rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 1.1 Output (1989=100)**

<!-- Start of picture text --> 110.0 120<br>100.0<br>100<br>90.0<br>80.0 80 Armenia<br>Azerbaijan<br>70.0 CEE Georgia<br>CIS non CIS-7 60 Kyrgyz Republic<br>60.0 CIS-7 Moldova<br>50.0 40 TajikistanUzbekistan<br>40.0<br>30.0 20<br>20.0<br>0<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> Source: Unicef TransMONEE database (2002) As in other CIS countries, _employment_ declined during the 1990s but at a much slower rate than output while employment ratios in the CIS-7 have been kept above those of other CIS economies (figure 1.2). During the 1989-1995 recession, employment fell in all countries with the exception of Uzbekistan where it actually increased by 10%. Georgia, the country that experienced the deepest recession, also experienced the deepest fall in employment by about 30%. Moldova followed with a 20% employment decline while all other countries constrained employment fall within 10% points. It is also evident that the employment ratio for most transitional economies continued to decline throughout the growth period making growth 'jobless'. The employment decline has also been larger for the non-CIS countries that evidently continued to gain productivity margins over the CIS economies.

**Figure 1.2 Employment ratio***

<!-- Start of picture text --> 90.00<br>90.0<br>85.0 85.00<br>80.0 80.00<br>Armenia<br>75.0 CEE 75.00 AzerbaijanGeorgia<br>70.0 CIS non CIS-7 70.00 Kyrgyz
partial reallocation failure, considering that about 38 percent of displaced workers remain unemployed.

We also find that these climate-induced impacts are unequally distributed across educational levels and gender, with workers with no education, females and informal workers the most affected. In addition, the sensitivity analysis, based on an increasing treatment intensity, shows a sharp increase in the magnitude and significance of the impacts when drought shocks become severe, corresponding to SPEI values lower than -1.8 s.d. in all three labor market outcomes. Overall, our results are robust to various specifications and falsification tests.

The remainder of the paper proceeds as follows. Section 2 presents the data, our measures of drought shocks and some descriptive analysis. Section 3 outlines our empirical strategy, Section 4 presents the results, and Section 5 presents a set of robustness checks. Section 6 offers a discussion of the findings and some policy implications, and Section 7 concludes. Additional information on the data employed and empirical analysis can be found in the Appendix.

# **2 Data**

We employed two data sources. First, we used the Enquête Nationale sur l’Emploi (ENE), which provides nationally representative socioeconomic information at the individual level from 2000 to 2009. The ENE is conducted by the Haut Commissariat au Plan (HCP) and consists of demographics and labor market data. The sampling follows a two-phase stratification approach with an urban-rural strata and a regional strata. Our analysis is restricted to the period from 2000 to 2009 since the provincial identifier was not provided in the survey rounds from 2010 to 2019.<sup>3</sup> Despite the richness of the available information, the survey consists of repeated cross-sections, which does not allow for tracking individuals over time. We started with an initial sample of nearly 2.7 million observations across 54 provinces.

> 3 While from 2000 to 2005 around 40,000 households and 230,000 individuals were sampled, from 2006 these numbers increased to around 60,000 and 270,
not available, which is qualitatively consistent with the empirical drop in borrowing associated with the use of the employment program documented in the empirical section.

In sum, the model yields four lessons. First, the aggregate expected credit loss and the government’s burden remain limited even after accounting for firms’ and banks’ endogenous credit response and the general equilibrium effects of the credit policy. We obtain comparable theoretical and empirical counterfactual results. Second, although the credit guarantee raises credit and default, the interest rate cap constrains them. Eliminating the cap increases credit and also expected credit losses. An analogous empirical counterfactual that allows firms in default to receive credit produces a similar outcome. An additional insight concerns endogenous recovery rates under default, which further reduce the government’s burden. Given that default and interest rates are comparable in the data and banks face a funding cost, the model implies that recovery rates must be substantial for lenders to break even. Third, when the lower interest rate cap is absent, credit and credit loss are higher in the theoretical than in the empirical counterfactuals because banks are endogenously willing to lend more to high-risk firms by charging a higher interest rate. The resulting increase in default probability is similar to that observed under a systemic shock. Lastly, under a systemic shock such as the Global Financial Crisis, risks rise sharply, with expected credit losses more than doubling relative to normal times.

