rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10923

# **Abstract**

Using global data from the World Bank’s Enterprise Surveys that includes the precise geo-location of surveyed firms, this paper examines how dry spells and precipitation shocks influence firm performance. The study finds that firms in areas that experience dry spells have lower performance in terms of sales. This is particularly true for smaller firms and those in developing economies. A higher number of extreme dry days also increases the chances that a firm will exit the market. The main channels are largely through labor productivity and infrastructure service disruptions such as water and power outages. There is also some evidence of limited access to finance due to negative precipitation shocks.

Governance may be an exacerbating factor, with negative precipitation shocks increasing exposure to corruption. Yet, there is also some indication that digitally connected and innovative firms are more resilient to negative precipitation shocks. Process innovation, website ownership, and use of technology licensed from foreign firms mediate the effects of negative precipitation shocks on firm performance. However, there is little evidence of adaptation. Negative precipitation shocks have no effect on the presence of green management practices or green investments for a subset of firms for which such data is available.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region and the Office of the Chief Economist, Planet Vice Presidency. 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 aislam@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
<!-- Start of picture text --> 2,500 100<br>Ome 80<br>2,000<br>rm 60 4<br>Cc Cc<br>mB2 «1900 | 40 52<br>vu vu<br>Ww Ww<br>=) =)<br>500<br>, —20<br>0 —40<br>2001 2002 2003 2004 2005 2006 2007 2008 2009<br>ma monthly increase of foreign exchange reserve (right axis)<br>—— heading foreign exchange reserve<br><!-- End of picture text -->
**Table 12: Barriers and policy responses to address these barriers**

|**Barrier**|**Options for policy response**|
|---|---|
|**Economic and market barriers**||
|-<br>Not cost-competitive under current market conditions||
|High cost for non-grain sugar and starch fuel-based<br>ethanol and oil-bearing trees-based biodiesel (Wang,<br>Zhao, & Zhang, et al., 2010)<br>`·`<br>Inefficient subsidies policies for energy crops<br>planting (Wang, Zhao, & Zhang, et al., 2010);<br>`·`<br>Lack of support policies for non-grain-fuel ethanol<br>conversion;|`· `Increase<br>subsidies<br>to<br>feedstock<br>production,<br>especially that of oil-bearing trees(Wang, Zhao, &<br>Zhang, et al., 2010);<br>`· `Financial support to build new demonstration<br>projects of non-grain fuel ethanol(Wang, Zhao, &<br>Zhang, et al., 2010);<br>`· `Tax exemption for fuel ethanol derived from<br>non-grain feedstocks(Wu, et al., 2010);<br>`· `Increase subsidy or establish special subsidy fund<br>for non-grain fuel ethanol development (Wu, et al.,<br>2010; RGCMLEDS,2011);<br>`· `Encourage technology penetration of energy crops,<br>and enhance the land productivity through scientific<br>research(EBSDEC,2009);<br>`· `Encourage international cooperation to introduce<br>good<br>species<br>of<br>energy<br>and<br>support<br>their<br>demonstration projects (EBSDEC,2009);|
|High feedstock cost for waste oil biodiesel<br>`· `Lack of recycling mechanism of waste oil|`· `Build recycling mechanism of waste oil for biodiesel<br>production(Ji & Wang, 2007; Chang, et al.2012)|
|High capital costs and production costs for 2<sup>nd</sup>generation<br>biofuels<br>`· `Lack of transparent support policies on 2<sup>nd</sup><br>generation biofuels(RGCREDS, 2008)|`· `Financial support demonstration projects of 2<sup>nd</sup><br>generation biofuels (Wang, Zhao, & Zhang, et al.,<br>2010; Qiu & Wang, 2010; Qiu & Huang.2010;<br>Chang et al. 2012);<br>`· `Provide higher subsidies to 2<sup>nd</sup>generation biofuels<br>than that for existing grain-ethanol(Song, 2010);|
|-<br>Limited access to finance||
|Limited access to finance on non-grain sugar and starch<br>fuel ethanol|`· `Establish stable and long-term policy framework and<br>targets to build trust in future markets (Chang,|
|Limited access to finance on oil-bearing trees biodiesel|Zhang, Chai, 2008);|
|Limited access to finance on 2<sup>nd</sup>generation biofuels<br>(Chang, Zhang, Chai, 2008; Song,2010)|`· `Set up venture capital fund for non-grain fuel<br>ethanol (Wu, et al., 2009);<br>`· `Set up industry development fund for 2<sup>nd</sup>generation<br>biofuels (Song,2010)|
|**Administrative and legal barriers**||
|Lack of strategy guideline and technology roadmap on<br>biofuels development (Wang, Zhao, & Zhang, et al.,<br>2010)|`· `Specific planning on biofuels (RGCREDS, 2008;<br>Wang, Zhao, & Zhang, et al., 2010; Wu et al. 2010,<br>RGCMLEDS,2011)|
|Lack of policies on access of newly added biofuels<br>(Wang, Zhao, & Zhang, et al., 2010)|`· `Clarify approvement indicators or guidelines for<br>non-grain fuel ethanol projects;|
|`· `Lack of transparent approvement procedure for|`· `Clarify policies on access to the distribution system|

