rafmacalaba/data-use-mentions-extended huggingface dataset:
collates the findings. This step was originally included in order to contribute to the Voices of the Poor global research exercise, but has turned out to be a valuable document in its own right. First, for those who lack time to read individual reports, it presents the highlights. Secondly, it provides an opportunity to reinforce findings that arise across the different sites in a variety of different social, economic and geophysical environments. Many of the problems of poverty were found to be just as crucial in Ho Chi Minh City as in remote, upland villages. This in turn helps to respond to some of the criticisms that the PPAs were simply collecting anecdotes that were isolated or extreme cases. Where findings crop up repeatedly, although different research teams in different areas using different methodology are presenting them, it suggests that the analysis is cutting through the biases of any one particular research team. The national report also identifies problems that are specific to particular locations. Thirdly, it made the work of incorporating the findings into the national analysis of poverty and merging it with the quantitative findings much easier. The national synthesis report has been published in both Vietnamese and English and circulated widely.

Diagrarn 2 shows the many levels of documentation that flowed from the PPAs. The bolcd arrows that direct the PPA analysis into the national poverty assessment mark the route that brought the PPA findings to the attention of national policymakers. The dotted line to the Voices of the Poor and the WDR2000 exercises indicates a less direct impact at a national level, although the association with these high-profile international studies has 16
∗</sup>|7.506<sup>∗∗∗</sup>|8.034<sup>∗∗∗</sup>|7.552<sup>∗∗∗</sup>|8.119<sup>∗∗∗</sup>|
||(0.519)|(0.578)|(0.504)|(0.566)|(0.430)|(0.516)|
|Controls|No|Yes|No|Yes|No|Yes|
|Province FE|No|Yes|No|Yes|No|Yes|
|Observations|858|853|909|904|963|958|
|_R_<sup>2</sup>|0.097|0.133|0.088|0.121|0.070|0.102|

_Notes:_ Authors’ analysis based on data described in the text. This table reports OLS estimates for the second definition of demobilization ( _D_<sup>_CNDD_</sup> _i_<sup>+2</sup> and _Di_<sup>_FNL_2</sup> ). We grouped households into five employment categories: farming, small business, construction, public sector, and extractive activity. We define as the main occupation the one which generated the highest income the year before the survey. Constructing this variable was problematic for households that did not report the revenue associated with their activities. These households are therefore excluded in columns (1) and (2). In columns (3) and (4), households that did not report their revenue but only engaged in one type of activity are included. In columns (5) and (6), we also add the households that did not report their revenue and engaged in multiple activities by assuming that their main income generating activity was the nonagricultural one. In all columns, the
Figure 5. Annual changes in the labor income Gini, Latin America and selected countries, ca. 1993–2003 and ca. 2003–13 <!-- Start of picture text --> 0.8 Argentina Uruguay Peru<br>Colombia<br>0.4 SwedenDenmark United Kingdom United StatesPanama Mexico Honduras Paraguay<br>0.0 FinlandSwitzerland KazakhstanItaly Chile<br>Greece Israel Nicaragua<br>-0.4 Germany Ireland El Salvador Bolivia<br>Mauritius<br>Philippines Brazil<br>-0.8<br>-1.2<br>-1.6<br>15 20 25 30 35 40 45 50 55<br>Labor earnings Gini, ca 1993 (Gini points)<br>0.8 Germany France<br>Denmark<br>0.4 Switzerland Sri Lanka Costa Rica<br>Ireland<br>United States<br>0.0 Finland Sweden Poland Israel Mexico Honduras<br>El Salvador<br>-0.4 Greece Colombia Brazil<br>-0.8 Kazakhstan Kyrgyz Republic Bolivia<br>Argentina Ecuador Peru<br>-1.2 Nicaragua<br>Venezuela, RB<br>-1.6<br>15 20 25 30 35 40 45 50 55<br>Labor earnings Gini, ca 2003 (Gini points)<br>LAC Non-LAC<br>ca 1993-2003 (Gini points)<br>Annual change in labor earnings Gini,<br>ca 2003-2013 (Gini points)<br>Annual change in labor earnings Gini,<br><!-- End of picture text --> _Source:_ Venezuela, RB and the non–Latin American countries: Global Wage Report, International Labour Organization. Seventeen Latin American countries: SEDLAC database. See annex A.3 for details about the underlying data and Annex B for circa periods. _Note:_ The data have been multiplied by 100. For
254|-0.301|-0.293|-0.073|-0.113|-0.119|
||(0.201)|(0.203)|(0.200)|(0.118)|(0.123)|(0.122)|
|_Geography_|||||||
|Share of Plain (%)|0.011|0.011|0.011|-0.000|0.000|-0.001|
||(0.011)|(0.011)|(0.011)|(0.008)|(0.008)|(0.008)|
|Distance to Xiamen|0.001|0.001|0.001|-0.000|-0.000|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Obs.|55|55|55|51|51|51|
|_R_<sup>2</sup>|0.087|0.095|0.095|0.084|0.105|0.110|
|_H_0 : _b_0 = _b_1|||0.309|||0.221|

