rafmacalaba/datause-encoder-data huggingface dataset:
The results are reported in Table 3.

**Table 3: Estimation results for determinants of sovereign ratings**

|**Dependent variable:**|**S&P 2008**|**S&P 2012**|**2008-2012**|**S&P 2008**|**S&P 2012**|
|---|---|---|---|---|---|
|**Sovereign rating**|**OLS**|**OLS**|**Pooled**|**O. Probit**|**O. Probit**|
|GDP growth|-0.43**<br>(0.23)|-.55**<br>(0.27)|-0.53***<br>(0.17)|-0.10<br>(0.06)|-0.13**<br>(0.06)|
|Log of GNI per capita|-3.66***|-3.60***|-3.82***|-0.80***|-0.87***|
||(0.81)|(0.86)|(0.58)|(0.26)|(0.20)|
|Reserves to import and|-4.07***|-3.78*|-3.92***|-0.97**|-0.70|
|ST debt|(1.21)|(1.99)|(1.03)|(0.39)|(0.45)|
|External debt to exports|0.59**|0.73|0.78***|0.16**|0.15*|
|& remittances|(0.25)|(0.51)|(0.27)|(0.06)|(0.09)|
|GDP volatility|1.58***|-0.13|-0.17|0.40**|-0.05|
||(0.56)|(0.33)|(0.25)|(0.16)|(0.07)|
|Rule of law|-6.34***|-7.91***|-7.46***|-1.74***|-1.64***|
||(1.03)|(0.91)|(0.71)|(0.27)|(0.23)|
|Inflation|0.48**|0.12**|0.19***|0.12**|0.04***|
||(0.20)|(0.05)|(0.05)|(0.05)|(0.01)|
|Govt. debt|0.08***|0.14***|0.11***|0.02***|0.03***|
||(0.02)|(0.03)|(0.02)|(0.004)|(0.006)|
|Log of GDP|-1.65***|-2.32***|-1.94***|-0.49***|-0.52***|
||(0.28)|(0.31)|(0.23)|(0.09)|(0.09)|
|High income dummy|-5.83***|-3.8*|-3.82**|-1.56***|-0.53|
||(1.84)|(2.10)|(1.51)|(0.52)|(0.41)|
|Intercept|96.47***<br>(7.68)|113.56***<br>(8.21)|107.48***<br>(5.77)|||
|GDP volatility X 2008<br>dummy|||1.61***<br>(0.57)|||
|2008 dummy|||-2.59**<br>(1.18)|||
|Observations|94|90|191|94|90|
|Adj. R-sq. (OLS)|0.93|0.87|0.90|||
|Pseudo-R-sq. (O. Probit)||||0.40|0.30|
|Logpseudo-likelihood||||-172.99|-202.81|

_* significant at 10%; ** significant at 5%; *** significant at 1%. Robust standard errors in parenthesis._ The empirical estimates are arrived at using ordinary least squares (OLS). OLS is appropriate if we are willing to assume that the ratings scale is cardinal and adjacent rating/outlook states have equal distance. However, credit ratings are often estimated using ordered probit which relaxes this assumption and requires the scale to be merely ordinal. We therefore also report ordered probit estimates. But for estimating ratings for developing countries and shadow ratings for unrated countries, ordered probit suffers from some drawbacks. Its ability to estimate ratings at the lower end of the ratings spectrum where data 11
108 sources, make services more convenient and, as mentioned, usually cover the cost of service delivery and commodities. The effects of nev employee benefits for family planning include: shifting the source of service from other public or private sources; shifting users from traditional or less effective methods to more effective contraceptive methods; or, increasing the level of overall acceptance from improved information and access. Which of these is most common is unclear, although some programs have assessed method shifting, increases in knowledge, and nev users at the site (ignoring the possibility of a shift from outside sources).

Vhereas firms that elect to supplement employees salaries through family planning services are providing an additional benefit, donors that are serving the factory population through subsidized services are aiming their subsidies at iqiddle income earners, even if that means lover middle income. It is unclear vhy those services need to be subsidized by donors or hov long the donor vill be able to bear the cost of the program. Thus there is an issue of whether there should even be a commodity subsidy on an ongoing basis, and how long such<sup>an</sup> arrangement should or can go on. Social marketing programs, on the other hand, are aimed at lower middle or upper lover income households under the assumption that the poorest couples will seek free public services. However, there is equal access for all users and since no assessment of social marketing users<sup>has</sup> ever been undertaken, no evidence exists on who uses the products. The SOHARC project in the Dominican Republic, hovever, is planning to undertake such a study.

