rafmacalaba/datause-encoder-data huggingface dataset:
not identical across all regions and countries, but the World Bank’s Data for Goals group undertook considerable effort to develop a harmonized data set to facilitate cross-regional and cross-country comparisons.

In addition to harmonization, a further methodological challenge is the limited representativeness of the surveys overall and within countries, as they were conducted via phone and used different sampling methodologies. In particular, most countries in Sub-Saharan Africa used a sampling frame based on a previous survey and explicitly sought to interview household heads, while surveys in Latin America and the Caribbean used random digit dialing to collect data (Table 3). This complicates comparisons of individual-level characteristics such as employment across regions. Because of the challenges regarding non-representative sampling of individuals, we report results under two different weighting methods. We rely on the household weights in the HFPS data for our main results and complement them with another weighting method as a robustness check. As a robustness check, we add an additional individual-level adjustment to the weights based on the World Bank’s Global Monitoring Database (GMD).<sup>1</sup> Using this method, we assign an inverse probability weight to each individual observed in the data to make the distribution of individual characteristics (age, gender, education, and urban status) more aligned with the GMD.<sup>2</sup> In the robustness check, we examine how outcomes compare when we rely only on the household weights in the HFPS data and our second weighting method and generally find similar results.

We first report results on high-level measures of work stoppage based on questions that ask respondents whether they were working pre-pandemic and whether they were working at the time of the survey. We find that work stoppage was common. Taking a simple average across countries, 34% of respondents reported stopping work. The average across countries in our data is 21% in the EAP region, 29% in the ECA region, 48% in the LAC region, 45% in the MENA region, and 26% in the SSA region. The cross-country average is 19% for

> 1Part of the World Bank’s harmonized survey repository, the GMD is a collection of globally harmonized household surveys that allow for cross-country and over-time analyses of representative samples.

> 2A further complication is that we cannot observe each of these variables for all countries in the HFPS data. We therefore use the widest available set of characteristics for adjustment.

3
**Table C-2: Tests of between-Subject factors**

|Dependent Variable: Difference(year 5)||||||
|---|---|---|---|---|---|
|Source|Type I Sum of<br>Squares|df|Mean<br>Square|F|Sig.|
|Corrected Model|21220<sup>a</sup>|40|531|6.446|.023|
|Intercept|1337|1|1337|16.243|.010|
|Literacy rate|244|1|244|2.969|.145|
|Aid (capital formation)|13|1|13|.162|.704|
|Aid (percent of import and exports)|764|1|764|9.284|.029|
|Capital Stock loss (log) [logCapLoss]|1802|1|1802|21.888|.005|
|Aid (percent of GNI)|2230|1|2230|27.093|.003|
|Remittances [Remit]|1849|1|1849|22.467|.005|
|Capital Stock (log)|20|1|20|.238|.646|
|GDP (log)|80|1|80|.971|.370|
|Land Area (log)|0|1|0|.003|.956|
|I_debt * Remit|4108|2|2054|24.959|.003|
|I_Income * Remit|1|1|1|.008|.931|
|I_SIDS * Remit|97|1|97|1.174|.328|
|I_debt * I_Income * Remit|965|1|965|11.723|.019|
|I_debt * I_SIDS * Remit|653|1|653|7.932|.037|
|I_debt * I_Hazard * Remit|4155|8|519|6.310|.029|
|I_Income * I_SIDS * Remit|369|1|369|4.483|.088|
|I_Income * I_Hazard * Remit|106|1|106|1.291|.307|
|I_SIDS * I_Hazard * Remit|245|3|82|.991|.468|
|I_debt * logCapLoss|727|2|364|4.418|.079|
|I_Income * logCapLoss|698|1|698|8.475|.033|
|I_SIDS * logCapLoss|5|1|5|.063|.812|
|I_Hazard * logCapLoss|1805|4|451|5.482|.045|
|I_debt * I_Income * logCapLoss|82|1|82|.998|.364|
|I_debt * I_SIDS * logCapLoss|140|1|140|1.706|.248|
|I_debt * I_Hazard * logCapLoss|63|2|31|.381|.702|
|I_Income * I_SIDS * logCapLoss|0|0|.|.|.|
|I_Income * I_Hazard * logCapLoss|0|0|.|.|.|
|I_SIDS * I_Hazard * logCapLoss|0|0|.|.|.|
|Error|412|5|82|||
|Total|22969|46||||
|Corrected Total<br>R Squared = .981 (Adjusted R Squ<br>a.|21632<br>ared = .829)|45||||

38
in their new locations, facing living conditions less favorable than those of the urban poor (V ́elez, 2002; Ib ́a ̃nez and Moya, 2006). Ib ́a ̃nez and Moya (2006), more particularly, estimate that the consumption of displaced households falls by 35.7 percentage points upon migration and drops even further during the year following displacement.