Table 17: The Effect of Factions and Guerrilla Presence on the Changes in the Average Years of Schooling for Individuals Born in 1952-1972 Relative to Those Born Prior to 1930, as Observed in the 1990 Census. Note: The dependent variable is the difference in log of the average education level of the cohort born between 1952 and 1970 and the log of the average education of the cohort born before 1930, as observed in the 1990 Census. In the right panel, the 5% tail observations are trimmed from the analysis
# **1 Introduction**

The diffusion of global production networks has called for new statistical tools providing a representation of complex production linkages between and within economies. New types of data sources, the Inter-Country Input-Output (ICIO) tables, and new analytical frameworks have been developed to measure supply and demand contributions of countries and sectors in global value chains (GVCs).<sup>1</sup> In this paper we describe `icio` , a new Stata command that computes countries’ and sectors’ participation in GVCs as well as relevant measures of trade in valueadded, following the conceptual framework proposed by Borin and Mancini (2019), – – which in turn extends, refines and reconciles the other main contributions in this strand of the literature.<sup>2</sup> The command is flexible in many aspects. It allows to choose from different accounting methodologies, called perspectives. Each of these perspectives is best suited to address specific empirical questions, such as tracking production-demand linkages, assessing countries’ participation to the global production sharing, quantifying value-added embedded in countries’ and sectors’ exports, evaluating the potential exposure to macroeconomic and trade policy shocks. It exploits the most famous ICIO tables - the World Input-Output Database (Timmer et al. 2015), the OECD TiVA database (OECD, 2018), and the Eora Global Supply Chain Database (Lenzen et al. 2013). Moreover, any user-provided ICIO table can be straightforwardly loaded and used to compute value-added trade and GVC participation measures.

More specifically, `icio` encompasses the most relevant measures of value-added in exports and imports at the aggregate, bilateral and sectoral levels. For a given trade flow, it disentangles the source country/sector and the destination country/sector of value-added content. Moreover, for export flows at any level of disaggregation, `icio` computes the component related to GVC trade, i.e., the one entailing more than a single border crossing. This measure - and its backward and forward
)|(0.004)|(0.016)|
|Observations|8,004|8,076|3,924|
|R-squared|0.097|0.124|0.267|
|Country FE|YES|YES|YES|
|Year FE|YES<br>**Pan**|YES<br>**el B**|YES|
||(1)|(2)|(3)|
||GDP growth|GDP growth|Night light growth|
|Confict Incidence|-0.020***|-0.029***|-0.075***|
||(0.005)|(0.005)|(0.024)|
|Observations|8,004|8,076|3,924|
|R-squared|0.100|0.130|0.268|
|Country FE|YES|YES|YES|
|Year FE|YES|YES|YES|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The dependent variable in columns (1) and (2) is the GDP per capita growth computed using Penn World Table data and World Bank data respectively. Column (3) uses growth of night light per capita form the National Oceanic and Atmospheric Administration (NOAA). Panel A uses as “conflict incidence” a dummy that takes a value of one if in country _i_ at time _t_ the number of battle related deaths is higher than 0. In Panel B “Conflict incidence” dummy takes a value of one if the number of battle deaths is above a threshold of 0.008 fatalities per 1000 population.

