rafmacalaba/datause-encoder-data huggingface dataset:
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_

**Table 9: Distribution of years of education firms and workers**

||**< 6yrs**|**6-9yrs**|**10-12yrs**|**12 +yrs**|
|---|---|---|---|---|
|Kenya (2003)|3.3|23.4|71.5|0.9|
|**Kenya (2003)**|4.8|22.3|36.5|36.4|
|Uganda (2003)|24.7|40.8|31.4|3|
|**Uganda (2003)**|7.1|17.7|26.5|48.7|
|Benin (2004)|30.5|34.9|18.7|15|
|**Benin (2004)**|17.5|35.1|18.8|28.6|
|Madagascar (2005)|21.7|28.2|15.6|9.5|
|**Madagascar (2005)**|7.2|22.9|26.7|43.2|
|Mali (2003)|34.2|26.2|27.2|11|
|**Mali (2003)**|15.4|24.9|13.8|46|
|Senegal (2003)|40.6|22|18.3|16.5|
|**Senegal(2003)**|7.1|27.7|20.2|45|

Note: The first row corresponds to the distribution of workers by education years at the firm level (comes from the firm survey). Shaded rows denote averages from the worker's surveys.

Source: Authors' calculations from Enterprise Surveys 35
Note also that the extent to which the model splits this increase in the trade deficit (due to the remittance shock) across adjustments in exports and imports depends on economic structure, including the relative responsiveness of exports and imports to exchange rate changes; in the model, the level and relative values of trade elasticities should reflect such structural features (cf. elasticity data in Table 2.2).

**Figure 3.6. Macro growth by simulation (%‐age point deviation for average annual growth from base)**

<!-- Start of picture text --> ‐0.4 ‐0.2 0.0 0.2 0.4 0.6 0.8 1.0<br>ginv‐tdir<br>ginv‐dbor<br>ginv‐fbor<br>pwe<br>remit<br>Absorption Exports Imports GDP factor cost<br><!-- End of picture text --> Source: Authors’ calculations based on simulation results.

‐24‐
10

**Table 4. Sales and Productivity in Polish Industry, 1992-93 (avg quarterly growth rates)**

||**SOEs**<br>**Production**<br>**Produ**|**ctivity**<br>|**Privat**<br>**Production**|**e Firms**<br>**Productivity**|
|---|---|---|---|---|
|Coal|0.6|2.0|10.2|<br>2.0|
|Power|-0.5|-0.8|11.2|<br>9.5|
|Iron & Steel|**-1.9**|0.2|19.3|<br>-1.3|
|Non-ferrous metals|-3.3|-1.5|1.2|<br>2.5|
|Metal Products|1.3|2.5|4.7|<br>3.4|
|Engineering|0.3|2.9|10.1|<br>_5.3_|
|Precision Instruments|0.7|7.6|14.9|<br>8.8|
|Trans. Equipment|9.8|4.7|5.0|<br>0.8|
|Electricity|1.7|_5.0_|<br>15.6|<br>7.8|
|Chemistry|-0.5|0.3|<br>16.2|<br>9.1|
|Building Materials|4.1|6.1|<br>18.0|<br>8.8|
|Glass|-0.4|3.2|<br>8.1|<br>3.3|
|Pottery|0.0|1.9|<br>23.2|<br>10.2|
|Wood|-2.2|2.4|<br>6.5|<br>3.1|
|Paper|-4.2|-1.8|<br>4.9|<br>2.3|
|Textiles|0.0|2.4|<br>9.1|<br>4.6|
|Clothing|0.2|2.2|<br>3.9|<br>2.4|
|Leather Goods|0.4|4.4|<br>6.1|<br>4.4|
|Food Processing|-2.5|-0.4|<br>11.1|<br>3.8|
|**Total Industry**|**0.3**|**2.6**|<br>**11.1**|<br>**5.4**|

**Source: GUS, Authors' calculations**

This is an unweighted average across all industries contained in our sample. For the whole economy, sales growth in the public sector was somewhat higher.

