rafmacalaba/datause-encoder-data huggingface dataset:
combine geographic targeting with other forms of targeting such as self-targeting.<sup>18</sup> However, if the individual-level concentration curve were OJ’B instead of OJB, there would be almost no room for improvement. In this case, the policy-maker will not gain much from devising a more complex targeting scheme.

We propose _relative budgetary gains_ to measure how much a particular targeting policy is capturing the possible gains from targeting. In Figure 9(a), the relative budgetary gain from community-level targeting given the goal of malnutrition reduction OI is DGFG<sup>.Bydefinition,</sup> the _relative budgetary gain_ is 1 for perfect targeting and 0 for uniform targeting regardless of the goal of FGT reduction.

There may not be an obvious choice of the goal OI or the budget OF. It is convenient to have an overall measure of the efficiency measure of targeting. A natural choice would be the negative concentration index. For the commune-level targeting, it is twice the heavily <u>X</u> shaded area X in Figure 9(a). Notice that 2 _·_ X = X+Y+Z<sup>.Thismeasureisthebudgetary</sup> gain averaged over all the possible values of FGT reduction goals. Hence, we shall call this _average budgetary gains_ . An alternative interpretation of the average budgetary gains is the equivalence gains averaged over the interval between zero and one. Similarly, we shall call the ratio of negative concentration index for commune-level targeting to that for perfect targeting _average relative budgetary gains_ , which is X+YX<sup>inFigure9(a).Itisalsoequalto</sup> _N_ _<u>CIB</u>_<sup>orthebetween-groupnegativeconcentrationindexovertheGiniindex.</sup> _GI_<sup>,</sup> Using the point estimates of the prevalence of stunting and underweight, we drew the concentration curves for different levels of geographic aggregation as shown in Figure 10(a). The concentration curves for underweight look similar and are given in Figure 10(b). There are four points to note here. First, the commune-level targeting is a large improvement over the stratum-level targeting, which is the best policy-makers can do with the DHS data only.

> 18An example of a self-targeting program is the food-for-work program, because only poor people without better outside option are willing to participate in the program to receive food. Food-for-work targeted to a particular region is an example of the combination of geographic targeting and self-targeting.

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difference gets smaller and insignificant once we condition on pre-COVID-19 industry of work (columns 5- 6).

An important result in Table 2 is that education is the key predictor of job status in February/March 2021 (columns 3-4): conditional on working pre-COVID-19, the least educated respondents are four to seven percentage points more likely to be jobless a year later compared to those with primary or higher education.<sup>17</sup> This is different from the early-phase results, where we found some evidence for a positive relationship between education and job loss. The education effects are similar between columns 3 and 4, indicating they are not driven by differential impacts across industries. Sub-sample results (Table A10) show that the education gradient is strongest in urban areas.

Overall, we find that disparities in the early-phase labor market impacts by gender, age, and rural/urban smoothed out after October 2020. Instead, education appears as the main dimension of inequality a year after the start of the pandemic, when joblessness was significantly higher among the least educated individuals.

> 17 If we include, in columns 3-4, all respondents in the February/March HFPS round who were working preCOVID-19 (instead of the subsample of respondents who participated in _all_ HFPS rounds up to Feb/March 2021), the results are very similar (available from the authors).

14
collaboration with the Agency for Communication under the Government of Tajikistan. The messages were sent to all mobile phones and were not “targeted” to particular users or groups at elevated risk.

The campaign used existing SMS-systems maintained by the country’s three largest mobile operators<sup>8</sup> From May to December 2020, the project sent COVID-19 related public health messages to approximately 5.5 million mobile phone subscribers in both Russian and Tajik languages (Table 1). The content of the messages was developed in consultation with the MoHSP based on recommendations from the World Health Organization’s COVID-19 SMS database. The messages were directly attributed to the Ministry in each message and focused on preventing the spread of COVID-19. The SMS messages focused on specific behaviors from the list including handwashing, avoiding handshakes and hugs, more frequent cleaning and disinfecting items, not touching one’s face and mouth, wearing masks, quarantining sick individuals, and social distancing. For a full list of the messages sent, see Appendix A.