11
(volunteers for _poskambling_ (security posts), checkpoints to see who is entering the village), and the distance to the nearest police post. We find some evidence in rural areas that the presence of communal security arrangements is associated with a reduced likelihood of conflict. In contrast, distance to a police station reduces the likelihood of conflict.<sup>21</sup> These associations must be interpreted with extreme caution, as this may actually reflect good targeting (posts are built nearer to likely trouble spots) or simply reflect that more isolated villages are less prone to conflict. These associations should provide an impetus to further on-going qualitative work to evaluate potential dynamics behind these associations, and subsequent means to better test these quantitatively.

# **5. Conclusions**

The paper has presented the results of a unique large-scale effort in a developing country to monitor the incidence of local conflict, Indonesia’s 2003 PODES. We found that conflict is significant across Indonesia, rather than being highly concentrated in just a few areas. This has significant implications for how we understand the roots of conflict in Indonesia. It suggests the need to investigate systemic factors associated with conflict, in addition to understanding often highly localized dynamics through qualitative approaches. From a policy perspective, the potential for conflict must be taken into account when designing development interventions for areas across Indonesia. From a research perspective, it implies the consideration and exploration of conflicts in areas beyond those normally thought of as being ‘high conflict’.

> 21 We also tested this with another variable that asked whether a police post was difficult to reach with similar results.

30
existence of corruption within the judiciary, and the ability of individuals and businesses to enforce contracts. _Political stability_ (World Governance Indicators), which ranges from -2.5 to 2.5 = best, and _corruption_ (Heritage data based on Transparency International’s Corruption Perceptions Index) take governance into account, while the _doing business_ frontier gap (WDI, from 2004 on) and _domestic competition_ and _foreign competition_ estimates (WEF, from 2006 on) refer to the business climate. The former is based on the regulatory environment for the starting and operation of a local firm and takes values from 0 to 100 = best. The latter are composite indices concerned with market concentration, antimonopolistic policies, investment incentives, and tax policy (domestic) as well as tariffs, nontariff measures, and investment promotion (foreign).<sup>7</sup> _Financial and labor market policy_ is captured on the financial side by the value of credit given to the private sector as percentage of GDP ( _private credit_ ) from the WDI. This quantitative variable is complemented by an estimate of _financial freedom_ (Heritage), which measures banking efficiency as well as independence from government control and interference in the financial sector. It relies on various underlying data sources, including from the International Monetary Fund, OECD, and official government publications of each country, among others. _Labor market policy_ is examined using Heritage’s _labor freedom_ index that captures labor market institutions. It includes various aspects of the legal and regulatory framework of a country’s labor market, such as minimum wages; laws inhibiting layoffs; severance requirements; and measurable regulatory burdens on hiring, hours, and so forth. The measure is mainly based on data from the World Bank’s Doing Business study. We also use an index of unit labor costs (ULC) by the OECD that proxies for a country’s cost competitiveness (base year = 2005). It measures the average cost of labor per unit of output and is calculated as the
to be matched with census data. This allows us to create a year-ISIC panel with information about exports and imports on 86 industry sectors in Peru.

# **4.2 Outcome Variables**

In the first part of our analysis, we examine the impact of local labor market conditions on self-reported information on experiencing discrimination as reported by Venezuelans surveyed in ENPOVE. Overall, 36.4% of Venezuelans report having experienced discrimination, with this being slightly more common among women (38.1%) than men (35.0%). Figure 2 shows the distribution of reported discrimination in different municipalities in Peru. There is clearly variation both across and within regions. Report discrimination is least common in Tumbes (23.4%), which is the typical entry point to Peru for Venezuelans and currently hosts 5% of ENPOVE sample, while Cusco and Lima, where 7% and 48% of Venezuelans are located, show the highest (47.8%) and median levels (37.1%) of reported discrimination, respectively. Individuals who experienced discrimination are then asked in which locations did the episode took place. We examine reports for the three most common locations, at work (20.0%), on the streets/in public places (25.0%), and on public transit (9.8%).