# **III Data**

## **III.1 Firm data**

Our main source of information is the _Encuesta Anual Manufacturera_ (Annual Manufacturing Survey), collected by the _Departamento Nacional de Estad ́ıstica_ (DANE), the Colombian statistics agency. These data set is a census of all manufacturing plants with ten or more workers or with a total output value larger than 65 million in 1992 Colombian pesos (approximately USD$95,000). Once a plant is included in the survey, it is followed over time until it goes out of business. The survey includes information on all production-related variables, including employment and wages. In conjunction with the standard plant information, the census contains information on all physical quantities and prices (valued at factory-gate prices) of each output and input used or produced by each plant. In this article, firms’ prices are defined as the plant-product-year observation estimated by dividing the value of revenues or expenditures by physical quantities. These data is regarded as one of the best and most complete sources of information for studying firm behavior in developing countries (Kugler and Verhoogen, 2011). The data set covers the years 1995 to 2010, though its data distinguishing employment and wages by white- and blue-collar workers is only available after the year 1999.<sup>9</sup> Our sample consists of all municipalities where more than two formal firms with ten or more workers are observed.<sup>10</sup> These municipalities are typically more populated and have higher eco-

> 9The data also contains information on firms’ average output and input prices. We do not use this data, however, as it highly concentrated in the biggest cities for which geographic variation is considerably lost. 10Municipalities where only one firm was observed were dropped from the sample by DANE due to confidentiality concerns.

10
Dalgaard, C., Hansen, H. and Tarp, F. (2004), On the Empirics of Foreign Aid and Growth. _The Economic Journal_ , 114, F191-F216.

De Soto, Hernando (1985) _El Otro Sendero_ . (American edition: The Other Path. New York: Kindle Books, 2002.) Devarajan, S., S. Suthiwart-Narueput, and K. E. Thierfelder (2001), The Marginal Cost of Public Funds in Developing Countries. In _Policy Evaluation with Computable General Equilibrium Models._ London: Routledge.

Diewer, W. E. and D. A. Lawrence (1994), The Marginal Costs of Taxation in New Zealand. Report prepared for the New Zealand Business Roundtable by Swan Consultants Pty. Ltd., Canberra, Australia.

Dreher, Axel, Kotsigiannis, Christos and McCorriston, Steven (2005a), How do Institutions Affect Corruption and the Shadow Economy? Working Paper, University of Konstanz and University of Exeter.

Dreher, Axel, Kotsigiannis, Christos and McCorriston, Steven (2005b), Corruption Around the World: Evidence from a Structural Model. Working Paper, University of Konstanz and University of Exeter.

Dreher, Axel and Schneider, Friedrich (2006), Corruption and the Shadow Economy: An Emprical Analysis. Discussion Paper, University of Linz.

Durham, K. B. (2004), Absorptive Capacity and the Effects of Foreign Direct Investment and Equity Foreign Portfolio Investment on Economic Growth. _European Economic Review_ , 48, 285-306.

Easterly, William (1999), The Ghost of Financing Gap: Testing the Growth Model Used in the International Financial Institutions. _Journal of Development Economics_ , 60, 423-438.

Easterly, William (2003), Can Foreign Aid Buy Growth? _Journal of Economic Perspectives_ , 17, 2348.

Easterly, William, Levine, Ross and Roodman, David (2004), Aid, Policies and Growth 2000: Comment. _American Economic Review_ , 94, 774-780 The Economist (1997), The Dutch Disease. November 26, 1997, pp 82-83.

Farah, Abdikarim, Sacerdoti, Emilio, and Salinas, Gonzalo (2009), The Macroeconomic Impact of Scaled-Up Adi: The Case of Niger. IMF Working Paper on WP/09/36. Washington DC: International Monetary Fund.

Feldstein, Martin (1999), Tax Avoidance and the Deadweight Loss of the Income Tax. _Review of Economics and Statistics_ , 81, 674-680.

Findlay, C. C. and R. L. Jones (1982), The Marginal Cost of Austrlian Income Taxation. _The Economic Record_ , 58, 253-262.

Fortin, B. and G. Lacroix (1994), Labour Supply, Tax Evasion and the Marginal Cost of Public Funds: An Empirical Investigation. _Journal of Public Economics_ , 55, 407-431.

