rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

- Aldasoro, I., & Faia, E. (2024). Energy subsidies and economic efficiency: Market distortions in times of crisis. _Economic Policy_ , 39(120), 711-738.

- Aldy, J. E., & Pizer, W. A. (2015). "The Competitiveness Impacts of Climate Change Mitigation Policies." _Journal of the Association of Environmental and Resource Economists_ , 2(4), 565–595.

- Andre, C., Costa, H., Demmou, L. and G. Franco (2023). Rising Energy Prices and Productivity: Short Run Pain, Long term gain? OECD Economic Department Working Paper # 1755.

- Ari, A., Arregui, N., Black, S., Celasun, O., Iakova, D., Mineshima, A., Mylonas, V., Parry, I., Teodoru, I., & Zhunussova, K. (2022). Surging Energy Prices in Europe in the Aftermath of the War: How to Support the Vulnerable and Speed up the Transition Away from Fossil Fuels. IMF Working Papers, 2022/152

- Bartelsman, E. J., Haltiwanger, J., & Scarpetta, S. (2004). Microeconomic Evidence of Creative Destruction in Industrial and Developing Countries. World Bank Policy Research Working Paper, No. 3464.

- Battistini, N, Bobasu, A and J. Gareis (2023). Who foots the bill? The uneven impact of the recent energy price shock. _European Central Bank Economic Bulletin_ , issue 2/2023.

- Battiston S., Monasterolo I., van Ruijven B., Krey V. (2022) Mapping economic activities into climate scenarios and transition risk classes: the NACE-CPRS-IAM classification, NGFS Technical notes.

- Battiston, S., Mandel, A., Monasterolo, I., Schütze, F., & Visentin, G. (2017). A Climate stress-test of the financial system. _Nature Climate Change_ , 7(4), 283–288. htps://doi.org/doi:10.1038/nclimate3255)

- Black, S., Liu, A., Parry, I., and N. Vermon (2023). IMF Fossil Fuel Subsidies Data: 2023 Update. IMF Working Paper # P/23/169

Bloom, N. (2009). The Impact of Uncertainty Shocks. _Econometrica_ , 77(3), 623–685.

Bloom, N., Genakos, C., Martin, R., & Sadun, R. (2010). "Modern Management: Good for the Environment or Just Hot Air?" _Economic Journal_ , 120(544), 551–572.

- _American_

- Bovenberg, A. L., & de Mooij, R. A. (1994). Environmental Levies and Distortionary Taxation. _Economic Review_ , 84(4), 1085–1089.

- fuel prices on firms. _Journal of Environmental Economics and Management_ 111.

- Cali, M., Cantore, N., Marin, G., Mazzanti, M., Nicolli, F., Presidente, G. (2023). Energy prices and the economic performance of firms in emerging countries. _Structural Change and Economic Dynamics_ 66.

- Caves, D., Christensen, L., & Diewert, W. (1982). "Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers." _The Economic Journal_ , 92(365), 73–86.

- Clements, B., Gupta, S., & Keen, M. (2024). The Fiscal Costs of Energy Subsidies: A Macroeconomic Perspective. IMF Working Papers, 24(081).

13
how important training and re-education embedded in management, supervisors or workers is considered inside the firm. In the latter case we model the answers as dummy variables, with the variables being equal to 1 for a positive answer to the above questions. Because of the qualitative nature of these data, and because of the limitation of the perceptional variable (as reported above), the importance of avoiding putting too much emphasis on the results related to it still holds. It is nevertheless worth reporting the degree to which training is perceived to be relevant in assuring a certain level of knowledge, and therefore in helping firms to be competitive and innovative.

The answers to questions in the Chilean survey regarding possible obstacles to the firms’ innovative activity have also been modeled as explanatory variables of innovation inputs. They have been modeled as dummies, with 1 if the firms consider that either lack of skilled personnel or lack of experienced personnel has been an obstacle for them. There is a caveat in this however: firms might not experience any obstacle to their innovation activity simply because they are not innovating and therefore sometimes this variable does not necessarily represent the lack of a negative condition but rather the absence of a positive action (which is innovation activity).

