rafmacalaba/data-use-mentions-extended huggingface dataset:
of absolute energy expenditure (M/2); (3) a household is energy poor as soon as equivalized energy expenditure is below half the median of absolute energy expenditure (M/2 (equ.)); (4) A household is considered energy poor as soon as the share of energy expenditure is above 10 percent. Source: Own estimates based on HBS (2021)._

**An additional measure of energy poverty that is increasingly used, the low-income-high-cost measure (LIHC), is below the 2M and (equivalized) M/2 measures in Bulgaria.** This measure is close to the newly announced official measure but uses observed energy expenditures from the household budget survey rather than modeled energy expenditures. The energy poverty rate resulting from the low-income-high-cost measure is 7.1 percent (Figure 9), below the 2M and M/2 measures. However, it is important to note that the at-risk of poverty rate using income reported in the HBS 2021 results in slightly higher rates than the official measures from the EU-SILC survey (25 percent versus 22.1 percent).<sup>36</sup> The difference in the estimates reflects differences in the income distribution generated by the household budget survey compared to the EU-SILC.

> 36 We create the LIHC measure of energy poverty by utilizing the official poverty line from the European Union Statistics on Income and Living Conditions (EU-SILC) rather than the poverty line derived from the Household Budget Survey (HBS). The purpose is to align our measure more closely with the official definition, which relies on the poverty line from the SILC as a benchmark. Using the poverty line from the HBS could potentially result in a higher incidence of energy poverty. This is because the HBS poverty line is higher than the one estimated using SILC, leading to higher income poverty rates.

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transfers, which increases up to 92 percent for countries following the conditional model for domestic data processing, as shown in Figure 2. A smaller fraction of trade within that portion takes place between countries sharing the open model of cross-border transfers and processing of data, with a share of respectively around 20 percent and 4 percent. Only a minority of trade happens between country-pairs sharing the limited model. The share is about 1.5 percent considering the component of cross-border data transfers and 4.5 percent considering the component of domestic data processing.

> 12 Typically, digital trade also covers trade in digital goods such as computer, electronic and optical products (ISIC Rev. 4 code 26), which has been left out from this study because of its focus on digital services trade. 13 In what follows, the categorization of countries is based on the component of cross-border data transfers, given that this component more directly connected to trade.

12
the GDP of rich countries to the terms of trade faced by LIC and to the international interest rate. This ordering permits the international interest rate to react contemporaneously to the state of the global economy, but imposes that the feedback from the international interest rate to global output operates only with a lag. As pointed out by Ahmed (2003), this assumption is standard in studies of U.S. monetary policy that use quarterly or monthly data, but may be overly strong when using annual data as in our case. Placing terms-of-trade below the GDP of rich countries assumes that changes in the demand for commodities resulting from changes in the state of the international economy translate into changes in the relative price of these products contemporaneously, but changes in commodity prices affect rich countries’ output only with a lag. This assumption is also common in VAR studies of U.S. monetary policy that control for the price puzzle by including indexes of commodity prices (see, for example, Christiano et al., 1998, and references therein). The ordering of the terms-of-trade index and the international interest rate also follows the typical ordering of commodity price indexes and interest rates in these studies. Nevertheless, in the robustness section we will also determine the impact on the results of assuming several alternative orderings among these variables. For the case of aid, we also impose a block triangular structure in the matrix of contemporaneous relations between the _y_ variables, _A_<sup>0</sup> 22<sup>, which</sup> assumes that output responds contemporaneously to changes in aid, but aid flows to a country respond to changes in its economic conditions only after a year. Given the usual delays in the process of aid allocation (see Odedokun, 2003), we believe that this is a sensible assumption.

The identification assumptions described above translate into the following structure for the matrices of structural coefficients: where _m_ is the number of variables included in _y_ excluding _AID_ , and the size of the different sub-matrices conform
# **5. Discussion and Further Results**

# **5.1 Enrollment vs. Retention**

We found relatively large effects of the program on enrollment – especially for girls – in 2012 and 2013. How much of the program effect on total enrollment was driven by new students enrolling versus students being retained?