# **8 Conclusions**

This paper uses a large-scale episode of a credit program and an employment program together with unique financial and real data for the universe of firms and banks in Chile to shed new light on how these policies influence the distribution of credit and the implied potential financial risks. The programs give different incentives to firms: firms internalize the cost of using the employment program, but do not do so for the credit program. As a result, higher-risk firms disproportionately borrow through the credit program, which leads to a rapid and substantial increase in indebtedness across a broad class of firms. Still
literature that studies the impact of various firm-level and market-level shocks on wages and employment (see Card, Cardoso, Heining and Kline, 2018 and more recently Kline, Petkova, Williams and Zidar, 2019; J ̈ager, Schoefer, Young and Zweim ̈uller, 2020; **?** ; Lamadon, Mogstad and Setzler, 2022). The documented interaction between worker and firm heterogeneity in pass-through rates has not been fully appreciated in this recent literature.

The remainder of this paper proceeds as follows. Section 2 introduces a search model with heterogeneous firms to illustrate the effect of payroll taxes in the presence of rents across firms. In Section 3, we provide background on the payroll tax reform we study and describe the Hungarian administrative data used for our empirical analysis. We present our employment results in Section 4 and wage results in Section 5. We discuss welfare effects in Section 6. In Section 7 we provide results for younger workers and women excluded from our main analyses. Section 8 concludes.

6
and taking into account the initial benchmark estimate from Nehru and Dareshwar (1993), a second capital stock series has been calculated with disaggregated investment data for the period 1970-2002.

The initial values are obtained from a pre-estimate starting in 1950. The data gaps for the sectoral composition of the eight assets groups prior to 1970 are filled in by extrapolation techniques. These values, however, do _not_ enter in the later regression or growth accounting exercise. They only provide reasonable initial values for the

> 16 The potential error of the estimate of initial capital stock diminishes over time due to depreciation. Based on international data, Nehru and Dhareshwar (1993) offer an estimate of the capital stock for Guatemala that was taken as a benchmark.

> 17 UNDP (2002) provides a brief summary of the associated empirical consequences and causes that prevented an actualization of the Guatemalan National Accounts.

> 18 In addition, the following results of the quality index for the physical capital stock differ. This may be due to the possibility of an oversight in the logarithmic transformation by Morán and Valle (2002), as was pointed out in a personal communication with Estuardo Morán, Banco de Guatemala, October 15, 2003.

-24-
|**Country**|**Total dispatched or**<br>**purchased(GWh)**|**Sales (GWh)**|**Sales (kWh per**<br>**person)**|**T&D losses**<br>**(GWh)**|**T&D losses (% of GWh**<br>**dispatched orpurchased)**|
|---|---|---|---|---|---|
|Nigeria_2014|30,715|18,819|105|11,896|39%|
|Rwanda_2013|517|382|32|135|26%|
|São Tomé and||||||
|Príncipe_2014|90|51|258|38|43%|
|Senegal_2013|3,038|2,507|177|531|17%|
|Seychelles_2014|357|313|3,415|45|12%|
|Sierra Leone_2012|195|120|20|75|39%|
|Somalia_2011|310|288|29|22|7%|
|South Africa_2014|238,201|216,274|4,005|21,927|9%|
|South Sudan_2013|99|70|6|29|29%|
|Sudan_2014|11,359|9,709|250|1,649|15%|
|Swaziland_2014|1,209|1,074|848|135|11%|
|Tanzania_2015|9,434|7,754|147|1,680|18%|
|Togo_2013|1,091|784|115|307|28%|
|Uganda_2014|2,894|2,277|59|617|21%|
|Zambia_2014|13,812|12,104|806|
findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* For useful comments and discussion we thank Pierre-Richard Agénor, Ricardo Caballero, Luis Felipe Céspedes, William Easterly, Linda Kaltani, Mauricio Larraín, Claudio Raddatz, Luis Servén, Raimundo Soto, and participants at the IFPRI/Cornell Conference on Threshold Effects and Non-Linearities in Growth and Development, the Annual Conference of the Central Bank of Chile, the Econometric Society World Congress, the Latin American Meetings of the Econometric Society, the Annual Meetings of the Chilean Society of Economics, and seminars at the Bank of International Settlements, Catholic University of Chile, University of Chile, University of La Plata, and Central Bank of Portugal.