Our strategy to disentangle structural from cyclical determinants of productivity growth is to decompose sectoral productivity growth into aggregate, cyclical components and structural, industry-specific components, with the help of a statistical model. In the spirit of Stockman (1988), we propose the use of a fixed-effects analysis-of-variance model:

# AY(i, j, t) = At+ c(i) + s(j, t) + m(t) + u(i, j, t)

where AY(i, j, t) is the rate of growth of labor productivity in three-digit industry i at time t. J is the index of the two-digit sector to which industry i belongs. The term , is the overall mean. The term c(i) is a constant term specific to three-digit industry i. The term s(j, t) represents the interaction of a fixed effect for sector j with a fixed effect for time t; in other words, s(j, t) is a set of dummy variables specific to sector j (i.e., common to all three-digit industries belonging to sector j) and time t. The term m(t) is a fixed time effect, that is, m(t)
costly due to factors such as geographic conditions or low population density, and where local governments do not have a sufficient tax base to be economically viable. For instance, class sizes may be smaller in schools, public facilities may be used at lower capacities, and a premium may have to be paid to attract qualified teachers, doctors, and administrative staff to work in remote rural areas.<sup>8</sup> Most remote communities do not offer a great many services. In industrialized countries in the OECD, rural local governments draw on a mix of local taxes, user charges, and transfers to meet national standards for minimum service provision – for basic education, health care, sanitation, and infrastructural services. Given their lower revenue capacities and higher expenditure needs, these governments are relatively more dependent on transfers than their urban counterparts.

In China the problems of financing rural governments are not confined to those in remote locations, however, but apply more generally. While high costs and weak administrative capacities are also present in localities in remote regions, virtually all rural counties and township governments (and villages) face large fiscal gaps that are rooted in China’s highly decentralized system of finance and the collapse of redistribution during the long fiscal decline of 1980-1996 (discussed later in this section).

# **2.1.  The Structure of Intergovernmental Fiscal Arrangements**

China is among the most decentralized countries in the world. Subnational governments at the provincial, municipal, county, and township levels account for more than 70 percent of national budgetary expenditures, and this share has grown in recent years (Table 1). Since the late 1980s, these expenditure shares have corresponded largely to financing responsibilities (World Bank 2002, 2008, Wong 2007). Two other features are salient for China’s rural public finance.

First, while in most countries decentralization is to the second, intermediate tier of government, in China it reaches all the way down to the lowest levels, with significant expenditure shares (and thus de facto responsibilities for service delivery) distributed among

> 8 In Canada, municipal spending on all services is considerably higher in remote areas than in the rest of the country – 41 percent higher than in the country on average in the Yukon; 173 percent higher in the Northwest Territories; and 171 percent higher in Nunavut (Kitchen and Slack, year?). In China there is a similar arrangement to provide hardship and remote area subsidies for public employees in remote areas.

6
13 Saharan Africa suggest that much short-term debt is publicly guaranteed, but this is probably not so for Latin America and Asia with their freer private sector. Including short-term debt can thus be seen as calculating a upper bound of capital flight as measured by this method, and excluding the short term as a lower bound. For the purposes of this paper, short-term debt is included.<sup>14</sup>

# Data Convention: Interest Compounding

Given the assumption made for the Dooley measure of flows on the imputed stocks of reported foreign assets (that is, no reinvested earnings are reported in the capital account) and the corresponding calculation of the flow figures, all flows reported here are on the same basis and are not compounded for interest earned abroad. All flows can be grossed **ip** using an international interest rate for a flight capital measuire which includes the cumulative returns on the assets held abroad.

14Erbe (1985) uses OECD data on medium and long-term gross external indebtedness with estimates of short-term debt for some countries. Cumby and Levich (1987) use public and publicly guaranteed longtern debt and all short-term debt. Morgan Guaranty (1986) excludes the increase in short-term foreign assets of the banking system from the increase in total private sector claims. Acquisition of foreign assets by nonbank agents, however, continues to be considered capital flight by Morgan. Since Morgan offers no explanation for treating the banking system differently from other firms and individuals. we do not pursue its distinction here.
started in 2012 or 2013. The differences between the schools that started in different years are not significant in any of the years, suggesting that there was no significant difference (after controlling for observed differences) in trends between the schools that received the Single-Shift policy in the first year of the policy compared to those that received it later. This result is particularly interesting considering the fact that the first year of the policy received extra attention for being an “experimental” program.