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size of the economy can not explain the quantity of merger cases handled by a competition agency<sup>8</sup> .

Some competition laws set minimum annual revenue requirements of the merging parties for the competition agency to consider a merger, while others set requirements that mandate competition agencies to analyze almost all mergers in the economy, independently of their size. Given the lack of correlation between the level of economic activity and the number of mergers analyzed by competition agencies, we can safely argue that the workload of a competition agency is more influenced by the legal requirements embedded in competition laws than by the size of the economy<sup>9</sup> .

<!-- Start of picture text --> Figure 3<br>GNP and Mergers<br>800<br>700<br>600<br>500<br>400 R 2 = 0.108<br>300<br>200<br>100<br>0<br>0.00E+00 2.00E+11 4.00E+11 6.00E+11 8.00E+11 1.00E+12 1.20E+12 1.40E+12 1.60E+12 1.80E+12<br>GNP (1995 U$S dollars)<br>Mergers (last 12 months)<br><!-- End of picture text -->

# _c. Anticompetitive conduct cases filed in the previous 12 months_

> 8 The R2 in the graph is 0,10. This value is upward biased because we excluded an average sized country that handled more than 1,700 mergers. Including this country the R<sup>2</sup> would be 0,06.

> 9 A reinforcing factor of this result is the fact that no response is in the range “11-20 mergers” which would be the case if the size of the economy had a high explanatory power in the quantity of cases handled by a competition agency.

7
# **1 Introduction**

The corporate income tax is a key source of government revenue, particularly for developing countries where it constitutes 15-20% of tax revenue. Yet, a common perception is that many firms, and especially large corporations, increasingly pay lower taxes on their profits. This view is fueled both by a race to the bottom in statutory tax rates and by increasing competition between governments to offer tax incentives to attract investment, such as tax credits, income exemptions and reduced rates. In this paper, we document how the take-up of tax incentives lowers effective tax rates and To describe effective tax burdens, we leverage a unique dataset comprising all firm-level corporate income tax returns in 13 developing countries spanning a wide range of income levels. On average, we find that countries spend a substantial amount — 0.9% of GDP — on corporate tax incentives, consistent with the common labeling of these incentives as ‘tax expenditures’. The benefits of tax expenditures accrue disproportionately to both small firms and very large firms (top 1% in sales). In the median country in our sample, 30% of the largest 1% of firms face effective tax rates (ETRs) below 15%, the minimum tax rate agreed upon by the OECD/G20 Inclusive framework’s Pillar II. Given the incentives that a global minimum tax could create, we estimate that corporate tax revenue could increase by up to a quarter in the median sample country.

While tax evasion and profit shifting have been extensively studied before (e.g. Carrillo et al. 2017; Tørsløv et al. 2022), tax expenditures have received less attention. This is in part due to limited access to high-quality microdata. Our first contribution is to develop a consistent measure of effective tax rates across multiple countries, relying on comparable data sources and definitions. The data cover 13 countries with varied income levels and population sizes in Africa (Ethiopia, Eswatini, Rwanda, Senegal, and Uganda), Latin America (Costa Rica, Dominican Republic, Ecuador, Guatemala, Honduras, and Mexico), and Eastern Europe (Albania and Montenegro). By using administrative data, we include all tax-registered firms, a much larger group than those covered by survey or financial data in developing countries.<sup>1</sup> Although tax reporting requirements are not homogeneous across countries, we harmonize the relevant variables used to define

> 1Tax return data miss the informal sector, but they still have much wider coverage than financial data which in most developing countries cover only the largest firms. Survey data often have poor coverage among the largest firms. Firm censuses, where they exist, cover all firms but rarely contain data on tax liabilities and profits.

