rafmacalaba/data-use-mentions-extended huggingface dataset:
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- 1995. In contrast to their findings, the sample of countries that have experienced at least ten continuous years of democracy is evenly split between presidential and parliamentary systems (22 countries in each), rather than weighted in favor of parliamentary systems. Table 1 does not necessarily refute the argument in Stepan and Skach (1993). It does suggest that with more extensive institutional databases, research can begin to accommodate new levels of hypothesisbuilding in institutional analysis, especially hypotheses that can explain such different findings as those in Table 1.

Table 1: Presidentialism, parliamentarism, and democratic consolidation among nonOECD countries

|||Regimetyp<br>(Legislative <br>|eduringthedemoc<br>IndexofElectoralCo<br>eaterthan5<br>|raticperiod<br>mpetitiveness<br>)<br>|
|---|---|---|---|---|
|||Presidential|Parliamentary|_Indirectly-<br>elected<br>president<br>(DPI) or semi-<br>presidential<br>Stepan-Skach|
|Non-OECD countries<br>thatweredemocratic<br>at leastoneyear<br>during1975- 1995|DPI Indicators<br>(1975 - 95, 122<br>countries)<br>Stepan-Skach<br>(1973 - 89, 53<br>countries)|68<br>28|40<br>25|14<br>0|
|Number ofnon-<br>OECD countriesthat<br>werecontinuously<br>democratic for at least<br>ten consecutiveyears,|DPI Indicators<br>(1975 - 95, 47<br>countries)<br>Stepan-Skach<br>(1973-89, 22.<br>countries)<br>.|22<br>5|22<br>17|5<br>.<br>0|

Note: This replicates Table 3, Stepan and Skach (1993), using variables
context to the IDA country results, Table 1 also presents results available for the United States. Results all relate to the particular year of the underlying microdata used in the analysis. Note that income survey microdata was available in only two of the six IDA countries (Honduras and Nicaragua).<sup>21</sup> In the other four countries, consumption data was used to proxy for income.<sup>22</sup> 17 A characteristic of the Gini-based dispersion measures on which the Kakwani and Reynolds-Smolensky indices are based is that they are less responsive to changes at the tails as compared to the middle of the distribution being examined. An alternative approach to measure redistribution is to calculate the difference in the Atkinson inequality index before and after tax. The Atkinson index enables greater emphasis to be applied to the bottom of the income distribution by specifying the degree of inequality aversion in its underlying social welfare function.

18 For example, Lambert’s conundrum illustrates that, due to path dependency, the combined impact of a regressive tax and a progressive transfer can lead to lower inequality than without the regressive tax (Lambert, 2001). 19 See Lustig (2018) for more detail on the CEQ methodology.

> 20 These remain _effective_ progressivity measures as they are the averages of the modelled tax burdens of the households in each decile or quintile.

21 Income survey data is now also available for Ethiopia and will be used in distributional analysis to be undertaken by the World Bank’s Fiscal Policy and Sustainable Growth Unit in the second half of 2022.

22 Because richer households tend to save more than poorer households, using consumption data can be expected to underestimate the income levels of high-income households and the resulting metrics may therefore underestimate the total progressivity achieved in the country. To a smaller extent this can also occur for the countries based on income survey data due to the underreporting of top earners in income survey data.

12
Policy Research Working Paper 10641

# **Abstract**

This paper assesses the medium-run effects of government support to firms during the COVID-19 crisis and whether the effectiveness of this support varied with its timing. Using data from three rounds of the World Bank’s Enterprise Surveys COVID-19 Follow-up Surveys carried out between May 2020 and April 2022, it relates government support in Round 1 (received in the first half of 2020) and Round 2 (received during the second half of 2020 or early 2021) with firm performance in Round 3 (generally mid-2021). Controlling for a host of background characteristics, firms that received support in Round 1 performed better in terms of Round 3 sales, but only if they did not have continued support. Firms that also received support in Round 2 had similar Round 3 sales as those that received no support and were more likely to decrease employment. Firms that received government support only in Round 2 experienced no boost in Round 3 performance. The findings suggest that government support should be provided promptly, but it should also be phased out quickly.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at dsinger@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