**Table 1: SMS-messaging through Mobile Engage in Tajikistan, May to December 2021**

|Mobile operator|Period|Approx. subscribers|Messages<br>(Tajik and<br>Russian)|Total SMS sent|
|---|---|---|---|---|
|TCell (round 1)|May 2020|2,477,274|8 (T/R)|39,636,384|
|TCell (round 2)|Jul-Sept 2020|2,477,274|7 (T/R)|34,681,836|
|Babilon M|Nov-Dec 2020|1,056,586|6 (T+R)|6,339,517|
|Megafon|May2020|~2,000,000|<br>1(T+R)|2,000,000|
||Total|5,533,860||82,657,737|

Alongside the Mobile Engage project, the Government of Tajikistan also endorsed an expansion of the existing Listening to Tajikistan (L2T) survey, another World Bank initiative, to collect individual and household data on the social and economic impacts of the COVID-19 pandemic. L2T is a continuous monthly household panel survey conducted over the phone with a nationally representative sample of about 1,200 households. The L2T survey was first launched in 2015, and because interviews are conducted over the phone, continued operation without interruption during the COVID-19 pandemic when in-person interviews were not possible. This study uses the national panel survey data to assess the impact of the Mobile Engage project on reported risk reducing behaviors.

The L2T survey was organized around two primary components. The first was a comprehensive inperson baseline survey, conducted in 2015, and supplemented with a second similar comprehensive in-person survey completed in 2017. The baseline sample design used a stratified two-stage clustered approach, with a target of national as well as regional representativeness for household consumption and expenditure. The sample frame was the national census of Tajikistan conducted in 2010. In the first stage, 150 clusters (census tracts) were randomly selected from the census, with a probability of selection proportional to size. In the second stage, 3,000 households were randomly selected from

> 8 TCell, Megafon, and Babilon M

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TABLR 8 3 **_SuTry_ ReAults** for Manufacturinx **Output** and Productivity Brazil/USA and Mexico/USA (1975)

|Brazil/USA|<br>Mexico/USA|
|---|---|
|Value Added (Former<br>National<br>Accounts<br>Concept)<br>as a percentage<br>of the USA<br>10.55|3.76|
|Value Added (Former<br>National<br>Accounts<br>Concept)<br>per Person Engaged<br>as a percentage<br>of the USA49.33|38.52|
|Value Added (Former<br>National<br>Accounts<br>Concept)<br>per Head of Population<br>as a percentage<br>of<br>the USA<br>21.72|13.48|
|Persons Engaged<br>in Manufacturing<br>as a percentage<br>of the USA<br>21.38|9.75|
|Population<br>as a percentage<br>of the USA<br>48.55|27.85|

Note: figures in the three upper lines are geometric averages.

- Source: Value added from table 3.15 and 3.16; value added<sup>per person em-</sup> ployed from table 7.1; value added per head of population derived from tables 7.5, 7.6 and 7.7; persons engaged and population from table 7.5.

# TABLE 8.4 Sumary **Results for Manufacturing Output and Productivi** Mexico/Brazil (1975)

|Direct<br>Mexico/Brazi|Inferential<br>l<br>Mexico/Brazil|
|---|---|
|Value Added (former<br>national<br>accounts<br>concept)<br>as a percentage<br>of Brazil<br>37.71|35.64|
|Value Added (former<br>national<br>accounts<br>concept)<br>per Person Engaged<br>as a<br>percentage<br>of Brazil<br>82.70|78.09|
|Value Added (former<br>national<br>accounts<br>concept)<br>per Head of Population<br>as a<br>percentage<br>of Brazil<br>65.74|62.06|
|Persons<br>Engaged<br>in Manufacturing<br>as a<br>percentage<br>of Brazil<br>45.60|45.60|
|Population<br>as a percentage<br>of Brazil<br>57.36|57.36|

Note: figures in the three upper lines are geometric averages.

