rafmacalaba/datause-encoder-data huggingface dataset:
6

# TABLE I

## IMPORTS AND IMPORT ELASTICrTES<sup>OP INDUSTIUAL</sup> COIRIEmrnu

||....-----<br>1<br>GDP|990----------<br>fmjorts<br>|----------<br>manuf.jmrts<br>GDP|1965-80<br>inportela|1980-90<br>sticity|
|---|---|---|---|---|---|
|||GDP||||
||Sbilltons<br>|||098|116|
|Ireland<br>|42500<br>|0.4874<br>|0.409<br>|.<br>|.<br>1|
|N.Zealand<br>|42760<br>|0.2214<br>|0.188<br> <br>|0.46<br> <br>|.90<br>307|
|Portugal<br>|56820<br>|0.4458<br>|0.348<br> <br>|<br>0.67<br>|.<br>239|
|Greece<br>|57900<br>|0.3403<br>|0.262<br>|0.90<br>|.<br>086|
|Norway<br>|105830<br>|0.2541<br>|0.229<br>|0.68<br>|.<br>165|
|Denmark<br>|130960<br>|0.2410<br>|0.195<br>|0.61<br>|.<br>138|
|Finland<br>|137250<br>|0.1970<br>|0.164<br>|0.78<br>|.<br>152|
|Austria<br>|157380<br>|0.3174<br>|0.283<br>|1.42<br>|.<br>155|
|Belgium<br>|201113<br>|0.5950<br>|0.488<br>|1.33<br>|.<br>|
|Switzerland<br>|224850<br>|0.3088<br>|0.275<br>|2.25<br>|1.73<br>|
|Sweden<br>|228110<br>|0.2390<br>|0.203<br> <br>|0.67<br> <br>|1.59<br>|
|Netherlands<br>|279150<br>|0.4510<br>|0.352<br> <br>|:<br>1.12<br>|1.84<br>|
|Australta<br>|296300<br>|0.1341<br>|0.121<br>|0.25<br>|1.38<br>|
|Spain<br>|491240<br>|0.1781<br>|0.139<br>|0.96<br>|2.90<br>|
|Cana'ta<br>|570150<br>|0.2032<br>|0.179<br>|0.52<br>|2.47<br>|
|U._i._<br>|975150<br>|0.2306<br>|0.194<br>|0.60<br>|1.58<br>|
|Italy<br>|1090750<br>|0.1615<br>|0.124<br>|0.81<br> <br>|1.75<br>145|
|France<br>|1190780<br>|0.1953<br>|0.158<br> <br>|:<br>1.08<br> <br>160|.<br>186|
|Germany<br>|1488210<br>|0.2293<br>|0.188<br> <br> <br>|:<br>.<br> <br>|.<br>|
|Japan<br>|2942890<br>|0.0786<br>|0.056<br> <br>|:<br>0.76<br> <br>|1.37<br>|
|U. s.|5392200|0.0956|0.077|:<br>2.03|2.23|
|North America<br>|5962350<br>|0.078<br>|0.058<br>|||
|E. C.|60045'3|0.099|0.062|:||
|OECD|15728210.0|0.1566||1.11|1.67|

6Source: _World Development Indicators_<sup>_(WDI),_the World Bank, Washington D. C. 1992. The share</sup> attributed to mranufacures imports is often different from that in GATT<sup>data; they probably include commodities</sup> classified by GATr as emi-mranufactures. Data on total imports are very similar. Data for Belgium include trade and GDP<sup>of Luxembourg.</sup> Data for *Nonth America' exclude trade<sup>between the United States and Canada. Data for "BC- exclude trade</sup> among EC member countries.

Data for the 'OECD' refer to country<sup>averages as shown in WDI. They include mutual trade among OECD</sup> members. They exclude countries not classified<sup>as "high income" by the World Bank, i.e. Turkey (not shown</sup> in the Table), Greece and Portugal.