- (iii) **Collaboration on innovation:** in this group of variables collaboration with different partners from both public and private sectors is considered. In the case of Chile, the variables are constructed as dummies with positive value if the firms attribute importance (on a scale from 1 to 4) to collaboration with each of the following: universities, consultants, suppliers and other firms in influencing their innovation activity. For Colombia the variables are defined as dummies with value 1 if the firm reports collaboration with each of the following partners: universities, public and private research centers, suppliers, and/or other firms.

- (iv) **Absorption capacity:** in this group, variables related to the firms’ capacity to transform knowledge into new products or processes are considered. These variables are:<sup>19</sup>

- The presence of an R&D department inside the firm.

- Innovation intensity (defined as the share of innovation expenses per employee).

- Investment in education related to innovation.

- Permanent innovation activities (if it is considered important to have permanent innovation activity within the firm).

These variables, with the exception of the innovation intensity variable, are dummy variables. Innovation intensity is defined as the amount of innovation expenses related to the size of the firm, in terms of number of employees.

# **5. DESCRIPTIVE STATISTICS**

> 19 For the last three variables data are available for Chile only.

11
Montenegro and Patrinos, 2014). Although historically most research found the highest payoffs came from primary education, recent evidence from a study comparing 139 economies (Montenegro and Patrinos, 2014) suggests that the returns to tertiary education surpass those at the primary and secondary education levels. These findings may be an indication of important shifts taking place, whether as a result of massive expansion in educational attainment, because of so-called skill-biased technological change (Katz and Murphy, 1992; Autor, Katz and Krueger, 1998) or because of rising inequality in the wage structure (DiNardo and Pishcke, 1997; DiNardo and Card, 2002).

However, while findings on the returns to schooling have proven to be robust and useful (Heckman, Lochner, and Todd, 2001), there are limitations (i.e., unobserved ability and effort, endogeneity, etc.) and methodological challenges associated with using schooling as a proxy for estimating schooling returns (Card, 1999). For example, Hanushek et al. (2013) have pointed out that Mincer’s formulation assumes that schooling is the only systematic source of skill differences.<sup>5</sup> But research shows that other factors, such as an individual’s ability, family inputs, and school and labor market characteristics also determine skill acquisition (Card, 2001 Heckman, and Vytlacil, 2001). Further, not all skills are acquired or demonstrated through years of formal schooling. Cognitive and noncogntive skills are important determinants of labor market success (Bowles, Gintis, and Osborne, 2001; Borghans et al., Duckworth, Heckman, and ter Weel, 2008; Cawley, et al., 1996; Heckman, Stixrud, and Urzua, 2006). Using schooling alone as a proxy for general skills severely underestimates the returns to human capital and obscures our understanding of how the labor market rewards various skills.

In response to the conceptual and methodological limitations of the returns-to-schooling studies noted above, a substantial literature has examined how cognitive skills, and to a lesser extent noncognitive skills, are rewarded in the labor market (Cawley, et al., 1996; Hanushek and Zhang, 2006; Hanushek et al., 2013). Although available metrics are fairly heterogeneous and seldom comparable across data sets, _cognitive skills_ tend to be measured through student achievement tests and reading and numeracy assessments. _Noncognitive skills_ are typically estimated through self-reported measures of behaviors and personality traits (such as the Big Five personality inventory dimensions).

Overall, findings indicate that, like years or level of schooling, cognitive skills tend to have a statistically significant effect on wages—the magnitude of which varies by the specific

> 5 “Mincer’s empirical innovation has perhaps been too successful as it has also led researchers to ignore many important and continuing measurement issues. Implicitly the Mincer formulation assumes that schooling is the sole systematic source of skills differences” (Hanushek et al., 2013, p. 4).

4
geographic centralization of deposit and loan decisions at headquarters reduces physical access and increases the opportunity costs for households and firms to access financial services.

Finally, we conducted a first-cut examination of the bank and country-level factors that explain variation in indicators of bank barriers. We provide suggestive evidence that variation in these barriers is associated with variation in bank size, physical infrastructure, contractual and informational frameworks, ownership structure in banking and the general degree of competitiveness, openness and transparency of economies. While much more research is needed in this area, these results have important implications for policy reforms to broaden access.