The other issue, vhich none of the projects consider, is how much of a subsidy is required to reach the target population. Some of these projects have modest budgets, mainly because contraceptives are not purchased for the clinic. What has not been assessed is the relative effectiveness of modest technical

**ml.tp:Fuily Plannng Papper (whol.e**
**Table 3**

**Regression Results of Public Transport Mobility with sectoral restriction (country fe)**

|**(2.a)**<br>**PANDEMIC & LOCKDOWN EVOLUTI**<br>1480***|**ON**<br>1304***|**(2.b)**<br>1260***|
|---|---|---|
|**Covid death (per Million)**<br>-.<br>(0.025)|-.<br>(0.025)|-.<br>(0.026)|
|**Public transport closure**<br>-12.669***<br>(0.155)|-11.974***<br>(0.136)|-11.996***<br>(0.136)|
|**Public transport facial covering**<br>1.170***<br>(0.080)|1.239***<br>(0.059)|1.219***<br>(0.077)|
|**CONTROLS**|12408***|12439***|
|**Working days**|.<br>(0.287)|.<br>(0.287)|
|**Min temp**|0.092***<br>(0.019)|0.093***<br>(0.019)|
|**Avg temp**|0.229**<br>(0.018)|0.232***<br>(0.018)|
|**Precipitation**|-1.378***<br>(0.079)|-1.363***<br>(0.071)|
|**City density**||-0.016<br>(0012)|
|**TRENDS IN MODAL COMPETITIO**|**N**|.<br>0.544***|
|**Car registration**||<br>(0.012)|
|**COUNTRY FE**<br>|||
|**Country FE**<br>Yes|Yes|Yes|
|**Monthly dummy**<br>Yes|Yes|Yes|
|**Constant**<br>-4.010<br>(7.454)|-21.899<br>(7.513)|-21.515***<br>(7.348)|
|**N**<br>59,777|59,777|59,777|
|**N cities**<br>186|186|186|
|**Wald χ2**<br>110836.80***|122569.48***|122635.90***|
|**Within R**<sup>**2**</sup><br>0.6498|0.6724|0.6725|
|**Between R**<sup>**2**</sup><br>0.6677|0.6573|0.6610|
|**Overall R**<sup>**2**</sup><br>0.6536|0.6697|0.6706|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent.

14
# **1. Introduction**

Income from off-farm employment<sup>2</sup> constitutes a significant share of the household livelihood portfolios across Sub-Saharan Africa (SSA) (Davis et al., 2017). It contributes to economic growth, poverty reduction and income smoothing (van den Berg and Kumbi, 2006; Bezu et al., 2012; Fox and Sohnesen, 2016) and can enable investments on the farm (Adjognon et al., 2017; Ayenew et al., 2017; Dedehouanou et al., 2018). It is further promoted to economically empower women by increasing their share over total household income (Buvinić and Furst-Nichols, 2014). However, access to qualitative employment is often confined to relatively better-off households (Alobo Loison, 2015), and within households, considerable heterogeneity exists with women and young people being disadvantaged in accessing employment (Hertz et al., 2008; Fox et al., 2016; Yeboah and Jayne, 2018). The ensuing implications for economic development depend on the prospect of entry into off-farm employment as well as the ability to remain employed (Bezu and Barrett, 2012).

While off-farm employment has received extensive attention in the existing literature, there are some important research gaps and methodological shortcomings. First, most studies look at household-level participation in off-farm employment, and not at individual-level. As women and youth are assumed to be disproportionally disadvantaged in both quantity and quality of employment, age and gender disaggregated data are a must to study the inclusiveness of off-farm employment. Second, most studies focus on settings where off-farm employment opportunities suddenly increased. Drawing general conclusions from these studies would lead to an overestimation of the importance of off-farm employment in more remote areas. Moreover, specific case studies do not allow to analyze linkages and influential factors from a broader perspective. Third, the available evidence is mainly informed by cross-sectional data. Such data cannot be used to investigate dynamics over time (e.g. entry into or exit from off-farm employment) and are likely to be prone to causal estimation issues. Fourth, while non-farm entrepreneurship in SSA has been elaborately investigated, the evidence regarding wage employment and rural labor markets is seriously lacking.