ISOURCE: _WORlD DEVELOPMENrINDICATORS. WORLD DEVEWLOPMNTREPORT1990,_ theWorld Bank, Tables 3 (GDP), 2 (growth of GDP),<sup>14 (unports and growth of impots) and 15 (manuSfcturs import)</sup>
**during the pandemic.** This question brings together the previous specifications into a single model. Again, we estimate an LPM of the following form:

Model (4) uses the same four outcome variables used in model (3), estimating the correlates of different coping strategies. Since we are interested in the relationship between enterprise income declines and coping mechanisms, the sample is limited to self-employed heads or households with enterprises. The variable _DFhc_ takes a value of 1 if earnings from a farm household enterprise declined compared to the period immediately before the pandemic started, while the variable _DNFhc_ takes a value of 1 if a non-farm household enterprise’s income declined. Thus, the coefficients βF and βNF represent the conditional correlation between an income decline and the prevalence of different coping mechanisms. The other control variables are the same as in model (3).

# **2.2 Evolving impacts of the pandemic**

**The impacts of the COVID-19 crisis varied over time.** We hypothesize that the share of household enterprises, captured by the share of self-employed workers or people working at a family business, fell in the first months of the pandemic when mobility constraints were imposed, and slowly recovered as the pandemic ran its course and mobility constraints were lifted.

**We use the larger sample of HFPS data from 27 countries and 11 waves covering 13 months from March 2020 to April 2021.** We only use the household head definition so that we can estimate how business owners with different characteristics evolved throughout the pandemic. In this exercise, the weights are normalized such that each country is given equal weight, to prevent estimates from being skewed towards populous countries. The results should therefore be interpreted as a simple average across included countries. For comparison purposes, we also estimate the trends of wage earners and of the non-working population.

**The timing of the survey varied by country.** Figure 1 shows the number of countries with available information by month. At the beginning of the analysis period, we only have information for two countries. In the subsequent months, the number of countries included in the analysis increase to 17 for the June 2020 wave (when disaggregating by gender) and oscillates between 2 and 17 in the following months.

11
123

# Expenditure 4: Health gxpendlture as Percent of Total Govezament Expenditure Suamary Table

||Number|<br>1975|1980|1985|
|---|---|---|---|---|
|ALL DEVELOPING<br>COUNTRIES<br>|49<br>|6.03<br>|6.23<br>|6.30<br>|
|LowIncome<br>|15<br>|5.34<br>|4.84|5.10|
|Middle Income|34|6.33|6.83|6.83|
|AFRICA<br>|11<br>|6.47<br>|6.06|6.14|
|LowIncome<br>|8<br>|6.27<br>|5.84|5.92|
|Middle Income|3|6.97|6.67|6.73|
|EAST ASIA<br>|6<br>|4.26|4.66|5.28|
|Low Income<br>|1|2.05|2.48|4.50|
|Middle Income|5|4.71|5.09|5.83|
|SOUTH ASIA<br>|6<br>|4.65<br>|3.91<br>|4.10|
|Low Income|6|4.65|3.91|4.10|
|EKENA<br>|12|4.51|5.48|6.11|
|Middle Income|12|4.51|5.48|6.11|
|EASTERN EUROPE|2|12.50|0.32|0.38|
|Middle Income|2|12.50|0.32|0.38|
|_LAC_|12|7.38|10.08|9.24|
|Middle Income|12|7.38|10.08|9.24|

Note: The summary table includes gonl the countries with complete data for the periods in the table.

