rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1: OLS Estimates of Change in Offshoring and Import Penetration Given Industry Skill Composition in 1983**

||Log Diffe|Dependent<br>rence in Em<br>(1983-|Variable:<br>ployment<br>2002)|Offshored|Depende<br>Import P|nt Variable:<br>enetration|
|---|---|---|---|---|---|---|
||Low I<br>Cou|ncome<br>ntries|High I<br>Cou|ncome<br>ntries|Diffe<br>(1983|rence<br>-2002)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Industry Share of Routine Jobs in|5.132**|5.926**|-0.980|-0.246|1.217***|1.337***|
|1983|(2.40)|(2.57)|(2.03)|(2.27)|(0.34)|(0.33)|
|Difference in log of price of||-0.056||-0.043||-0.018|
|investment between 1983 and 2002||(0.51)||(0.45)||(0.07)|
|Difference in total factor productivity||0.395||-0.364*||0.102***|
|level between 1983 and 2002||(0.24)||(0.22)||(0.03)|
|Difference in log of real price of||-0.175||0.210||-0.052**|
|shipments between 1983 and 2002||(0.19)||(0.17)||(0.02)|
|Difference in computer use rates||0.434||-0.227||0.023|
|between 1983 and 2002||(0.67)||(0.59)||(0.09)|
|Number of observations|66|59|66|59|66|61|
|R-squared|0.07|0.14|0.004|0.07|0.17|0.37|

_Source_ : Affiliate (or offshore) employment data are taken from the Bureau of Economic Analysis annual survey of US firms with multinational affiliates for 1983-2002. Low income countries are defined according to the World Bank income categories. Employment data are taken from all workers in the Current Population Surveys Merged Outgoing Rotation Groups for the same period. Import penetration and export share are taken from Bernard et al. (2006). Investment good prices, total factor productivity measures, and the real price of shipments are taken from the NBER productivity database. Computer use rates are taken from October CPS supplements during the sample period. Details for each of the data sources are available in the data appendix.

_Note:_ Robust standard errors reported in parentheses below coefficient estimates.
- [15] Hakura, D., 2001. Why does HOV fail? The role of technological differences within the EC. Journal of International Economics, 56 (2), 509511.

- [16] Harrigan, J., 1995. Factor Endowments and the International Location of Production: Econometric Evidence from the OECD, 1970-1985. Journal of International Economics, 39 (1/2), 123-141.

- [17] Harrigan, J., 1997. Technology, Factor Supplies and International Specialization: Testing the Neoclassical Model. American Economic Review, 87 (4), 475-494.

- [18] Harrigan, J. and E. Zakrajsek, 2000. Factor Supplies and Specialization in the World Economy. NBER Working Paper 7848.

- [19] Hotchkiss, J., 1991. The Definition of Part-Time Employment: A Switching Regression Model with Unknown Sample Selection. International Economic Review, 32 (4), 899-917.

- [20] Kennedy, C., 1964. Induced Bias in Innovation and the Theory of Distribution. Economic Journal, LXXIV, 541-547.

- [21] Klenow, P. and A. Rodriguez-Claire, 2005. Externalities and Growth. Handbook of Economic Growth, vol. 1A, P. Aghion and S Durlauf, eds. Chapter 11, 817-861.

- [22] Krugman, P., 1980. Scale Economies, Product Differentiation, and the Pattern of Trade. American Economic Review, 70 (5), 950-959.

- [23] Krugman, P. and M. Obstfeld, 1994. International Economics: Theory and Practice. Third edition, pp.64-65.

- [24] Leamer, E., 1984. The Commodity Composition of International Trade in Manufactures: An Empirical Analysis. Oxford Economic Papers, 26, 350-374.

- [25] Leontief, W., 1956. Factor Proportions and the Structure of American Trade: Further Theoretical and Empirical Analysis. Review of Economics and Statistics, 38, 386-407.