1
4 wage effecti on a women's marketed supply of labor, many factors must be evaluated together, as specified by a family model of decisionmaking Moreover, the distinctive role of women in managing<sup>investments in</sup> children may be a strong reason for incrtasing women's capabilities **and** their control over !amily resources. Improving **the** productivity of women is a social objective to advance economic development, and **also** possibly to channel that development toward socially desired<sup>investments</sup> in the food, health care, **and** schooling of children. Raising **women's** productivity often also leads to reduced child mortality and, at approximately the **same** time, to a reduction in fertility. If the latter more than offsets the former,<sup>the</sup> rate of population growth may be reduced and the proportion of the population in the labor force may be slowly increased, **as** the demographic transition progresses. Quantitative confirmation of these apparent linkages, however, requires such study and replication to build sound generalizations from oepirical regularities and to identify the special features in society that affect these behavioral tendencies. If these empiricai studies are<sup>guided bya</sup> coherent modeling framework, the accumulation of useful knowledge can be expected to occur more rapidly.

# Concepts of the Family Guiding Empirical **Analysis**

A conceptual and statistical framework is essential to draw inferences from notiaxperimental data about how family production, consumption, and behavior respond to changes. This applies to experimental pilot programs as well. It is hard to forecast how social institutions, such **as** families, firms, and government social programs will evolve over time. The first step is to work out the rules for decision-making in the family that account for observed producer and consumer behavior. This requires<sup>analysis of how individual,</sup>
# **Appendix 5: Results**

|**Table 5.1. Convergence Estimates f**|**or Education and**|**Fertility**|Education|||Fertility||
|---|---|---|---|---|---|---|---|
|||1.1|1.2|1.3|2.1|2.2|2.3|
||Baseline|Closure|SSA|OECD|net-repr.rate|SSA|OECD|
|**1. GDP**||||||||
|1.1. GDP (in USD)|$4,049,744,636|$4,388,335,127|$4,253,190,626|$4,400,110,268|$4,175,874,219|$4,072,908,921|<br>$4,199,841,582|
|1.1.1. Growth (in %)||8.36%|5.02%|8.65%|3.11%|0.57%|3.71%|
|1.2. GDP per capita (in USD)|$800.71|$867.66|$840.94|$869.99|$825.65|$805.29|$830.39|
|1.2.1. Growth (in %)||8.36%|5.02%|8.65%|3.11%|0.57%|3.71%|
|1.3. GDP per worker (in USD)|$2,125.44|$2,303.14|$2,232.21|$2,309.32|$2,159.22|$2,125.66|$2,169.19|
|1.3.1. Growth (in %)||8.36%|5.02%|8.65%|1.59%|0.01%|2.06%|
|**2. Women's Contribution**||||||||
|2.1. in USD, bil.|$1,084,390,549|$1,253,722,388|$1,184,810,896|$1,259,793,203|$1,150,278,839|$1,102,102,586|<br>$1,159,794,558|
|2.2. of GDP (in %)|26.78%|28.57%|27.86%|28.63%|27.55%|27.06%|27.62%|
|2.2.1. Growth (in p.p. %)||1.79%|1.08%|1.85%|0.77%|0.28%|0.84%|
|2.3. of Labor Output (in %)|39.04%|40.23%|39.74%|40.27%|39.61%|39.35%|39.61%|
|2.3.1. Growth (in p.p. %)||1.19%|0.70%|1.23%|0.57%|0.31%|0.57%|
|**3. Sectoral Value Added**||||||||
|3.1. traditional, female (in USD)|$647,951,018|$544,919,521|$587,085,006|$541,193,305|$634,051,255|$650,632,344|$628,268,957|
|3.1.1. Growth (in %)||-15.90%|-9.39%|-16.48%|-2.15%|0.41%|-3.04%|
|3.2. modern, female (in USD)<br>3.2.1. Growth (in %)|$436,439,531|$708,802,867<br>62.41%|$597,725,890<br>36.96%|$718,599,899<br>64.65%|$516,227,584<br>18.28%|$451,470,242<br>3.44%|$531,525,601<br>21.79%|
|3.3 traditional, male (in USD)|$735,858,976|$600,342,350|$653,652,082|$595,736,673|$711,563,750|$735,955,225|$704,207,490|
|3.3.1. Growth (in %)||-18.42%|-11.17%|-19.04%|-3.30%|0.01%|-4.30%|
|3.4. modern, male (in USD)|$957,620,009|$1,262,395,286|$1,142,852,546|$1,272,705,289|$1,042,156,527|$962,976,008|$1,063,964,432|
|3.4.1. Growth(in %)<br>_Source:_Authors’ simulation based on ex|panded version of|31.83%<br>CKW 2017 model.|19.34%|32.90%|8.83%|0.56%|11.11%|

