rafmacalaba/data-use-mentions-extended huggingface dataset:
conceptual analytical framework came from Loewe et al. (2021) in their introduction to a recent special issue of _World Development_ .<sup>25</sup> From a methodological standpoint, our objective is to contribute to making operational this conceptual framework for the purpose of characterizing the main features of an SC, in particular, that of MENA countries. An attractive feature of the operational framework that we propose in this first attempt is that it allows us to analyze the impact of digital transformation of society on this SC, a question that has recently drawn much attention.

# **3. Data and preliminary analysis**

This section will detail the construction of the MENA database and preliminary analysis. We first visualize the 3-P model of the SC and describe the raw data used to proxy the three dimensions of Provision, Protection, and Participation or those used as controls. We then examine descriptive statistics between MENA and OECD countries as an initial base of comparison, and proceed to process the data, including reducing dimensionality using a principal component approach (PCA). Finally, we discuss descriptive evidence from the transformed data on the three indices. Table A.6 in the appendix lists the MENA and OECD countries on which we collected data over the 2007-2020 period.

# **_Raw data for the 3-P model of the social contract_**

Following Loewe et al. (2021), we represent a given SC as a vector in a 3-dimensional space (see Figure 1) whose components are levels of Protection, Provision, and Participation, noted respectively P1, P2, and P3 hereafter.<sup>26</sup> For our empirical analysis, we collected data on variables that are good candidates for proxying each of the three dimensions. Table 1 lists these variables, along with control variables that may plausibly impact at least one of the three fundamental dimensions of the SC.<sup>27</sup>

> 25 ' We must indicate here that while each of the papers published in this
they were better able to coordinate and collude), then the reduction in quantity may reflect a move towards the monopoly quantity after the reform. In fact, a simple alternative explanation of the increase in prices and weakening of pass-through after the reform is that the DOT layer of the market was competitive before the reform, and the reform facilitated monopoly practices among the refiners. If this alternative model is correct, then the refiners would try to protect their monopoly profit by cooperating with the government to make the ban on DOTs permanent. In contrast, the model based on credit rationing faced by the wholesalers when

> 30They report that crude oil import by these two refiners in 2010 was 417,000 MT, but in 2011 the import drastically dropped to 254,000 MT, implying a 39 percent reduction in volume.

> 31The monthly import data is not adequate for a formal DID analysis.

32
the 1% SRS by PSU samples.

As expected, _CEBa_ estimates show a considerably tighter MSE spread than direct estimates, but still with outliers. Another interesting finding is that under CensusEB but with random effects specified at the PSU level ( _CEBc_ ), the empirical MSE is tighter than that of the direct estimates and also displays better properties than _ELLc_ as seen in Table 2. However, given the discussion in the model-based validation and the results shown here, the results with the random effects at the municipality level are preferred over the ones where the effect is specified at the PSU level. Perhaps the most surprising result is the low MSE of the _UC − CEBa_ method with only contextual variables and the other _UC − CEB_ variants, as proposed by Masaki et al. (2020). The results display a tight spread and the results from Table 2 corroborate the finding. Nevertheless, beyond _UC_ models being an alternative if contemporaneous census data is not available, the method presents a couple of advantages over traditional Fay Herriot (FH) area level models under sampling scenarios that follow a two-stage design like the one considered here. First, as noted toward the end of section 4.1, the majority of municipalities are represented by 1 PSU and thus have quite small sample sizes which makes the likelihood of direct FGT0 estimates being equal to 0 or 1 much higher. Under these cases, the method from Nguyen (2012) is a valid alternative to FH because FH is not applicable in these municipalities with a sampling variance of 0. An additional advantage is that the model can be used for multiple indicators whereas the FH requires a model for each indicator considered.

> 44Note that under the MI inspired bootstrap of ELL, a very poor model fit will be heavily penalized and will likely yield noise estimates for _UC − ELLc_ that fare worse than direct estimates in most applications.

28
court division. This is consistent with the World Bank Doing Business’ Index, which asserts that cases are in fact randomly assigned to judges in Kenya (World Bank 2021). Nonetheless, it must be noted that this randomized procedure may be a recent phenomenon. Indeed, introducing randomization was allegedly one of the goals of the reform team following the implementation of the 2010 Kenyan Constitution (Gainer 2015). Therefore, it is necessary to provide further evidence that case selection by judges has not been a common feature across our sample. To do so, we present balance tests in the following two subsections. Considering the possibility that assignment has become de jure random after 2010, in addition to conducting the tests for the full sample, we also split the balance tests for de facto randomization before 2011 and after 2010. As a robustness check, we also conduct the main analysis for only after 2010.

