rafmacalaba/datause-encoder-data huggingface dataset:
UNCTAD value is missing; otherwise, we use the UNCTAD data.<sup>29</sup> This approach of rescaling the UNCTAD data to the CDIS data results in aggregate values that are consistent over time and similar to the country-level aggregates reported in Lane and Milesi-Ferretti (2018).

The treatment of zeros and missing values in the combined CDIS-UNCTAD data set deserves some attention because countries are more likely to report zeros in the CDIS data than in the UNCTAD data.<sup>30</sup> Hence, it is likely that many missing observations in the UNCTAD data are actually true zero-valued observations, rather than missing values. In addition, the procedure of combining the two data sets yields some missing observations, even when data are actually available. Specifically, if for any country pair the CDIS and UNCTAD data do not overlap, we cannot calculate the ratio of the two series and thus cannot rescale the UNCTAD series to CDIS levels. Hence, in these instances, even if the UNCTAD data are available for 2001–08, our data set will have missing values for those years. These two features of the data result in a combined data set containing relatively more zeros from 2012 onward, as some true zeros appear as missing observations during 2001–08. To minimize this underreporting of zero-valued observations, we assume that if the first non-missing observation for a country pair is a zero, then all previous observations are also zero.<sup>31</sup>

# **A.4. International Reserves**

Unlike for the other investment types, holdings of international reserves are unavailable at the countryto-country level. Instead, we construct a country-to-region data set on international reserves by

> 29 We also considered an alternative methodology: start with the CDIS data and fill in backward the values for 2001–08 using the growth rates implied by the UNCTAD data. This approach yielded quantitatively similar results to the ones reported in the paper.

> 30 In the CDIS data, observations with zero value represent 12 percent of all observations, whereas they represent only 4 percent in the UNCTAD data.

> 31 For robustness, we considered two other approaches for country pairs that only have zero values in the sample. We alternatively replaced the missing values in 2001–08 (2001–18) with zeros. We obtained quantitatively similar results when using these alternative approaches.

36
are also relatively important for the UK. In terms of services, two sectors stand out as the most important exports: financial intermediation and business services. While roughly half of the value of financial intermediation is provided to the EU, almost two‐thirds of the value of business activities is consumed by other EU countries. Renting of machinery and equipment and other business services are the most imported services and more than half of the value comes from non‐EU countries. Tourism‐related and financial services are among the top imported sectors, mainly from outside the EU, but in terms of value they account for only about half of the business services sector.

_Figure 4:_ Top traded sectors <!-- Start of picture text --> UK Exports: Top 10 Products<br>Financial Intermediation<br>Renting of M&Eq and Other Business Activities<br>Other Community, Social and Personal Services<br>Water Transport<br>Hotels and Restaurants<br>Transport Equipment<br>Chemicals and Chemical Products<br>Electrical and Optical Equipment<br>Coke, Refined Petroleum and Nuclear Fuel<br>Machinery, Nec<br>$ bn $40 bn $80 bn<br>EU Extra‐EU<br>UK Imports: Top 10 Products<br>Renting of M&Eq and Other Business Activities<br>Hotels and Restaurants<br>Financial Intermediation<br>Other Community, Social and Personal Services<br>Air Transport<br>Transport Equipment<br>Electrical and Optical Equipment<br>Mining and Quarrying<br>Food, Beverages and Tobacco<br>Chemicals and Chemical Products<br>$ bn $40 bn $80 bn<br>EU Extra‐EU<br>Services<br>Goods<br>Services<br>Goods<br><!-- End of picture text --> We next look at the extent to which the UK economy is integrated in global value chains with the EU and other non‐EU partners. In a world of international production networks, gross trade statistics are inflated by the double‐counting of goods and services that cross borders multiple times. Wang et al. (2016) offer a decomposition of gross exports based on the work by Koopman et al. (2014). This decomposition allows 11
projects When regional staff indicated that a study had been completed,<sup>the regional</sup> Information Centers and Rer rd Center wer%.<sup>requested to search for relevart</sup> docmnents.