_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
# **Table A9: The effects of temperature on poverty and inequality – Heterogeneity analysis**

|Poverty $1.90|Gini index|
|---|---|
|(1)|(2)|
|_Panel A: Regime type (Reference group: Democracy)_||
|Temperature*Hybrid regime<br>0.735*|-0.004|
|(0.444)|(0.003)|
|Temperature*Authoritarian regime<br>1.395***<br>(0.431)|0.008**<br>(0.003)|
|_Panel B: Location (Reference group: Countries near equator)_||
|Temperature* Countries near equator<br>0.943***<br>(0.293)|0.011***<br>(0.004)|
|_Panel C: Share of agriculture in GDP (Reference group: Low s_|_hare)_|
|Temperature*High agriculture share<br>0.155***<br>(0.051)|0.001***<br>(0.000)|
|_Panel D: Share of manufacturing in GDP (Reference group: Lo_|_w share)_|
|Temperature*High manufacturing share<br>-0.076**<br>(0.039)|-0.001***<br>(0.000)|
|_Panel E: Share of trade in GDP (Reference group: Low share)_||
|Temperature*High trade share<br>-0.005<br>(0.003)|0.000<br>(0.000)|
|Controlling for rainfall<br>Yes|Yes|
|Subnational FE<br>Yes|Yes|

_Notes:_ Results of panel
>6.0<br>4.0 Pakistan<br>2.0<br>0.0<br>1940 1950 1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> In order to establish what Pakistan‘s per capita income would have been in the period 1900 to 2050 had it followed a different education policy, we must first establish a relationship between education and income. In this illustrative example such a relationship is obtained by fitting a Mincerian function using per capita income ( _Y_ ) and years of schooling ( _S_ ) circa 2009 in 128 countries:
We use both discovery filters described in section 3 on the HS 4-digit and HS 6-digit data during the 1990s. In addition, we examine the SITC data in the same manner, first with a crosssection of discoveries in the 1990s, and with a conditional fixed-effects negative-binomial regression on discoveries since 1972 (including year dummies)<sup>8</sup> . The results are shown in Table 4.

**<u>Table 4: Stages of Discovery</u>**

||**HS 4-Di**<br>|**git 1990s**<br>|**HS 6-Dig**<br>|**it 1990s**<br>|**SITC**<br>|**3-Digit**<br>|
|---|---|---|---|---|---|---|
||**Filter 1**|**Filter 2**|**Filter 1**|**Filter 2**|**1990s**|**1972-1991**<br>**(FE)**|
|Coefficient: ln<br>GDPpercapita|15.65175<br>(5.68)|13.95415<br>(3.72)|15.72022<br>(5.89)|12.06436<br>(4.57)|22.38211<br>(4.38)|19.53553<br>(7.47)|
|Coefficient: ln<br>GDP per capita<br>squared|-.9280328<br>(-5.92)|-.821797<br>(-3.85)|-.9114158<br>(-6.03)|-.695039<br>(-4.65)|-
set-up of household diaries with infrequent visits.

Once the diaries and questionnaires come back from the field, they go through several steps before being available in electronic format: administration and filing of forms, coding of items, double blind entry, reconciliation of the two entries, and so forth. We use information from the survey experiment to compute the time for these activities. On a given day, a data entry operator can process 2.86 personal diary households, 4.31 household diary households, or 8.51 recall questionnaire households.

Based on the person days for field work and data processing and with data entry staff in the office at half the cost of an interviewer in the field, we then compute the total costs of field work (Table 9, column 6). With the average cost to administer a recall questionnaire set at US$100 per household (the numeraire to scale the other field costs), the personal diary will fall between US$597 and US$974, depending on whether the household is visited every day or every two days. For a frequently visited household diary, the estimated costs lie between US$442 and US$726; for
Uniform Spacings approach of Ng (2006) and statistically assess the degree and pervasiveness of co-movement between commodity prices. Further, we use the factor model from Bai and Ng (2004) to examine the time series properties of the common and idiosyncratic elements and to investigate whether co-movement can be summarized by common factors. Finally, we consider potential determinants of such common elements, in real terms, using a Factor Augments VAR following Bernanke et al. (2005) and GARCH in Mean following Beck (2001).