|**Table 8: Testing Common T**|**rend Assumpt**<br>(1)|**ion - IDEB Midd**<br>(2)|**le Schools**<br>(3)|
|---|---|---|---|
||<br>IDEB Mid<br>(SD)|<br>IDEB Mid<br>(SD)|<br>IDEB Mid<br>(SD)|
|Single-Shift 2011*Post 2007|0.408||0.149|
||[0.209]*||[0.193]|
|Single-Shift 2011*Post 2009|0.075||0.211|
||[0.221]||[0.262]|
|Single-Shift 2011*Post 2011|0.664||0.270|
||[0.248]***||[0.266]|
|Single-Shift 2011*Post 2013|0.534||-0.241|
||[0.194]***||[0.168]|
|Single-Shift 2012/13*Post 2007||0.204<br>[0.158]||
|Single-Shift 2012/13*Post 2009||-0.214<br>[0.276]||
|Single-Shift 2012/13*Post 2011||0.295<br>[0.260]||
|Single-Shift 2012/13*Post 2013||0.865<br>[0.127]***||
|Constant|-0.528|-0.537|-0.838|
||[0.032]***|[0.032]***|[0.095]***|
|Observations|1854|1860|198|
|Number of schools|309|310|33|
|R-squared|0.44|0.42|0.67|
|School FE|Yes|Yes|Yes|
|Year FE<br>Robust standard errors in brackets|Yes|Yes|Yes|

<mark>* significant at 10%; ** significant at 5%; *** significant at 1%</mark> Notes: IDEB High Fundamental Education 2005, 2007, 2009, 2011, 2013 and 2015. Single-Shift schools include CMS in this model. Standard errors clustered in school level.

# _5.B. Results_

Tables 9 and 10 present the results of model (1) for the IDEB and IDERio early years, respectively. Column (1) of these tables shows the results controlling only for whether the schools are part of the programs “Schools of Tomorrow” and “More Education”, in addition to the school and time fixed effects. They compare the Single-Shift schools (including those in the model of CES) with 26
# [(Ih/Y) rh ]

This term can be further disaggregated into the contribution of each level of education to the rate of growth of the economy.

where subscripts p, s and u refer to primary, secondary and university education.

Education investment in Turkey has averaged 4.4 percent of GDP, nearly one-half of it spent on secondary education (see Table 7).

**<u>Table 7: Education Investment as Percent of GDP</u>**

|Level|Education expenditure<br>as % of GDP|
|---|---|
|Primary|1.1|
|Secondary|2.0|
|Higher|1.3|
|All levels|4.4|

Source: OECD (2018) Therefore, the contribution of education to growth has been 4.4% x 7.2 = 0.32 percentage points, or about one-third of a growth point. Among the three levels of education, higher education has contributed the most (see Table 8).

**Table 8: The Contribution of Education to Growth**

|Educational<br>level|Social<br>rate of<br>return<br>(%)|Education<br>expenditure<br>as % of<br>GDP|Percentage<br>points<br>contribution<br>to economic<br>growth rate|
|---|---|---|---|
|Primary|6.0|1.1|0.07|
|Secondary|5.2|2.0|0.10|
|Higher|10.4|1.3|0.14|
|Overall|7.2|4.4|0.32|

# **Conclusion**

While Turkey has had the highest growth in the respective region in recent years and aspires to become a high-income economy in the next decade, the country has witnessed a slowdown in economic growth since 2011, as private investment and productivity stagnated. This was in marked contrast to the previous decade, when total factor productivity growth made a considerable contribution to GDP growth. Therefore, productivity dynamics deserve attention from researchers 11
**Table 6:  Robustness Checks, 1**

