rafmacalaba/data-use-mentions-extended huggingface dataset:
and its workers from this unexpected shock.

Overall, our estimates imply very large increases in borrowing needs to cover tax revenue shortfalls and to guarantee loans to the corporate sector – a challenge for developing countries with limited fiscal space (Loayza and Pennings 2020, Benmelech and Tzur-Ilan 2020, and Abraham et al. 2020). Countries with limited access to international capital markets will require a substantial increase in budget support from donors. A silver lining is that poorer countries seem relatively less impacted on the dimensions we examine: the average loss in tax revenue from corporations in our five Sub-Saharan African countries is 1.0% (1.7%) of baseline GDP in the three-month (five-month) shock scenario, while it is 1.9% (3.0%) in the middle-income countries. This is because middle-income countries have larger formal sectors, higher effective tax rates and sectoral compositions which produce a slightly stronger revenue shock.

Our analysis faces several limitations. First, it only considers a demand shock— arguably the key direct impact of COVID-19 (Coibion et al. 2020)— and assumes that firms have no problems sourcing inputs; in practice the pandemic also induces a supply shock which can amplify the demand shock (Guerrieri et al. 2020). Second, we do not account for the indirect impacts of the shock through firms’ trade linkages which are potentially important (Baqaee and Farhi 2020, Bonadio et al. 2020). Third, in our framework firms do not adapt to the crisis (for example by changing products, selling online etc.). Fourth, profits in administrative tax data could be under-reported for tax minimization purposes, so that firms in our data might exhibit artificially low profitability in the pre-Covid baseline. Taking into account caveats (i)-(iii), the numbers in this paper could be considered as plausible lower bounds arising from direct effects, in partial equilibrium, without taking into account the full set of government support policies. Dynamic general 4
of extraordinary growth. This implies that, in the majority of cases, the output lost is not completely recovered in the recovery period. In addition, the similar results for light per capita growth suggest that the lack of an extraordinary growth period at the country level is not due to measurement issues. Regressions with levels of GDP per capita on the left hand side confirm this lack of a recovery.

This is, however, not a contradiction to the findings of strong recoveries in levels of output within country in the long run. As a check we use the data on night light at the ethnic group level as it offers a unique opportunity to study the dynamics of local economic activity on a yearly basis.

We run the following regression for group _i_ in year _t_ in country _c_ : where _ycit_ is the log of per capita night light, _incidencecit_ is conflict incidence and _recoverycit_ are a set of recovery dummies that capture the period after conflict, all at ethnic group level. The key addition in this regression is a set of of country/year fixed effects _θct_ which capture average economic activity at the country level in every year. In this way we compare how the level of economic activity in group _i_ compares to other groups in the same country _c_ during and after conflict. This mimics the existing 18
. Borrowers of the flexible loan appear to have slightly more businesses and to have started a secondary business. A new enterprise typically is an indication of risktaking, but of course could also be a diversification strategy, and thus we are not able to infer whether the increase in secondary businesses is indicative of flexible lending making risk-taking more palatable for the entrepreneurs.

Table 4 reports no changes in additional business or financing outcomes and no change in an overall loanrelated stress index, although borrowers of the flexible loan report thinking less about loan repayments and a decrease in anxiety in the days prior to loan payment deadlines. Table 4 also reports no change in a general stress index, though flexible loan borrowers report being less nervous or stressed.

In sum, we find no changes in revenues or profits in follow-up data collected about 10 months after loan disbursal but an increase in defaults among the Flexible Contract group. This group also reports lower stress and higher client satisfaction. Using Causal Forests to test for heterogenous treatment effects (Athey, Tibshirani, and Wager 2019; Chernozhukov et al. 2020), we do not find evidence that effects vary systematically as a function of important client or business characteristics pre-loan disbursement, such as gender, sales or household expenses.