> 2 Off-farm employment is defined as all economic activities that take place outside the agricultural household, hence excluding on-farm self-employment activities but including off-farm salaried and casual wage employment, and offfarm self-employment.

2
Table 3a: Industry-level distribution of female activity in unorganized manufacturing

||Estimat<br>owne|ed count of<br>d establish|female-<br>ments|Share o<br>industryt|f establishm<br>hat are fem|ents in<br>ale-owned|Estima|ted count of<br>employees|female|
|---|---|---|---|---|---|---|---|---|---|
||2001|2006|2010|2001|2006|2010|2001|2006|2010|
|**15** Food products and beverages|322,477|407,214|310,072|0.110|0.163|0.144|1,511,752|1,421,604|956,762|
|**16** Tobacco products|1,368,973|2,008,088|1,807,527|0.654|0.721|0.815|2,143,567|2,861,262|2,266,572|
|**17** Textiles|821,118|1,132,295|1,327,794|0.349|0.467|0.509|2,262,706|2,256,550|2,316,948|
|**18** Wearing apparel; dressing and dyeing of fur|906,643|1,452,207|2,214,195|0.327|0.464|0.526|1,128,173|1,633,362|2,573,955|
|**19** Leather tanning; luggage, handbags, footwear|8,017|12,125|10,538|0.047|0.089|0.100|35,845|35,690|35,905|
|**20** Wood and wood products; straw and plating articles|477,855|437,638|206,609|0.173|0.211|0.135|1,379,253|1,223,001|570,938|
|**21** Paper and paper products|37,462|125,430|50,259|0.435|0.782|0.432|75,048|198,602|94,871|
|**22** Publishing, printing and media reproduction|8,763|9,559|12,701|0.066|0.089|0.079|32,926|41,686|55,183|
|**23** Coke, refined petroleum and nuclear fuel|196|392|38|0.029|0.074|0.041|1,973|1,541|1,101|
|**24** Chemicals and chemical products|165,455|332,369|152,666|0.781|0.821|0.739|258,966|510,297|263,461|
|**25** Rubber and plastic products|23,952|14,017|78,498|0.284|0.220|0.297|52,187|52,163|125,981|
|**26** Other non-metallic mineral products|35,521|33,779|40,912|0.045|0.056|0.074|792,384|502,389|631,427|
|**27** Basic metals|643|1,373|10,000|0.018|0.044|0.186|3,530|4,967|14,910|
|**28** Fabricated metal products, except machinery|11,141|11,230|8,922|0.018|0.019|0.014|98,328|65,839|57,178|
|**29** Machinery and equipment, n.e.c.|3,575|3,782|1,141|0.023|0.023|0.005|19,153|15,363|20,484|
|**30** Office, accounting and computing machinery|0|0|0|0.000|0.000|0.000|82|519|550|
|**31** Electrical machinery and apparatus, n.e.c.|3,051|4,346|952|0.051|0.043|0.036|32,323|21,864|7,212|
|**32** Radio, television, and comm. equipment|275|565|2,084|0.048|0.124|0.004|3,233|936|10,124|
|**33** Medical, precision and optical instruments, watches|409|408|232|0.054|0.045|0.022|1,703|1,992|2,023|
|**34** Motor vehicles, trailers and semi-trailers|318|207|594|0.017|0.015|0.036|2,273|2,408|7,298|
|**35** Other transport equipment|212|8,020|568|0.014|0.337|0.032|1,170|2,738|2,286|
|**36** Furniture, manufacturing n.e.c.|221,350|179,464|411,169|0.170|0.162|0.252|475,311|323,674|700,604|
|Nationwide|4,417,408|6,174,508|6,647,471|0.266|0.375|0.386|10,311,885|11,178,449|10,715,774|

Notes: See Table 1.
However, recent migrant households near the Syrian border in 2013 are distinctively poorer than any previous group of migrant households.