8. From these activities, a comprehensive list of all<sup>planned. completed, and</sup> available studies in education projects, 1982-1990,<sup>was compiled.</sup>

# Type of Research

9. Studies were classified in two separate ways. First, to<sup>be comparable with Tan</sup> (1982), studies were identified as either "tracer" studies<sup>or not tracer studies 0. Second,</sup> studies were classified more extensively according to<sup>seven categories:</sup> (a) education inputs (this category represents descriptive<sup>studies covering</sup> topics such as textbook availability, teacher upgrading,<sup>school supervision,</sup> educational materials and classroom conditions, the improvement<sup>of teacher</sup> training, and curriculum development), (b) education outputs (student achievement testing, external<sup>efficiency,</sup> monitoring standards of institutions), (c) labor force paiticipation (manpower needs assessment. education/employment sector linkages and general vocational education coordination studies. Tracer studies were also included<sup>in this sub-group),</sup> (J) project feasibility (pre-investment or feasibility studies related<sup>to project</sup> generation or future projects, the design of which is based on<sup>the experience of</sup> the current project. Since these studies provide the bulk<sup>of research studies they</sup> were incorporated in other categories wherever possible.<sup>However, when the SAR</sup> or President's Report made only general references to<sup>pre-investment and</sup> feasibility studies thev were included in this general category), (e) statistical and information systems (NManagement Information<sup>Systems,</sup> evaluation of in-country propensity to gather statistical data,<sup>development of</sup> acrninistrative and finance svstems, development of dornestic<sup>information</sup> gatheiing and analysis, rationalization of domestic Ministries,<sup>and budget</sup> management. Also includes school mapping studies as part<sup>of the information</sup> system development).

(fl cost related studies (all studies related to cost and efficiency<sup>analysis,</sup> regardless of the topic focus), and

> 10 Tracer studies **are** cross vctionsil and longitudinal studies<sup>designed to</sup><sup>**analyze**specific graduating cohots to test the external</sup> efficiency of **educational** inpuu into **project** instituuons.<sup>Thesc studies generallyfocuson vocational training institutionu and their</sup> graduates. They take more t,me to corfplete, as they **rly** on<sup>**cohort data**of graduates enteringthelabor market, and often must</sup> extend beyond the fornul .ompletion of the project.

3
have to be converted into 6-Digit Harmonized System nomenclature (HS) and then averaged out into a common more aggregated nomenclature based on the International Standard Industrial Classification (ISIC-Rev. 2--4 Digits), to facilitate correlation with the preferential tariff data. For more than half of the observations of MFN rates, more than one source exists. We drop observations where MFN rates from different sources differed by more than 2 percentage points.

# **Appendix A2: Variable Descriptions**

MFN Applied MFN tariff. PREF Minimum preferential tariff offered to RTA partners. Share90 Share of total imports of current RTA members in the first period in the sample. MARG Difference between the MFN and the minimum preferential tariff: _MARGijt_ = _MFNijt_ – _PREFijt_ . MRG Dummy variable for observations in which the preference margin is greater than 2.5 percentage points. _MRGijt_ = 1 iff _MFNijt_ – _PREFijt_ > 2.5. MRG50 Dummy variable for observations in which the preference margin is above the median preference margin in the sample.

HiMFN_x Dummy variable for observations in which the MFN tariff is above the x<sup>th</sup> percentile in the sample, x = 25, 50. CUalign_x Variable defined to capture the alignment of external tariffs in customs union x, x = MERCOSUR, ANDEAN COMMUNITY. ⎡ _M_ ⎤ _CUalignijt_ ≡ ∑ ⎢ _MFN imt imt_ ⎥ − _MFN ijt_ , where _M_ ( _CU j_ ) _it_ ≡ ∑ _M imt m_ ⎢⎣ _M_ ( _CU j_ ) _it_ ⎥⎦ _m_ ∈ _CU j_ denotes total imports in industry _i_ , year _t_ , by members of the customs union.

Bind Dummy variable for observations in which the difference between bound and applied MFN tariff is not higher than three percentage points.

- Bind_0 Dummy variable for observations in which the applied MFN tariff is not lower than the bound MFN tariff.

40
pollution abatement expenditures in the country increase. For the US, Japan, and Germany, patents on these innovations are typically domestic patents. In contrast, for developing countries, the majority of these patents come from other countries. This is especially true of air pollution control technologies, which tend to be complex. Water pollution control technologies, on the other hand, are more frequently local innovations, as local conditions shape the requirements of these technologies, and are less likely to be patented elsewhere.

Dechezleprêtre _et al_ . (2011) examine climate-friendly innovation using patent data from 1978-2005 for 76 countries and covering a broad range of technologies, including renewable energy technologies, carbon capture and storage, and energy efficiency technologies for buildings, lighting, and cement manufacture. Like Lanjouw and Mody (1996), they find that most climate-friendly innovation occurs in developed countries. In fact, the US, Japan, and Germany together account for two-thirds of the innovations in their sample. Reflecting the role and impact of policy, innovation increases after the Kyoto Protocol in all Annex I countries except the US, which had not ratified Kyoto.<sup>12</sup> Dechezleprêtre _et al_ . (2011) do find some evidence of innovation in emerging economies, as measured by patents. In particular, China, South Korea, Russia and Brazil together accounted for 18.5% of climate-friendly innovations from 2000-2005. However, innovation in emerging economies is often of a different nature. For example, the most prevalent innovations in China, South Korea, Russia, and Brazil include technologies designed primarily for local markets, such as geothermal and cement manufacture. As a result, the share of high-value patents (defined as patent applications filed in multiple countries) from these four countries is just 7.2%. Consistent with Lanjouw and Mody (1996), Dechezleprêtre _et al_ . (2011) find that technologies of wider use