# _4.1 Data_

In order to reduce the noise to signal ratio in the data, we have run this investigation on a long span of yearly data. The quality of the historical data used in empirical work is one of the main disputes in the literature on commodity prices. This dispute was settled by Grilli and Yang (GY, 1988), who collected and summarized in a single trade weighted index 24 prices of internationally traded non-fuel commodities for the period 1900 to 1986.<sup>14</sup> Since, their dataset (or variants) has become the most widely used for empirical research on historical commodity prices. The original data from GY has been recently revised and updated to 2003 by Pfaffenzeller et al. (2007).

In order to investigate the behavior of historical commodity prices up to the most recent period, including the recent swings in prices, we have updated this data to 2008. This gives us a panel of time dimension _T_ =109 and cross sectional dimension _N_ =24. Please refer to the Data Appendix for a description of how the data has been updated and for some descriptive statistics.

> 14 Grilli-Yang‟s commodity price index ( _GYt_ ) is a simple arithmetic mean to individual commodity prices ( _CPit_ ), with weights ( _αi_ ) based upon trade shares _GYt = ΣαiCPit_ . Cuddington and Wei (1992) show how adopting a geometric average (i.e. _GYt = ΠCPitαi_ ) may affect a resultant commodity price index.

13
This information will lay the groundwork for new research in international economics and other fields. A large body of economic literature has looked at the effects of PTAs on international trade flows and on welfare.<sup>3</sup> However, this literature has two important limitations.<sup>4</sup> On the theoretical side, the study of PTAs is mostly based on Vinerian<sup>5</sup> logic, which focuses exclusively on tariffs, thus by construction excluding deep integration issues. On the empirical side, attempts to quantify the effects of PTAs suffer from a measurement error problem, as studies generally rely on dummies to identify trade agreements or distinguish between broad categories of agreements such as FTAs or CUs. The new data will help theorists to model DTAs and help empirical economists to properly identify their effects. Beyond economics, the data will inform research in other fields, primarily international law and international relations, on important issues such as the commonality or divergence of the rules set in PTAs and how they could evolve in the future.

The new data is the result of collaboration among the World Bank, the International Trade Center (ITC), the Organization for Economic Development and Cooperation (OECD), the WTO, and experts from academic institutions. It builds on previous research by the World Bank and others. A first database on the content of deep trade agreements was published in 2017 with the goal of documenting how the policy areas covered by PTAs had increased over time (Hofmann et al. 2019). This data set allowed researchers to construct a first series of indicators which capture the scope of trade agreements; i.e., what policy areas they cover. We refer to this as the _extensive margin_ of PTA depth. Based on this first data set, several research papers then looked, respectively, at the impact of deep trade agreements on trade, global value chains, foreign direct investment, and the effect of breaking up such agreements.<sup>6
learning.<sup>14</sup> But this should not be seen as an indication that children should be learning outside the school system. As the COVID-19 school closures have shown, schools have several other functions to protect and improve children’s health and well-being,<sup>15</sup> such as safety, nutrition,<sup>16</sup> socialization, facilitate parents' labor market participation,<sup>17</sup> and at the macro level can help build social cohesion,<sup>18</sup> democracy and peace,<sup>19</sup> All those complementary functions mean that schooling has an intrinsic value over and above learning and justifies its deprivation as a relevant component of learning poverty.<sup>20</sup> Hence, learning poverty is multivariate and unidimensional, using a nested approach. Table 1 illustrates learning poverty classification using a 2 x 2 contingency table based on the two deprivations, schooling and learning.<sup>21</sup>

