rafmacalaba/datause-encoder-data huggingface dataset:
what percentage of total workforce would you estimate the typical firm in your area of business reports for tax purposes?” In Table 8, we perform additional robustness tests of our regressions in Table 3 using the two new dependent variables. In addition to the usual set of control variables we also control for Profit Margin in cols. 1, 3, 5, and 7 and for Profitability Dummy in cols. 2, 4, 6, and 8. Specifically, in the OLS regressions in cols. 1-4, we regress the different measures of Tax Evasion on bribes paid to public officials. The results show that greater the bribes paid to public officials, firms under report their wage bill and labor for tax purposes. In cols. 5-8, we report instrumental variable specifications where we instrument for bribes using time spent with the government. Specifically, time spent with the government is the percent of senior management’s time over the last 12 months that was spent in dealing with public officials about the application and interpretation of laws and regulations and to get or to maintain access to public services? The IV regressions show that but for one specification in col. 7 where we control for profit margin in the Tax Evasion (Labor) regression, bribe payments influence the extent of tax evasion. The firms whose bribe paying behavior is altered by the time their managers spend dealing with public officials evade more taxes by reporting a lower wage bill and lower total workforce.<sup>31</sup>

# **_6.2 Are Bribe Payments a Tax on Innovation? Robustness Checks_**

In this section, we undertake several robustness checks to confirm that innovative firms are indeed victimized by corruption. In unreported regressions, we perform the following robustness tests: First when we re-estimate our regressions controlling for profit margin in the BEEPS sample, we find that profit margin is not significant and innovating firms still pay more bribes than non-innovators. For the same set of countries, we have surveys in 2002 which also have data on profit margin and we again find that profit margin by itself is not significantly associated with bribe payments whereas innovation is. The 2002 surveys also have data on past profit/sales ratio in 2001 and 1999. Past profitability, both in 1999 and 2001 is significantly associated with

> 31 The same sample of countries was surveyed in 2002. The firms surveyed in 2002 and 2005 were randomly chosen each year and hence we have panel data on less than 1500 firms that were surveyed by coincidence in both years. We do not run pooled regressions across both datasets since the profitability measures were defined differently in the two surveys and the 2002 survey does not have the variables Tax Evasion (Wagebill) and Tax Evasion (Labor). However, the 2002 survey has the main tax evasion variable and controlling for profitability, we again find that bribe payments are associated with tax evasion.

31
Table A.2: RISE discrepancies and variables removed from the analysis

||**Type of**<br>**discrepancy **|<sup>**Dummy**</sup>|**Year**|**Decision**|**Rationale**|
|---|---|---|---|---|---|
|1|Dummy and<br>year<br>discrepancy|0|Year should<br>not be<br>specified but<br>is specified|Favored the<br>year column|The year column is more specific<br>information than the dummy<br>column. If there is input for the<br>more specific column, then we<br>assume that it has been verified<br>and is correct.|
|2|Potential<br>dummy and<br>year<br>discrepancy|0|Year should<br>be 0, but it is<br>NA, N/A, not<br>applicable, or<br>missing|Favored the<br>dummy<br>column,<br>treated year<br>as “0”|We cannot use a year if we do<br>not have it.|
|3|Dummy and<br>year<br>discrepancy|1|Year should<br>be specified,<br>but is 0|Treated year<br>as NA (“.”)|Treating years as “no” (with 0)<br>would be incorrect because the<br>reform seems to have been made.<br>However, without a year, we<br>cannot count them in apanel.|
|4|Dummy and<br>year<br>discrepancy|1|Year should<br>be specified,<br>but is missing|Treated year<br>as NA (“.”)|Treating years as “no” would be<br>incorrect because the reform was<br>made according to the dummy<br>column. However, without a<br>year, we cannot count them in a<br>panel.|
|5|<sup>Year looks</sup><br>suspicious|1|Year seems<br>too early|No action|Some years are very early,<br>examples re.2.1.6.yr (1895) or<br>re.6.3.1.3.yr (1923). We give the<br>dataset the benefit of the doubt.|
|6|Dummy and<br>year<br>discrepancy|NA|Year should<br>be NA, but is<br>specified|Favored the<br>year column|The year column gives more<br>information than the dummy<br>column. If there is input for the<br>more specific column, then we<br>assume that it has been verified<br>and is correct.|
|7|Potential<br>dummy and<br>year<br>discrepancy|NA|Year should<br>be NA, but is<br>0|Favored the<br>dummy<br>column,<br>treated year<br>as NA (“.”)|Seems like the year column was<br>given a “0” because it was “NA”<br>in the dummy column. But we<br>treat missing in the dummy<br>column as “NA”. So, we favored<br>the dummycolumn.|