Source: IMF Government Financial Statistics: Consolidated Central Government Expenditure.
26

**Table 6. Simulation Results – Effect of Treaty-Shopping**

|Column<br>(1)|(2)|(3)|
|---|---|---|
|Subsample<br>All countries|DTT with investment hub|No DTT with<br>investment hub|
|Diversion (in%)<br>36.00|92.71|30.20|
|FDI Gain (in%)<br>0.96|2.90|0.76|
|CIT Revenue Loss (in%)<br>5.13|14.48|4.17|
|Notes: Based on the conceptual framework and estim<br>results in percent, differentiating between three countr|ation results of Table 5, this Tab<br>y groups.|le presents simulation|

# **VI.   CONCLUSION**

We analyze the cost and benefits of concluding Double Tax Treaties (DTTs) with investment hubs on FDI and government revenues for a sample of African economies. Our results imply that these economies are likely facing a substantial reduction in revenue when signing a treaty with investment hubs. At the same time, we find no evidence substantiating expectations of a corresponding benefit in terms of additional investment generation. The value of tax treaties to attract investors thus remains uncertain at best, especially where investments tend to be aimed at a country’s natural resources or specific market.

Further analysis is needed to assess the large magnitudes of treaty related base erosion and profit shifting in Sub-Saharan Africa identified in our analysis. While micro-level firm level information on MNEs remains very limited in commercial databases for many developing countries, including most economies in Sub-Saharan Africa (Platform for Collaboration on Tax 2017), future research drawing on micro level administrative information, including Country-by-Country reports of large MNEs, could potentially help fill this gap To assess the robustness of our findings and evaluate their potential underlying drivers, we examine the role of treaty shopping in driving nominal investment flows and provide initial empirical evidence on the importance of treaty-shopping in the region. Our analysis suggests that the conclusion of DTTs with investment hubs increases the rerouting of income flows. These results confirm that avoidance opportunities have a major impact on the overall costbenefit ratio of tax treaties. Ongoing policy reforms and international initiatives to include or improve anti-abuse provisions in tax treaties, including those signed with Mauritius, may help address this challenge.

The findings of this paper are relevant for tax policy makers in Sub-Saharan Africa when balancing revenue and investment promotion objectives and for wider discussions on the international tax architecture. Tax treaties can be beneficial where their benefits outweigh costs. Our paper raises further doubts on this being the case for treaties concluded with investment hubs.
approach identifies the effect of one option versus the next best–in our case, SC versus a mixture of NE and BP. Formally, where _ω_ is the share of compliers along the diversion margin, or diversion share. We estimate the LATE of SC programs vs. the next best, _βSC_ , by estimating equations (1) and (2) using TSLS. While we are able to identify the diversion share, the margin specific LATEs cannot be separately identified without additional assumptions.<sup>14</sup> Nonetheless, if the estimated share of compliers along one of the margins is low, one can view the LATE of SC programs vs. the next best as largely reflecting the local effect for students along the other margin.

# **4.2 Complier shares**

Next, we describe how to estimate _ω_ in equation (3) for the case of one binary instrument under partial monotonicity.<sup>15</sup> We assume that the enrollment choice, _Ddij_ with _d ∈{SC, BP, NE}_ , depends on _Hj_ , _Zj_ and _Xij_ . We also follow the logic of partial monotonicity and impose two additional assumptions. First, _DBPij_ (0 _, Hj_ ) _≥ DBPij_ (1 _, Hj_ ), so that having access to SC HEIs makes _BP_ weakly less preferable. Second, _DNEij_ (0 _, Hj_ ) _≥ DNEij_ (1 _, Hj_ ), so that access to SC HEIs makes _NE_ weakly less preferable. We can then identify the share of compliers switching at each margin as: and the total share of compliers as _E_ [ _DSCij|Zj_ = 1] _− E_ [ _DSCij|Zj_ = 0].

To estimate complier shares, we first estimate propensity scores for every enrollment choice in _d ∈{SC, BP, NE}_ . We posit the following linear probability model: where _Ddij_ is a binary variable equal to one if the student chooses option _d_ , and _εdij_ is the error term. Note that, for the probability of enrolling in SC programs, equation (1)

> 14See, for instance, Hull (2018); Kirkeboen et al. (2016); Kline and Walters (2016).

> 15 Mountjoy (2022) presents a formal derivation of the shares and estimates them using continuous instruments.