> 12 Annex I countries include all Annex B countries plus Belarus and Turkey.  These are the developed and transitioning economies required to reduce emissions under the Kyoto Protocol.  A list of Annex B countries can be found at http://unfccc.int/kyoto_protocol/items/3145.php

11
<!-- Start of picture text --> MRP # Poor te |<br>3 MRP # Vulnerable; 4 |<br>> | |<br>4 MRP # Lower-middle class aid<br>E<br>= MRP # Middle class ! -—e——_<br><!-- End of picture text -->

<!-- Start of picture text -->
# Upper-middle class ———_<br>z MRP # Poor et |<br><!-- End of picture text -->

<!-- Start of picture text --> fe} MRP #Lower-middle class tol |<br>s MRP # Middle class te<br>MRP# Upper-middle class HoH! |<br>0 1 2 3 4 5<br>Relative risk rate<br><!-- End of picture text -->

<!-- Start of picture text --> MRP # Poor +®— ; |<br>3 MRP # Vulnerable} +e—|<br>> | |<br>4 MRP # Lower-middle class al<br>|<br>= MRP # Middle class | }-—__—e—_____<br><!-- End of picture text -->

<!-- Start of picture text --> MRP # Upper-middle class rr<br>|<br>MRP # Poor rs |<br><!-- End of picture text -->

<!-- Start of picture text --> fe}<br>® MRP #Lower-middle class + |<br>s MRP # Middle class} -eL4<br>MRP# Upper-middle class HeH! |<br>0 1 2 3 4 5<br>Relative risk rate<br><!-- End of picture text -->
Substituting from equation (6) for<sup><u>@w</u></sup> @�<sup>�andrearrangingterms,wehavethefollowing:</sup> It is easy to check from equation (10) above, the increase in income is higher when the labor supply responses of the households are higher. In this model there are thus two sources of income gains following an increase in agricultural productivity: a reallocation of labor from home production to agriculture, and a higher productivity of agricultural activity. In other words, the income and poverty impacts of agricultural productivity will be larger when households can increase their labor supply to market work, which does not necessarily imply an increase in hired labor through the market as shown in proposition (2) earlier. This result is important because it underscores the importance of looking at the e¤ects on both price (wage) and total labor supplied to the market (not only hired labor) to understand the e¤ects of an agricultural productivity increase on the poor.

# (3) Empirical Framework

To estimate the e¤ects of agricultural productivity growth on wages, labor allocation across own farming and hired labor, and household consumption, we construct a subdistrict (upazila) level panel data set using three rounds of Household Income and Expenditure Surveys (HIESs). To test the theoretical predictions in propositions 1-3, a natural regression speci...cation is: where i indexes the outcome variables (e.g. share of employment in an activity, wage, 15
24

# **Table 2**

# India: Public Sector Real Gross Domestic Investment (percent of real GDP)

||1960/61-69/70|1970/71-79/80|1980/81-84/85|1985/86-89/90|1990/91-92/93|
|---|---|---|---|---|---|
|Agriculture|0.88|1.03|1.23|0.77|0.54|
|Electricity, water and<br>g as__|1.36<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|1.66<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|2.31|2.62|2.50|
|Transport and<br>communications|1.72|1.36|1.31|1.36|1.34|
|Public administration<br>and defense|1.39|1.38|1.57|1.40|1.17|
|Other a/|2.25|3.31|3.64|3.52|3.36|
|**TOTAL**|7.60|8.74|10.06|9.67|8.89|

Notes: -/ Includes manufacturing, mining, construction, trade and finance. Source: Central Statistical Office and own calculations.
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
|---|---|
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
|BI-Jiji and Mulembwe Hydropower|Pierre Jacques Lorillou|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|Ethiopia Geothermal Sector Development Project|Kenta Usui|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|StrengtheningSafetyNets Systems - MASAF IV|Ivan Drabek|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Municipal Governance and Services Project|Kwabena Amankwah-Ayeh|
|Malawi Economic RecoveryDevelopment PolicyOperation 1|Appolenia Mbowe|
|Malawi Economic RecoveryDevelopment PolicyOperation 1|Appolenia Mbowe|
|Mozambique - Financial Sector DPO|Mazen Bouri|
|Mozambique - Financial Sector DPO|Mazen Bouri|
# **1 Introduction**

Foreign direct investment (FDI) declined drastically during the recent global crisis. According to UNCTAD statistics, global FDI outflows decreased by 15% in 2008 and a further 43% in 2009. The fact that this abrupt fall coincided with a deterioration of credit conditions worldwide suggests that credit constraints have been one of the factors hindering the expansion of multinational enterprises (MNEs) abroad. While this possibility has been evoked in various policy reports, e.g. UNCTAD (2010), it has yet to be investigated rigorously. This is the aim of this paper.