** Calderón: The World Bank. Address: 1818 H St. N.W., Washington, DC 20433. Email: ccalderon@worldbank.org. Loayza: The World Bank. Address: 1818 H St. N.W., Washington, DC 20433. Email: nloayza@worldbank.org. Schmidt-Hebbel: Central Bank of Chile, Economic Research Division. Address: Agustinas 1180, Santiago de Chile. E-mail: kschmidt@bcentral.cl.
available to the government, it is possible to determine for any given budget how many cases would be treated or averted depending on the targeting strategy chosen. Looking across the entire distribution in the top panel, the targeted strategy based on information from mobile phones is consistently the most cost- eective one. Across the whole distribution, the cell phone data based targeting policy performs 11.15% better on 27This is based on data from Richard Toll where 3,386 out of 3,609 targeted travelers agreed to be tested.

30
Figure 14: _Officials in EAP prefer structured reading, in-service training and removing fees at the secondary level over supporting inclusion of children with disabilities._ <!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Building Providing Providing Hiring extra Removing In-service Giving<br>hospitals laptops to free lunches specialist fees for teacher teachers<br>schools in primary teachers for secondary training structured<br>school students school early grade<br>with<br>disabilities<br>project<br>Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP Non-EAP EAP<br>Share of respondants selecting the<br><!-- End of picture text --> Source: Authors’ calculations from the Discrete Choice Experiment as part of the 2022 survey. See probit model results in the annex. The probabilities have been computed for a similar “average” respondent in all countries. Building hospitals are the least often chosen among the proposed possibilities. Officials were asked “Now I'd like you to imagine that the Department/Ministry of Finance is considering an additional allocation for education to one of two policy areas. Which one of the following would you choose?” The percentages in the figure don’t add to one hundred since individual officials were randomly offered two of the six options listed above and chose one. This means that for investing in inclusion of children with disabilities, when presented with a choice between this and another option, respondents chose to invest in inclusion 45 percent of the time.

When analyzed by _type of official_ across all countries, respondents from ministries of education favor structured lessons (63 percent) and training (55.8 percent) over removing fees for secondary school (43.3 percent), whereas
, followed by Niger 2. Cameroon is the country with the lowest average per-capita consumption, but food security levels are even lower in Mali (HDDS).

# **5 Results**

# _5.1 Targeting performance_

# _Performance in reaching the poorest households_

We start by presenting the performance of PMT relative to CBT targeting schemes to reach the poorest households, based on CBT selection thresholds in each database. For example, if a CBT targeting scheme selected 20 percent of the population, we adjust the PMT threshold so that 20 percent of the population is

> 25 See Figure 1 for the per-capita consumption cumulative distribution by data set. Additional descriptive statistics by data set for our key variables are presented in Table A2 in the Addendum, available upon request. 26 The Senegal 2 study was conducted to assess the targeting performance of the Unique National Registry (RNU), which was conducted nationally. The sample comes from 4 regions. Likewise, the health insurance project studied in Burkina Faso 2 was rolled out in several regions, and the sample comes from 2 distinct regions.