_Source: ESMAP RISE dataset and authors’ elaboration based on dataset described in this paper._ 47
_Africa’s Macroeconomic Story_ increased significantly after debt relief was granted.<sup>15</sup> The Gambia, São Tomé and Príncipe, Guinea Bissau, Ghana and Malawi are exemptions. Debt-to-GDP ratios increased in all five countries and now exceed 50 percent (Figure 14). However, modest primary surpluses and strong growth are projected to reduce their debt burdens significantly (Table 3). In São Tomé and Príncipe, oil production beginning in 2015 is expected to generate a surge in growth (38 percent in 2015 and 48 percent in 2016) and revenues, resulting in a primary surplus at 20 percent of GDP in 2016 and 25 percent in 2017. In Ghana, an increase in oil production is also expected to generate a surge in growth and revenues, yet the debt burden is not projected to decline significantly. In Malawi, growth is expected to pick up over the medium term following the large exchange rate depreciation in 2012 (33 percent against the $US; almost 50 percent in real effective terms).

**Figure 14: Public Debt in African Countries that Received HICP/MDRI Debt Relief**

_Percent of GDP_ <!-- Start of picture text --> 75<br>50<br>25<br>0<br>2007 or HIPC Completion Point date 2012<br>... ... ...<br>The Gambia São Tomé and Guinea-Bissau Ghana Malawi Côte d'Ivoire Togo Mozambique Senegal Sierra Leone Guinea Tanzania Madagascar Democratic Uganda Benin Mali Burundi Niger Central African Liberia Rwanda Burkina Faso Zambia Ethiopia Republic of Congo Cameroon<br><!-- End of picture text --> Source: April 2013 IMF _World Economic Outlook_ database. _Note:_ Estimates for 2012

> 15 Merotto, Thomas and Stucka, (2013) examine debt incurred by all HIPC/MDRI recipients since 2007 whereas the above analysis focuses on recipient countries in the Africa region. They show that in cases where debt burdens have increased significantly, external debt (rather than domestic debt) accounts for most of the new borrowing.

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general tendency of consumers to shift to products declining in price, the export price indexes based on 1975 weights rise less than those based on 1963 weights aid the indexes based on 1986 weights rise still less (Table 3): Table 3 Effect on Export Price Index, 1991/1953, of Changing Weight Base Years Index on Later Weights as Percent of Index on Earlier Weights

||1975 Weights|<br>1986 Weightc|1986 Weights|
|---|---|---|---|
||1963 Weights|<br>1975 Weights|1963 Weights|
|Developed Country Exports|to World<br>94.47|92.84|87.71|
|Developed-Country<br>Exports||||
|to Developing Countries|<br>93.75|94.44|88.54|

Source: Appendix Tables 1 and 2.

The shift in commodity and country weights from 1963 to 1975 reduced the estimate of the price increase for manufactured goods by about 5 or 6 per cent, or about .15 per cent per year. The shift to a 1986 base reduced the rate of price increase still further, by about .19 per cent per year. Thus, the difference between the growth in prices calculated on 1963 weights and that based on 1986 weights is about a third of a per cent per year.

The estimates from fitting logarithmic time trends are that the change from 1963 to 1975 weights reduces the growth in prices of exports to the world by about .11 per cent per year, and the change from 1975 to 1986 weights reduces it by about another .17 per cent or more per year, a total reduction by a little over a quarter of a per cent per year. For exports to developing countries the effect of the 1963 to 1975 weight shift is about .14 per cent per year and of the 1975 to 1986 shift, another .19 per cent, a total reduction of about a third of a per cent per year.
**Table 2:  Crude Steel Production by Process (2008)**

||Production (million tonnes)|BOF (%)|EAF (%)|OHF (%)|
|---|---|---|---|---|
|EU(27)|198|58.2|41.4|0.3|
|U.S.|91.4|41.9|58|NA|
|Japan|118.7|75.2|24.8|NA|
|Brazil|33.7|74.8|23.5|NA|
|Russia|68.5|55.2|28.4|16.5|
|China(e)|500.5|90.9|9.1|NA|
|India(e)|55.2|40|58.2|1.8|
|World|1322.7|67.2|30.6|2.2|