37
(2009, ch. 4) for discussion). Given that the corresponding OLS estimate in Column 4 is significantly smaller than than the IV estimates in Columns 5-7, this suggests that the IV estimates are likely to be underestimates of the true relationships.

We now turn to the results for input prices in Table 6. The OLS estimates for the relationship between destination income and input prices are positive and significant, although these should be interpreted with caution because of the omitted-variables concerns discussed above. The key point of the table — indeed, of this paper — is that the IV estimates are positive and highly significant in Columns 5-7. As above, weak instruments are a concern. In Column 5, the Anderson-Rubin F-test indicates that we can reject the null that the coefficient on the destination-income term is zero with a high level of confidence. Regarding Columns 6 and 7, we again note that the IV estimates for the destination-income term are robust across specifications and that, if anything, since we expect IV to be biased towards OLS in the weak-instruments case, the reported coefficients are likely to be underestimates. As an additional check, we also follow a suggestion of Angrist and Pischke (2009, Section 4.6.4) and report limited-information maximum likelihood (LIML) estimates of the same model, since LIML estimates tend to be more robust to weakness of the instruments than IV. These appear in Table A5 and correspond to Columns 5-7 of Table 6. Reassuringly, the results are similar to those for IV, and are a bit larger, consistent with the idea that the IV estimates are more biased towards OLS than the LIML estimates. In unreported results, we have found that the basic patterns in Tables 5 and 6 survive a number of robustness checks: (1) using 50, 75 or 125 instruments instead of 100; (2) not winsorizing prices, or winsorizing by year; (3) including non-manufacturing outputs or inputs in the calculation of firm-level average prices; and (4) including Euro-zone destinations in the instrument set.

Considering Tables 5 and 6 together, a number of points are worth noting. First, and most obviously, the positive significant IV coefficients for both output and input prices are consistent with the hypothesis that exogenous shifts in exporting toward richer destinations are associated with an increase in average output and input quality within firms. Second, the magnitudes appear to be economically significant: our baseline estimates in Column 5 of each table suggest that a 10% increase in average destination income leads to a 20% increase in average output prices and a 7% increase in average input prices. Third, the fact that the estimates are larger for output prices than for input prices suggests that both mark-ups and product quality increase when firms sell to richer destinations. That is, the results provide evidence for both the pricing-to-market and the income-based quality-choice stories discussed in the introduction. Our key contention 15
Romanian application clearly identified a set of respondents who are reticent in answering survey questions.<sup>2</sup> AM define a reticent respondent as one who gives knowingly false answers with a nonzero probability when honest answers to a specific set of survey questions could lead to the inference that the respondent might have committed a sensitive act. Because their methodology cannot identify all reticent respondents, AM are only able to split their sample into 'reticent' and 'possibly candid' groups, the latter a mixture of candid and reticent respondents.

The AM methodology uses random response questions, which were developed in other contexts as a tool for encouraging more candid responses to sensitive questions, but which have been less than fully successful. In a random response question, a sensitive question is posed—for example, 'have you ever given a bribe?' But the respondent is instructed to privately toss a coin before answering and always answer 'yes' if the coin came up heads, while answering the sensitive question truthfully if the coin came up tails. The conventional rationale for random response questions is that they should encourage respondent candor because nobody but the respondent knows in any specific instance whether the answer reflects the sensitive activity or the coin toss. However, given a fair coin and a large sample, and assuming that respondents are candid and follow the protocol of the random response question, it is trivial to estimate the population percentage committing the sensitive act as [2*(percentage answering 'yes' – 50)]%.