28
||(0.362)|(0.312)|(0.366)|
|---|---|---|---|
|Wall material: Stone, Burnt bricks, Cement||||
|blocks/concrete, Other|0.954***|0.995***|0.683**|
||(0.264)|(0.223)|(0.300)|
|Floor material: Earth||||
|Floor material: Cement/concrete, stone,||||
|burnt bricks|-0.00460|-0.00310|0.0620|
||(0.247)|(0.275)|(0.311)|
|Floor material: Wood, vinyl tiles, ce-||||
|ramic/porcelain/granite/marble tiles, ter-||||
|razo/terrazo tiles, other|0.0441|0.102|0.0783|
||(0.543)|(0.548)|(0.657)|
|Expenditure||1.01e-05|2.35e-06|
|||(1.99e-05)|(1.86e-05)|
|High elevation||||
|Low elevation||-0.403|-0.168|
|||(0.307)|(0.257)|
|Female||||
|Male|||0.326**|
||||(0.133)|
|No preventive measure taken||||
|At least one preventive measure taken|||0.671***<br>(0.247)|
|Constant|6.616***|6.784***|5.688***|
||(0.308)|(0.542)|(0.490)|
|Observations|299|299|292|
|Prob > F|2.38e-05|5.66e-05|2.65e-09|

# **Appendix 8 – Descriptive statistics and regression tables for section 7 on Resilience**

_Table 26: Used savings as a coping mechanism by expenditure, main source of expenditure and gender of household head._

||Used savings|
|---|---|
|Expenditure|-3.96e-05|
||(3.28e-05)|
|Female|ref.|
|Male|0.478**|
||(0.222)|
|Main income source: Monthly Salary|ref.|
|Main income source: Casual labor|-1.952***|

49
A special case is the development of cash exchange outlets. So long as cash remains a critical part of the system then the establishment of a network of cash merchants to exchange cash and BEMs will be central to the functioning of the payments system. A network is both expensive and time consuming to develop and the ability of service providers to have exclusive relations with merchants may be necessary for them to invest in the creation of such networks. A requirement for competitors to be able to access a merchant network on particular terms or for merchants to act as agents of more than one service provider may limit the development of the agency network. We thus question policies that restrict exclusivity of agent networks. Moreover, new entrants into the account provision and transfer market are always free to establish competing networks and anyone can, in principle, set up a merchant business.

When services are unbundled and no longer part of a traditional bank the question arises as to which organization should regulate them. Financial regulators are the most competent for banking and financial services, and depending on the country prudential regulation and business conduct regulation may be under the same roof or carried out by different agencies (the “twinpeak” model of regulation). However, where competition issues arise in network industries, it is typically sensible to allocate responsibility to sector-specific regulators. Special rulings on matters like interconnection pricing and rulings on exclusivity arrangements may be better made by sector-specific regulators that have a grasp of the technical and organizational intricacies of the problem at hand. In some cases the regulatory system may require co-ordination between several different sector-specific regulators, for example when co-coordinating registration requirements for SIM cards and accounts. Most of the time, rulings by telecom regulators on competition issues in the telecom network and rulings on competition in the payments network by financial regulators can be made separately but some level of consultation about the nature and timing of decisions may be required.

Beyond financial inclusion, the M-PESA experiment provides important insights into the regulation of financial services in developed economies. There is an active debate in the UK and US amongst other countries about the separation of commercial and investment banking and 27
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debt declines from about 58 percent in 2002-07 to 49 percent in 2012-17 while that of multilateral debt decreases from 37 percent to 32 percent over the same time period.

**Figure 12. Share of Concessional and Multilateral Debt in Public External Debt** _(%)_

<!-- Start of picture text --> 60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>2002-07 2008-11 2012-17<br>Concessional External Debt Multilateral External Debt<br><!-- End of picture text --> _Source: World Bank – International Debt Statistics_

# **Fact II. The change in the composition of public external debt has rendered not only a riskier debt profile but also costlier external debt service.**

Rising debt owed to private creditors and non-Paris Club governments, which comes often at shorter maturities and higher interest rates, is rendering larger debt service burdens for borrowing countries in the region. Since 2010, total public external debt service in the region increased by about 4 percentage points of exports and this increase has been driven by service to bilateral creditors (mainly, non-Paris Club governments) and bondholders ( **Figure 13** ). Unlike government members of the Paris Club, these groups of creditors may not have the mechanisms to coordinate with other creditors —thus, hindering processes of debt resolution.