13
|Expected to give gifts to public officials (to<br>get things done) Y/N|Dummy variable that takes the value of 1<br>if the firm was expected to give gifts to<br>public officials to get things done|World Bank Enterprise Surveys|
|---|---|---|
|Ownership - government/state (%)|Percentage of firm owned by the<br>government|World Bank Enterprise Surveys|
|Purchased fixed asset Y/N|Dummy variable equal to 1 if the firm<br>purchased fixed assets in the last fiscal<br>year|World Bank Enterprise Surveys|
|Establishment has a line of credit or loan at<br>this time Y/N|Dummy variable equal to 1 if the firm has<br>a loan or line of credit at the time of survey|World Bank Enterprise Surveys|
|Exports 10% or more of sales Y/N|Dummy variable that takes the value of 1<br>if the firm exports at least 10 % or more of<br>its sales, and 0 otherwise|World Bank Enterprise Surveys|
|Security costs (% of sales)|Percentage of establishment’s total annual<br>sales that was paid for security|World Bank Enterprise Surveys|
|Establishment competes against<br>unregistered or informal firms Y/N|Dummy variable that takes the value of 1<br>if the establishment competes against<br>unregistered or informal firms, 0 otherwise|World Bank Enterprise Surveys|
|Number of years that firm was unregistered<br>at start|Difference between the year the firm<br>started operations, and when the firm was<br>formally registered. Variable takes a value<br>of zero if firm was registered when they<br>started operations|World Bank Enterprise Surveys|
|Bribery depth|% of public transactions where a gift or<br>informal payment was requested|World Bank Enterprise Surveys|
|Bribery incidence|Firms experiencing at least one bribe<br>payment request Y/N|World Bank Enterprise Surveys|
|Informal payment/gift requested for water<br>connection Y/N|Dummy variable that takes the value of 1<br>if the firm applied for a water connection<br>over the last 2 years before the survey and<br>an informal payment or gift was expected<br>or requested|World Bank Enterprise Surveys|
|Proportion of public transactions (excluding<br>water) where a gift or informal payment<br>required|Proportion of public transactions<br>(excluding water) that required a gift or<br>informal payment for firms that applied for<br>a water connection in the last 2 years|World Bank Enterprise Surveys|
|Firm experienced at least one public<br>transactions (excluding water) where a gift<br>or informal payment required|Dummy variable that takes the value of 1<br>if the firm was asked for an informal<br>payment or gift was expected or requested<br>for any public transaction excluding water<br>(this is only for firms that applied for a<br>water connection)|World Bank Enterprise Surveys|

30
# **Table 4: Distribution of non-farm household enterprises and returns by Sector, by poverty status, in Rural Malawi, 2010/2013**

|**Sector**|Rural|Malawi|Non|Poor|Po|or|p-value|difference|
|---|---|---|---|---|---|---|---|---|
||2010|2013|2010|2013|2010|2013|2010|2013|
|Participation|Pro|portion of e|nterprises|by sector o|f industry|(%)|||
|**Primary sector**<br>(mostly charcoal related activities and stone quarrying)|1.6|2.7|0.8|1.9|3.4|5.4|0.161|0.183|
|**Food, beverage, tobacco manufacturing**<br>(mostly food and drinks street vendors)|26.1|25.6|22.3|25.3|34.8|26.6|0.036|0.81|
|**Non-food manufacturing**<br>(mostly mats, baskets, and pottery, tailor, metal tinsmith)|16.2|13.6|15.9|12.7|16.9|16.7|0.786|0.248|
|**Wholesale and retail trade + restaurant + hotels**<br>(include a wide range of commerce & tourism related activities)|46.4|49.2|50.4|50.9|37.3|43.4|0.016|0.121|
|**Construction, services, and other sectors**<br>(mostly electrical and other repair shops, barber shop, hair salon)|9.7|8.9|10.7|9.1|7.6|8|0.329|0.674|
|Returns||Profit o|f enterpris|es by indu|stry (1000|KW)|||
|**Primary sector**|4.6|3.2|6.1|3.4|3.8|3.1|-|0.651|
|**Food, beverage, tobacco manufacturing**|3.8|3.7|5.1|4.3|1.7|2.1|0.038|0.006|
|**Non-food manufacturing**|4.8|5.8|5.9|6.7|2.4|3.1|0.052|0.086|
|**Wholesale and retail trade and restaurant & hotels**|6.6|7|7.6|8|3.6|3.3|0|0|
|**Construction, services, and other sectors**|8.6|12.2|10|13.8|2.8|6.5|0.017|0.109|
|Overall|5.7|6.4|7|7.3|2.6|3.2|0.000|0.000|