Table 4. Imputed Poverty Rates (LFS)

|||Ne<br>|arSyrian Bor<br>|der<br>|Re<br>|st ofthe Coun<br>|try<br>|
|---|---|---|---|---|---|---|---|
|||Host<br>Community|Settled<br>Migrants<br>(>4years)|Recent<br>Migrants<br>(<=4years)|Host<br>Community|Settled<br>Migrants<br>(>4years)|Recent<br>Migrants<br>(<=4years)|
|2009|Poverty Rate|44.2|27.5|15.6|21.1|9.1|7.3|
||S.E.|0.8|6.7|22.8|0.3|0.7|5.0|
|2010|Poverty Rate|42.6|23.8|27.1|20.8|10.7|5.2|
||S.E.|0.7|6.4|19.3|0.3|0.8|3.2|
|2011|Poverty Rate|40.9|12.5|17.5|19.3|9.3|13.3|
||S.E.|0.7|5.4|25.6|0.3|0.7|4.9|
|2012|Poverty Rate|41.1|21.3|37.0|18.4|7.0|13.2|
||S.E.|0.7|5.1|22.5|0.3|0.7|4.5|
|2013|Poverty Rate|40.3|17.7|46.7|17.9|6.6|16.1|
||S.E.|0.8|5.8|7.9|0.3|0.5|6.7|

_Notes_ : Regionally deflated, OLS imputation model, with time-invariant characteristics. The Near Syrian Border region (NSB) includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, TR62. Time in Turkey is based on the date of arrival of the head of household. _Source_ : LFS 2009-2014. World Bank author’s calculations.

Figure 2 illustrates poverty trends in 2009 and 2013, by region and migrant status. Regions are divided into the near Syrian border region, and the rest of the country. The NSB includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, and TR62. The population is also divided into host communities, recent migrant households, and settled migrant households.

Poverty is higher in regions near the Syrian border, for both host communities and migrants. The South Eastern region of Turkey has been historically the poorest area of the country. However, between 2009 and 2013, poverty has declined for host communities and settled migrant households. On the other hand, poverty of recent migrant households in the NSB region has been increasing, and significantly so in 2013 (Figure 3). The sharp increase in the poverty rates of recent migrant households is evident. Near the Syrian border, recent migrant households had a poverty rate of 15.6 percent (caveat on the small sample size and large standard error) in 2009, and a rate of 46.7 percent in 2013. There was a parallel increase among the recent migrant households in rest of the country, from 7.3 percent to 16.1 percent over the four years.

11
Djankov, S., C.S. Pham, and C. Freund. 2010. “Trading on Time.” _Review of Economics and Statistics_ 92(1): 166-173.

Escaith, H., Lindenberg, N., and S. Miroudot. 2011. “Global Value Chains and the Crisis: Reshaping the International Trade Elasticity?” in Cattaneo, O., Gereffi, G., and C. Staritz eds. Global Value Chains in a Postcrisis World. Washington, D.C.: World Bank.

Feenstra, R. 1996. “U.S. Imports: Data and Concordances.” National Bureau of Economic Research Working Paper 5515.

Haddad, M., and B. Shepherd. 2011. “Managing Openness: From Crisis to Export-Led Growth Version 2.0.” in M. Haddad and B. Shepherd eds. _Managing Openness: Trade and Outward-Oriented Growth after the Crisis_ . Washington, D.C.: World Bank.

Hanson, G., Mataloni, R., and M. Slaughter. 2005. “Vertical Production Networks in Multinational Firms.” Review of Economics and Statistics, 87(4), 664-678.

Haveman, J., and D. Hummels. 2004. “Alternative Hypotheses and the Volume of Trade: The Gravity Equation and the Extent of Specialization.” _Canadian Journal of Economics_ 37(1): 199-218.

Helble, M., B. Shepherd, and J.S. Wilson. 2009. “Transparency and Regional Integration in the AsiaPacific.” _World Economy_ 32(3): 479-508.

Helpman, E. 2006. “Trade, FDI, and the Organization of Firms.” Journal of Economic Literature 44(3), 589–630.

Helpman, E., M. Melitz, and Y. Rubinstein. 2008. “Estimating Trade Flows: Trading Partners and Trading Volumes.” _Quarterly Journal of Economics_ 123(2): 441-487.