Debt levels for the region remain modest in comparison to emerging markets and are still lower than they were at the start of the 2000s. However, there are a few countries where the debt outlook is worrisome. Sub-Saharan Africa’s public debt problems are currently concentrated in Mozambique, Angola, the Republic of Congo, Ghana, Nigeria and Zambia. However, a small shock may prompt a crisis in other countries (say, Kenya). Mozambique already had to renegotiate its debt while debt service costs account for more than 15% of government revenue in Angola and Kenya and more than 20 percent in Zambia, Ghana and Nigeria.

13
**Figure 30: Average Share of Trade Deficit of East Asian Countries in U.S. Trade Deficit**

<!-- Start of picture text --> 1990-1999 2000-2009<br>East<br>Asian<br>Other East Other<br>Countries<br>Asian<br>Countries Countries<br>38%<br>49% Countries 62%<br>51%<br><!-- End of picture text --> Source: United Nations Comtrade, World Integrated Trade Solution.

# **_Global savings glut and interest rates_**

48. One of the most important channels through which global imbalances are considered to have caused the global economic crisis is through the impact on interest rates (Krugman 2009; Summers 2008). The hypothesis is that the global savings glut put extreme downward pressure on interest rates and thereby catalyzed the real estate boom in many countries and the risky innovations in the financial sector that were crucial to the global contagion of the financial crisis. However, recent research<sup>27</sup> highlights that the global savings glut or ―excess savings‖ may not be related to the downward pressure on world interest rates and the financing of the booms in those countries.

49. A paper by Borio and Disyatat (2011) on the relationship between excess savings associated with current account imbalances and world interest rates concludes that this link may be tenuous. The authors stress that only financial flows can have an impact on world interest rates. However, current accounts and net capital flows say little about financing, as they do not allow statements on the underlying changes in gross flows and their contributions to existing stocks, including the transactions involving trade in financial assets. As a result, current account imbalances provide little information on the role a country plays in international borrowing, lending and financial intermediation, about the degree to which its real investments are financed from abroad and about the impact of cross-border capital flows on domestic conditions.

> 27 Borio and Disyatat (2011), Bank for International Settlements.  Laibson and Mollerstrom (2010).

37
ter and D. Card (eds.), Handbook of Labor Economics 3A, Elsevier, Amsterdam, North Holland.

- Stiglitz, J (2012), The Price of Inequality, W. W. Norton and Company.

- The Economist (2012), “For Richer, For Poorer”, Special Report on Inequality by Zanny

- Minton Beddoes, October 13th 2012.

United Nations. 1989. “Household Income and Expenditure Surveys: A Technical

- Study.”, National Household Survey Capability Programme, New York.

World Development Report (2006), Equity and Development, Oxford University Press.

24
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**-53-**

|**Quantities**<br>**(Value Added**<br>of<br>|**. Former**<br>Manufactur<br>atBrazili<br>|**TABLE 3.15**<br>**National**<br>**A**<br>ing.<br>Brazi<br><br>an<br>"price|**ccounts**<br>l/USA.<br>...______<br>s"<br>|**Concept)**<br>1975<br>__________<br> <br>|**by MaJor B**<br>_________<br>atUS"pric|**ranch**<br>________<br>es"<br>|
|---|---|---|---|---|---|---|
||Brazil<br>1975<br>(1975 Cr.|USA<br>1975<br>million)|Brazil/<br>USA<br>(%)|<br>Brazil<br>1975<br>(1975 USS|<br>USA<br> <br>1975<br>million)|Brazil/<br>USA<br>(%)|
|Food Products<br>|27,759<br>|<br>144,744<br>|19.18<br>|<br>10,337<br>|<br>25,421|40.66|
|Beverages<br>|4,565|41,774|10.93|<br>590|<br>5,155|11.45|
|Tobacco Products<br>|2,987|12,203|24.48|<br>682|<br>2,469|27.62|
|Textiles and Wearing Apparel<br>|22<sup>,</sup>94<sup>0a</sup><br>|<br>270,854<br><br>|8.47<br>|<br>2,339<br>|<br>20,325|11.51|
|Footwear and Leather Products<br>|3,977a<br>|<br>13,145<br> <br>|30.26<br>|<br>833<br>|<br>2,339|35.63|
|Wood and Paper Products<br>|27,696<br>|<br>482,290<br>|5.74<br>|<br>3,362<br>|<br>45,788|7.34|
|Chemical Products<br>|42.511<br>|388,781<br> <br>|10.93<br>|<br>4,286<br>|<br>32,627|13.14|
|Rubber and Plastic Products<br>|10,260<br>|<br>121,560<br>|8.44|<br>881|<br>9,231|9.55|
|Stone, Clay and Glass Product<br>|s 15,365<br>|<br>84,899<br> <br>|18.10<br>|<br>3,130<br> <br>|<br>10,817<br> <br>|28.93<br>|
|Metal products<br>|31,176<br>|<br>470,798<br> <br>|6.62<br>|<br>4,289<br>|<br>52,617|8.15|
|Electrical Machinery<br>|15,437|<br>211,184|7.31|<br>2,020|<br>27.369|7.38|
|Machinery and Transport<br>|||||||
|Equipment<br>|44,231|<br>513,071|8.62|<br>6.996|<br>79,087|8.85|
|Other|8,109|132,811|6.11|<br>1,295|<br>15,099|8.58|
|Total<br>|257,012|2,888,112|8.90|<br>41,039|328,343|12.50|