Source: Malawi IHS3 panel sample (2010) and IHPS (2013).

Note: The information in this table is at the enterprise level. Profit is the net profit generated by the enterprise over the month of operation prior to the interview, as reported by the enterprise owner. Kwachas (MWK), local currency used in Malawi.

29
existing technologies by local firms. In addition, the import of green goods may be a response to domestic demand-side green policies in developing countries. However, there is no significant upward trend in any region in particular.

**Figure 6: Imports of Green Goods and Services** (as % of all imports)

<!-- Start of picture text --> 8%<br>6%<br>4%<br>2%<br>2000<br>0%<br>2005<br>2010<br><!-- End of picture text --> Source: COMTRADE and OECD List of environmental 6-digit HS categories Even if developing countries are not increasing their exports of green products, they could be increasingly capable of moving into green sectors to the extent that they are producing non-green goods and services that enable them to produce green products because of similarities in the required inputs or technologies. To examine this broader ̳capability‘ for green exports, we utilize the concept of ̳proximity‘ between products.<sup>13</sup> For example, a country with the ability to export apples will probably have most of the conditions suitable to export pears, but not necessarily those for producing electronics. Figure 7 shows in general, the trade in green and ̳close-to-green‘ products is about three to five times that in green products alone. Moreover, some developing regions like EAP and LAC are even comparable to high-income countries in this respect.

Proximity between two products does not necessarily imply that a country producing one also has comparative advantage in the other. But as described in Hausmann and Klinger (2007), as countries change their export mix –in other words, as comparative advantage _evolves_ – there is a strong tendency to move towards proximate goods rather than to goods that are farther away. Thus, Figure 7 suggests that some developing countries have opportunities comparable to developed countries to leap to green products close to products they already export.

Policies targeting the sources of proximity between products could enable such leaps. The pattern of relatedness of products is only partially explained by similarity in broad factor or technological intensities, suggesting that the relevant determinants are much more product-specific, such as productspecific human capital acquired through learning-by-doing (Hausmann and Klinger, 2007). Therefore, any policy intervention should be predicated on better information on the sources of proximity, including crucial technological differences and access to complementary inputs. Further, given the double

> 13 Following Hausmann and Klinger (2007), we measure proximity between goods by using an outcome-based method which is based on the hypothesis that similar products are more likely to be exported in tandem. Details are given in the data annex.

9
The absence of risk pooling in the RM market keeps insurance costs high. Public pension plans benefit from the cross-subsidization of longevity risk: citizens who die early essentially support those with a longer life span. In contrast, when a mortgagor passes away, no more than the individual’s outstanding loan balance has to be returned. Given these constraints on risk pooling, FHA-sponsored insurance programs may have to retain relatively high premia to be financially sustainable (Shan, 2011).