||**Removing**|**Influential**|**Observations**|**Using No**|**n-Interest**<br>**Spendin**|**Government**<br>**g**|**Using**<br>**on Gov**|**National A**<br>**ernment C**|**ccounts Data**<br>**onsumption**|**Controlli**<br>|**ng for Appr**<br>**Disbursing **|**ovals of Slow-**<br>**Loans**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Sample of Countries|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|
|_(Dependent variable is Change in Real G_|_DP)_||||||||||||
|**_Panel A: OLS Estimates_**|||||||||||||
|Change in Government Spending|0.313***|0.266***|0.275***|0.292***|0.239***|0.262***|0.343***|0.269***|0.289***|0.305***|0.257***|0.276***|
||(0.0380)|(0.0505)|(0.0437)|(0.0399)|(0.0535)|(0.0414)|(0.0639)|(0.0596)|(0.0581)|(0.0375)|(0.0498)|(0.0429)|
|Approvals of Slow-DisbursingProjects||||||||||0.0591**|0.0700**|0.0535*|
|||||||||||(0.0272)|(0.0318)|(0.0278)|
|**_Panel B: 2SLS Estimates_**|||||||||||||
|Change in Government Spending|0.371|0.387*|0.389**|0.462|0.445**|0.496**|0.741|0.740*|0.808|0.335|0.364*|0.389*|
||(0.268)|(0.194)|(0.191)|(0.297)|(0.213)|(0.230)|(0.536)|(0.402)|(0.495)|(0.249)|(0.193)|(0.202)|
|Approvals of Slow-DisbursingProjects||||||||||0.0586**|0.0676**|0.0520*|
|||||||||||(0.0267)|(0.0311)|(0.0272)|
|_First-Stage F-Statistic_|11.48|41.20|30.34|7.87|19.94|15.97|6.51|11.02|8.69|12.20|26.65|22.98|
|_Number of Observations_|2786|1503|1939|2744|1481|1909|2672|1407|1865|2787|1494|1935|
|_Number of Countries_<br>*** (**) (*) denotes significance at the<br>year data and include a full set of count<br>Equation (1). Panel B reports 2SLS estim<br>reduced-form regressions for the bench<br>Columns (7)-(9) use national accounts d<br>correspond to the full sample of countri<br>government spendingover the sample|102<br>1 (5) (10) pe<br>ry and year<br>ates of Equ<br>mark speci<br>ata on gove<br>es, the sam<br>period.|60<br>rcent level.<br>fixed effect<br>ation (1). C<br>fications in<br>rnment con<br>ple of IDA-|70<br>Heteroskedasti<br>s. Changes in GD<br>olumns (1)-(3) re<br>Table 4. Column<br>sumption spend<br>eligible countries|102<br>city-consiste<br>P, governm<br>port results<br>s (4)-(6) subt<br>ing , Column<br>, and the sa|60<br>nt standard<br>ent spendin<br>after using<br>ract interes<br>s (10)-(12)<br>mple of cou|70<br>errors are cluste<br>g, and disbursem<br>the Hadi (1992) p<br>t payments on p<br>control for conte<br>ntries where dis|102<br>red at the c<br>ents are all<br>rocedure to<br>ublic and pu<br>mporaneou<br>bursements|60<br>ountry lev<br>scaled by<br>remove i<br>blicly-gua<br>s approval<br>on loans f|70<br>el. All regressio<br>lagged GDP. Pa<br>nfluential obser<br>ranteed debt fro<br>s of slow-disbur<br>rom official cred|102<br>ns are estima<br>nel A reports<br>vations from<br>m total gove<br>sing loans. T<br>itors averag|60<br>ted using p<br>OLS estima<br>the first-st<br>rnment sp<br>he three co<br>e at least 10|70<br>ooled country-<br>tes of<br>age and<br>ending.<br>untry samples<br>percent of|
TABLE A1: RELATIONSHIP BETWEEN INSTRUMENT AND FOOD AID SHIPMENTS BY FARM BILL ERA

