deficit as
in 2014. Low rates of formal saving are prevalent in most developing economies in ECA. Exceptions include Bulgaria, Croatia, and Turkey, where most savers save formally (figure 10).

**Figure 9. Formal saving is more common in high‐income economies than in developing economies**

<!-- Start of picture text --> 80%<br>60%<br>40%<br>20%<br>0%<br>High‐income economies Developing economies<br>Saved formally Saved semi‐formally Saved using other methods only<br>2016<br>Share of adults saving any money in<br><!-- End of picture text -->

# _Source_ : Global Findex database.

_Note_ : People may save in multiple ways, but the categories here are constructed to be mutually exclusive. “Saved formally” includes all adults who saved any money formally. “Saved semi‐formally” includes all adults who saved any money semi‐formally but not formally. Data on semi‐formal saving are not collected in most high‐income economies. Semi‐formal methods may include saving money with a savings club or a person outside the family.

20
# Figure 7

## Panel A

<!-- Start of picture text --> Labor Productivity: Current and Potential Frontier<br>Sri Lanka Industrial Clusters, 2003<br>Bars represent province-sector-size clusters<br>Performance Frontier - Multiplicative Performance Frontier - Additive<br>Trend - Reported Value Trend - Primary Frontier<br>Mean Output per Worker<br>Data source: Sri Lanka Census of Industry 2004. World Bank Enterprise Surveys.<br>Graphic shows current (reported) metric and estimated frontier given relaxation of parametric constraints. Y-axis in logs.<br>15<br>12<br>10<br>logLP_mean7<br>5<br><!-- End of picture text -->

## Panel B

<!-- Start of picture text --> Value Added per Worker: Current and Potential Frontier<br>Sri Lanka Industrial Clusters, 2003<br>Bars represent province-sector-size clusters<br>Performance Frontier - Multiplicative Performance Frontier - Additive<br>Trend - Reported Value Trend - Primary Frontier<br>Value Added per Worker<br>Data source: Sri Lanka Census of Industry 2004. World Bank Enterprise Surveys.<br>Graphic shows current (reported) metric and estimated frontier given relaxation of parametric constraints. Y-axis in logs.<br>15<br>12<br>10<br>7<br>logVA_perEmp<br>5<br><!-- End of picture text -->
correlation between the number of IPT-G sessions attended and psychological well-being, but we cannot ascribe a causal interpretation to this finding.

At the 12-month follow-up (Table 6 Panel B), we find that the modest but statistically significant effects of therapy on primary mental health outcomes remained: the likelihood of minimal depression was higher in the IPT-G arm (no longer pooled with the combined treatment arm of IPT-G+) by 5.4 pp over a control group mean of 18.4% using the PHQ-8 (q = 0.017) and by also 5.4 pp over a control group mean of 27.8% using the GHQ-12; q = 0.030). The reductions in the discrete scores are both less than 0.1 SD and not statistically significant. Figure 3 shows that the improvements in the scores largely occurred around the cutoff values for minimal depression, explaining the effects on the primary outcomes.<sup>21</sup> Table A5 presents the impact of IPT-G on binary indicators for moderate (PHQ-8 _≥_ 10) and severe depression (PHQ-8 _≥_ 15) in each follow-up survey round: the impact estimates are

> least 14 (or 87.5%) of the 16 total sessions, compared with only 28% of the participants in our study, who attended at least 12 (or 85.7%) of the 14 total sessions.

> 20Table A3 replicates the same table with covariate adjustments.

> 21Data on other secondary outcomes were not collected during this round as the questionnaire had to be shortened for the phone survey that was implemented during the COVID-19 lockdown.

14
## 2.3 Schooling externalities

We also consider the possibility of a positive externality from highly educated workers, in the spirit of the recent literature (Acemoglu and Angrist 2000, Ciccone and Peri 2006, Moretti 2004a, 2004b and Iranzo and Peri 2009). There is a large body of growth literature (beginning with Lucas 1988, and extending to Azariadis and Drazen 1990, Benhabib and Spiegel 2005, Cohen and Soto 2007 and Vandennbussche et al 2009) that emphasizes the role of human capital (schooling) on technological progress, innovation and growth of GDP per capita. More recently, however, the empirical literature has pointed out that while it is sometimes hard to ...nd an e¤ect of human capital on growth of income per capita (Benhabib and Spiegel 2005), there seems to be evidence that human capital contributes to the level of income per person beyond its private returns. This implies that TFP is an increasing function of the schooling intensity in the domestic labor force. Such formulation is particularly appropriate to be included in our model and, based on the expressions used in Moretti (2004a, 2004b), the TFP can be expressed as follows , where A0 captures the part of TFP independent of the human capital externality, and � is the semi-elasticity of the modi...ed TFP to the share of highly skilled in the economy, <u>Qh;t</u> 5 Qt . Acemoglu and Angrist (2000) and Iranzo and Peri (2009) use a similar formulation to express schooling externalities and we use their estimates of the parameter �. Relying on this structure and using migration data from national Censuses, we can simulate the e¤ects of immigration and emigration on wages of non-migrants in a range of countries for a range of values obtained from the literature for the three key parameters �q; �I and �.

# 3 Data description

Assessing the national wage e¤ects of immigration to and emigration from diverse set countries across the globe requires country-level
livestock sale is due to actual market participation. However, total sale is still a good measure for commercialization, whatever is produced.

Finally, we were not able to assess whether the observed effects lead to better livelihood outcomes as our data does not contain information on consumption, full spectrum of assets and income sources. While this needs to be explored further in the presence of refugee communities, previous studies have shown that livelihood commercialization and diversification are associated with better livelihood and welfare outcomes (see e.g., Ochieng et al. 2019; Loison 2015).

# **Acknowledgments**

We are grateful to Matthew Stephens, Sandra Viviana Rozo Villarraga, and Kibrom Tafere at the World Bank for providing comments on earlier versions of the paper. We thank the UNHCR office in Addis Ababa for providing the refugee population data in Ethiopia and to Berhanu Ayalew at Food and Agricultural Organization for providing information on local contexts. This work is part of the program “ _Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership_ ''. The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO). It is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.

21