In the second part of our analysis, we examine the impact of Venezuelans on a wide variety of outcomes for Peruvians. First, we examine impact on labor market outcomes, specifically employment, formal employment, log wages if employed, log household income and log household expenditure. Second, we examine the impact on crime and opinions about personal safety. Specifically, we look at the reported (log) number of crime in each district from administrative data split into non-violent and violent crimes (data starting in 2011, means 3.54 for log violent crime and 3.31 for log non-violent crime), from ENAHO whether crime is a major
Financial Regulation and Government Revenue analogue to our reference because it catches all costs, including rents, local wages, utility bills, equipment, and so on. As well as interviewing banks, we tested the plausibility of this number by using the annual report data and inferred branch costs. In Appendix C, we present a detailed exercise, which backs out the average cost of a branch from the yearly operational expenses of the CBE between 2006 and 2012. In line with interviews with private sector banks, we obtain an average cost of a branch of 303,000 birr. Given that the minimum business-lifecycle of a branch is five years to collect the peak deposit from a location, this makes the average total cost of branch 18 million birr (ranging between 6 and 30), and equivalent to 900,000 USD. Therefore the total cost of opening 91 new branches is 1,638 million birr (ranging between 546 and 2,730).

Similarly to the previous case, we can calculate the revenue gains on the Ethiopian Treasury, by subtracting the NBE bill interest payment from this liability expansion measure. This overall cost is composed of two components: a fixed cost, the installation of 91 new branches, _BI_ ; and a variable cost, which is the net loss incurred by the CBE on collecting 13.976,75 million birr of deposits remunerated at an average rate of 5.4% and their use for NBE bill purchase, which only give a 3% fixed return. Therefore, the final gain can be written as follows which places the revenue gains of introducing the NBE bills at 1,554.2 million birr (between 462.2 and 2,646.2). Overall, this gain is 24% higher than the previous gain and makes the savings of this measure 19% of the deficit and 2.6% of tax revenue.

# **5 Concluding Remarks**

In this paper, we show that the government revenue gains of a specific piece of financial
Value of saving repaid to savers (excl. int.)|28|201|443|32|
|V4_23|Value of saving repaid to savers who quit/died|6|27|843|8|
|V4_25_1|Repayment of shares (baht)|8|17|1,233|8|
|V4_20|Value ofprincipal repaid to institutions|1,000|971|530|187|
||**_Memo item_**|||||
|V4_12|Total principal repaid by borrowers (on time)<br>**Revenue**|1,059|1,068|2,363|917|
|V4_13|Total value of interest earned on loans|64|89|2,359|76|
|V4_14|Fees|2|22|633|5|
||**Expenditure**|||||
|V4_19|Value of interest paid to institutions|50|56|537|11|
|V4_21|Value of interest paid to savers|12|22|921|7|
|V4_28_2|VF “profit”|60|89|2,393|77|
||**Uses of profit**|||||
|V4_24|Payments to shareholders|6|12|1,889|8|
|V4_27|Payments for welfare programs|6|9|1,749|6|
|V4_26|Payments to VF committees|15|17|2,158|12|
|V4_25_2|Repayment of shares|1|5|465|1|
|V$_28_1|Share of VF reporting profit(%)||92.3|2,631||

Source: Village Fund Survey, 2010. Totals do not always add up exactly, because the samples vary somewhat from item to item. Includes only those VFs that reported the amount of lending.

# **6. Measuring Village Fund Performance**
011|**|0.282|0.284|0.925||
|Would use mortgage to buy land|dlctln2|0.107|0.088|0.276||0.100|0.104|0.819||
|Land area in square meter|gpsarea|1,297|1,566|0.010|***|1,306|1,278|0.690||

_Source:_ Own computation from MSU 2013 baseline survey. Note: The standardized mean and median covariate bias before matching were 17.6 and 15.5 percent, respectively. The kernel matching technique reduces the mean and median percentage bias to 3.8 and 3.2, respectively. One-to-one and 2-nearest neighbor matching strategies reduce the mean and median percentage bias (4.9 and 4.1; and 5.6 and 4.3, respectively), suggesting that the kernel-based matching performs slightly better than the latter two approaches.

38
, controlling for a range of pre-program village

> 1 Our study closely follows Jalan and Ravallion (1998), who examine the impact of a poor-area development program on growth in household consumption using panel data collected from targeted and non-targeted areas across four provinces in China.  Using county yearbooks over 1980–87 and 1982 census data, they employ a Generalized Method of Moments estimation procedure on an AD(1,1) model for household consumption growth, including initial area conditions on the right-hand side, and using second and higher lags of consumption as instruments for lagged consumption to obtain consistent estimates of a dynamic growth model with panel data.