Notes: OHF: Open Hearth Furnace; (e): estimate; NA=not applicable Source: World Steel in Figures 2009 (World Steel Association) We now consider the consequences for net revenue for the various types of steel in the different jurisdictions under the assumption that the U.S. has imposed a simple domestic carbon tax of $25/tonne of CO2.<sup>15</sup> In the scenarios below (summarized in Table 3), I will use average 2009 spot prices for hot-rolled coil (HRC) of $540, which is a common benchmark product in the steel industry.<sup>16</sup> I will assume that the EU and Japan have implemented similar, if not more stringent carbon reductions policies than the U.S., so that they can be ignored for the analysis below.

**Table 3:  Marginal revenue for hot-rolled coil**

||Technique|Carbon<br>intensity|Baseline<br>Scenario:<br>No<br>border<br>tax|Scenario 1:<br>Firm-<br>specific tax|Scenario<br>2:<br>Average<br>foreign<br>emissions|<br>Scenario<br>3:<br>Average<br>domestic<br>emissions|<br>Scenario 4:<br>"Best<br>available"<br>U.S.<br>technology|<br>Scenario 5:<br>"Worst<br>available"<br>U.S.<br>technology<br>with foreign<br>firm<br>submissions|
|---|---|---|---|---|---|---|---|---|
|China|BOF|3.9|540|443|480|510|525|490|
||EAF(Scrap)|0.9|540|518|480|510|525|518|
|Brazil|BOF|2.5|540|478|505|510|525|490|
||EAF(Scrap)|0.3|540|533|505|510|525|533|
|US|BOF|2|490|490|490|490|490|490|
||EAF(Scrap)|0.6|525|525|525|525|525|525|
||EAF(DRI)|1|515|515|515|515|515|515|

Notes and sources: (1) Carbon intensity based on OECD (2002); “Brazil” is South America in that study while “U.S.” is North America; (2) Hot-rolled coil price is average of 2009 spot price. In all scenarios, tax is assumed fully absorbed by the producer.

> 15 One could also analyze a more complicated emission permit emission program but the basic insights would be unchanged.

> 16 These prices were obtained from www.steelonthenet.com/price_info.html

12
nowcasting but we include all of them here – as we do with WDI and WEO – to remain agnostic about which ones are relevant and which ones are not.

For all of the variables from the three sources above we also calculate annualized growth rates of the variables between two household surveys for a country. For all variables that are expressed as percentages, rates, or indices, we also calculate the annualized change between two household surveys for a country.

The only variables we remove from what is described above are (1) variables with more than 90% missing information in 2020, the nearcasting year, (2) variables with more than 90% missing information for country-years with poverty estimates, and (3) variables that are not comparable between countries, such as variables expressed in local currency units. The first two criteria help speed up the models by removing variables with too little information to be relevant. The latter removes variables where exploiting cross-country variation is not meaningful.

Depending on which month of the year this exercise is carried out, the removal of variables with more than 90% in the nearcasting year removes a large fraction of the WDI. The reason is that early in the year, most variables do not yet have information for the prior year, meaning the information used for nearcasting is not much better than the information that can be used for nowcasting. When conducted in July (as the current exercise is), only about 300 WDI variables meet that criterion. All in all, we are left with a bit more than 1,000 features across the various data sources.

Not all poverty trends within countries are comparable over time due to changes in the survey methodology or the welfare aggregate. This matters for our predictions of in a variable. Even if we knew the exact causes changes/growth target of poverty in a country, if welfare aggregates are not comparable, then we would not be able to predict the changes in poverty between two surveys. Though this also makes it more difficult to predict levels in our target variables, the problem is arguably larger when predicting changes. Though we can restrict our sample to comparable spells only, our main results will cover all data points, even the ones that are not comparable over time. As one of our findings will be that nowcasting poverty by predicting changes from a past distribution gives more accurate results than predicting levels directly, the decision to include non-comparable spells makes this result more conversative, as it precisely penalizes the methods we find superior. As a robustness check we restrict our data to comparable spells only.

14
For each disaster type and each event, there is uncertainty on the impacts. We therefore build three different scenarios, which are described in Table 1.