15
a _monetary income_ definition, which is composed of wages and salaries (monetary and in-kind), earnings from self-employment, self-provision of goods produced by the household, rents, interest, dividends, retirements, pensions, private transfers, and public monetary transfers. In the case of poverty, the measurement is based on a _total income_ definition, which is equivalent to monetary income plus imputed rent. It is important to highlight that the methodology for measuring income changed in 2013 and that such new approach is the one employed in this paper. Specifically, household income is no longer adjusted to national accounts, and the new estimation of the imputed rent considers not only owneroccupied dwellings, but also dwellings which were donated, given as work benefit, or dwellings in usufruct.<sup>12</sup> This paper exploits the 2013 National Socioeconomic Characterization Survey (CASEN) carried out by the Ministry of Social Development, which is a nationally representative sample collecting detailed information on household incomes, as well as on individual and dwelling characteristics. This survey is employed as the primary source of data in the incidence analysis as it is the official data set to measure the levels of poverty and income inequality in Chile. Since the CASEN does not collect information on household spending, the Family Budget Survey (EPF) 2011-2012 is employed as a secondary source to estimate indirect taxes on household consumption. This survey was carried out by the National Institute of Statistics and is aimed at identifying the structure and characteristics of final consumption of urban households in the regional capitals of the country. In addition, the analysis exploits official data on government revenues and expenditures from the 2013 executed budgets reports published by the Ministry of Finance’s Budget Office, the Ministry of Social Development, the National Institute of Statistics, and the National Audit Office.

In order to assess the distributional effects of fiscal interventions, the core building block of the fiscal incidence analysis is the definition and construction of the income concepts using the previous data sources (Figure 1). The allocation
index of industrial products in order to separately identify quantity<sup>6</sup> . _Kijt_ , capital, is defined as the value of fixed assets, which is deflated by the fixed assets investment index, and _Lijt_ is the total number of employees. _Mijt_ represents the intermediate inputs purchased by firms to use for production of final products, which is deflated by the intermediate input price index.<sup>7</sup> _ForeignShareHKTMijt_ , _ForeignShareFRijt_ and _StateShareijt_ are defined as the share of the firm‘s total equity owned by Hong Kong-Taiwan-Macau investors, foreign investors, and the state respectively. The omitted share, the non-state domestically-owned share, is represented by the constant term. By construction, these three firm-level controls are continuous variables and range from 0 to 1 in value<sup>8</sup> .

The motivation for separating foreign share into two types is two-fold. First, we would like to see whether some types of foreign investment are more likely to result in productivity spillovers than others. Second, anecdotal evidence suggests large quantities of so-called foreign investors in China are actually domestic investors who channel investment through Hong Kong–Taiwan-Macau in order to take advantage of special treatment for foreign firms (so-called ―round tripping‖). If this is the case, then we would expect that foreign investment of this type might have a smaller impact on domestic firms.

Following Javorcik (2004), we define three sector-level FDI variables. First, _Horizontaljt_ captures the extent of foreign presence in sector _j_ at time _t_ and is defined as foreign equity participation averaged over all firms in the sector, weighted by each firm‘s share in sectoral output. In other words, where _ForeignShareit_ is the sum of _ForeignShareHKTM_ and _ForeignShareFR_ .

Second, _Backwardjt_ captures the foreign presence in the sectors that are supplied by

> 6 Sector-specific ex-factory price indices for industrial products came from China Urban Life and Price Yearbook (2008, Table 4-3-3). The price indices are published for 29 individual sectors.

> 7 Price indices
to estimate poverty among refugees even in the absence of income or consumption data (Dang and Verme, 2021, Beltramo et al., 2021). The mobile nature of refugees and IDPs also lends itself to experimenting with new methodologies to measure poverty with alternative methods such as mobile phones (Blumenstock et al., 2015; Pape et al. 2020, Wieser et al., 2021), or satellite imagery and remote sensing data (Abelson et al., 2014; Neal et al., 2016). For example, night lights or types of infrastructures captured by satellite imagery can provide gross estimates of poverty. In essence, poverty measurement among FD populations benefits from decades of developments in the poverty measurement field, but also provides new opportunities to expand the field because of the atypical characteristics of these populations.