21
$40.<sup>19</sup> Because this value measures the private cost to consumers and producers in the market, it should be compared to the social benefit of the lower emissions. Intragency Working Group (2021) provides a preliminary estimate of social benefits equal to about $47 per ton of carbon dioxide. Van den Bremer and Van der Ploeg (2021) report higher estimates after adjusting for climate and economic risk, although their estimate is generally lower than our estimated welfare costs.

Table 9: Emissions and cost-effectiveness

|**Variable**|**No**<br>**Policy**<br>**(level)**|**Current**<br>**Policy**<br>**(diff)**|**Sales tax**<br>**reduction**<br>**only (diff)**|**Tariff**<br>**reduction**<br>**only (diff)**|**Carbon**<br>**tax**<br>**(diff)**|
|---|---|---|---|---|---|
||**Panel A: Ye**<br> <br>|**ar 2019** <br>||||
|Lifetime CO2emissions (thousand tons)|<br>3416.8|-18.2|-3.0|-9.0|-59.6|
|Fiscal cost (USD) per ton of CO2|0.0|353.3|140.9|247.3|106.7|
|Welfare cost(USD) per ton of CO2|0.0|39.6|-30.8|76.1|-497.9|
||**Panel B: Ye**|**ar 2020**||||
|Lifetime CO2emissions (thousand tons)|<br>2126.0|-18.4|-3.3|-9.1|-40.6|
|Fiscal cost (USD) per ton of CO2|0.0|509.2|307.9|449.3|-740.6|
|Welfare cost(USD) per ton of CO2|0.0|47.8|135.8|265.7|-486.1|

Note: This table shows the counterfactual simulations of emissions and cost-effectiveness for the five scenarios in 2019 and 2020. The "No Policy" scenario’s calculations are in levels. All other scenarios’ calculations are increments from the "No Policy" scenario. The Lifetime CO2 emissions are computed by multiplying the emissions produced per vehicle times the average mileage driven per year in Colombia times the expected lifetime of a vehicle. We assume that the average mileage driven in Colombia is 12 thousand miles, while a vehicle's lifetime is ten years. The "Fiscal cost per ton of CO2" is computed by dividing the fiscal cost by the difference in lifetime emissions in each scenario. The "Welfare cost per ton of CO2" results from dividing the difference in total welfare by the difference in lifetime emissions in each scenario.

Panel B in Tables 8 and 9 report results using the 2020 market for the simulations. Qualitatively, the results are similar to the 2019 results: the status quo has high fiscal costs and welfare costs that are comparable to global climate benefits, the sales tax reduction has lower average fiscal and welfare costs, and the carbon tax proposal has lower welfare costs than the sales tax or tariff policies. An important difference between the 2019 and 2020 results is that the average costs of the policies tend to be larger in 2020. Much of that difference is explained by the fact that

> 19 This estimate is based on assumption that carbon emissions from electric cars is zero. If we account for emissions from electricity generation in the cost of the policy, the welfare cost per ton of carbon dioxide increases to $63 (see Table A.1 in Appendix).

26
Infrastructure Complementarities and Local Economic Growth: Evidence from Electrification and Highway Construction in Brazil<sup>∗</sup> Harris Selod<sup>1</sup> , Jevgenijs Steinbuks<sup>1</sup> , Ian Trotter<sup>2</sup> , and Brian Blankespoor<sup>1</sup>

> 2Universidade Federal de Vi ̧cosa, Brazil

Originally published in the Policy Research Working Paper Series on _May 2024._ This version is updated on _June 2024._ To obtain the originally published version, please email prwp@worldbank.org.

JEL: O13, O18, O47, O54 Keywords: Brazil, electricity, highways, infrastructure complementarities, local economic development

> ∗The authors thank Cl ́ement de Chaisemartin, David McKenzie, Niclas Moneke and St ́ephane Straub as well as attendees of the NCID workshop on Energy and Environmental Issues in Developing Countries, the European Meeting of the Urban Economics Association, the annual workshop on the Empirical Methods in Energy Economics conference, and the Urbanization and Poverty Reduction Research conference for useful advice, comments and suggestions.
<!-- Start of picture text --> TACNA<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>TRUJILLO<br>7<br>5<br>3<br>1<br>-1<br>-3<br>-5<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>Celsius)<br>average for 1960-1990 (degrees<br>Temperature Anomaly compared to<br>Celsius)<br>average for 1960-1990 (degrees<br>Temperature Anomaly compared to<br><!-- End of picture text --> <!-- Start of picture text --> TALARA<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>TUMBES<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>Temperature Anomaly compared to<br>average for 1960-1990 (degrees Celsius)<br>Temperature Anomaly compared to<br>average for 1960-1990 (degrees Celsius)<br><!-- End of picture text -->