On the demand side, concerns about old-age financial literacy and consumer protection are a key constraint. Reverse mortgages are a relatively complex financial product that many young people struggle to understand. An industry survey from the 2010s reveals that of 1,000 Americans surveyed, 70% failed a 10-question quiz on RM (Reversemortgages.com, n.d.). The elderly face greater challenges in financial decision-making as their cognitive skills decline. They are more vulnerable to fraud (particularly from family and/ or caregivers) and other types of financial abuse than society overall (American Association for Justice, 2017, G20/OECD Task Force on Financial Consumer Protection 2019). One common example of RM fraud in the 1990s is that of contractors who offer house repairs without a down payment to elderly people. The contract offered turned out to be an RM with exorbitant origination and house repair fees (Caplin, 2002). Fraud opportunities and perverse incentives may be even more salient, in particular in the case of RM which are ‘sale-and-leaseback agreements’ where the lender has a financial interest in a short lifespan of the counterparty.

High RM adoption rates among poor elderly people make consumer protection concerns even more salient. Data from the United States suggest that over the last decade, poor elderly homeowners have become a key target group for RM lenders. Only 2% of the elderly homeowner population at large have an RM, but market penetration reaches over 5% for the lowest income quintile and even 17% for lowincome households of age 90 and above (Nakajima & Telyukova, 2017). RM origination sustained its growth even during the Great Recession, a potentially worrying sign that cash-strapped homeowners signed RM in financial distress in spite of the significant drop in their housing wealth.

Non-interest fees are relatively high **.** In addition to interest on the RM loan, a borrower has to pay a loan origination fee, a servicing fee to the lender and/or broker, initial and monthly mortgage insurance premiums, and third-party charges (title insurance, credit report, appraisal costs). In the United States, such non-interest fees are estimated to be roughly equivalent to 6.8% of the loan (Davidoff & Welke, 2004). These fees are usually financed rather than paid in cash up front, but they reduce the loan available to the borrower significantly.

19
varies across firms, which corresponds to the incomplete shift-share case described in Borusyak, Hull, and Jaravel (2022). We therefore adopt their proposed correction, by controlling for the interaction between this sum (the export share of sales of the firm in the initial period) and a period dummy in all our regressions, defined as _XSi_ 0 _∗ ιp_ .

The firm fixed effects account for the role of any time-invariant (observable or unobservable) firm attribute. The industry-period effects absorb common shocks to all firms in an industry in each period, while the region-period effects capture the impacts of common shocks across firms operating in the same region in a given period. We report standard errors clustered by firm. Since the forecast error is defined as the difference between actual and forecasted GDP growth in export destinations, in order to isolate its effect on the dependent variable we need to control for the weighted forecast growth, which would be expected to have a direct impact on firm-level outcomes. Our identification assumption is that after controlling for the weighted forecast growth and these fixed effects, subsequent variations in weighted forecast errors are uncorrelated with firm-specific shocks to firm performance and worker compensation.<sup>11</sup>

# **5 Results**

## **5.1 Summary statistics**

Before turning to the econometric analysis, we report descriptive statistics on several variables underlying our empirical strategy. Our firm-level baseline estimation sample is composed of manufacturing firms that exported in the initial year, and for which it is possible to link information from all the data sets described above (except the IPG survey, which is available only for a subset of firms in 2016). Table 1 reports summary statistics on firms from the estimation sample, both in levels and in changes. These statistics reveal that there exists considerable variation across firms and over time with regard to the weighted average of actual and forecast growth. Table A1 in the Appendix provides summary statistics on firms in the estimation sample for each 3-year period considered in the econometric analysis. Once again, these descriptive statistics show considerable variation across firms in the main variables of interest, both within and across periods. In Table A2 in the Appendix we also show summary statistics for firm age,

> 11Since isolating the effects of the weighted forecast error on firm-level exports and sales requires controlling for the weighted forecast growth, our empirical methodology is not amenable to an instrumental variable approach. If both variables were included in the first stage in order to predict changes in firm-level exports or sales, in the second stage we could only identify the total effect of both expected and unexpected GDP growth in export destinations on wage setting. We therefore adopt a more reduced-form approach in which both the weighted forecast errors and weighted forecast growth are regressed directly on wages, which allows us to isolate the effect of the unexpected component of demand shocks across destinations on wage setting.

9