Gelb, Alan (1988), _Windfall Gains: Blessing or Curse?_ Oxford: Oxford University Press.

31
reform of electricity and water tariffs, are below 20%. This is the area of the graphs that is most of interest in Morocco today.

**Figure 3 – Sensitivity of changes in Poverty and Government Revenues to Changes in Prices**

<!-- Start of picture text --> .3 2.50e+09<br> Sugar Sugar<br> Flour Flour<br>2.00e+09<br>.2<br>1.50e+09<br>1.00e+09<br>.1<br>5.00e+08<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Increase in price in % The increase in prices (in %)<br>.2 8.00e+09<br>Gas LPG Gas LPG<br>Gasoline Gasoline<br>Diesel Diesel<br>.15 6.00e+09<br>.1 4.00e+09<br>.05 2.00e+09<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Increase in price in % The increase in prices (in %)<br>8.00e+09<br>2.5 Electricity Electricity<br>Water Water<br>2 6.00e+09<br>1.5<br>4.00e+09<br>1<br>2.00e+09<br>.5<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Increase in price in % The increase in prices (in %)<br><!-- End of picture text --> Source: Authors’ estimations using SUBSIM.

# **5.2.2. Indirect effects**

As a reminder, the scenario we are considering is the elimination of subsidies in October 2014. In October 2014, subsidies for gasoline had been already removed and this product will not be considered here. We also do not consider LPG and flour, assuming that these products do not have indirect effects. This is not entirely true because there may be some enterprises using some LPG bottles while some 19
discusses the data and methodology used in the paper and Section 3.0 presents the findings on the characteristics of households who migrate. Section 4.0 describes the costs of the migration process, sources of financing as well as the process of remittance transfer. Section 5.0 discusses the results from the multivariate analysis of the impact of migration. Conclusions and policy implications are made in Section 6.0.

# **2.0. Data**

Ideally we would have preferred to address our study objectives using a nationally representative sample of migrant and non-migrant households. However, because the most recent Bangladesh Household Income and Expenditure Survey conducted by the Bangladesh Bureau of Statistics contained no information on transactions involved in either securing overseas employment and or accessing remittance services, we could not use this survey for the purposes of our work. As a result, we designed and fielded a migration-focused survey in 2007. Given budgetary constraints and considering that one of our key objectives was to get detailed information on various components of transactions costs, we selected several communities in Bangladesh that had higher than average rates of migration and drew a sample of households from these. Thus, while the sampling framework used is not representative of the entire population of Bangladesh, it provides a fairly representative picture of the migration process and remittance-related transactions in areas where international migration is highly prevalent. The sampling method used is described below.

In the initial design phase of this survey, key informant interviews were administered to travel agents and private manpower exporting agencies in Dhaka in order to identify districts that had higher than average rates of migration. These interviews identified 32 such districts. In the next step, 10 districts were randomly chosen from this list and one _upazila_ (sub-district) was 4
share of sugarcane processed into ethanol declined (Figure 5). We do not analyze the impacts of U.S. ethanol policy on Brazilian ethanol and sugar markets, but with trade in ethanol, the importance and need for this paper for future research takes on added significance.

The rest of this paper is organized as follows. After a brief description of how our approach relates to the literature, we develop an analytical model in Section 3. Section 4 incorporates the shift in the demand curves for fuel and E100. Section 5 presents the comparative static results. Data and calibration used in our empirical model are described in Section 6. Empirical findings are presented in Section 7; the last section provides key concluding remarks.

**Figure 5: Fuel Prices in São Paulo**

<!-- Start of picture text --> 3.0<br>E100 Fuel Parity<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Source: ANP 2012<br>liter<br>$/<br>R<br><!-- End of picture text -->

# **2. Relation to the Literature**

There is a fledgling literature on the market effects of Brazilian ethanol policies (e.g., Schmitz et al., 2003; Elobeid and Togkoz, 2008; Balcombe and Rapsomanikis, 2008; Serra et al., 2008; Kliauga et al. 2010; Rajcaniova et al., 2011). Elobeid and Togkoz (2008) analyze the 7
**Table 3: Regression Results of Dynamic Inklaar Equation (22) without the Inequality Variable**

**Dependent Variable: D ln (PPP)**

|**Explanatory Variables**|**Estimated P**|**arameters**|
|---|---|---|
|**Trend**<br>|-0.000<br>(0.995)||
|**D ln (GDP Deflator)**|0.325<br>(0.278)|0.323<br>(0.153)|
|**D ln (GDPPC)**|0.555*<br>(0.074)|0.555**<br>(0.050)|
|**D ln (Exchange rate)**|0.749***<br>(0.003)|0.749***<br>(0.003)|
|**R**<sup>**2**</sup>|0.6846|0.6846|
|**No. of observations**|311|311|
|**No. of countries**|120|120|
|**F-statistic for testing**���1|1.25<br>(0.275)|1.26<br>(0.273)|

Note: (1) Figures in parentheses are the p-values. **: significant at 5% level; ***: significant at 1% level (2) The operator _D_ denotes difference over time.