> 2 In addition to the RRMIMP, the 2009 study examined the effects of the Rural Development Project (RDP), another road-paving project, using a similar household panel survey of project and control villages; however the third survey round was limited to the RRMIMP project and control areas.

3
<!-- Start of picture text --> 7C: Investment rate has increased over the years 7D: Financial development in India has<br>increased over the years 20<br>Investment Rate Domestic Credit Provided by<br>40 Financial Sector (as % of GDP)<br>35 100<br>30<br>80<br>25<br>20 60<br>15<br>40<br>10<br>20<br>5<br>0 0<br>India Median in EM7 countries India Median of EM7<br>7E: India has strong and reliable institutions<br>Rule of Law Index* Index of Bureaucracy<br>Effectiveness**<br>0.2<br>4<br>0.1<br>3<br>0<br>2<br>-0.1<br>-0.2 1<br>-0.3 0<br>India Mean of EM7 India Mean of EM7<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016<br><!-- End of picture text --> Note: Data is from World Development Indicators and IMF International Financial Statistics. The latter’s date range is 1960 to 2011. *: World Bank Worldwide Governance Indicators, Kaufmann et. al. 2002. Rule of Law Index (Value between -2.5 and 2.5). Date Range: 1996 to 2016 (with gaps); **: Political Risk Service (values between 0 and 4, 4 being the best). Date Range: 1984 to 2014. Bars show average over time and, in the case of EM7, across countries Below we look at India’s growth record since the early 1990s more granularly, in order to reconcile its positive long-term growth
% in 1990 and 4.4% in 2000. This appears in ...gure 3 (...g 3.A and 3.B) which gives skilled migration rates evaluated under three measurement methods: (i) a method fully based on Censuses and administrative data (our method), (ii) a method based on OECD statistics and US educational attainment data (Carrington and Detragriache), (iii) an intermediate method based on Census/administrative data and US educational attainment data. In comparison to the Census method, the second method clearly underestimates the brain drain for a large majority of countries. On the contrary, the third method overestimates the brain drain.

The two sources of bias cancel each other in a couple of cases. However, the brain drain is particularly overestimated in countries such as Algeria, Tunisia, Morocco, Turkey, Somalia. By transposing the educational structure observed in the US, Carrington and Detragiache and Adams obtain high emigration rates of tertiary education workers for these countries (between 55 and 65 percent for North Africa and Turkey). Taking into account the low level of education observed among emigrants to Europe (where the large majority of these migrants live), we obtain much lower skilled emigration rates (between 5 and 10 percent).

26
child health as a consequence of the COVID-19 pandemic includes Roberton et al. (2020), who use a health care seeking and supply model, and posit reductions in care seeking and available quality of care. In contrast, our approach uses the data on past contractions to calibrate the under-5 mortality semi-elasticity with respect to the economic shock.

The rest of the paper is structured as follows. Section 2 lays out the quantitative framework. Section 3 details the calibration, and 4 presents the quantitative results. Section 5 concludes. The Appendix contains additional details on theory, quantification, and robustness.

# **2 Quantitative Framework**

This section builds a macro-SIR framework along the lines of Eichenbaum et al. (2021) with the added feature that households comprise several members in different age groups (Acemoglu et al., 2020). Our key innovations are (i) to model income shocks as a source of mortality not related to COVID-19; and (ii) to calibrate the model to 85 countries with different underlying characteristics.

## **2.1 Economic Environment**

We consider a discrete and infinite time horizon model, _t_ = 0 _,_ 1 _, · · · , ∞_ , and a continuum of households indexed by _j ∈_ [0 _,_ 1]. A model time period corresponds to one week. The measure of households is normalized to 1 in the initial period. Households are formed by individuals differentiated by age group _m ∈{_ 1 _,_ 2 _,_ 3 _}_ to which they belong. Type _m_ = 1 individuals are children ages 0-14, type _m_ = 2 are working prime-age adults ages 15-59 and _m_ = 3 household members are the elderly aged 60 and over. Denote by _l_<sup>_m_</sup> the mass of individuals of age group _m_ so that<sup>�3</sup> _m_ =1<sup>_lm_= 1.Weomitcountryindicesintheexposition</sup> to streamline