_Table 1 Low, medium and high values for the impact of disasters_

||Low|Medium|High|
|---|---|---|---|
|Vulnerability to floods,<br>storm surges,<br>cyclones, earthquakes,<br>tsunamis|Medium ‐30%|Combination of the<br>USGS PAGER building<br>inventory and simple<br>vulnerabilitycurves|Medium +30%|
|Exposure to storm<br>surges, cyclones,<br>earthquakes,tsunamis|Medium ‐30%|GAR|Medium +30%|
|Exposure to floods|Medium ‐30%|GLOFRIS|Medium +30%|
|Exposure to drought|Medium ‐30%|GLOFRIS|Medium +30%|
|Impact of droughts on<br>farmers’ income|0|10|20%|
|Number of people<br>affected by higher<br>food prices for each<br>farmer affected by a<br>drought|Medium ‐30%|50|Medium +30%|
|Impact of drought on<br>local foodprices|10%|55%|100%|

_Table 2 Reconstruction time after disasters by return period and for the three scenarios_

|||R<br>|et|urnperiod<br>||
|---|---|---|---|---|---|
|Scenarios|0‐10|10‐100||100‐500|>500|
|Low|0.5|1||2|3|
|Medium|1|2||3|5|
|High|3|3||5|10|

Note that we are building a counterfactual scenario in which disasters never hit, starting from surveys in which households’ income factors in the impacts of disasters. We therefore apply positive shocks on households, i.e. we _increase_ the income of each household when they are affected by a disaster, to reverse current disasters’ effects on people’s income.

The effect of natural disasters generally lasts more than a year. To simplify, we assume that the economy goes back to normal linearly during a “reconstruction” time that depends on the return period (Table 2). Here again we build three scenarios that account for the uncertainty around reconstruction times. For each event, therefore, we estimate the total impact (over time) by multiplying the yearly impact by N/2 (where N is the reconstruction time).

The impact of each event is represented in Figure 2, with the number of people in poverty because of an event multiplied by the probability of this event.

5
# **References**

Amato, Paul (2000). “The Consequences of Divorce for Adults and Children.” _Journal of Marriage and Family_ 62(4): 1269-1287.

Antoine, Philippe and Fatou Binetou Dial (2003). "Mariage, divorce et remariage à Dakar et Lomé." Actes du colloque de l'AUF : Familles du Nord, familles du Sud, Marseille 23-26 juin.

Appleton, Simon (1996). “Women-Headed Households and Household Welfare: An Empirical Deconstruction for Uganda.” _World Development_ 24(12): 1811-1827.

Bongaarts, John (2001). “Household Size and Composition in the Developing World in the 1990s.” _Population Studies_ 55(3): 263-79.

Bowen, John (2017). “Gender, Islam and Law.” WIDER Working Paper 2017-xxx. Helsinki: UNU-WIDER.

Brossier **,** Marie (2004). “Les débats sur le droit de la famille au Sénégal : Une mise en question des fondements de l’autorité légitime?” _Politique Africaine_ 96 :78-98 DOI : 10.3917/polaf.096.0078 Chapoto, Antony, T. S. Jayne, and Nicole Mason (2011). “Widows’ Land Security in the Era of HIV/AIDS: Panel Survey Evidence from Zambia.” _Economic Development and Cultural Change_ 59(3): 511-47.

Cherchye, Laurens, Bram De Rock, Frederic Vermeulen and Selma Walther (2016), “Should I Stay or Should I Go: Marriage and Divorce in Malawi” mimeo Clark, Shelley, and Sarah Brauner-Otto (2015). "Divorce in Sub-Saharan Africa: Are Unions Becoming Less Stable?" _Population and Development Review_ 41(4): 583-605.

De Vreyer, Philippe, Sylvie Lambert, Abla Safir, and Momar B. Sylla (2008). "Pauvreté et structure familiale, pourquoi une nouvelle enquête ?" _Stateco_ , 102: 261-275.

Feinstein, Jonathan (1993). “The relationship between socioeconomic status and health: A review of the literature.” _The Milbank Quarterly_ 71(2): 279-322.