- Source: Direct Mexico/Brazil comparison: Value added from table 4.10; value added per person engaged from table 7.2; value added per head of population derived from tables 7.5 and 7.8; persons engaged and population from table 7.5; Inferential Mexico/Brazil comparison from table 8.3.
**Figure 4.6: Services trade restriction in Turkey by typology (2016)**

<!-- Start of picture text --> Establishment vs Operations<br>0.5<br>0.45<br>0.4<br>0.35<br>0.3 Legal<br>0.25<br>Courier<br>0.2<br>Logistics freight Broadcasting Air transport<br>0.15 ArchitectureComputer forwarding<br>0.1Maritime transportEngineeriInsuraMotion picturesCommercial banking n ceg Telecom Logistics cargo‐handling<br>Rail freight transport<br>0.05 Construction<br>Distribution Sound recording Logistics storage and<br>0 Road freight transport Logistics customs warehouse<br>0 0.05 0.1 0.15 0.2brokerage0.25 0.3 0.35 0.4 0.45 0.5<br>Operations<br>Discriminatory vs Non‐discriminatory<br>0.5<br>0.45<br>0.4<br>Air transport<br>Insurance<br>0.35<br>0.3<br>Logistics cargo‐<br>0.25 handling<br>Logistics storage and Logistics customs<br>0.2 warehouse brokerage<br>Computer Construction<br>Engineering<br>0.15 Architecture<br>Legal<br>Commercial banking<br>0.1 Broadcasting Logistics freight<br>DistributionCourier forwarding<br>0.05 Rail freight transport<br>Motion pictures Maritime transport<br>0<br>0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5<br>Non‐discriminatory<br>Establishment<br>Discriminatory<br><!-- End of picture text --> Source: OECD STRI; author’s calculations; the scales on both panels refer to the percentage division of the number of restrictions for each type, which together add up to a sector’s overall restrictiveness index.

**Restrictions on firm operations can dampen growth.** Cross‐country differences in the post‐entry performance of all firms tend to be more marked than differences in entry and exit patterns (Bartelsman et al., 2003), which suggests an important role for services conduct regulations (Van der Marel et al., 2016). Furthermore, the OECD (2016) finds that post‐entry growth patterns between countries explain firms’ ability to achieve sufficient scale to reach global markets, and the share of small firms that are relatively old negatively affects aggregate productivity and employment growth. Put differently, in countries with

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**______________________________________________________________________________**

## **Table 4.1: Trend Tests for Thematic Shares**

||Climate|Pollution|Land|Biodiversity<sup>8</sup>|Water|Policy|Other|
|---|---|---|---|---|---|---|---|
|Time|0.318|-0.167|0.639|0.074|0.008|-1.071|0.199|
||(0.63)|(0.19)|(1.38)|(0.37)|(0.01)|(1.65)|(1.09)|
|Constant|8.337|33.404|8.529|2.215|20.456|24.930|2.128|
||(2.44)*|(5.54)**|(2.72)*|(1.61)|(5.86)**|(5.67)**|(1.71)|
|Obs.|11|11|11|11|11|11|11|
|R-squared|0.04|0.00|0.17|0.01|0.00|0.23|0.12|

Absolute values of t statistics in parentheses * significant at 5%; ** significant at 1%

**______________________________________________________________________________**

Produced by thousands of interactions between the Bank and its partner countries, these results suggest very strong continuity in the relative valuation of thematic objectives. We will return to this point in Section 7, which develops a model for the optimal allocation of environmental resource allocation by the Bank.

# **5.  How Has Environment Mattered in the Aggregate?**

We begin our assessment by analyzing the match between environmental lending, AAA and environmental problems at the country level. Our overall environmental indicator is the mean of the five thematic indicators.<sup>9</sup> We use log values for the analysis because the size distributions of country indicators and resource allocations are extremely

> 8  For biodiversity, our data include only Bank lending.  Grants by the Global Environment Facility (GEF) for biodiversity conservation are not included in this analysis, but the GEF is currently conducting a parallel analysis of its own resource allocation.

> 9  All indicators are normalized to the range [0-100], so they have equal weight in determining the mean indicator.