As a first attempt at capturing quantitative measures of cross-country differences in barriers to banking along the dimensions of physical access, affordability and eligibility, this paper is complementary to other efforts to collect data on access to financial services at the aggregate, firm- and household levels. We are still very much in the beginning of this work and richer data sources and in-depth analysis are needed to improve our understanding of access and its impact on economic outcomes. Going forward, several fruitful approaches can be envisioned. First, the type of analysis conducted in this paper is very useful in identifying outlier countries, i.e. those with high access barriers, as potential case studies to investigate financial access in greater depth. Case studies for individual countries that combine detailed supply and demand data from financial institutions, households and firms would be able to more thoroughly assess access to and use of financial services, barriers faced by different users, and potential policies to reduce these barriers. Compared to cross-country studies, such country-case studies can better take into account idiosyncratic characteristics and better exploit the richness of institutional detail at and below the country level.

Second, while household and firm surveys at the country level are useful instruments, important empirical challenges remain in measuring the causal impact of improved access to 28
# **Table 1: Relationship between historical malaria and current productivity (yield)**

||Rice|Cassava|Banana|Ground Nut|Other Oilseeds|
|---|---|---|---|---|---|
|Proportion of Area Under||||||
|LDO|1,589**|-1,513|-1,645|843.5***|-314.1|
||(2.058)|(-0.736)|(-1.417)|(3.593)|(-0.275)|
|Malaria Incidence*rainfall|-0.386|-14.24|-2.003|-0.574|-1.77|
||(-0.105)|(-1.541)|(-0.366)|(-0.400)|(-0.325)|
|Observations|118|90|98|57|101|

Note: Regressions control for rainfall, average slope, proportion of irrigated land and dummy for within 5 km of a river.

Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%

**Table 2: Land under Restrictions, Historical Malaria and Health Status**

||Anemia among n|on-pregnan|t women|% su<br>Illn|ffering Chroni<br>ess/disability|c|
|---|---|---|---|---|---|---|
||Mild/Moderate|Severe|Any|Male|Female|All|
|Proportion of Area Under LDO|-27.82|1.859|-25.96|-4.912|-9.060|6.093|
||(-1.017)|(1.139)|(-0.966)|(-0.576)|(-1.474)|(0.372)|
|Malaria Incidence*rainfall|-0.240|1.05e-02|-0.230|7.30e-03|-1.48e-02|3.79e-02|
||(-1.626)|(1.117)|(-1.581)|(0.150)|(-0.397)|(0.409)|

Robust t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%

**Table 3: Land market restrictions, Female Labor Force Participation and Wages** OLS Regression Results

||Labor<br>Partici<br>1|Force<br>pation<br>2|Log(Real A<br>3|nnual wage)<br>4|
|---|---|---|---|---|
|Proportion of area under LDO|-0.0148|0.280***|-0.240|-1.424***|
||(-0.150)|(2.752)|(-1.058)|(-6.931)|
|Travel Time to Large City|0.0187***|0.00944*|-0.0594***|-0.0512***|
||(3.862)|(1.911)|(-5.235)|(-3.821)|
|Selection Term|||1.032***|2.474***|
||||(4.804)|(9.722)|
|Observations|10,850|10,850|2,918|2,918|
|Individual/household characteristics|Yes|Yes|Yes|Yes|
|District Fixed Effect,Area Characteristics|No|Yes|No|Yes|

All regressions include individual's age, education, marital status, and dummies for household's religion/ethnicity Robust t statistics in parentheses. Standard errors corrected for clustering at the sub-district level (DSD) * significant at 10%; ** significant at 5%; *** significant at 1%
# **3.2 Data**

The empirical approach uses the data from the sixth wave of the World Value Survey (WVS) on four African countries: Algeria, Ghana, Morocco, and Nigeria.<sup>10</sup> In the survey, respondents are interviewed on their attitudes concerning economic and social contemporary issues, family, religion, and work. The WVS is widely used in political economics, sociology and economy in many papers, including Knack and Keefer (1997), Zak and Knack (2001), and Nunn and Wantchecon (2011). Moreover, using the experimental data of the _Reader’s Digest_ ,<sup>11</sup> Knack and Keefer (1997) provide empirical proof of the validity of these data. Indeed, they show the existence of a strong correlation between the measure of trust in the WVS and the number of wallets lost and returned with their content intact in many countries.