# _<u>Mountain stations</u>_

<!-- Start of picture text --> AREQUIPA AUGUSTO WEBERBAUER<br>5 5<br>4 4<br>3 3<br>2 2<br>1<br>1<br>0<br>0<br>-1<br>-1<br>-2<br>-2<br>-3<br>-3<br>-4<br>-4<br>-5<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03 -5<br>may-48 oct-53 abr-59 oct-64 mar-70 sep-75 mar-81 ago-86 feb-92 ago-97 feb-03<br>Temperature Anomaly compared to average for 1960-1990 (degrees Celsius) Temperature Anomaly compared to average for 1960-1990 (degrees Celsius)<br><!-- End of picture text --> 28
FIGURE __________ 6.1 54

- 23 -

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# Table 2: FDI and Growth, Top 20 Percent Most Regulated Countries (OLS)

||||De<br>|pendent Vari<br>|able: Growth<br>|in current per<br>|iod<br>|||
|---|---|---|---|---|---|---|---|---|---|
|Independent Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Constant|1.25**|1.37***|1.31***|1.22***|1.43***|0.90**|1.12**|1.26**|0.84*|
||(2.48)|(2.84)|(2.61)|(2.78)|(2.81)|(2.06)|(2.28)|(2.34)|(1.71)|
|Change over previous period in average FDI|0.28***|0.35***|0.33***|0.36***|0.35***|0.30***|0.33***|0.26***|0.31***|
||(2.92)|(3.86)|(3.58)|(3.67)|(3.95)|(3.20)|(4.00)|(2.55)|(3.65)|
|Change over previous period in average||-0.36**|<br>-0.32*|<br>-0.35**|<br>-0.38**|<br>-0.26*|-0.31**|<br>-0.17|-0.23|
|FDI*Regulation Dummy||(-2.08)|(-1.74)|(-2.02)|(-2.07)|(-1.71)|(-1.95)|(-1.09)|(-1.58)|
|Growth in previous period|0.33***|0.31***|0.32***|0.32***|0.29***|0.40***|0.28***|0.36***|<br>0.35***|
||(3.44)|(3.21)|(3.20)|(3.60)|(2.82)|(4.41)|(3.07)|(3.95)|(3.62)|
|Change over previous period in average rule of law|0.23|0.21|0.27|0.20|0.22|0.36**|0.20|0.37**|0.37**|
||(1.42)|(1.41)|(1.60)|(1.29)|(1.48)|(2.46)|(1.35)|(2.29)|(2.43)|
|Change over previous period in average inflation rate|||0.10<br>(0.29)||||||0.39<br>(1.12)|
|Change over previous period in average population||||-0.26|||||0.20|
|growth||||(-0.54)|||||(0.55)|
|Change over previous period in average government|||||0.03||||0.03|
|consumption|||||(0.59)||||(0.40)|
|Change over previous period in average BMP||||||-0.05|||-0.19|
|||||||(-0.15)|||(-0.52)|
|Change over previous period in average trade|||||||1.92***||1.41*|
||||||||(2.75)||(1.73)|
|Regional dummies<sup>1</sup>|No|No|No|No|No|No|No|Yes|No|
|F-value for joint test on change over previous period||7.64***|6.78***|6.80***|7.94***|5.16***|8.17***|3.28**|6.87***|
|in average FDI and change over previous period in||||||||||
|average FDI*Regulation Dummy<br>||||||||||
|R<sup>2</sup>|0.29|0.34|0.35|0.34|0.34|0.43|0.38|0.47|0.48|
|Observations|89|89|88|89|89|82|89|89|82|

Notes:<sup>1</sup> North America has been excluded; current and previous period refer to the periods 1994-2003 and 1984-1993, respectively; t-values, which have been corrected for heteroskedasticity, are reported in parentheses; multicollinearity has been tested by the creation of variance inflation factors (VIF); all regressions pass at conventional levels; *** significant at 1% level; ** significant at 5% level; * significant at 10% level.

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