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Table 13: Basel III and SME access to finance: Repeated cross-sections

|VARIABLES|(1)|(2)<br>Ac|(3)<br>cess tofinanc|(4)<br>e as a constr|(5)<br>aint|(6)|
|---|---|---|---|---|---|---|
|Basel Dummy Gest|-0.944**|-0.965**|-1.162***|-0.914***|-0.994***|-0.973**|
|First period dummy|-1.051***|-1.074***|-1.196***|-1.094***|-1.131***|-1.148***|
|ES gap|-0.157*|-0.157*|-0.336***|-0.323***|-0.337***|-0.371***|
|Employees||0.002***|0.002***|0.002***|0.003***|0.002***|
|Age||0.003|0.004**|0.004**|0.003*|0.005***|
|Foreign owners share (%)||0.004***|0.005***|0.005***|0.001|0.007***|
|Government-owned share (%)||0.001|0.001|0.001|0.017***|-0.005|
|Direct exports share (%)||0.001|-0.000|-0.001|0.000|-0.001|
|Annual sales paid after delivery (%)||-0.001|-0.002**|-0.002***|-0.000|-0.003***|
|Log of GDP per capita (constant 2005|||-0.103|-0.132|-0.249|-0.078|
|US$)|||||||
|Log of population|||0.087|-0.093|-0.317**|-0.057|
|Ease of Doing Business|||0.006|-0.004|-0.010|-0.007|
|Getting Credit|||-0.002|0.005|0.010**|0.005|
|<br>Enforcing Contracts|||0.030***|0.020**|0.014|0.025**|
|Financial Crisis Dummy|||-0.876***|-1.024***|-1.211***|-1.066***|
|Bank return on equity (%, after-tax)||||-0.022**|-0.007|-0.027***|
|Credit to government and state-owned||||-0.006|0.000|0.004|
|enterprises to GDP (%)|||||||
|Bank nonperforming loans to gross loans||||-0.012|0.015|-0.036*|
|(%)|||||||
|Bank credit to bank deposits (%)||||-0.003|-0.004|-0.000|
|Bank regulatory capital to risk-weighted<br>assets (%)||||0.009|0.016|-0.006|
|Lerner index||||-0.835*|-1.034**|-0.968*|
|Liquid assets to short-term liabilities (%)||||-0.001***|-0.002***|-0.002***|
|Observations|16,462|16,462|16,462|16,462|6,448|10,014|
|Countries|20|20|20|20|20|20|
|Pseudo R-squared|0.025|0.027|0.045|0.047|0.037|0.058|
|Selection equation|||Basel III Im|plementation|||
|Log of GDP (current US$)|1.083**|1.083**|1.083**|1.083**|1.166**|1.001**|
|<br>Foreign bank assets among total bank|-0.020|-0.020|-0.020|-0.020|-0.009|-0.035|
|<br>assets (%)|||||||
|<br>G20 Developing Dummy|1.322|1.322|1.322|1.322|0.967|1.477|
|Observations|16,462|16,462|16,462|16,462|6,448|10,014|
|Countries|20|20|20|20|20|20|
|Pseudo R-squared|0.555|0.555|0.555|0.555|0.481|0.616|

_Note:_ This table presents the estimates of the effect of Basel III implementation on the firm’s subjective assessment of access to finance, which was estimated with an ordered logit and for the repeated cross-section sample. The models were estimated with the Heckman selection model—with the selection equation being estimated with a logit model. Standard errors clustered at the country level are in parentheses. Column (5) presents the results for the firms that had a bank account and a loan; column (6) shows the results for firms with a bank account but without a loan. ES = Enterprise Survey; GDP = gross domestic product; SME = small and medium-size enterprises. *** indicates significance at the 1% level, ** at the 5% level, and * at the 10% level.

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**Figure 2: Distribution of time usage for husbands and wives by country and labor market state**

<!-- Start of picture text --> Albania<br>Husbands Wives<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>NP 34 UE 36 PT 12 FT 18 NP 65 UE 22 PT 3 FT 10<br>Bosnia and Herzegovina<br>Husbands Wives<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>NP 53 UE 27 PT 7 FT 13 NP 85 UE 12 PT 1 FT 2<br>Montenegro<br>Husbands Wives<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>NP 59 UE 7 PT 9 FT 24 NP 98 UE 1 PT 1 FT 1<br>Macedonia<br>Husbands Wives<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>NP 37 UE 34 PT 7 FT 22 NP 70 UE 20 PT 2 FT 7<br>Serbia<br>Husbands Wives<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>NP 47 UE 22 PT 16 FT 16 NP 83 UE 10 PT 4 FT 4<br>Kosovo<br>Husbands Wives<br>100% 100% Husbands100%<br>80% 80% 80%<br>60% 60% 60%<br>40% 40% 40%<br>20%<br>20% 20%<br>0%<br>0% 0%<br>NP 57 UE 22 PT 10 FT 10<br>NP 57 UE 22 PT 10 FT 10 NP 94 UE 4 PT 1 FT 1<br>Home production Leisure Work<br><!-- End of picture text --> Source: Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey.