# **4.2 Gender Balance Tests**

To confirm that judge assignment to cases is random in terms of gender majority, we use the following balance test for the analysis sample, for case _i_ filed in court _c_ at time _t_ as: where Φ _c,t_ is a court-year fixed effect and _Xi,c,t_ is a vector of additional control variables, which may include: binary variables for judge, defendant, and plaintiff plurality ethnicity; variables for the numbers of judges, plaintiffs, and defendants; a binary variable indicating whether the case is an appeal; and binary variables indicating the case type. Court-year fixed effects are used to ensure that we are comparing defendants and plaintiffs that are in the same court at the same time. Court-year periods with insufficient variation for regression analysis are dropped from the regressions. For this and all other models, we cluster standard errors at the judge level. As a robustness check for the main analyses, we use HC1 robust standard errors.

The results of the balance test are shown in Table D1 in Appendix D. Column (1) does not include
Finally, in ancillary regressions not reported in the paper, we control for heterogeneity in the industry by including dummy variables constructed using the two-digit standard industrial classification system. We also drop from the sample the country with the largest number of observations (India) to test whether the results are driven by this country. Results, available from the authors upon request, remain qualitatively the same.

# **4 Concluding remarks**

Firm innovation plays a key role for economic development. Using the World Bank Enterprise Surveys data for a large sample of countries, we find that bank funding of fixed assets is strongly and positively associated with both the decision to innovate and the intensity of firm-level innovation. The relationship is stronger for invention. Funding by NBFI is strongly associated with the overall extent of innovation and adoption, especially in countries with more developed financial markets and institutions. Funding through other sources (including informal financing though family and friends) is also significantly related to the extent of innovation and adoption, indicating the positive association between heterogeneity in the sources of funding and firm innovation.

The most important source in terms of magnitude is NBFI funding, implying that the development of financing from non-bank financial intermediaries could bring larger benefits than other sources of funding. Nonetheless, the high variability around the point estimates cautions that further analyses are needed to dig deeper in the heterogeneity of financing by non-bank financial institutions to capture what country and firm circumstances bring the higher benefits in terms of firm innovation.

20
sheets and income statements of firms compiled by Bureau Van Dijk from various national sources. After data cleaning, our sample covers 38,227 firms with total outstanding debt of US$ 2,112 billion. Data is as of the most recent available for each firm between 2018 and 2019. Table 4 reports the number of firms in each country and sector. Data on CO2 emissions are sourced from Climate Watch.

# **Methodology**

# **_Physical risks_**

To obtain a first indication of the value-at-risk of banks’ loan portfolios related to physical risks, we first compare the provincial distribution of banks' nonfinancial corporate credit portfolios with country-province-specific hazards. Our physical risk assessment is grounded in first-level administrative units, which carries unavoidable caveats. Banks’ credit portfolios are commonly available at the first level administrative units in our country sample (denoted as provinces throughout the paper for simplicity), so we use them as our unit of analysis across countries. This approach entails gains in terms of getting a homogeneous identification approach across countries in our sample, yet we lose the richness of exploiting more granular data. Besides, we follow a conservative approach for the province hazards’ classification by adopting the higher

> 17 For instance, the hazard level of an earthquake is assessed as high if there is more than 20 percent chance of potentially-damaging earthquake shaking in the next 50 years; the hazard level of water scarcity (drought) is classified as medium if there is up to a 20 percent chance droughts will occur in the coming 10 years. For additional reference, see https://gfdrr.github.io/thinkhazardmethods/#about-thinkhazard.

9
|
|Number of distinct participants|189,738|330,745|50,236|94,864|175,024|119,322|959,929|
|Number of distinct suppliers|137,997|301,447|43,548|66,466|154,301|91,609|795,368|
|Firms among suppliers (%)|55.4|83.2|47.8|66.4|71.6|79.0|72.3|
|**_Competitive tenders_**||||||||
|Avg. number of participants per tender|2.8|2.3|2.6|3.9|3.2|3.6|3.1|
|Avg. number of suppliers per tender|1.5|2.1|2.0|1.4|2.2|2.2|1.9|
|Number of distinct municipalities|184|853|223|184|392|497|2333|

**Notes** : This table presents the descriptive statistics from the public procurement dataset. Deserted tenders refers to a situation where no proposals were submitted by potential bidders in response to a tender notice, while unsuccessful tenders occur when proposals were submitted but didentifier not meet the requirements or the tender process was cancelled or revoked. The variables “Has item information” and “Has participant information” refer to the percentage of tender identifiers with any information related to items or participants, respectively. The definition of non-competitive tender encompasses both _dispensa_ and _inexigibilidade_ . To calculate the percentage of “Non-competitive tenders value ” we use the variable valor_corrigido winsorized at percentiles 0.01 and 99.9. Additionally, the percentage of firms among suppliers is calculated as the number of distinct firms divided by the number of distinct suppliers, where firms are those whose identifier has 14 digits.