# **5.** **<u>Conclusion</u>**

We study a flexible lending contract for first-time microcredit borrowers. We find that while flexibility was used by clients, there are no differences in the characteristics or take-up rates between flexible loan
# _The spatial dimension_

The question is then whether these convergence clubs translate into geographical “regions” as well. Quah identified a similar “twin peaks” phenomenon in the kernel derived from a international cross section of per capita incomes and found it to be geographically driven- regional clusters of poor countries were getting poorer, the agglomerations of rich countries were getting richer. To use the kernel density plots to see if the same geographical patterns are emerging in Mexico, we regenerate the kernels replacing _t_ +5 with the income of the state relative to the average income of its contiguous neighbors (“neighbor-relative”) in _t_ If the local and economy wide distributions of income are similar, that is, there are no clusters of states with similar incomes, we would find a concentration of probabilities along the main diagonal. If, on the other hand, poor states are found with poor states and rich with rich, we should expect a rotation toward a vertical line at unity- a country-relative poor state will have the same income as its neighborhood.

Figure 4a plots the spatially conditioned kernel density plots. Several points merit note. First, geography does not determine destiny in Mexico to the degree that it appears to globally. Had the income clusters identified previously been totally determined by space, the three observed peaks would have been aligned along the vertical line at unity on the Y axis. However, the post 85 figure is fairly similar to the multiple peaked unconditional kernel of figure 3b and there is a large group of states (for example Zacatecas, Tlaxacala, Michoacan, Nuevo Leon) whose mass lies largely on the 45 degree line. In the post -85 period the intermediate peak mainly consists of the northern states of Baja California Norte, Baja California Sur, Coahila and some successful central states such as Queretaro, Jalisco and, Aguascalientes (recently arrived) who show negative spatial correlation with neighbors. Quintana Roo and Mexico similarly constitute unusually high income areas, the former joining Mexico and Nuevo Leon as especially rich states in the
system. Following on from the horizontal equity analysis that does find a role for block grants in decreasing inequality, however, there is at least a suggestion of causality.

# **2. Conclusions**

We explore the institutional contradiction of implementing aid-financed programs to combat inequality in federal countries by analyzing results from the largest donor-financed program in the world: Ethiopia’s Promotion of Basic Services Program. Aid relies on the principle of local officials’ upward accountability to higher levels of government, and eventually to donors. But federalism shifts the accountability of subnational officials downwards to regional and local voters. What happens when aid agencies fund anti-inequality programs in federal countries? Does federalism undermine aid? Does aid undermine federalism? Or can the political and fiscal relations that define a federal system resolve this contradiction internally?

We present evidence showing that Ethiopia’s woreda block grants system promotes geographic and vertical equity in health and education outcomes. Block grants are positively associated with the poverty level: more resources are transferred to woredas with higher poverty rates. Poverty is negatively associated with basic service sector outcomes: a higher poverty rate implies a lower level of outcomes. And spending at the woreda level increases outcomes for most basic service sector indicators. These results imply that Ethiopia’s block grant system is helping narrow the gap between lagging and better-off woredas, and also between poorer and wealthier households. Ethiopia’s health and education indicators have shown impressive improvements across the board since the 1990s. But poorer woredas and poorer households have improved faster. These results dominate other cultural and socioeconomic factors that push in the opposite direction.

Our results should be read with two important caveats. First, our evidence concerns service delivery outcomes, not ultimate outcomes, such as increases in students’ knowledge or improvements in citizens’ health, which cannot necessarily be assumed to follow. Second, decreasing inequality should not obscure the fact that the quality of public services still varies greatly across woredas. The administrative data we use do not permit the measurement of service quality.
allocated to the treatment group in the first or second of two sequential list experiments. This approach means that estimates across the two list experiments can be directly compared, serving as an internal robustness check to demonstrate the accuracy of the results. We implement this double list experiment as part of one of the largest World Bank Enterprise Surveys (WBES) to date, covering a nationally representative sample of 2,955 registered firms with at least five employees. WBES are among the most comprehensive and reliable surveys about the activities of small, medium, and large firms, containing detailed information about firm characteristics and beliefs, which allows us to examine the heterogeneity of tax evasion extensively.