To answer this question, we match student enrollment records in 2011, to 2012 and 2013 and identify three samples of students: _Baseline_ , _Newly Enrolled_ (students not enrolled in grades three or four in 2011 or 2012, but enrolled in grades four or five in 2012 or 2013, respectively), and _New Cohort_ (students enrolled in grade three in either 2012 or 2013). To match students across 2011, 2012 and 2013 enrollment records, we use information regarding each student’s school, village, gender, age, last and first name, and father’s name. In some cases – for example, when a student name is common or when we are missing demographic information – there are multiple possible matches. Any student who matches to subsequent enrollment records (uniquely or otherwise), is coded as retained to the relevant year.<sup>17</sup> Appendix Table A7 estimates Equation (1) on enrollment numbers, disaggregating total enrollment by sample of student. In 2012, we find that a portion of the treatment effects on enrollment are driven by baseline sample girls – a coefficient of 0.28 (Panel A, Column 3), but the majority of the effects are coming from newly enrolled fourth and fifth grade girls – a coefficient of 1.1 (Panel A, Column 6). We detect no enrollment effects on new third graders (Panel A, Columns 7-9). In 2013, there are positive coefficients on the treatment effect on enrollment for

> 17 Matching baseline students to 2012 yields 97.1 percent unique matches (99.1 percent in the treatment and 95.1 in the control). Similarly, there
promote genuine responses.<sup>9</sup> 4. _Policy preferences:_ This index summarized the respondents’ stances on various policies related to Syrian refugees, including their living arrangements (e.g., a hypothetical requirement to live in camps) and employment rights.

In addition, the neighbor survey collected comparable measures of economic and psychological well-being to those measured among the refugee sample, including housing expenditures, total consumption, and mental health, to assess program spillovers.

# **3.3 Data sources**

The study employs four different data sources, with the first obtained from partners at the implementing organization, and the other three original survey data sets collected by the research team. They include:

1. _Baseline administrative data:_ The implementing organization collected baseline data from program applicants to establish their eligibility for the HSP. This assessment form collected information on household demographics, housing quality, health and disability, employment, and education, which were used to construct a housing vulnerability index employed as a baseline covariate in the empirical analysis.

2. _Surveys collected among refugee households:_ The study collected surveys during program implementation (the midline survey), immediately after (endline survey), and 1.5 years after all assistance was delivered (the follow-up survey) (see Figure 2). In the midline survey, 1,619 participants were surveyed by phone in 2020, during the first year of the COVID19 pandemic. The endline survey in 2021 collected in-person data from 1,534 participants shortly after HSP assistance had ended. Finally, in the 1.5 year follow-up collected in late 9A random one third of respondents were offered financial incentives whereas the others were told it was hypothetical. We cannot reject that the same choices were made in both groups on average.

14
benefits that are not captured by the lending institution, such as increased income due to more intensive input application by fingerprinted farmers.<sup>26</sup>

# **8. Conclusion**

We conducted a field experiment where we randomly selected a subset of potential loan applicants to be fingerprinted, which improved the effectiveness of dynamic repayment incentives for these individuals. For all the recent empirical work on microcredit markets in developing countries, to our knowledge this is the first randomized field experiment of its kind, and the first to shed light (thanks to a detailed follow-up survey of borrowers) on the specific behaviors germane to the presence of asymmetric information problems.

Consistent with a simple model of asymmetric information in credit markets, we find heterogeneous effects of being fingerprinted, with the strongest effects among borrowers expected (ex ante) to have the worst repayment performance. Fingerprinting leads these “worst” borrowers to raise their repayment rates dramatically, partly as a result of voluntarily choosing lower loan sizes as well as devoting more agricultural inputs to the cash crop that the loan was intended to finance. The treatment-induced reduction in loan size represents a reduction in adverse selection, while the increased use of agricultural inputs on the cash crop represents a reduction in ex-ante moral hazard.