Note that if everybody is candid, 50% is a lower bound for those answering 'yes'. But many applications of random response, including most reported below, result in fewer than 50%, which means that there is an implausible number of answers implying that a tails was obtained in the coin-toss. The AM procedure leverages this observation by asking a series of random response questions.<sup>3</sup> If a single respondent answers 'no' repeatedly, then the respondent must be reticent with a very high probability. In an application to Romanian businesses, AM showed that respondents identified as reticent typically had different response patterns to sensitive questions posed elsewhere in the

> 2 Azfar, Omar and Peter Murrell (2009): Identifying Reticent Respondents: Assessing the Quality of Survey Data on Corruption and Values, _Economic Development and Cultural Change_ , University of Chicago Press, Vol. 57(2), 387-411.

> 3 See section 2 for further details of the Nigerian implementation.

3
# **2. The Model and Data**

# **2.1. A Brief Description of Model Structure**

For our simulations, we utilize a dynamic global CGE model developed at the World Bank. The basic information for the calibration of this model is derived from the GTAP 7.0 database with base year 2004. All results presented are derived from a version of the model with 28 sectors and 25 countries/regions.

The 28 sectors are depicted by a set of nested constant elasticity of substitution (CES) production functions. At the top level of the production structure, that is, in the final goods sectors, firms minimize costs, i.e., maximize profits, by choosing an optimal combination of _non energy intermediate input_ (ND) and _value added + energy input_ (VAE). The inputs are assumed to be perfect complements so that the elasticity of substitution between the two factors equals 0 as dictated by a Leontief function. Based on the demand for ND and VAE, the firms on the second level of the production tree again minimize costs by choosing combinations of inputs that are supplied by firms on the next lower production level, and so forth. In total, the economy is represented by 7 production tiers (see Figures 1a-1c).

The government derives revenues from a number of indirect taxes, import duties and a direct tax on households. Government expenditures are an exogenously determined share of nominal GDP. Government revenues equal the sum of government expenditures and government savings; the direct tax on households is adjusted each period to ensure a balanced public budget.

Households maximize utility, which is represented by a non-homothetic Constant Difference of Elasticities (CDE) function<sup>4</sup> , subject to the budget constraint. The households’ disposable income consists of the factor incomes (net of taxes) minus the direct tax. A household savings rate determines the fraction of disposable income that is saved and thus

> 4 See Surry (1993) for details on this functional specification .

4
**<u>Table B4. SCM Detailed Results for All IHC Firms – Import Value</u>**

|IHC firm #|6-month average<br>treatment effect:<br>change in log<br>import value<br>(�_������<br>�<br>�<sup>)</sup>|Number of donor<br>firms chosen by<br>synthetic control<br>procedure|Rank of IHC firm<br>relative to 25 placebo<br>firms|Percentile rank<br>statistic<br>(rank/26)|
|---|---|---|---|---|
|1|0.404|9|24|0.923|
|2|0.121|9|21|0.808|
|3|-0.002|7|14|0.538|
|4|0.048|9|15|0.577|
|5|0.236|7|22|0.846|
|6|0.104|8|17|0.654|
|7|0.387|68|21|0.808|
|8|-0.448|9|2|0.077|
|9|0.370|9|21|0.808|
|10|-0.197|12|5|0.192|
|11|0.323|8|20|0.769|
|12|-0.163|8|6|0.231|
|13|-0.652|8|1|0.038|
|14|-0.126|9|10|0.385|
|15|0.147|7|20|0.769|
|16|0.365|4|24|0.923|
|17|0.106|6|20|0.769|
|18|-0.224|2|9|0.346|
|19|-0.778|10|2|0.077|
|20|0.520|9|24|0.923|
|21|0.368|11|23|0.885|
|Mean % change in<br>import value|0.043||Mean percentile rank<br>statistic(�)|0.588|
|Median %<br>change in import<br>value|0.106||||

Notes: Synthetic control firms were constructed with the benchmark specification that includes three pre-treatment values of the outcome variable as part of the set of conditioning variables. The summary statistics at the bottom left of the table are obtained as the mean and the median of the treatment effect across the 21 firms. The statistic at the bottom right of the table �̅ given 21 firms has 5% critical values of 0.377 and 0.623 (based on Appendix table A1 in Dube and Zipperer (2015)).

51