|VARIABLES|(1)<br>US wheat<br>aid<br>(1,000<br>MT)|(2)<br>US wheat<br>aid<br>(1,000<br>MT)|(3)<br>US wheat<br>aid<br>(1,000<br>MT)|(4)<br>US wheat<br>aid<br>(1,000<br>MT)|(5)<br>US wheat<br>aid<br>(1,000<br>MT)|
|---|---|---|---|---|---|
|_Panel A: Pre-1985_||||||
|Baseline interaction instrument|0.00416<br>(0.00290)|0.00261<br>(0.00181)|0.00262<br>(0.00195)|0.00216<br>(0.00148)|0.00256<br>(0.00175)|
|Observations|1,460|1,460|1,460|1,460|1,460|
|R-squared|0.73574|0.73809|0.75020|0.75813|0.76167|
|_Panel B: 1985 to 1996_|-0.00043|-0.00211|-0.00261|-0.00192|-0.00218|
|Baseline interaction instrument|(0.00132)|(0.00214)|(0.00274)|(0.00229)|(0.00246)|
|Observations|1,384|1,384|1,384|1,384|1,384|
|R-squared|0.60490|0.63565|0.65284|0.65952|0.66482|
|_Panel C: Post-1996_|0.00149|0.00039|0.00100|0.00165|0.00181|
|Baseline interaction instrument|(0.00083)|(0.00076)|(0.00094)|(0.00107)|(0.00114)|
|Observations|1,245|1,245|1,245|1,245|1,245|
|R-squared|0.69145|0.69852|0.72413|0.73353|0.73564|
|Controls (for all panels):||||||
|Country FE|Yes|Yes|Yes|Yes|Yes|
|Region-year FE|Yes|Yes|Yes|Yes|Yes|
|US real per capita GDP x avg.<br>prob. of any US food aid|No|Yes|Yes|Yes|Yes|
|US Democratic president x. avg.<br>prob. of any US food aid|No|Yes|Yes|Yes|Yes|
|Oil price x avg. prob. of any US<br>food aid|No|Yes|Yes|Yes|Yes|
|Monthly recipient temperature<br>and precipitation|No|No|Yes|Yes|Yes|
|Monthly weather x avg. prob. Of||||||
|any US food aid|No|No|Yes|Yes|Yes|
|<br>Avg. US military aid x year FE|No|No|No|Yes|Yes|
|<br>Avg. US economic aid x year FE|No|No|No|Yes|Yes|
|Avg. recipient cereal imports x<br>year FE|No|No|No|No|Yes|
|Avg. recipient cereal production x||||||
|<br> year FE|No|No|No|No|Yes|

Notes: An observation is a country and a year. The sample includes 125 non-OECD countries for the years 1971-1984 (panel A), 1985 to 1996 (Panel B), and 1997 to 2006 (Panel C). Coefficients are reported with standard errors clustered at the country level in <u>parentheses. Data are from the NQ replication file.</u> 38
|**Table 6: Do thegrants affect the selection of who**|**owns a bu**<br>Cu|**siness?**<br>rrently Ow|n a Busine|ss|Ever|Closed|Wage|Not|
|---|---|---|---|---|---|---|---|---|
||||||Own|Business|Work|working|
||Round 2|Round 3|Round 4|Round 5|Round 5|Round 5|Round 5|Round 5|
|_Panel A: Interaction with Raven score_|||||||||
|Assigned to Cash if finish Training|0.232***|0.149*|0.0956|0.161*|0.0846|-0.0224|0.0406|-0.195**|
||(0.0840)|(0.0892)|(0.0916)|(0.0869)|(0.0769)|(0.0711)|(0.0550)|(0.0822)|
|Assigned to Training only|0.0640|0.0163|0.0450|0.101|0.0320|-0.0503|-0.0645|-0.0187|
||(0.0835)|(0.0857)|(0.0894)|(0.0860)|(0.0799)|(0.0633)|(0.0550)|(0.0841)|
|Assigned to Cash * Raven Score|-0.0111|-0.00344|-0.0246|-0.0492*|-0.00500|0.0308|-0.0147|0.0586**|
||(0.0254)|(0.0274)|(0.0269)|(0.0253)|(0.0221)|(0.0211)|(0.0144)|(0.0242)|
|Assigned to Training * Raven Score|0.0105|0.0188|-0.00248|-0.0451*|-0.00708|0.0246|0.0180|0.0209|
||(0.0261)|(0.0271)|(0.0267)|(0.0255)|(0.0241)|(0.0178)|(0.0176)|(0.0240)|
|_Panel B: Interaction with Digitspan recall_|||||||||
|Assigned to Cash if finish Training|0.220|0.0472|-0.147|-0.141|-0.117|0.0699|0.0640|0.0762|
||(0.229)|(0.234)|(0.244)|(0.232)|(0.218)|(0.185)|(0.147)|(0.222)|
|Assigned to Training only|0.189|0.244|0.163|0.168|0.0560|-0.115|-0.105|-0.0766|
||(0.224)|(0.240)|(0.249)|(0.238)|(0.222)|(0.199)|(0.137)|(0.237)|
|Assigned to Cash * Digitspan recall|-0.00315|0.0153|0.0283|0.0273|0.0310|-0.00106|-0.0107|-0.0178|
||(0.0371)|(0.0379)|(0.0393)|(0.0375)|(0.0353)|(0.0297)|(0.0234)|(0.0360)|
|Assigned to Training * Digitspan recall|-0.0167|-0.0304|-0.0212|-0.0316|-0.00728|0.0221|0.0144|0.0197|
||(0.0365)|(0.0395)|(0.0408)|(0.0390)|(0.0364)|(0.0327)|(0.0222)|(0.0389)|
|_Panel C: Interaction with household wealth index_|||||||||
|Assigned to Cash if finish Training|0.198***|0.137***|0.0198|0.0177|0.0696|0.0684*|-4.25e-06|-0.0249|
||(0.0468)|(0.0487)|(0.0500)|(0.0484)|(0.0438)|(0.0386)|(0.0314)|(0.0454)|
|Assigned to Training only|0.0855*|0.0652|0.0344|-0.0226|0.0121|0.0174|-0.0187|0.0423|
||(0.0466)|(0.0492)|(0.0500)|(0.0492)|(0.0447)|(0.0368)|(0.0305)|(0.0473)|
|Assigned to Cash * Household Wealth|-0.0369|-0.0276|-0.0594**|-0.0540*|-0.0144|0.0356|0.0185|0.0385|
||(0.0293)|(0.0295)|(0.0289)|(0.0282)|(0.0254)|(0.0227)|(0.0184)|(0.0259)|
|Assigned to Training * Household Wealth|-0.0515*|0.00990|-0.0290|-0.0142|-0.00663|0.00817|0.00815|0.00904|
||(0.0302)|(0.0309)|(0.0299)|(0.0298)|(0.0258)|(0.0222)|(0.0179)|(0.0283)|
|Number of Firms|607|601|582|609|620|609|606|606|
|<br>_Notes:_Results shown are for OLS regressions of o|utcome on|treatmen|t assignme|nt.|||||
|<br>Robust standard errors in parentheses , *** p<0.0<br>All specifications include randomization strata du|<br>1, ** p<0.05<br>mmies and|<br>, * p<0.1<br>level effe|<br>ct of intera|<br>cting|||||