The results show that around one-quarter of firms report evading taxes and evasion rates vary substantially on only a relatively small number of dimensions. The ”double” list experiment produced internally consistent estimates of around 26 percent (varying from 25 to 27 percent) of firms admitting to not paying all the taxes they are required to pay (which is an extensive margin estimate of tax evasion). This result is almost identical across both experiments, which provides considerable reassurance of its accuracy, and it represents a lower bound estimate as it is based on self-reported tax evasion, which is likely to be lower than actual tax evasion. A back-of-the-envelope calculation based on this result suggests that revenue lost from tax evasion by registered firms is around two percent of GDP. We use machine learning to identify, among an extensive set of firm characteristics, the dimensions in which substantial and consistent variation in tax evasion exists. This revealed that firms that do not export, face substantial competition from the informal sector, and believe tax administration is a major obstacle to their business activities are the most likely to evade taxes. There was limited variation across both list experiments in reported tax evasion based on other dimensions, highlighting just how widespread this behavior is.

These results provide important insights into how common tax evasion is among firms and the specific firm characteristics and beliefs most associated with evading tax.
- - <u>Table 3: R2 decomposition of LASSO Selected Features (% of the overall r squared)</u>

||(1)<br>Growth rate (actual)|(2)<br>Growth rate<br>(deviation from<br>predicted)|(3)<br>Stringency Index|
|---|---|---|---|
|**Price factors**|20.08|23.83|22.71|
|**Google mobility factors**|52.49|63.37|55.65|
|**Satellite-derived data**|2.84||3.86|
|**Search terms factors**|18.85|10.1|13.77|
|**Macro variables**|5.74|2.7|4.01|

NOTES: The table shows the contribution of each group of real-time indicators to the overall goodness of fit of each regression using Shorrocks–Shapley decomposition of r-squared.

35
A more credible approach to parallel trends. _Review of Economic Studies_ **90** , 2555–2591 (2023).

- [99] Abadie, A., Athey, S., Imbens, G. W. & Wooldridge, J. M. When should you adjust standard errors for clustering? _The Quarterly Journal of Economics_ **138** , 1–35 (2023).

- [100] Mu ̃noz Sabater, J. ERA5-Land monthly averaged data from 1981 to present. Accessed: October 20, 2023 (2019). URL https://doi.org/10.24381/

22
– – September) and 633 kWh per month more than double in the winter (November to February). On average, households have 8 electricity-using durables<sup>40</sup> and many households (39 percent) report heating with electricity.<sup>41</sup> A small proportion have an electric hot water heater (14 percent), and almost none (2 percent) have air conditioners.

Households largely indicate that they both frequently worry about saving electricity (95 percent) and take measures to save electricity (86 percent). More than half the households report knowing about energy efficient lightbulbs (56 percent). However, CFLs use was low. Only a few households had them, and in small numbers (resulting in 0.17 CFLs per household, on average). The majority of households did not know or believe that CFLs consume less electricity (70 percent), did not expect savings in their electricity bill from replace incandescent bulbs with CFLs (72 percent), and did not believe the electricity savings would pay back the upfront costs of the CFLs (69 percent).<sup>42</sup>

# **4.3 Randomization balance**

Baseline characteristics were compared both at the transformer and household levels to understand the outcome of randomization. Comparisons use both household survey data and data on transformer characteristics, provided by the electricity utility.

Transformer-level balance test results are shown in Appendix Table 1. There are no statistically significant differences between the transformers treated with a low saturation and the control transformers, nor are there any statistically significant differences between the high saturation transformers and the control transformers. The high and low saturation transformers do have one significant difference from each other. They differ in the number of households within the transformers. Following Bruhn and McKenzie (2008), we chose not to re-randomize and instead control for this characteristic in related regressions and perform additional robustness checks.