Figure 1 depicts the distribution of tax morale by country. As the figure shows, the majority of respondents declare high levels of tax morale, while a minority state lower tax morale. In Ghana and Morocco, the share of respondents stating that cheating on taxes is never justified is close to 80 percent, while the same proportion is about 60 and 40 percent in Algeria and Nigeria respectively. This highlights heterogeneity in attitudes toward taxes both between and within countries. Figure 2 shows the relationship between tax morale and trust in the data graphically. It plots the average trust in public institutions and the neighborhood against tax morale. As expected, both trust in public institutions and the neighborhood are positively correlated with tax morale, with a more pronounced relationship for trust in public institutions.

In Table 2, I present some individual-level descriptive statistics. The standard errors are presented in parenthesis. The first columns show the summary statistics for each country while the last column gives the statistics for the full sample. Respondents in Algeria report a lower level of tax morale (7.12) compared to respondents in Morocco (9.49), Ghana (9.35), and

> 10The sixth wave of the WVS covers three additional African countries, which are the Arab Republic of Egypt, Rwanda, and Zimbabwe. Those countries are not considered in this study as ethnic origins of the respondents are not well documented, making it impossible to employ the identification strategy.

> 11This experimental study was conducted in several European countries and the United States. The experiment took place in several cities and consisted of losing the wallets containing $50 each, with the owner’s address and phone number. See _The Economist of 22 June 1996_ .

15
# Growth Rates and Aggregate Welfare: An International Comparison

# by Nanak Kakwani*

# TABLE OF CONTENTS

|1.|<br>Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>1|
|---|---|
|2.|<br>The Least Square Growth Rate . . . . . . . . . . . . . . . . . . . .<br>3|
|3.|<br>Alternative Procedures . . . . . . . . . . . . . . . . . . . . . . .<br>6|
|4.|<br>Growth Rates of GN± per Capita in Seventy-seven<br>Developing countries,<br>1970-87 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10|
|5.|<br>Welfare Interpretation<br>of Aggregate Growth Rate . . . . . . . . . . . 15|
|6.|<br>An Evaluation of Alterr.-tive<br>Procedures . . . . . . . . . . . . . . . 19|
|7.|<br>A New Procedure .... . . . . . . . . . . . . . . . . . . . . . .<br>. 24|
|8.|<br>Sub-period Growth Rates .... . . . . . . . . . . . . . . . . . .<br>. 27|
|9.|<br>Relationship Between Welfare Change and Sub-period Growth Rates.<br>. . 30|
|10.|Testing for Significance<br>of Growth Rates . . . . . . . . . . . . . . 31|
|11.|Growth Performance of 83 Developing Countries: Further analysis . . 37|
|12.|Aggregation over Countries . . . . . . . . . . . . . . . . . . . . . 47|
|13.|<br>An Alternative Procedure .....................<br>. 53|
|14.|A Comparison of Growth Performance of Countries by Alternative<br>Classifications<br>..........................<br>. 56|
|15.|Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59|
|Refe|rences ...............................<br>. 62|

# LIST OF TABLES

- Table 1: Growth Rates of GNP per Capita 1970-87; Based on alternative procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

- Table 2: Characteristics of Countries with Negative Per Capita Growth Rates 19t0-87 . . . . . . . . . . . . . . . . . . . . . . . . . 15

- Welfare and Human Resources Division, the World Bank and Department of Econometrics, The University of New South Wales, Australia. The author gratefully acknowledges Martin Ravallion, Jacques van der Gaag, **..** Subbarao and Ravi Kanbur for their helpful comments on this paper. I am most grateful to Bela Balassa for carefully reading the paper and making extremely useful comments.
# _Lagged Effects_

We further explore the possibility that there may be a sequence in the formalization process of the firm, by using lagged variables. We replicate our main regression analysis, but instead of using contemporary formality variables as explanatory variables, we use their lagged counterparts. This is possible because we have two observations for each firm in the sample (for fiscal years 2011 and 2012).