- a) the footwear industry (2,675.9 million cruzeiros) was reallocated from wearing apparel to footwear and leather.

- Note: The breakdown between food products and beverages for the US on a national accounts basis was assumed to be proportionately the same as on a US Census basis (1975 figures derived from Annual Survey of Manufactures).

Source: Brazil value added in national currencies from Censo Industrial (see table 2.1 which does not exclude bank costs). US value added in national currencies from National Income and Products Accounts of the United States: 1929-76 Statistica. Tables (1981c) after adjustment for inventories indirect taxes and subsidies **an** net intrest (see table 2.6). PPPs from table 3.14.
(LCU), we use end of period USD/LCU exchange rates from the International Financial Statistics (IFS) to obtain sales in USD.

**_Firm Age_** is defined as the number of years since the firm began operations in the country. We compute age at the end of the last fiscal year to be consistent with the employment question.

We winsorize the top and bottom 1% of firm size, size at birth, and labor productivity (before taking logs) to minimize the effect of outliers.

Table1 presents the summary statistics and correlations between the main variables. Panel A of Table 1 shows that the mean firm size in our sample is 59 employees. The mean firm size ratio is 5.64. Thus, the average firm in our sample is 5.6 times its size when it started operations. The ratio extends from a very small number to 860. The firms with ratios in excess of 100 are spread out across countries and industries.<sup>18</sup> For instance, there are two firms with a size ratio of 860, one is a Columbian firm surveyed in 2010 in the industry sector _Metals and Machinery_ which is privately held and started operations in 1940 with 1 employee and had 860 employees in 2009 and the other is a publicly listed company in Mauritius in _Non-metallic and Plastic Materials_ surveyed in 2009 which started operations in 1953 with 1 employee. 55% of our sample is composed of manufacturing firms and while the average log labor productivity is 10.08, there is wide heterogeneity as seen by the range between 5.81 and 17.2. Panel B shows that older firms in our sample are larger and more productive. Firm Size Ratio and Age are also positively correlated suggesting that firms grow as they age.

> 18 Our results are robust to using median regressions rather than OLS for the specifications with size ratios.

11
13 differences between the K/S/V study and that summarized in Anderson are that the former’s product sample is smaller, its country sample is smaller (in particular, it omits the biggest developing countries of China, India and Indonesia), and it provides only unweighted averages of distortions to farmer incentives across its developing countries.

# **Distortions to Agricultural Incentives in Developing Countries before 1985**

We turn now to summarizing first the stylized facts that emerged from K/S/V for the period to 1984 as compared with the findings of Anderson and Valenzuela’s (2008) compilation and aggregation of NRAs and related indicators. We begin by focusing on just those developing countries included in the K/S/V sample, and then show how much the new estimates for the fuller sample of 41 developing countries differ from those for the more-limited K/S/V sample of 17 countries. We leave until the following two sections a discussion of the estimates for more-advanced economies pre-1985, and of the period since the K/S/V era for both sets of countries.