39
sizeable. We find that a 1 pp increase in inflation reduces the probability that establishments without access to a formal bank loan innovate by 4.1%; having access to a formal bank loan reduces this probability by an additional 0.5 pp (to 4.6%).

A more direct test of our model predictions is possible by examining the importance of retained earnings for working capital finance (columns 3-6). We find that the adverse impact of inflation on innovation increases with establishments’ reliance on retained earnings to finance their working capital. For establishments that finance less than half of their working capital expenditure through retained earnings (column 5), a 1 pp increase in inflation reduces the probability to innovate by 2.4%. Recall, however, that the majority of firms do not have (sufficient) access to formal bank loans for financing their working capital expenditure and thus need to rely on internal funds or retained earnings. Among the establishments for whom retained earnings are the main source of working capital finance (78% of the sample, column 4), the adverse effect of inflation on innovation increases to 4.4%. And for the establishments that rely exclusively on retained earnings (47% of the sample, column 3), the innovation probability is reduced by 4.6%.

Table 4 provides the results of the generalized difference-in-difference estimation following the approach of Rajan and Zingales (1998). We test for the causal impact of inflation on innovation, exploiting variation in industry characteristics. The results confirm all four model predictions and address any remaining concerns about the endogeneity of country-level inflation for establishments’ innovation decision. In line with Proposition 1, we find that the impact of inflation on establishments’ innovation decision is larger in (i) more volatile sectors, (ii) sectors with higher value added growth, (iii) more R&D intensive sectors, and (iv) sectors with higher asset tangibility. The common ground here is that these sectors are more exposed to advanced but risky technologies so that the liquidity mechanism becomes particularly relevant.

# **4.4 Robustness**

While the WBES include detailed information on firms’ sources for financing their working capital expenditure, it does not provide direct balance sheet information on their short-term liquidity holdings. Balance sheet information on corporate holdings of cash and cash equivalents is, however, necessary to directly test the second key component of the model mechanism, 23
**-4-**

# AlI. Income **Tax**

11. The income tax gknerates about one-fourth of total tax revenues. This tax has a large number of exemptions. The collection effort for the income tax has been very ws k. The maximum marginal rate is 402 for incomes, exceeding t 10 ml.ion (t - lempiras, US$1 - t 2).

12. Conclusions. In the short-term, the collection effort haa to be intensified, but existing rates should not be increased to avoid a recessionary impact.

# A.2. Propertv Taxes

13. Collection from this tax has been limited, averaging only 0.81 of total Central Government current revenues in 1987. The tax suffers from outdated valuations, the absence of forceful enforcement and the lack of a cadaster.

14. Conclusions. In view of the lack of a cadaster and of up-to-date valuations, collections can not be expected to increase substantially in the short-term. However, for the longer-term, it is suggested that the Government prepare a cadaster and update valuations with a view to increasing collections.

# A.3. Sales Tax

15. The sales tax is levied on both domestic and 4.mported goods at a flat 51 rate--except for tobacco, which is taxed at 101. The tax is mostly evaded by store owners, who generally collect the tax from customers but do not pay the Treasury. A 5S tax rate should provide sufficient incentives to intensify collection efforts. However, consideration has to be given to increasing the rate to 71 or perhaps 10? to reduce projected fiscal deficits to sustainable levels in the next several years. Compared to other developing countries, the rate of the tax is low (Table 2). It also has a number of exemptions. Moreover, the Government may set the tax on the basis of the physical characteristics of the establishments that are indicative of their sales volume (e.g. size of store, number of employees). Collection for this tax has averaged less than 22 of GDP or about 121 of Central Government current revenues in the recent past. This compares unfavorably with other developing countries.