Results from the household-level balance tests are shown in Appendix Table 2. The
NO2 (LHS) Stringency index (RHS) <!-- Start of picture text --> China Italy<br>60 Yearly NO2 percent change Stringency index 100 60 Yearly NO2 percent change Stringency index 100<br>Wuhan lockdown Northern Italy<br>lockdown<br>40 80 40 80<br>20 20<br>60 60<br>0 0<br>40 40<br>−20 −20<br>−40 20 −40 20<br>−60 0 −60 0<br>Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr<br>2019 2020 2021 2019 2020 2021<br>United States of America France<br>60 Yearly NO2 percent change Stringency index 100 60 Yearly NO2 percent change Stringency index 100<br>40 80 40 80<br>20 20<br>60 60<br>0 0<br>40 40<br>−20 −20<br>−40 20 −40 20<br>−60 0 −60 0<br>Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr<br>2019 2020 2021 2019 2020 2021<br><!-- End of picture text --> Figure S5: COVID-19 Lockdown Stringency Index and NO2, by-Country. _Notes._ NO2: percentage change in the 91 day rolling. Stringency Index: World Bank, Our World in Data.

_N_ = 3 _,_ 073 and _R_<sup>2</sup> = 0 _._ 01. The constant is negative both due to overall downward trend in NO2, and overall nationwide decline in NO2 in 2020 due to the pandemic. The effect of the Democratic margin is negative, consistent with the collective inaction thesis. While this result is interesting, there are important robustness checks to this incomplete claim. For instance, we make
The data on SP spending in response to the pandemic form a single cross-sectional data set, as distinct from the longitudinal (panel) data we assembled for looking at SP spending prior to the pandemic. So, we cannot include country fixed effects. However, as a check for bias associated with latent country characteristics, we include the estimated country effect from the pre-pandemic panel data regressions as an extra control in the encompassing model.<sup>34</sup> Notice that, on _a priori_ grounds, the sign on this latent effect could go either way. On the one hand, countries with greater prior experience with SP interventions may have seen lower costs of implementing an extra SP effort during the pandemic. But, on the other hand, they would presumably have less need for extra SP effort during the pandemic.

Similarly to our analysis of the pre-pandemic data, we build up our tests using regression controls to augment Figure 6. <u>Table 5</u> provides the results in testing the hypotheses one-at-atime. Relative prices for human development services do not shift the SPEC much. We find mixed support for the Accountability Hypothesis. In the linear regression, the VA and Polyarchy indices have positive coefficients, significant at the 5% level, but these effects are not robust to allowing for nonlinearity using the PLREG estimator. The GDP effect remains strong, however, so we cannot conclude that the Accountability Hypothesis explains that effect.

There is stronger support for the Governance 2 Hypothesis in the SP response to the pandemic. Strikingly, we find that GE knocks out GDP per capita, leaving it with a small effect not significantly different from zero. The GE indicator has a positive and significant effect in both the linear and non-linear SPECs.

We also find support for the Technology Hypothesis, with mobile phone usage having a significant effect at the 5% level; the GDP effect is attenuated somewhat when we control for mobile phones but remains strong. In contrast to the pre-pandemic SP spending data, we find no
transfer, is positive, significant, and strikingly high when self-reported information about demobilization status is cross-checked against official demobilization registers (columns [1] to [4]). It suggests that the consumption of demobilized FNL households is between 77% and 144% higher<sup>23</sup> than the consumption levels of FNL _returnee_ households that did not benefit from reinsertion allowances but had the same consumption level in 2006 as the demobilized households. This effect is also positive when using the definition of demobilization status based on self-reported information (columns [5] and [6]), but it loses its significance. This reduced precision can be explained by the presence of noise due to misreporting (Hausman, 2001).