32
.7<br>19.8 19.6<br>19.6<br>19.4<br>19.2<br>1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> Note: 12-month moving average for the period June/2018-June/2019. 95% confidence intervals shown in orange. _Source_ : World Bank calculations using data from TUIK.

# **5.2** **_Implications on the estimation of poverty and inequality_**

Starting in 2016, TUIK stopped reporting absolute poverty and instead began to measure it in a relative sense, namely the share of people with incomes below 50% of the national median income. For that reason, Turkey does not use an absolute poverty line to monitor the evolution of 16
>0.07444|9 1,754c|
|R2-adjusted||0.12376||0.035|
|F||23.599a||7.713a|
|N|480||548||
|Memorandum<br>item:|||||
|Maximum total contribution<br>of PARE program|<br>0.475484||0.20166|3|
|a - Significant at the 1% level or more|||||
|b -Significant at the 5% level or more<br>c - Significant<br>at the 10%/o<br>level or more|||||
|d - Significant<br>at the 20% level or more|||||

Dependent variable: Difference in normalized test scores between 6th and 4th grade. Source: Own calculations based on PARE's database.

An objection may be raised, nonetheless, about the measure of performance. What if small differences in test score are very imperfect measures of relative capabilities and/or achievements? To try to get around this issue, we perform a final experiment on the test scores. We stratify the samples in two sub-samples each: those of students with performance above and below their respective medians. These results are shown in Table 8. Once again the estimates are consistent. The program has a positive and significant impact, and especially so for the native population.

10
that future research should control for long-term plot-level CSA effects on vulnerability and resilience.

- Lastly, although our income-based poverty measures are presumably broadly aligned with consumption expenditure-based measures, they may be an imperfect substitute for expenditure-based metrics, which are more commonly used in the country.

# 7. Conclusion

This paper assessed the extent to which smallholder farmers in Zambia are exposed to climate shocks using both exogenous and self-reported shock measures. It then evaluated the impacts of climate shocks on vulnerability and household resilience and assessed the extent to which climate-smart agriculture (CSA) practices—defined as minimum tillage, inorganic fertilizers, and hybrid maize seed—influence outcomes. We used a three-wave household panel data obtained from the nationally representative Rural Agricultural Livelihoods Survey (RALS). We restricted our analysis to a subset of households re-interviewed over the three-waves for a total sample of about 6,531 households and applied an instrumented probit regression that 23
exercise. We also look at countries that are believed to be floating, to offer a contrast to those that are believed to follow basket bands.

This paper explores the amount of information that it takes for market participants to verify announced exchange rate regimes from observed data. The goal of the paper is to show the difficulty to verify exchange rate regimes and how this varies with regimes. To our knowledge, this is the first paper that performs this type of exercise. We use observed exchange rate data and simulated data to provide empirical estimates. The fact that countries vary their exchange rate regime over time allows us to run this experiment for regimes of different complexity.

Regarding bands, the paper confirms the intuitive notion that wide bands are harder to verify than narrow bands. It is often difficult or impossible to estimate the weights of the central parity with only one or two years of data. Regarding regimes announced as free-floating, the paper shows that in some cases the exchange rates observed under such regimes are correlated with those of major currencies. In this sense, they behave similarly to the basket countries. It is not straightforward to verify them, when only using exchange rate data.

To complement the tests performed with real data, we run Monte Carlo experiments to obtain more general conclusions and to provide results regarding the amount of information necessary to estimate regimes of interest. Monte Carlo experiments, displayed in the Appendix, confirm that more complex regimes take a larger amount of data to be verified. The Monte Carlo exercise shows the role of a number of factors in determining verifiability: the band size, number of currencies in the basket, the rate of crawl, sample period, periodic adjustments of the central parity. The results 4
most dominant strategies include: (i) spent savings, (ii) reduced food consumption, (iii) borrowed money from others, (iv) reduced health and education expenditure, (v) reduced expenditure on agricultural inputs, and (vi) sold assets. Some of these coping strategies are likely to have lasting adverse impact on the livelihoods of rural households. For instance, decrease in education and health related investments will adversely affect human capital accumulation and reduce future earnings. Similarly, reduction in agricultural investments may limit households’ production potential.

Table 5 presents single difference estimates as data on coping strategy were only collected in June 2020 and thus should be interpreted cautiously. It shows that households who received 15
Policy Research Working Paper 5544

# **Abstract**

This paper analyzes the patterns of and the challenges for youth employment in Pakistan and examines whether these challenges are youth-specific. Using the 2005/2006 Labor Force Survey, the analysis includes determinants of unemployment, determinants of working in the formal sector, rate of return on education, and determinants of working hours. The paper finds that many of the challenges to youth employment in Pakistan are not youth-specific. Policies should thus emphasize broader labor market reforms, even in the context of tackling youth employment issues. Still, some challenges are youth-specific, such as a higher youth unemployment rate and insufficient returns to better-educated youth. To address these challenges, more youth-specific interventions are needed.