**Table** 2: **Rate** of Volue Added **_Taxes_**<sup>**_1_**</sup> **_/_** by Selected Countries<sup>2</sup> l. _1987_

|Count-leg<br>1||
|---|---|
|**Haiti**|_10.0_|
|**Grenada**|_20.0_|
|**Peru**|_18.0_|
|**Bolivia**|10.0|
|**Chile**|_ao.o_|
|**Brazil**|1.00|
|Indonote*|_10.0_|
|**Madagascar**|**16.0**|

Source: **IMF. T-ilue added tax** or **a** sales **tax.** _J_ Only thoso countries with **a single rate *re** included In **the table.**
_Why Infrastructure Financing Facilities Often Fall Short_<sup>_of Their Objectives_</sup> _35_ address and mitigate market failure and ensure that<sup>there is demand for such an</sup> instrument, (ii) to pursue overall consistent government policies,<sup>and (iii) to get the</sup> pricing parameters of the instruments right.

94. As the Pakistan and India case already illustrated, the<sup>government should conduct</sup> a thorough demand analysis that is aimed at:

- Assessing impediments to private participation (in the Moldovan<sup>case: why</sup> are foreign banks not providing working capital to Moldovan<sup>enterprises; and</sup> is there a lack of foreign capital);

- Providing guidance on the design of the instrument<sup>that could possibly</sup> mitigate the market failure (in the Moldovan case:<sup>would foreign banks be</sup> offering working capital to Moldovan firms? Or are foreign<sup>banks reluctant to</sup> offer financing because of commercial risks or other<sup>factors?</sup> What institutions are currently providing working capital and at what<sup>prices (market</sup> based; non-market based?)

- Identifying demand (in the Moldovan case: if political risk<sup>insurance were to</sup> be offered would there be demand for it? Is there any<sup>other entity (be it</sup> private or public) that offers similar instrument? If so, does<sup>the government</sup> have any comparative advantage to offer this instrument?)

- Evaluating the overall consistency and adequacy of government<sup>policy (in the</sup> Moldovan case: is there any other government program that offers<sup>guarantees</sup> to this sector of the economy? At what prices? Is the success of the instrument contingent on the implementation of structural<sup>changes?).</sup>

95. In Moldova, a demand survey would have revealed that<sup>most of the working</sup> capital financing was being provided by multi-lateral institutions<sup>(thus demand for</sup> political risk insurance would be limited), that the facility<sup>would be crowded out by the</sup> Government's own guarantee program if the government<sup>did not discontinue the</sup> program, and that demand would be sluggish to begin with because<sup>the Government had</sup> stalled on structural reforms.

96. Finally, the Moldovan case study highlights the importance<sup>of an adequate pricing</sup> policy for the facilities' instruments. This is, however, not an<sup>easy undertaking especially</sup> in the case of contingent instruments, the prices of which<sup>basically have to be derived</sup> from complex option price models. If reference prices<sup>are used, the Moldovan case</sup> illustrates that it is important to ensure that the employed<sup>reference price matches the</sup> main features of the instruments offered in terms of maturity<sup>and types of risk covered.</sup>
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

**Table 18. Dakar home electricity: coverage insights**

|||# of EAs<br>with no<br>||Coverage<br>|(percen<br>|tage of hou<br>Acros|seholds wit<br>s constitue<br>|h access)<br>nt EAs||
|---|---|---|---|---|---|---|---|---|---|
|Division|Number of EAs|electricity|Number of HHs|Across divisions|min|max|med|mean|sd|
|Nolivet/Barak|4|1|40|27|0|73|17|27|32|
|Arafat|11|0|200|87|65|100|89|87|11|
|Hann|12|0|250|86|75|96|88|86|8|
|Dalifort|4|0|60|70|60|75|72|70|7|
|Santiaba|4|0|80|81|70|100|76|81|13|
|Taïf|10|0|200|79|65|90|83|79|9|
|Guinaw|16|0|400|85|73|95|88|85|9|
|Médina|16|0|300|80|60|100|80|80|12|
|Wakhinane|9|0|180|83|75|95|80|83|7|
|Diokoul|13|0|250|93|75|100|95|93|8|
|ALL|99|1|1960|82|0|100|85|82|16|

**Table 19. Nairobi home electricity: logistic regression results for household access**

|Variable||Odds-ratio|Standard error|p-value|
|---|---|---|---|---|
|Poor||0.574|0.096|0.001|
|Tenant||0.371|0.111|0.001|
|Locations|||||
|1. Central (base)|||||
|2. Makadara||0.305|0.132|0.008|
|3. Kasarani||0.325|0.141|0.011|
|4. Embakasi||0.219|0.134|0.015|
|5. Pumwani||1.497|0.986|0.542|
|6. Westlands||0.769|0.594|0.735|
|7. Dagoretti||2.032|0.953|0.134|
|8. Kibera||0.330|0.161|0.026|
||N<br>1,755||||
||U2<br>0.14||||

44