The long-run evolution of total consumption per adult equivalent does not seem to differ between civilian households and CNDD+ _returnee_ households that did not benefit from the DDR. In contrast, the long-run evolution of total consumption turns out to be negative for households that benefited from the first wave of the DDR program from 2004 (hypothesis 2). The coefficient of the variable _D_<sup>_CNDD_</sup> _i_<sup>+</sup> is significant and large but negative. These estimates suggest that the consumption of CNDD+ households that benefited from the first wave of the program is between 40% and 50% lower in 2010 than the total consumption of nonbeneficiary CNDD+ households that had the same consumption level in 2006 as beneficiary households. This observation holds for all three definitions. The coefficients associated with the variables _Di_<sup>_FNL_</sup> and _D_<sup>_CNDD_</sup> _i_<sup>+</sup> are of similar size, but of opposite sign (the F-test _p_ -values reported at the bottom of table 3 are above 0.48 for all specifications).

Table 3 also highlights the presence of spillovers (hypothesis 3). The coefficient associated with the proportion of ex-FNLs in collines _S i_<sup>
.71|
|Mean population|5.26|8.35|5.68|9.34|6.06|10.10|6.22|11.04|
|Percent national park|0.00|29.06|-|-|-|-|-|-|
|Percent wildlife preserve|29.38|0.00|-|-|-|-|-|-|
|Mean distance to road (km)|0.29|0.11|-|-|-|-|-|-|
|Observations|1,511|413|1,511|413|1,511|413|1,511|413|

Source: authors’ calculations based on a random sample of pixels in the Cox’s Bazar district. For each pixel, we determine land-use / land cover (LULC) classified maps using Landsat remote sensing imagery for the district in the years 2010, 2014, 2017, and 2020. We determine the population using Worldpop unconstrained individual country and UN adjusted data for 2010-2020. The night lights score for each pixel comes from the VIIRS Night Lights Index data for 2014-2020. Static statistics only appear in the first and second columns. The classification of pixels into national parks and wildlife preserves comes from Protected Planet’s World Database of Protected Areas map. We derive the pixel’s geodesic distance to the nearest main road using the Google Maps API. The distance buffer, which we use to assign pixels to treatment and control groups, is determined by the pixel’s minimum geodesic distance to the nearest camp border. Treated pixels are 0-5 km from the nearest camp, and control pixels are 10-15 km from the nearest camp 17
# **1. Introduction**

Recent studies show that SME development is closely linked with growth. For example, Beck _et al._ (2005a) find a robust, positive relationship between the relative size of the SME sector and economic growth, even when controlling for other growth determinants. According to Ayyagari _et al._ (2007), in high-income countries formal SMEs contribute to 50 percent of GDP on average. Furthermore, in many economies the majority of jobs are provided by SMEs. In OECD countries, for example, SMEs with less than 250 employees employ two-thirds of the formal work force (Beck _et al._ , 2008b; Dietrich, 2010). Using country-level data, Ayyagari _et al._ estimate that, on average, SMEs account for close to 60 percent of employment in the manufacturing sector. According to SME Performance Review (EC, 2009), between 2002 and 2008, the number of jobs in SMEs increased at an average annual rate of 1.9 percent while the number of jobs in large enterprises increased by only 0.8 percent. In absolute numbers, 9.4 million jobs were created in the SME sector in EU-27 between 2002 and 2008. Also, it is often argued that SMEs are more innovative than larger firms. In developed countries, SMEs commonly follow “niche strategies,” using high product quality, flexibility, and responsiveness to customer needs as a means of competing with large-scale mass producers (see, for example, Hallberg, 2000, and Snodgrass and Biggs, 1996).

As the world economies are recovering from the financial crisis of 2008-9, many economies urgently need to create employment opportunities for their citizens. In this respect, creation and growth of SMEs is an important item on the policy agenda due to evidence that points to significant contributions by SMEs to employment. In addition, regulatory measures are necessary to ease access to formal financial services by SMEs. Historically, SMEs have been more likely than larger firms to be denied new loans during