**Table 1 Contingency table of schooling and learning deprivations and learning poverty classification**

|||Learning<br>|deprivation<br>|
|---|---|---|---|
|||No|Yes|
|vation|No|_Not School Deprived_and_Not_<br>_Learning Deprived_<br>=|_Not School Deprived_and_Learning_<br>_Deprived_<br>_=_|
|depri||**Not Learning Poor**|**Learning poor**|
|Schooling|Yes|_School Deprived_and_Not Learning_<br>_Deprived_<br>_=_<br>**Learning poor**|_School Deprived_and_Learning Deprived_<br>_=_<br>**Learning poor**|

Note: Given the anchoring on the learning poverty measure on the SDGs, learning deprivation at the End of Primary is measured using schoolbased learning assessments. Hence
<!-- Start of picture text --> Wm % Revenue lost due to distribution losses lll % Revenue lost due to collection rate<br>—®— Average Tariffs LCU Index —@=— Average Tariffs USD Index<br>30% 490%<br>25%<br>n 390%<br>3<br>Cc yn<br>S 20% 2<br>s 290% a]<br>E©@ae15% .o<br>=x 190% vu®<br>" g 10% g<br>Z<br>x<br>90%<br>5%<br>No data (1990-2009)<br>sd<br>0% -10%<br>PPPSoSSY PPBob 9?o> I of PPPSF0 © SSV PPL0 O DQ LLLPGy Dm FFL& © © FPFSo LPMO SPWO AD SPMPADV SKSWD SPAh 89<br><!-- End of picture text -->
37-52.

De Mel, S., McKenzie, D., &Woodruff, C. (2011). What is the Cost of Formality? Experimentally Estimating the Demand for Formalization. Working Paper, University of Warwick, Coventry. De Rosa, D., Gooroochurn, N., & Görg, H. (2010). Corruption and Productivity: Firm-level Evidence from the BEEPS Survey. World Bank Policy Research Working Paper 5348, The World Bank, Washington DC. Distinguin, I., Rugemintwari, C., & Tacneng, R. (2016). Can Informal Firms Hurt Registered SMEs’ Access to Credit? _World Development_ , 84: 18 – 40.

Djankov, S., and Murrell, P. (2002). Enterprise Restructuring in Transition: A Quantitative Survey. _Journal of Economic Literature_ 40(3): 739-792.

Dollar, D., Hallward-Driemeier, M., & Mengistae, T. (2006). Investment Climate and International Integration. _World Development_ , 34 (9): 1498 – 1516.

Du Rietz, A. & Henrekson, M. (2000). Testing the Female Underperformance Hypothesis. _Small Business Economics_ , 14 (1): 1 – 10.

Farrell, D. (2004). The Hidden Dangers of Informal Economy. _McKinsey Quarterly_ , 3: 27 – 37. Fisman, R., & Svensson, J. (2007). Are Corruption and Taxation Really Harmful to Growth? Firm Level Evidence. _Journal of Development Economics_ , 83 (1): 63 – 75.

Gonzalez, A., & Lamanna, F. (2007). Who Fears Competition from Informal Firms? Policy Research Working Paper Series 4316, World Bank, Washington DC.

Haltiwanger, John C., Ron S. Jarmin, and Javier Miranda. (2010). Who Creates Jobs? Small vs. Large vs. Young. NBER Working Paper 16300, NBER.

ILO (2018). Women and Men in the Informal Economy:
Figure 2: Days of Cash in Hand across Industries <!-- Start of picture text --> Multiline & specialty retail<br>Restaurants<br>Consumer staples<br>Textiles, apparel, & luxury goods<br>Auto & components<br>Other transport<br>Utilities<br>Industrials<br>Hotels, resorts, & cruise lines<br>Other consumer discretionary<br>Airlines<br>Materials<br>Information technology<br>Communication services<br>Health care<br>Energy<br>0 30 60 90 120 150 180<br>Number of days<br><!-- End of picture text -->

**Note** : This figure shows the number of days of operating expenses covered by cash held across U.S. listed firms. The figure shows 2000-2016 averages by industry. **Source:** Authors’ calculations based on Compustat data.