The key empirical findings from the study by Krueger, Schiff and Valdés (1988; 1991) and their authors’ detailed country case studies are based on the estimates shown in table 2, for 4 groups of countries classified according to their level of national per capita income at the time.

The most important findings, based on the unweighted average estimates across developing countries for the entire period from 1960 to 1984 roughly (see exact years for each country in note b of table 2), are:

- The direct rate of assistance (DRA) to farmers, due to agricultural policies, was negative (average DRA of -8 percent), but tended to be more negative the lower a country’s per capita income (as low as -23 percent for the lowest-income group, but +24 percent for the highest-income group);

- Even more important were non-agricultural policies, particularly manufacturing protection, which on average were three times as harmful to farmers as agricultural policies;
# **Appendix Table 2. Shocks and Transfers from Regular Clients for Other Shocks (Daily)**

||(1)<br>D|(2)<br>ependent|(3)<br>Variable: G|(4)<br>ifts from Re|(5)<br> gular Clien|(6)<br>ts|
|---|---|---|---|---|---|---|
|Mean of Dependent Variable<sup>1</sup>|||76|.10|||
|**Panel A. Respondent was Asked for a**|**Gift or Loan**||||||
|Respondent was Asked for|24.26|24.25|24.81|47.05|46.84|47.97|
|a Gift or Loan Today|(18.62)|(18.57)|(17.80)|(29.45)|(29.10)|(28.41)*|
|Respondent was Asked for||||21.71|21.24|20.33|
|a Gift or Loan Yesterday||||(26.38)|(26.58)|(26.39)|
|Respondent was Asked Today *||||-57.05|-57.47|-58.28|
|Respondent was Asked Yesterday||||(77.07)|(76.12)|(76.45)|
|# of Regular Clients Seen||5.37<br>(11.47)|||6.02<br>(12.22)||
|# of Casual Clients Seen||-6.02<br>(8.73)|||-7.80<br>(8.78)||
|Activity Controls|No|No|Yes|No|No|Yes|
|Observations|5465|5465|5465|4929|4929|4929|
|Number of Women|108|108|108|108|108|108|
|_p_-value for asked for gift/loan today +<br>asked for gift/loan yesterday = 0|-|-|-|0.84|0.86|0.86|
|**Panel B. Respondent had an STI**|||||||
|Respondent had an STI|19.52|17.02|16.88|82.39|78.22|78.72|
|Today|(21.90)|(21.97)|(22.25)|(54.17)|(54.01)|(54.58)|
|Respondent had an STI||||53.21|53.93|53.34|
|Yesterday||||(30.37)*|(30.52)*|(29.85)*|
|Respondent had an STI Today *||||-135.64|-135.17|-135.55|
|Respondent had an STI Yesterday||||(69.81)*|(69.20)*|(69.93)*|
|# of Regular Clients Seen||6.49<br>(7.03)|||5.77<br>(7.35)||
|# of Casual Clients Seen||-8.71<br>(4.69)*|||-10.04<br>(4.75)**||
|Activity Controls|No|No|Yes|No|No|Yes|
|Observations|12321|12321|12321|11330|11330|11330|
|Number of Women|192|192|192|192|192|192|
|_p_-value for STI today +|-|-|-|1.00|0.89|0.87|
|STIyesterday= 0|||||||

Notes: All variables are measured at the daily (rather than client) level, as we are not able to match clientspecific transfers to client-specific activities. Regressions are fixed effects regressions with controls for the day of the week and the month of the year. Sexual activity controls include vaginal sex, anal sex, oral sex, kissing, massage, stripping, whether the women gave a client company, whether the woman had unprotected sex, and whether she engaged in any other activities. All sexual activity variables measure the number of clients with whom the woman engaged in the given activity at least once, since we did not record the number of repetitions for all activities. Sexual activities were missing for some women. To avoid dropping them from the regressions, we code the activity as 0 and generate dummies for whether the activity is missing. See text for more details.

1Mean of Dependent variable is mean when all shocks equal 0. Exchange rate was roughly 70 Kenyan shillings to US $1 during the sample period.

* significant at 10%; ** significant at 5%; *** significant at 1%