Note: Abbreviations: FT= employed full-time; PT= employed part-time; U= unemployed; NP= nonparticipant. The numbers next to the labor market labels are the proportion of husbands and wives in each labor market state for each country. Per panel, the proportion of husbands and wives should sum up to 100. Because of approximation, for some panels, the sum is equal to 101. Each bar represents the average time allocation between home production, leisure, and work of husbands and wives on each labor market state.

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-7-

One way to deal with the problem of heterogeneity is to estimate production functions and input demands as a simultaneous system. Rosenzweig and Schultz, for example, use Cobb-Douglas and translog production functions for birth weight in the United States with inputs as endogenous. Guilkey e' al. (1989) use a similar model to examine the effec-. of prenatal care on birth outcomes in the Philippines.7 Frequently, however, it is difficult to find identifying restrictions for niore than one or two simultaneously determined health care inputs. Even when such restrictions are plausible, the identifying instruments may have little explanatory power, leading to imprecise, although unbiased, estimates in the nutrition production function. Part of the difficulty is that a limited range of prices are often the only potential identifying instruments for variable **s** uch as health care utilization. For example, the availability of, or distance to, a health care facility is often found to determine health care choices (cf. Gertler and van der Gaag (1990) and the references therein). Unless, however, there are a number of distinct health service centers, such time prices only provide identification for a small number of instruments. Moreover, the quality of services may be a stronger determinant of health care utilization but, nevertheless, is often unobservable to the researcher. Finally, for many health care practices, community diffusion and interactions provide much of the 7This study utilizes a rather extraordinary longitudinal data set which contains a great deal of data on household health care decisions as well as community infrastructure. See also Cebu Study Team (1989).
2011). As a result, econometric estimations of the effects of IFFs on tax revenues could be criticized for their lack of external validity related to the contingency of IFFs data on a given definition and/or methodology. Second—from a methodological perspective—the effect of IFFs on tax revenues may be subject to endogeneity issues arising (in addition to measurement errors related to the mere IFFs’ definition) from reversed causality. Indeed, poor fiscal institutions can result—aside from low collecting rates and therefore low tax revenues—into poor fiscal control and enforcement that may ultimately favor IFFs.

Addressing these issues looks like an unfeasible task, given that it requires finding timevarying reliable instruments for a variable whose mere measurement may be unreliable. Consequently, in this paper we adopt a different perspective that exploits the fact that combating IFFs requires compliance with international standards, as defined by the Financial Action Task Force (FATF) recommendations on domestic tax revenue mobilization.<sup>**††**</sup> The FATF recommendations formulate a comprehensive and consistent regulatory framework that has been endorsed and recognized since 2003 by more than 180 countries as the international standard for anti-money laundering (AML) and countering the financing of terrorism (CFT). Conversely, the lack of compliance with these recommendations is considered as a serious signal for not combating IFFs, and is made visible through the inclusion of such countries on the FATF “blacklist”. Therefore, we develop an impact assessment analysis in which the presence of a country on the FATF list is considered as a “treatment” variable signaling the presence of major IFFs, i.e. a Non-Cooperative country, as opposed to Cooperative countries that are not on the FATF list. Of course, since being on the FATF list can hardly be seen as a random event, we draw upon appropriate techniques in order to make this event exogenous conditional upon a wide set of covariates that may influence the decision of becoming a listed country.

Our results are as follows. First, propensity score matching estimations reveal that countries on the FATF list experience significantly lower tax revenues with respect to comparable countries that are not on this list. This causal effect is economically meaningful, as the tax revenue loss—estimated always above 2 GDP percentage points—represents between one-third and almost half of the standard deviation of tax revenues depending on the considered estimator. Second, a wide robustness section confirms the soundness of our finding with respect to alternative matching estimators (e.g. including several “doubly robust” estimators), the use

> **††** Created in 1989, the FATF is an international institution that develops and promotes policies to protect the global financial system against money laundering, terrorist financing, and the financing of proliferation of weapons of mass destruction.

3