> 5 Note that many microdata sets are present in both the WB and the UNHCR microdata libraries.

6
**Figure 4: Impact of increase in petty corruption on employment growth for firms that face low vs. high levels of financial constraints**

<!-- Start of picture text --> Figure 2A: Petty Corruption Incidence<br>0.6 0.54<br>0.5<br>0.4<br>0.3<br>0.2 0.15<br>0.1<br>0<br>Low Corruption High Corruption Low Corruption High Corruption<br>-0.1<br>Low financial constraint High financial constraint<br>-0.2<br>-0.21<br>-0.3<br>-0.26<br>Figure 2B: Petty Corruption Depth<br>0.5<br>0.41<br>0.4<br>0.3<br>0.2<br>0.13<br>0.1<br>-0.002<br>0<br>Low Corruption High Corruption Low Corruption High Corruption<br>-0.1<br>Low financial constraint High financial constraint<br>-0.2<br>-0.3<br>-0.27<br>-0.4<br>residuals)<br>Employment growth rate (%,<br>Employment growth rate (%, residuals)<br><!-- End of picture text --> Source: Enterprise Surveys. Based on authors’ own calculations.

Notes: The figure is a partial scatter plot of the residuals of growth rate of employment plotted against residuals of petty corruption (Petty Corruption Incidence in Figure 4A and Petty Corruption Depth in Figure 4B) and residuals of financial constraints variable. The residuals of employment growth are obtained by regression employment growth on country fixed effects, industry fixed effects and initial employment at the firm
the general equilibrium model.

The authors validate their model against the historical facts via two steps. First, they set aside the CGE model and simply impose observed historical changes in the linking variables that drive the micro-simulation model. They then compare the changes in poverty produced by the latter to changes actually observed over the crisis period. Results indicate the model captures the qualitative flavor of the impacts across population segments, but over-predicts the total increase in poverty (+239% estimated vs. +67% observed in the poverty headcount).

Second, they add the CGE component, which permits them to relate the poverty changes to the underlying factors driving this crisis, including the: foreign credit crunch, domestic credit crunch, real devaluation, and El Nino drought. They find that the financial crisis did indeed contribute the majority 19
2009, aggregating the case-level data to each hospital-month-level.<sup>9</sup> Only hospitals with enough month-level observations over the 2002-2009 period were kept, resulting in a total sample of 11,861 hospital-month-level observations across all 5 procedures.

The ITS regressions confirm some of the descriptive statistic results (see Table 3). Each column of each panel represents one regression, and the reported coefficients and standard errors are for the linear combinations of PPTP ( _β4_ + _β5*48)_ and DTS ( _β2_ + _β3*12_ ) described earlier.<sup>10</sup> The most significant results are for pneumonia and appendectomy. For pneumonia (Col 2), there is a statistically significant reduction in ALOS of 3 days associated with the PPTP period (2005-2008), and a significant increase of almost 1.5 days associated with the DTS period (2009). At the same time, there was a reduction of almost 9 cases for the average Croatian hospital performing pneumonia procedures under the PPTP period, and an increase of ~13 cases under the DTS period. Similarly, there was a substantial reduction in ALOS for appendectomy (Col 4) by ~1.3 days under PPTP and a continued decline of about 1 day under DTS. Under PPTP, the volume of cases actually increased for appendectomy but decreased under DTS. Taking the PPTP and DTS results together (Total effect of DRG reforms), the results suggest that the introduction of case-based payment systems from 20052009 reduced ALOS for pneumonia and appendectomy procedures by over 2 days, and led to little change in the volume of cases. ALOS also declined for bypass, although the complexity of bypass surgeries results in less precision for this estimate. There was little change in ALOS from 2005-2009 for cataracts or hip replacement as both were already experience declining ALOS.

# **Quality – Readmissions**

Although attribution to the introduction of PPTP or DTS is difficult, efficiency has improved in hospitals as Table 3 shows declining ALOS over time for all