The short-term improvements in repayment estimated in this paper may indeed be smaller than the effects that would be found over a longer horizon. First of all, borrowers’ assessments of the effectiveness of the technology and the credibility of the threat to withhold credit would likely rise over time as they gained further exposure to the system, observed that their past credit performance was being correctly retrieved by the lender, and saw that credit history information was indeed being shared with other lenders. In addition, the lender should be able to selectively allocate credit to the pool of good-performing borrowers over time, further improving overall repayment performance of the borrowing pool. Finally, because there is less risk involved for the

> 26 Unfortunately, our estimates of the impact of fingerprinting on profits are
# **1 Introduction**

Crisis-driven migration flows have significantly increased in the past two decades (Bhabha, 2018). The arrival of a large number of migrants and refugees have triggered fierce political disputes over its impact on local labor markets and have been the center of much discussion in the academic literature, as well as in the media. While local’s perceptions about the effects of immigration on the labor market determine their behavior and attitudes towards immigrants, the way in which these perceptions are formed are much less well understood.

We study the economic underpinnings of hostility and discrimination against immigrants. The current crisis in Venezuela has led about 5.6 million people to emigrate (R4V, 2021). This migration wave intensified in 2017 when the country’s political instability added to the worsening economic situation in the country. While many Venezuelans chose neighboring Colombia, Brazil and Ecuador as their destinations, at the time, the Peruvian economy was experiencing sustained economic growth, thus about one million Venezuelans were attracted to the country. This large inflow of immigrants potentially put pressure on local labor markets, especially in large cities, and increased the negative public discourse against immigrants in the media (Winter, 2020; Freier et al., 2021). Some of these sources claiming, not only that the large wave of newcomers had led to an increase in unemployment, but it had also contributed to an upsurge in urban violence.

Our analysis has two parts. First, we use a specialized survey of Venezuelan immigrants in Peru to identify the causal effect of the quality of local labor markets on discrimination against immigrants. Then, we turn to the economic determinants of attitudes towards immigrants, and study the flip-side of the first analysis, namely, how does the presence of immigrants affect Peruvians’ labor market outcomes, overall criminal activity, and their perceptions about crime and their local community. In both analyses, the main identification issue is that Venezuelans are not randomly allocated to specific locations in Peru and hence unobserved characteristics of both
of _A_<sup> ̃</sup> ) under the _A_<sup> ̃</sup> decomposition of Ωand the _m ×_ 1 dimensional column vector _ri_ ( _k_ ) as the vector response [ _r_ 1 _i_ ( _k_ ) _, ..., rmi_ ( _k_ )]’, the _m ×_ 1-dimensional impulse response _ra_ ( _k_ )<sup>11</sup> at horizon _k_ to the impulse vector _a_ is given by: where _qi_ is the _i_<sup>_th_</sup> entry of _q_ . Identification of the structural shock _s_ , that is the impulse vector _a_ = _a_<sup>(</sup><sup>_s_)</sup> with sign restriction consists of selecting the right impulse vector _a_ = _a_<sup>(</sup><sup>_s_)</sup> that satisfies sign restrictions imposed on the IRFs. The procedure is explained in detail in the Appendix.

# **2.3 Data description**

The VAR-system is composed of two blocks _y_ 1 _t_ and _y_ 2 _t_ of quarterly data from the first quarter of 1970 to fourth quarter of 2010. The first block _y_ 1 _t_ contains Canadian variables: (a) GDP, (b) private consumption, (c) private non-residential investment, (d) government spending, (e) net tax revenue, (f) GDP deflator, (g) REER, (h) net-exports-to-GDP ratio and (i) the 3-month T-bill rate. Variables (a) to (e) are presented in real per-capita terms by dividing each real variable by the labor force.