There are several channels through which a credit crisis<sup>1</sup> can have an influence on outward FDI.<sup>2</sup> In addition to curtailing access to external finance, it depresses demand, reduces firms’ self-financing capabilities, and increases uncertainty. Testing directly for the impact of a credit crisis on outward FDI would amount to confounding these different effects. Hence, to identify a potential “credit channel" impact of the global financial crisis, we adopt a difference-in-differences approach, originally suggested by Kroszner et al. (2007), where we simultaneously exploit the variation in financial vulnerability across manufacturing sectors,<sup>3</sup> the variation in financial development across source countries,<sup>4</sup> and the widespread tightening of credit conditions during the 2008-2010 period. More precisely, in a model including country-sector, time-varying country, and time-varying sector fixed effects, we investigate whether the relative impact of the global financial crisis on outward FDI in more financially

> 1We define a credit crisis as an episode during which there is a severe decline in the terms and availability of credit for consumers and firms. Papers in the special issue of the _Journal of Financial Economics_ on the 2007-2008 financial crisis in the United States (Kashyap and Zingales (eds.), 2010) provide a good overview of the financing difficulties typically faced by firms during a credit crisis.

> 2In the context of this paper, FDI is defined as the initial fixed costs incurred by a firm expanding its activities outside the territorial boundaries of its home country through the establishment (greenfield FDI) or the acquisition (M&A FDI) of a foreign affiliate, whatever the sources of funds for this expansion.

> 3Following Manova (2013), we define financially vulnerable firms as firms with high requirements for external capital and/or firms with few assets that can be used as collateral. The varying prevalence of these firms in each sector translates into sectors which differ in their financial vulnerability.

> 4The World Economic Forum (World Economic Forum, 2012) defines financial development in its 2012 _Financial Development Report_ as “the factors, policies, and institutions that lead to effective financial intermediation and markets, as well as deep and broad access to capital and financial services” (p.3).

1
- 23 -

Table 2. Female Education, GDP per capita and Desired Family Size

|Independent Variables|Dependent Variable, DFS,|1988|
|---|---|---|
|Constant|3.404|3.817***|
|Female Secondary Gross Enrollment, 1975|-0.109|-2.781***|
|Female Secondary Gross Enrollment2|0.001|2.579***|
|Male Secondary Gross EnroUlment, 1975|0.018|0.320|
|Male Secondary Gross Enrollment2|0.00001|0.125|
|GDP per capita, 1988|0.001|2.381**|
|GDP per capita2|-0.0000001|-2.162**|
|Africa Regional Dummy|3.181|7.301***|
|Asia Regional Dummy|-0.147|-0.361|
|LAC Regional Dummy|0.392|0.865|
|Adjusted R<sup>2</sup>|.74||
|nm|~~~|~~~~~~~~~~~~~~32|
|4 solute values of t-ratios, whem,<br>,and<br>dicate|sigificance levels of 10, 5, and 1 per|cent respectively.|

Table 3. Female Education, GDP per capita and Contraceptive Prevalence

||Independent Variables|Dependent|Variable, CP, 1988|
|---|---|---|
||_<br>_<br>_<br>_<br>__|_ _Coefficient<br>t-ratio|
|Constant|-8.166|-I-.572|
|Fanale<br>Secondary Gross Enrollrnent,<br>1975|1.437|2.683***|
|Female Secondary Gross Enrolmnent2|-0.012|-1.924*|
|Male Secondary Gross Enrollment, 1975|-0.040|-0.093|
|Male Secondary Gross EnroilmenC^|-0.005|-0.970|
|GDP per capita 1988|4.007|-3.1078**|
|GDP per capitas|-0.000001|-2.655***|
|Family planning services 1982|0.526|7.7018**|
|Africa Regional Dummy|-0.525|4.150|
|Asia Regional Dummy|-5.351|-1.451|
|LAC Regional Dummny|-3.738 _4.8|17|
|Rz||.83|
|n||61|

Absolute values of t-ratios, where **8, 88,** and ****** indicate significance levels of 10, 5, and I percent respecdvely.
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