39
# **1 Introduction**

Most countries devote considerable attention and resources to attracting foreign direct investment (FDI). This is done in the hope not only of generating benefits like jobs, foreign exchange, and tax revenues, but perhaps more importantly in realizing dynamic benefits to the domestic economy through so-called “spillovers” from FDI. These “spillovers” generally refer to productivity improvements resulting from knowledge diffusion from multinational affiliates to domestic firms – both in the form of unintentional transmission or intentional transfer if the multinational is not compensated for by the domestic firm – encompassing both technology and all forms of codified and ‘tacit knowledge’ related to production, including management and organizational practices. The existence of spillovers is based on the assumption that multinational firms enjoy technological and other advantages and have, therefore, higher levels of productivity (Hoekman and Javorcik 2006).

Numerous econometric studies show ambiguous effects of FDI on domestic firm productivity within the same sector, also known as horizontal spillovers (see, e.g., extensive literature reviews in Görg and Greenaway 2004; Lipsey and Sjöholm 2005; Smeets 2008, among others). Other studies have shifted the focus to vertical spillovers to domestic firms in upstream and downstream sectors (e.g., Javorcik 2004, Blalock and Gertler 2008, Havranek and Irsova 2011). These studies support the existence of positive backward spillovers from multinationals on local suppliers, while evidence on forward spillovers is mixed.

Significant policy relevant research gaps remain, as identified in a recent survey of the empirical literature (Javorcik 2009). Among the gaps identified, there is the need to (i) determine the conditions under which spillovers are likely to materialize; (ii) understand more specifically the mechanisms behind the observed patterns; and (iii) extend the scope of investigations beyond the manufacturing sector (Javorcik 2009). The second research gap is also a function of the FDI measure being used. The econometric studies above, for example, measure FDI only at the broad sectoral level, but don’t include direct supplier relationships with multinational firms which are based on survey data and could reveal the exact underlying mechanisms (Javorcik and Spatareanu 2009).

Using newly collected survey data on direct supplier-multinational linkages in Chile, Ghana, Kenya, Lesotho, Mozambique, Swaziland, and Vietnam, this paper addresses these research gaps as follows. We first evaluate whether foreign investors differ from domestic producers in terms of 2