Except for the net-exports-to-GDP ratio and the 3-month T-bill rate, all the variables are expressed in log form. Private consumption is comprised of non-durable goods and services. Government spending is government purchases (i.e. the sum of current consumption and public investment). Net tax revenue is the sum of personal and corporate income tax revenues, social
the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of Egypt in each of the countries of destination of Egyptian migrants at the time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, conditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

56
||
|12.|Ghana|Verhage et. al. (2002)||
|13.|Hungary|Mardas (2001)||
|14.|India|Srivastava (1999), Khorana (2002), and Das<br>(2004)||
|15.|Kenya|Obiri(2003)||
|16.|Korea|Choi(1999), Seong andLee (2004)||
|17.|Kosovo|Piselli(2003)||
|18.|Laos|Trepte (1998)|Yes|
|19.|Nicaragua|Cohen(n.d.)||
|20.|Nigeria|Ekpenkhio (2003), Onyekpere (2005).||
|21.|Pakistan|TransparencyInternational(2002)||
|22.|Poland|Glebocki(2000),Piasta (2000), andLemke (2003)||
|23.|South Africa|Wittig (1999), Caron(2002), Watermeyer(2004)||
|24.|Tanzania|Nkingu (2003)|Yes|
|25.|Uganda|Wittig (1999),Agaba (2003)||

_Notes_ : This list is based on a search of (i) the websites of the World Bank, OECD, the International Trade Centre, the United Nations Commission on International Trade Law, and the WTO; (ii) the Social Science Research Network database, and (iii) the tables of contents of the leading public procurement journals, including the _Public Procurement Law Review_ and the _Journal of Public Procurement_ , and (iv) recent conferences on public procurement reform in developing countries. This list focuses on the writings of individuals and excludes documents produced by official bodies or reports of international organizations.<sup>10</sup> 9 We are well aware that there is a growing literature of theoretical analyses of public procurement matters and indeed have surveyed such literature in other papers. Here our focus is
60. In line with the Standard Model, the analysis finds that countries with private-sector participation in distribution are more likely to have achieved cost recovery. Indeed, the country case studies show a distinct trend of cases with private sector participation in distribution companies achieving higher fullcost recovery levels and being responsible for almost all of the net improvement since the pre-reform period (see Figure 16a-b).

**Figure 16: Utility Ownership and Cost Recovery**

<!-- Start of picture text --> a) Average Full-Cost Recovery Level 2010-17 b) Change compared to Pre-Reform<br>by Type of Utility (C3, %) Period (Percentage Points)<br>Private Reformed (Fully Private) 26.5%<br>Reformed (Public and<br>Mixed 34.3%<br>Private)<br>Public Remained Public -0.4%<br>0% 20% 40% 60% 80% 100% 120% -10% 0% 10% 20% 30% 40%<br><!-- End of picture text --> _Notes:_ ‘Mixed’ refers to the presence of both privately and publicly owned distribution companies in the country/state. _Source:_ World Bank staff.

61. This quantitative finding is borne out by the qualitative evidence, which suggests several underlying mechanisms including improved efficiency and commercialization (and thus depoliticization) of the power sector. Colombia’s privatization of utilities resulted in lower losses, higher collections, and full financial cost recovery for CODENSA (one of the privatized utilities). In Uganda, the private sector participation in UMEME under a concession contract brought immediate results as collections rose and distribution losses declined, improving cost recovery. The concession also guaranteed the investor 14 percent ROR, so the government increased tariffs to reflect costs. The impact of utility governance reforms on cost-recovery is robust also when looking at broader indicators of sector-wide private-sector participation and comprehensive indicators of utility governance (see Figure 17a-b)
invests in Türkiye, which corresponds to F/X flows for investment purposes (e.g., FDI, short term capital movements etc.).