<u>Table 6: Using Lagged Explanatory Variables</u>

|Variable|(1)<br>OLS<br>License|(2)<br>2SLS<br>License|(3)<br>OLS<br>Payroll|(4)<br>2SLS<br>Payroll|
|---|---|---|---|---|
|Payroll registry, t-1|0.0132<br>(0.0324)|-0.245<br>(0.590)|||
|License, t-1|||0.176***<br>(0.0378)|0.573**<br>(0.258)|
|Log distance to SUNAT|-0.0771***<br>(0.0190)|-0.0735***<br>(0.0212)|||
|Firms in E-Payroll / firms in SUNAT|||0.0186***<br>(0.0047)|0.0195***<br>(0.0049)|
|Log product per worker|0.0328**|0.0518|0.0446**|0.0252|
||(0.0159)|(0.0461)|(0.0175)|(0.0222)|
|Log number of workers|0.0730***|0.0973*|0.0909***|0.0798***|
||(0.0168)|(0.0580)|(0.0182)|(0.0204)|
|Incorporated business|0.174***|0.263|0.306***|0.253***|
||(0.0321)|(0.205)|(0.0338)|(0.0494)|
|Age 5-8|0.0360|0.0241|-0.0198|-0.0479|
||(0.0372)|(0.0467)|(0.0414)|(0.0471)|
|Age 9-12|0.115***|0.123***|0.0278|-0.00616|
||(0.0391)|(0.0436)|(0.0435)|(0.0506)|
|Age 13-16|0.149***|0.149***|-0.0917*|-0.164**|
||(0.0430)|(0.0441)|(0.0484)|(0.0691)|
|Age 17-+|0.201***|0.223***|-0.00724|-0.0728|
||(0.0390)|(0.0637)|(0.0437)|(0.0624)|
|Constant|0.288|0.156|-0.749**|-0.863**|
||(0.268)|(0.406)|(0.313)|(0.337)|
|Observations|888|888|888|888|
|R-squared|0.182|0.122|0.278|0.187|
|Weak identification test||2.767||21.11|

_Note:_ Standard errors are in parentheses. Significance: *** p<0.01, ** p<0.05, * p<0.1. Stock-Yogo weak ID test critical values: 16.38 (10% maximal IV size); 8.96 (15% maximal IV size).

Again, we use OLS and 2SLS regression models. For the 2SLS estimation, we use as instruments our proxy variables for enforcement as we did before. We report the results in table 23
reaches companies in the form of debt, it tends to increase their indebtedness and default risk (Myers (1997); Hennessy et al. (2007)).<sup>13</sup> Thus, matching support instrument with the dominant channel of transmission of the shock for the firm is critical in making firm support effective.

Figure 10: Relationship between access to public support and key firm-level outcomes <!-- Start of picture text --> Public access and expected change in sales Public access and financial fragility<br>SAR countries SAR countries<br>20 1.2<br>1<br>0<br>.8<br>.6<br>-20<br>.4<br>-40 .2<br>0 .2 .4 .6 0 .2 .4 .6<br>Average predicted probability of accesing public support Average predicted probability of accesing public support<br>Expected change in sales<br>Probability of falling in arrears<br><!-- End of picture text -->

# **5. Conclusion and policy recommendations**

Firms in South Asia had a disproportionately larger impact of the pandemic. Evidence from Business Pulse Surveys conducted by the World Bank in collaboration with the IFC in South Asian countries suggests that only two-thirds of the businesses are currently open in the region compared to 80% in other non-high income countries. The average revenues among these businesses have declined by 64% relative to a 46% decline in other non-high income countries. Results also point to the elevated financial fragility among South Asian firms and the dwindling confidence about their future during the crisis. BPS results show that nearly two-thirds of the South Asian firms are likely to fall into arrears compared to 43% in other non-high-income countries. South Asian firms also have a more pessimistic outlook such that they expect their sales to decline by 19% relative to the expectation of an 8% decline in other non-high income countries.

Within the region, COVID-19 did not affect all firms equally. Micro and small firms have generally suffered more in terms of business closures and revenue drops. While female-led firms suffered with respect to business closures, exporters remained resilient to firm closures, impact on sales and financial fragility. This may be related to firm’s ability to adjust their operations model. Compared to a global average of 34%, only 23% of the firms in South Asia adjusted to COVID-induced shocks by increasing the use of digital platforms. Micro and small firms, and non-exporters have considerably lagged behind in this regard. It may also coincide with access to public support programs which remained low for the most affected groups, including female-led firms, perhaps due to lack of organized business networks which can potentially alleviate information gaps. The fact that micro, small and non-exporting firms represent over 80% of the region’s workers suggests that the pandemic

> 13In the sample, 72% of the large firms in the accommodation and food services sector have a higher probability of falling into arrears. These are also the firms that are likely to receive public support.

18