This paper is a product of the Human Development Unit, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at xhou@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
(Chile, Czechia, and India) and demonstrate that inflation rises for several months before falling in response to monetary tightening, suggesting the price puzzle. Héricourt (2005) argues that despite numerous proposed solutions, the price puzzle remains unresolved in the case of Czechia. The inclusion of a broad commodity price index has been insufficient to resolve the puzzle. Additionally, Giordani’s (2004) proposition of simultaneous inclusion of output and output gap in the VAR to mimic IS and Philips curves has not resulted in any change in price behavior.

Javid and Munir (2010) attribute the currency depreciations as a source of price puzzle, citing cases where exchange rate depreciation follows monetary tightening. Creel and Levasseur (2005) rationalize this exchange rate puzzle in terms of market expectations regarding the sustainability of sovereign debt and the probability of default, particularly with high levels of government debt and deficits. Beckers (2020) finds evidence of the price puzzle in Australia data, even when controlling for the systemic response of the cash rate to the Reserve Bank’s own inflation forecasts. This is attributed to an additional but omitted systematic response of the cash rate to credit market shocks, leading to a positive correlation between cash rate changes and future inflation.

47
B. A. (2009). The Causes and Consequences of Land Use Regulation: Evidence from Greater Boston. _Journal of Urban Economics_ 65(3), 265-278.

- Gnagey, M. & Tans, R. (2018). Property-Price Determinants in Indonesia _. Bulletin of Indonesian Economic Studies_ 54(1), 61-84.

- Goodhart, C. & Hofmann, B. (2008). House Prices, Money, Credit, and the Macroeconomy. _Oxford Review of Economic Policy_ 24 (1):180-205.

- Goodman, J. L. & Ittner, J. B. (1992). The Accuracy of Home Owners’ Estimates of House Value. _Journal of Housing Economics_ 2 (4), 339-57.

- Gröbel, S. & Thomschke, L. (2018). Hedonic Pricing and the Spatial Structure of Housing Data - An Application to Berlin. _Journal of Property Research_ 35(3), 185-208.

34
>(0.0252)|
|Observations|900|900|900|900|900|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Note: The treatment corresponds to 1 while the control group is 0. ATET denotes the weighted average treatment effect on the treated. ATET estimates are adjusted for sector/panel effects and time effects. The sample period is January 2019 to September 2022.

# **4.3 Drivers of Growth in Fully Liberalized Sectors and Those Linked to SDGs**

We then assess the growth in realized FDI, DDI and planned FDI to check the drivers of growth in these sectors and whether FDI can be linked with sustainable development sectors. This will enable us to assess, to the extent possible, the quality in addition to the quantity of FDI. For that, we use the UNCTAD list of ”SDG (Sustainable Development Goals) investment” – project finance in infrastructure, food security, water and sanitation, and health (UNCTAD, 2022). Due to the data challenges outlined in the Data Section and the level of aggregation of our data, we are unable to pinpoint exact investments that are not defined at the 2-digit KBLI level. As such, in this 17
shows, the areas with higher self-reported Native identity tend to also have higher measures of Native genetic ancestry. This shows that Native self-reported identity correlates with Native ancestry. Note, however, that Native genetic ancestry is several times larger than the self-identified measure we use in this paper. As such, the results discussed in this paper do not correspond directly to people with genetic Native ancestry but to those who self-identify as Natives or descendant of Natives.

Figure 2: Genetic and Self-Reported Native Prevalence <!-- Start of picture text --> Salta<br>Salta<br>Catamarca Chubut<br>NEA Río Negro Chubut<br>Tucumán<br>Río Negro<br>Formosa<br>Buenos Aires<br>Corrientes<br>Buenos Aires<br>Misiones<br>0 2 4 6 8 10<br>% Identifies as Native or Descendant (Census 2010)<br>Parolin et al (2019) Avena et al (2012) Corach et al (2010) Wang et al (2008)<br>80<br>60<br>40<br>20<br>Average Native Genetic Ancestry<br>0<br><!-- End of picture text --> _Sources:_ REDATAM INDEC Census Argentina (Instituto Nacional de Estad ́ıstica y Censos, 2010), Parolin et al. (2019), Avena et al. (2012), Corach et al. (2010) and Wang et al. (2008). _Notes:_ NEA (Northeast of Argentina) includes the provinces of Formosa, Chaco, Corrientes, and Misiones.

6