The model follows a savings-driven closure where aggregate investment is flexible and equals the available volume of saving. Foreign saving is exogenous and fixed as a share of GDP, while government and household savings are endogenous. In effect, the rate of return on capital adjusts to equalize investment to the saving. Hence, the model has the crowding out effect where government investment displaces private investment.

The model dynamics follows the neo-classical growth framework (Solow-Swan growth model) implying that the long-run growth rate of the economy is determined by three main factors: capital accumulation, labor supply growth, and increases in productivity. The stock of capital is endogenous, while the latter two are exogenously determined. The capital stock in each period is the sum of depreciated capital from the previous period and new investments. For each type of labor, the maximum stock of labor available in each period grows exogenously based on population projections by age cohort and cohort-specific participation rates. The technical progress specific to sector and production factors are calibrated to replicate the GDP growth in the baseline and equals that calibrated level in simulations.

On the data side, the model is calibrated to replicate the 2018 Social Accounting Matrix (SAM) for Türkiye, which consists of the 2018 macro-aggregates, the 2012 Input-Output (I-O) table, and household surveys. It includes 38 sectors and products, 6 factors of production, and 10 types of households by income deciles. The SAM also distinguishes between public and private investment demand and includes seven electricity activities that produce a homogeneous electricity product: coal, gas, nuclear, hydropower, wind, solar and other. The single electricity sector in the original I-O table is divided based on the GTAP electricity database with ad-hoc adjustments for the Turkish electricity balance tables. Nuclear power is introduced with a small share in electricity supply based on the average cost structure of nuclear power activities in the GTAP power
# **Appendix A: Trade Share Data and Tariff Rates for Kenya’s Trade Partners**

# **Trade Share Data**

To obtain the shares of imports and exports from the different regions of our model, we used trade data for 2007 obtained from WITS access to the COMTRADE database.

The regions of our model are Kenya, the European Union, the East African Customs Union plus COMESA and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. In this appendix, we calculate and report data for the East African Customs Union and COMESA separately. For the East African Customs Union, we took Tanzania, Uganda, Rwanda and Burundi. For COMESA, in order to avoid double counting, we took the COMESA countries less those in the East African Customs Union, i.e., Comoros, Congo, Djibuti, Egypt, Eritrea, Ethiopia, Libya, Madagascar, Malawi, Mauritius Seychelles, Sudan, Swaziland, Zambia and Zimbabwe. Trade shares for the ―Africa‖ region in our model is the sum of East Africa Customs Union plus COMESA as defined above. Rest of the World is the residual.

We mapped two digit sectors from the COMTRADE database into the sectors of our model. The exact mapping is defined in the first table below.

We used Kenya as the reporter country for both exports and imports. Results for both exports and imports are reported in the subsequent three tables, by CRTS and IRTS goods in our model separately.

## **Tariff Rate Calculations**

**Tariff and Sales Tax Data** .  We started with MFN tariff rates at the eight digit level  taken from

the website of the Kenyan government: <u>www.kra.go.ke/customs/customsdownloads.php. These tariff</u> rates were then aggregated to the sectors of our model, using simple averages.

We obtained data on the total taxes on imports and the total value of imports and
above requires that productivity and markups be identified consistently if the economic impacts of regulation and enforcement are to be accurately characterized. A widely used methodology to estimate productivity is Ackerberg et al. (2015) and a popular extension for estimating markups is De Loecker et al. (2020). Recent critiques of both methodologies require a change in assumptions or a very specific data set. As an example, Bond et al. (2020) show that it is theoretically impossible to recover markups if the firm-level data do not contain separate entries for quantities and prices. Another possible problem arises in the presence of demand shifters, which may render estimates biased (Doraszelski & Jaumandreu (2019)).

On these terms, the data sets used here would be criticized; we do not observe price and quantity data separately (only price times quantity) and we need to account for several demand shifters. Sampi et al. (2021) recently addressed some of these criticisms and produced unbiased policy estimates in the presence of demand shifters as well as missing price data. The methodology, which entails several deviations from Ackerberg et al. (2015) 15