Dial, Fatou B (2008). _Mariage et divorce à Dakar. Itinéraires féminins_ , Col : Hommes et Sociétés, Khatalas-Crepos, ISBN 9782845869608 Hertrich, Véronique (1994). _Dynamique démographique et changements familiaux en milieu rural africain. Une étude chez les Bwa au Mali_ , Thèse de doctorat, Université de Paris 1 Horrell, Sara and Pramila Krishnan (2007). “Poverty and Productivity in Female-Headed Households in Zimbabwe.” _Journal of Development Studies_ 43(8):1351-1380.

Hurd, Michael and David Wise (1989). “The Wealth and Poverty of Widows: Assets Before and After the Husband’s Death,’ in David Wise (ed.) _The Economics of Aging_ , University of Chicago Press.

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# **1. Introduction**

Financial sector openness is expected to be directly linked to economic growth through enhancing the access to financial services, and indirectly through increasing competitiveness of domestic financial markets, both of which reduce the cost of financing. This, in turn, spurs economic growth through higher capital accumulation and higher efficiency. These links have been extensively investigated in the literature, giving special emphasis on the indirect one since previous empirical studies have failed to find a robust direct link between financial market openness and economic growth.<sup>1</sup> The indirect link between openness and economic growth is investigated in two steps: i) financial development is essential for economic growth, and ii) financial openness improves the level of financial development. Financial development is one of the important engines of economic growth.<sup>2</sup> The basic roles played by financial markets in the process of transferring savings to borrowers are risk sharing, providing liquidity, information, and improving allocative efficiency. The development level of financial markets depends on how well they provide these financial services. If financial markets are well-developed, they improve the availability of funds to support domestic borrowers, and channel the funds to where the rates of returns are higher. These positive effects are expected to increase economic growth through larger capital accumulation due to a lower cost of financing, and increased efficiency. However, in developing countries, financial markets are often underdeveloped and market failures exist everywhere.

> 1 Levine (1996 and 2001) provides an extensive survey study about the link between international financial liberalization and growth. He points out the importance of international financial integration in promoting growth through improvements in the domestic financial markets.

> 2 Examples of papers investigating the link between economic growth and financial development include Levine (1997, 1998), Levine, Loayza, and Beck (2000), King and Levine (1993a and b), Demirguc-Kunt and Maksimovic (1998), and Rajan and Zingales (1998).

2
The model also predicts that lower transaction costs should result in more frequent and smaller transactions. We have shown in Table 5 that individuals encouraged to open an account with a Microcred agent engage in more transactions with Microcred agents. Table 6, however, shows that these individuals do not necessarily transact smaller amounts. The coefficient on the dummy variable Agent is positive and significant, suggesting that withdrawals at the branch are larger for individuals encouraged to open the account with the agent rather than at the branch. Using data from all transactions, the average transactions for individuals sent to the branch versus an agent are 112 USD and 147 USD, respectively.<sup>21</sup> Transactions are, on average, larger among individuals sent to a Microcred agent, and that difference is statistically significant.

It is also clear from columns 1 and 3 of Table 6 that individuals encouraged to open the Sukaliku account with a Microcred agent, choose to make deposits and withdrawals with the agent that are significantly smaller than those made at the branch. Column 3 focuses on individuals sent to the agent. For this group, the average transaction at the branch is 219 USD compared to 96.50 USD for transactions with a Microcred agent.<sup>22</sup> There are at least three possible reasons for this behavior. First, agents may be unable to accommodate requests for large withdrawals due to lack of liquidity and may refuse large deposits due to concerns about security and fears of theft. According to the endline survey, however, none of the respondents who had visited a Microcred agent reported that the agent had ever refused a transaction. The agent module in the neighborhood survey also asked agents (both those of Microcred and others) if they had been unable to complete a transaction during the last week. About 25 percent of all agents in the area report having refused at least one transaction. Of those, about one-third of the transactions were refused due to liquidity concerns while the remaining two-thirds had to do with internet connectivity problems. None of the 6 Microcred agents, however, reported problems completing transactions during the last week. As Appendix Table OA2 shows, Microcred agents are better off financially than other banking and mobile money agents and thus they may run larger businesses with more reliable internet and fewer liquidity problems. Alternatively, clients, rather than the agents, may feel unsecure depositing or withdrawing large amounts with the agents for fear of theft. While each branch has a security guard, some Microcred agents do not have one. However, the selection process to become a Microcred agent ensures that the establishment has brick walls and concrete ceilings. In fact, none of the clients in the study ever complained about

> 21 See Footnote 20.

> 22 See Footnote 20.

20