The resilience of the corporate sector is also tightly linked to the duration of the pandemic shock. The exogenous health emergency is transitory. Eventually a vaccine or an effective treatment will be developed, or the disease will run its course, and the source of the crisis will basically disappear. Nonetheless, the timeline for these exits from the pandemic are still unknown and hard to predict. Hence, the duration of the pandemic crisis remains a crucial source of uncertainty. The longer the pandemic lasts, the tougher it would be for firms to withstand the economic downturn, survive, and recover. To the extent that the shock does not persist for too long, a significant proportion of firms are likely to remain viable. That is, their net worth will still be positive as the economy returns to pre-pandemic levels. However, not all firms can survive a prolonged crisis, with extensive lockdowns and other containment measures. While industries themselves will weather the shock and survive (e.g., the restaurant or airline industries will not disappear), the
constraints exist. Also, they do not fully eliminate some degree of arbitrariness (for example, on the number of indicators, functional forms, or aggregation of indicators per components). However, they allow for monitoring, profiling, benchmarking, and analytical functions in a systematic way. They also allow for multiple robustness checks that confirm that our key results are not predetermined by these choices.

Our empirical exploration highlights three main results. First, a given country’s social sustainability is positively correlated with per capita income levels and negatively correlated with poverty headcounts and income inequality. This is also true for each of the four components of social sustainability. Second, these associations are not equally strong. Process legitimacy and social inclusion tend to be more strongly associated with higher income and lower levels of poverty and inequality, the opposite being true for resilience and social cohesion. Third, results are generally robust to the choice and number of social indicators, different functional forms, and alternative developmental outcomes.

Both the global database and the analysis can be further expanded and improved. The global database would benefit from including a previous time period (and future ones) to enable longterm monitoring. Further variables need to be explored to increase the number of countries for which social sustainability indices can be constructed. Currently, only 40 countries have information for every resilience indicator, compared with 139 that have data for every process legitimacy indicator, something which has implications for the precision of each dimension. More analysis is also needed to understand how the variation of social indicators across vulnerability groups within a country affects our results. In addition, both the global database and analysis are conducted at national levels. Scholars working on social sustainability in the future will hopefully be able to draw on data sets from more countries with data representative at the subnational and even city level. This will allow for an enhanced within- and between-country monitoring of social sustainability. More analysis is also needed to understand the interactions between different components of social sustainability. For example, we know
only 54 observations informing the two means. For this reason, standard errors are not shown. Nonetheless, table 7 shows suggestive results that relate food characteristics and the degree of reporting error.

The frequency of purchase of a particular food appears to convey a salience reflected in the accuracy of the reporting. For frequently purchased items (defined as those above the median purchase incidence of 1.4 times in a two-week period), the incidence error is lower in all five modules investigated, but especially in the usual month, where the error magnitude is less than half as much as the overreporting for infrequently purchased foods. The value error is also far lower for frequently purchased goods among the recall modules. Combined, this suggests that additional attention must be paid to infrequently purchased goods to improve the accuracy in recall surveys of both the reported incidence and the reported value conditional on incidence. For the diaries, the frequency of purchase is not substantially related to the value of the good recorded as we might expect in settings if transactions are recorded soon after they occur, regardless of purchase frequency.

There is no apparent relation of the share of home production to incidence reporting error: across all five modules, the incidence error for goods more commonly consumed from home production and those seldom consumed are close. The same holds true of consumption value error except for the 14- and 7-day recalls. Under the 14-day recall, the value error is positive for foods consumed with a high share of home production, while negative for foods with a low share. On the other hand, the 7-day module exhibits a much higher degree of overreporting for foods with a low share of consumption from home production.

In terms of storability, we define 26 individual food items as storable, e.g. dry goods such as maize flour, and 28 as perishable, e.g. various types of fresh vegetables. There are some apparent differences in errors between the two characteristics. The incidence error is always greater in absolute magnitude for perishable goods: