rafmacalaba/datause-encoder-data huggingface dataset:
Dweck, C. S. (1999). Caution: Praise can be dangerous _. American Educator, 23_ (1), 1–5.

- Dweck, C. S. (2002). Messages that motivate: How praise molds students’ beliefs, motivation, and performance (in surprising ways). _Improving academic achievement: Impact of Psychological Factors on Education_ , 37-60.

- Dweck, C. S. (2013). _Self-theories: Their role in motivation, personality, and development._ Psychology Press.

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- <mark>Emmer, E. T., & Stough, L. M. (2001). Classroom management: A critical part of educational psychology, with implications for teacher education.</mark> _<mark>Educational Psychologist</mark>_ <mark>,</mark> _<mark>36</mark>_ <mark>(2), 103-112.</mark>

- Eskreis-Winkler, L., Duckworth, A. L., Shulman, E. P., & Beal, S. (2014). The grit effect: Predicting retention in the military, the workplace, school and marriage. _Frontiers in Psychology, 5_ , 36.

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- Evertson, C. M., Anderson, C. W., Anderson, L. M., & Brophy, J. E. (1980). Relationships between classroom behaviors and student outcomes in junior high mathematics and English classes. _American Educational Research Journal_ , _17_ (1), 43-60.

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- Farmer, T. W., Dawes, M., Hamm, J. V., Lee, D., Mehtaji, M., Hoffman, A. S., & Brooks, D. S. (2018). Classroom social dynamics management: Why the invisible hand of the teacher matters for special education. _Remedial and Special Education_ , _39_ (3), 177192.

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- Felder, R. M., & Brent, R. (2007). Cooperative learning. In _Active learning: Models from the analytical sciences, ACS Symposium Series_ (Vol. 970, pp. 34-53).

48
**<u>Table 7: Impact of Sharecropping on Productivity (IV Estimates): Household Fixed Effects with Landlord Characteristics</u>**

|||**Methodol**|**ogy used**||
|---|---|---|---|---|
||**2SLS**|**GMM**|**LIML**|**Fuller**|
|Only output sharing (γ)|-0.247***|-0.229***|-0.245***|-0.245***|
||(3.70)|(3.66)|(3.58)|(3.59)|
|Output and input sharing (θ)|-0.305|-0.336|-0.333|-0.331|
||(0.70)|(0.79)|(0.71)|(0.71)|
|Farming is landlord‟s main activity (β)|0.137**|0.136**|0.138**|0.138**|
|(if sharecropped only)|(2.19)|(2.19)|(2.19)|(2.19)|
|No. of adult children per ha of rented out land|0.008**|0.008**|0.008**|0.008**|
||(2.16)|(2.01)|(2.16)|(2.16)|
|Number of years possessed|-0.005*|-0.005*|-0.005*|-0.005*|
||(1.72)|(1.86)|(1.74)|(1.73)|
|Good soil quality|0.291***|0.287***|0.292***|0.292***|
||(6.03)|(5.70)|(6.03)|(6.03)|
|Medium soil quality|0.170***|0.167***|0.170***|0.170***|
||(3.70)|(3.46)|(3.70)|(3.70)|
|Flat land|0.045|0.033|0.044|0.044|
||(0.60)|(0.45)|(0.59)|(0.59)|
|Gently sloped land|0.054|0.033|0.053|0.053|
||(0.69)|(0.43)|(0.68)|(0.68)|
|Irrigated land|0.034|0.017|0.033|0.033|
||(0.42)|(0.21)|(0.40)|(0.40)|
|Teff|0.561***|0.575***|0.561***|0.561***|
||(8.69)|(9.11)|(8.69)|(8.69)|
|Wheat|0.464***|0.481***|0.464***|0.464***|
||(7.04)|(7.41)|(7.04)|(7.04)|
|Sorghum|-0.076|-0.060|-0.076|-0.076|
||(1.15)|(0.90)|(1.16)|(1.16)|
|Pulses|0.215***|0.239***|0.215***|0.215***|
||(2.96)|(2.98)|(2.96)|(2.96)|
|Number of observations|3743|3743|3743|3743|
|Number of households|635|635|635|635|
|_R_<sup>2</sup>|0.113|0.113|0.113|0.113|
|Sargan/Hansen J Statistic|14.599|14.948|14.596|14.596|
|Sargan/Hansen J Chi2|0.147|0.134|0.148|0.148|
|Anderson-Rubin Statistic|||14.630|14.630|
|Anderson-Rubin Chi2|||0.146|0.146|
|Anderson/Kleibergen-Paap Statistic|107.203|46.157|107.203|107.203|
|<br>Anderson/Kleibergen-Paap Chi2|0.000|0.000|0.000|0.000|
|Cragg-Donald/Kleibergen-PaapStatistic|9.181|4.127|9.181|9.181|
|**Coefficient tests**|||||
|γ +β= 0|14.58***|14.29***|14.45***|14.46***|
|<br>θ +β= 0|0.90|1.08|0.90|0.90|

**Note:** Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. Contract choice (sharecropping only, output, or input sharing) is instrumented using landlord characteristics, in particular plot distance from homestead, land ownership, household composition (number of dependents, number of male adults, number of female adults), assets (no. of oxen, value of other livestock, iron roof), and characteristics of the head (age, sex, literacy level, and farming as a primary activity). First-stage regressions are reported in appendix table 6.

23
Table 5: The role of bank branches from the regional trade-partner country: before and after the crisis

|Crisis? (Yes=1)|-1.033***|-0.930***|-0.925***|-0.823***|-0.925***|-0.230|
|---|---|---|---|---|---|---|
||(0.307)|(0.308)|(0.315)|(0.316)|(0.315)|(0.333)|
|Financial<br>|||1.000***|1.008***|1.000***|0.797***|
|development ( <sup>_Loan_</sup><br>_GV A_<sup>)</sup>|||(0.205)|(0.205)|(0.205)|(0.206)|
|Financial<br>|||-0.348|-0.360|-0.348|-0.164|
|development ( <sup>_Loan_</sup><br>_GV A_<sup>)</sup> <sup>_×_ Crisis?</sup>|||(0.390)|(0.390)|(0.390)|(0.391)|
|Regional TP bank?|0.340***||0.338***||0.000||
|(Yes=1)|(0.090)||(0.090)||(.)||
|Regional TP bank?|-0.470***||-0.472***||0.000||
|_×_ Crisis?|(0.072)||(0.072)||(.)||
|Share(Regional TP||-0.002*||-0.002*||-0.003**|
|branches)||(0.001)||(0.001)||(0.001)|
|Share(Regional TP||-0.006***||-0.006***||-0.035***|
|branches) _×_ Crisis?||(0.001)||(0.001)||(0.003)|
|TP bank? (Yes=1)|||||0.338***<br>(0.090)||
|TP bank? _×_|||||-0.472***||
|Crisis?|||||(0.072)||
|Share(TP branches)||||||0.067***<br>(0.006)|
|Share(TP branches)<br>_×_ Crisis?||||||0.093***<br>(0.012)|
|Log(Sub-regional|0.227***|0.243***|0.143*|0.158*|0.143*|-0.034|
|GVA)|(0.085)|(0.085)|(0.086)|(0.086)|(0.086)|(0.089)|
|Log(Importer GVA)|0.620***<br>(0.074)|0.627***<br>(0.074)|0.595***<br>(0.073)|0.602***<br>(0.073)|0.595***<br>(0.073)|0.633***<br>(0.074)|
|Log(Distance)|-1.309***|-1.304***|-1.309***|-1.304***|-1.309***|-1.266***|
||(0.161)|(0.161)|(0.161)|(0.161)|(0.161)|(0.159)|
|Log(Sub-regional|0.121***|0.118***|0.111***|0.109***|0.111***|0.057***|
|GVA) _×_ Crisis?|(0.019)|(0.019)|(0.020)|(0.020)|(0.020)|(0.022)|
|Log(Importer GVA)|0.089***|0.089***|0.089***|0.089***|0.089***|0.086***|
|_×_ Crisis?|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|Log(Distance)|-0.153***|-0.162***|-0.153***|-0.162***|-0.153***|-0.155***|
|_×_ Crisis?|(0.021)|(0.021)|(0.021)|(0.021)|(0.021)|(0.021)|
|Regional (exporter) FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country (importer) FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.99|0.99|0.99|0.99|0.99|0.99|
|R<sup>2</sup>|0.549|0.549|0.549|0.549|0.549|0.555|
|N|42120|42120|42120|42120|42120|42120|

The dependent variable is the _Log_ ( _Exportit_ ) expressed in 100,000 constant USD. Robust standard errors clustered at the pair level are reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level.

14
**Table 11. Wage gap decompositions, quantile regressions**

|**Quantiles**|**Armenia**|**Bolivia**|**Colombia**|**Georgia**|**Ghana**|**Kenya**|**Serbia**|**Ukraine**|**Vietnam**|
|---|---|---|---|---|---|---|---|---|---|
||||**C**|**onditional wage g**|**ap: F****_Y[m,m]_ - F****_Y[f,f]_**|||||
|0.10|0.334***|0.479***|0.400***|0.331***|0.679***|0.282***|-0.138|0.214***|0.357***|
||(0.0772)|(0.0830)|(0.0766)|(0.112)|(0.123)|(0.0818)|(0.268)|(0.0631)|(0.0511)|
|0.20|0.382***|0.395***|0.329***|0.309***|0.511***|0.232***|-0.0124|0.347***|0.355***|
||(0.0721)|(0.0666)|(0.0546)|(0.0789)|(0.114)|(0.0641)|(0.0650)|(0.0534)|(0.0435)|
|0.50|0.482***|0.338***|0.270***|0.172***|0.417***|0.187***|-0.0247|0.406***|0.306***|
||(0.0583)|(0.0688)|(0.0382)|(0.0630)|(0.0981)|(0.0621)|(0.0398)|(0.0500)|(0.0422)|
|0.80|0.370***|0.298***|0.171**|0.244***|0.161|0.141*|-0.0634|0.444***|0.234***|
||(0.0618)|(0.0881)|(0.0794)|(0.0895)|(0.109)|(0.0733)|(0.0519)|(0.0574)|(0.0623)|
|0.90|0.329***|0.327***|0.168*|0.359***|0.122|0.0826|-0.0555|0.476***|0.253***|
||(0.0813)|(0.101)|(0.0903)|(0.113)|(0.120)|(0.0894)|(0.0589)|(0.0663)|(0.0853)|
|||||**Covariates gap:**|**F****_Y[m,m]_ - F****_Y[m,f]_**|||||
|0.10|0.0422|0.129**|-0.00274|-0.0542|-0.000536|0.105***|-0.0226|-0.0344|0.0398|
||(0.0461)|(0.0542)|(0.0358)|(0.0635)|(0.0579)|(0.0266)|(0.112)|(0.0584)|(0.0321)|
|0.20|-0.00319|0.112**|0.0192|-0.0497|0.0250|0.108***|-0.0428|-0.0317|0.0489*|
||(0.0373)|(0.0501)|(0.0253)|(0.0514)|(0.0499)|(0.0253)|(0.0376)|(0.0499)|(0.0260)|
|0.50|-0.00852|0.117**|0.0181|-0.0791*|0.0664|0.145***|-0.0916***|-0.0289|0.0351|
||(0.0297)|(0.0513)|(0.0267)|(0.0436)|(0.0519)|(0.0297)|(0.0270)|(0.0410)|(0.0241)|
|0.80|-0.0226|0.108**|0.0316|-0.0842|0.0720|0.201***|-0.0881***|0.0198|0.0425|
||(0.0360)|(0.0511)|(0.0442)|(0.0548)|(0.0514)|(0.0453)|(0.0307)|(0.0490)|(0.0300)|
|0.90|-0.0175|0.0953|0.0246|-0.0659|0.0932|0.210***|-0.0804**|0.0389|0.0477|
||(0.0427)|(0.0599)|(0.0524)|(0.0576)|(0.0605)|(0.0503)|(0.0363)|(0.0549)|(0.0355)|
|||||**Coefficients gap**|**: F****_Y[m,f]_ - F****_Y[f,f]_**|||||
|0.10|0.291***|0.351***|0.403***|0.385***|0.679***|0.177**|-0.116|0.249***|0.318***|
||(0.0810)|(0.0986)|(0.0786)|(0.117)|(0.129)|(0.0879)|(0.318)|(0.0745)|(0.0495)|
|0.20|0.385***|0.283***|0.310***|0.358***|0.486***|0.124*|0.0304|0.379***|0.306***|
||(0.0696)|(0.0783)|(0.0565)|(0.0937)|(0.115)|(0.0655)|(0.0781)|(0.0655)|(0.0427)|
|0.50|0.491***|0.221***|0.252***|0.251***|0.351***|0.0415|0.0670|0.435***|0.271***|
||(0.0617)|(0.0704)|(0.0388)|(0.0662)|(0.0919)|(0.0544)|(0.0420)|(0.0647)|(0.0422)|
|0.80|<br>0.393***|<br>0.190**|<br>0.140*|<br>0.329***|<br>0.0892|<br>-0.0596|<br>0.0247|<br>0.425***|<br>0.191***|
||(0.0701)|(0.0896)|(0.0815)|(0.0902)|(0.110)|(0.0644)|(0.0520)|(0.0640)|(0.0643)|
|0.90|0.346***|0.232**|0.144|0.425***|0.0284|-0.128|0.0249|0.437***|0.205**|
||(0.0918)|(0.110)|(0.0918)|(0.114)|(0.125)|(0.0824)|(0.0635)|(0.0753)|(0.0875)|
|Observations|660|1,093|1,168|636|851|1,586|866|723|1,708|

These results derive from the full model, with controls for schooling, cognitive and noncognitive skills. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 54
property rights, revising policies, reneging on commitments, and capriciously imposing new regulations. In short, they prevent short term actions, like precipitous expropriation, in the event of a negative shock. Executive constraints, especially if safeguarded by political competition, should reinforce the attractiveness of current business opportunities to foreign direct investment by providing credible assurances about the permanence of those policies.

Finally, the cost of doing business in any country depends on intangible factors – the mentality of its residents. Ample empirical evidence shows that “culture” affects economic outcomes by affecting economic agents’ prior beliefs, preferred economic activities, and political choices. For a comprehensive and informative survey, see Guiso, Sapienza and Zingales (2006). Mostly recently, Siegel, Licht, and Schwartz (2006) show that attitudes (in this case, attitudes towards egalitarianism) affect cross-border economic exchanges because they affect how people interact and negotiate on economic matters. A huge management literature emphasizes how the commercial success of FDI hinges on how well a firm protects its property rights and overcomes a range of agency and information asymmetry problem – foreign investors are particularly handicapped to achieve these goals. Consequently, if locals make transparent and predictable use of norms, or established legal and political institutions, to adjudicate disputes, this liability of being foreign diminishes and FDI flows in more abundantly.

In summary, inward FDI is attracted by basic sets of economic and institutional factors. The economic factors include the size of the market, expectations on growth, normal costs of doing business such as labor and input costs, infrastructure development, and financial market development. Obviously, the host country’s currency may affect foreign firms’ local input factor costs. The institutional factors include general measures of “good government” such as the public’s attitude towards law and order, the quality of government bureaucracy, and the efficiency of the legal system, as well as specific factors like the strength of constraints on executive power.

10
# TABLE OF CONTENTS

|||PAGE NO,|
|---|---|---|
|A.|INTRODUCTION ..............................................|....<br>1|
|B.|RECENT TRENDS IN INFLATION, INTEREST AND EXCHANGE RATES ....|....<br>2|
|C.|CONCEPTUAL FRAMEWORK .......................................|....<br>11|
|D.|EMPIRICAL EVIDENCE AND POLICY IMPLICATIONS .................|....<br>14|
|E.|CONCLUDING REMARKS .........................................|....<br>24|
|BIB|LIOGRAPHY ...................................................|....<br>26|

This working paper was prepared for the Macroeconomic Mar.agement Study, as background input to the 1989 Economic Report for Indonesia. Sadiq Ahmed is a senior economist with the World Bank's resident office in Jakarta. Basant .. Kapur (consultant) is an associate professor with the National University of Singapore. We would like to thank Professor William Branson, Professor John Harris, William Easterly and Ajay Chhibber for helpful comments.
|Registered at the beginning (yes=1)|0.382|0.700|
|---|---|---|
||(0.72)|(1.52)|
|Foreign technology (yes=1)|0.566|-1.202**|
||(1.54)|(-2.50)|
|Imported inputs (yes=1)|1.593***|-0.373|
||(5.30)|(-1.17)|
|Female labor intensity (yes=1)|0.152|0.971***|
||(0.56)|(2.58)|
|Year dummies|Yes|Yes|
|Sector dummies|Yes|Yes|
|Region dummies|Yes|Yes|
|Constant|-0.766|-1.058|
||(-0.55)|(-0.69)|
|Observations|184|150|
|Robust z-statistics in parentheses|||
|*** p<0.01, ** p<0.05, * p<0.1|||
|1/Measured in logarithms.|||

Table 5.5 considers _total employment_ as the dependent variable and estimates the correlation between it and firms’ characteristics and other independent variables. Results show that employment is higher among firms that have always exported, while it is lower for those firms that are new exporters. In addition, total employment was high among firms that stopped exporting. Other factors previously mentioned that also play a role in the level of firms’ employment are age and the incorporation of foreign technology. As in previous models, female intensity is associated with a lower level of employment, meaning larger firms are male-intensive, while smaller firms are female-intensive.

**<mark>Table 5.5. Employment and Exporting Status</mark>**

|**D**|**ependent Variabl**<br>|**e: Total Employm**<br>|**ent**<sup>**1/**</sup><br>||
|---|---|---|---|---|
|VARIABLES|1<br>New<br>Exporters|2<br>Stopped<br>exporting|3<br>Always<br>exported|4<br>Never<br>exported|
|New exporters (yes=1)|-0.613*||||
||(-1.91)||||
|Stopped exporting (yes=1)||0.697*|||
|||(1.95)|||
|Always exported (yes=1)|||0.770**||
||||(2.03)||
|New exported (yes=1)||||-0.421|
|||||(-1.41)|

33 | P a g e
32 data.

Between these two extremes, there are other methodologies that may be applicable depending on data availability and the particular focus of the reform program. Furthermore, if a country faces special issues, a partial analysis may be relevant. For example, with significant trade liberalization programs, a partial analysis of the export sector, and its contribution to income inequality, relative to the sectors producing nontradables and import substitutes, will be important. 26 A second example is in countries that must rely on inflation for tax revenue, an inflation tax incidence study may be useful to identify which groups bear the costs. 2 7 Finally, this review also shows that a study of macropolicy impacts on distribution will benefit from an analysis of microeconomic issues which address how the poor, and other groups, respond to the changed environment following an adjustment program. These responses can have significant effects on the outcome of real incomes and poverty. The importance of these responses and feedback effects implies that wherever possible, general equilibrium effects should be considered.

26The sectoral contribution to incomes and employment is analyzed by Bourguignon and Morrisson (1989) and Krueger (1983).

27The incidence of the inflation tax on income deciles is examined by Diaz (1987) for Mexico.
# Appendix A: Cross national data and results

This appendix provides details on four aspects of the cross national regressions: data on health status, data on independent variables, data on health sector strategy.

# a) Data on health status

The data used here are as reported by UNICEF (1992) except for the mortality rates for Zaire which are unbelievable: under-5 mortality rate for 1990 is reported as 130 and infant mortality rate as 79. These are replaced with the, more reliable, estimates reported in United Nations (1992) for 1984. These are 200 for under-5 mortality and 126 for infant mortaLlity.

# b) Data on non-health sector variables

|_Income_|Real GDP per capita in 1995 international dollars (i.e. adjusted for Purchasing Power Parity)<br>are from the Penn World Tables 5.6.|
|---|---|
|_Education_|Average education levels for men and women over 15 are from Barro and Lee (1996).|
|_Income inequality_|Gini coefficient as calculated by Deininger and Squire (1996) multiplied by 100.|
|_PercRenturban_|Percent of the country's population that lives in urban areas. From the World Bank's Social<br>Indicators of Development database (World Bank, 1997).|
|_Predominantly Muslim_|Dummy equal to one if over 90 percent of the country's population is Muslim.|
|_Ethnolinguistic_<br>_fractionalization_|Index of ethnolinguistic fractionalization for 1960. Measures the probability that two randomly<br>selected people from a given country will not belong to the same ethnolinguistic group as<br>reported in Easterly and Levine (1996).|
|_Tropical country_|Dummy equal to one if part of the country's territory lies within 20 degrees of the equator.|
|_Access to safe water_|Percent of population with access to safe water. From the World Bank's Social Indicators of<br>Development database (World Bank, 1997).|
|_Oil exporter_<br>_Oil exporter_|Dummy equal to one if the country primary export is fuels (mainly oil) as classified by the<br>World Bank's World Development Indicators (1996) plus Kuwait.|
|_Years independent_|The percentage of years since 1776 that a cotntry has been independent, as reported in Easterly<br>and Levine (1996).|
|_Defense spending_|Defense spending as a share of GDP, as reported in CIA (1994)|

# c) Data on health sector variables_

|_Health Expenditures_|Updates of health expenditures from Murray, Govindaraj, and<br>Musgrove (1995) which appear in World Bank (1993)|
|---|---|
|_Perrentage of national health expenditures_<br>_devoted to local health services_|As reported in the WHO's Health for All Database. The observation<br>closest to 1990 in the 1986-1993 period is used.|
|_Percentage of the population with local health_<br>_services, including availability of essential_<br>_drugs, within one hour's walk or travel_|As reported in the WHO's Health for All Database. The observation<br>closest to 1990 in the 1,986-1993 period is used.|

57
|She returns home<br>from work after 5<br>pm|0.92<br>(0.01)|0.78<br>(0.02)|0.84<br>(0.02)|0.69<br>(0.02)|-0.08***|-0.08***|
|---|---|---|---|---|---|---|
|She works in a|0.92|0.91|0.90|0.90|-0.02|-0.01|
|governmentjob|(0.01)|(0.01)|(0.01)|(0.01)|||
|Her workplace is<br>not close to her|0.82<br>(0.02)|0.65<br>(0.02)|0.76<br>(0.02)|0.56<br>(0.02)|-0.06***|-0.09***|
|home|||||||
|The job she is in<br>pays verywell|0.99<br>(0.01)|0.97<br>(0.01)|0.97<br>(0.01)|0.96<br>(0.01)|-0.02*|-0.01|
|She has to<br>occasionally travel<br>for work outside<br>the village/city|0.74<br>(0.02)|0.47<br>(0.02)|0.63<br>(0.02)|0.41<br>(0.02)|-0.12***|-0.06**|
|Her job is|0.67|0.50|0.63|0.47|-0.04|-0.03|
|physically<br>demanding|(0.02)|(0.02)|(0.02)|(0.02)|||
|She works in a|0.56|0.31|0.45|0.25|-0.11***|-0.06**|
|hotel/restaurant|(0.02)|(0.02)|(0.02)|(0.02)|||
|Observations|5|15|6|35|1,|150|

**Table 18.2 Approval of women’s work under different contexts, non-migrant vs. migrant households**

||Females w/o<br>Mea<br>|migrant spouse<br>n/(SE)<br>|Females with<br>Mea<br>|migrant spouse<br>n/(SE)<br>|Pairwi<br>differen<br>|se t-test Mean<br>ce<br>|
|---|---|---|---|---|---|---|
||**Individual**<br>**approval**<br>**(PNB)**|**Perceived**<br>**community**<br>**approval**<br>**(SNE)**|**Individual**<br>**approval**<br>**(PNB)**|**Perceived**<br>**community**<br>**approval**<br>**(SNE)**|**Individual**<br>**approval**<br>**(PNB)**|**Perceived**<br>**community**<br>**approval**<br>**(SNE)**|
|**A woman's charact**<br>|**eristics and ro**<br>|**les**<br>|||||
|She is married|0.98<br>(0.00)|0.95<br>(0.01)|0.99<br>(0.01)|0.94<br>(0.01)|-0.01|0.01|
|She has small<br>children under 5<br>who can be<br>looked after by<br>familymembers|0.86<br>(0.01)|0.75<br>(0.02)|0.89<br>(0.02)|0.77<br>(0.02)|-0.03|-0.02|
|She has small<br>children under age<br>5, who she will<br>need to drop with<br>non-family<br>members while<br>she is at work|0.25<br>(0.02)|0.16<br>(0.01)|0.30<br>(0.03)|0.17<br>(0.02)|-0.05*|-0.02|

54
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_

**Table 5: Selected firm characteristics**

||Exporter<br>(percent)|Privatized<br>(percent)|Foreign<br>owned<br>(percent)|Part. foreign<br>owned<br>(percent)|Provides<br>training<br>(percent)|Workers<br>reported for<br>taxes<br>(percent)|
|---|---|---|---|---|---|---|
|Anglophone|25|9|11|13|33|49.2|
|Francophone|25|3|19|24|29|66.9|

Source: Enterprise Surveys But overall, the within group differences are much greater than those between groups: the share of exporting firms varies from 4 percent in Cape Verde to 59 percent in Kenya. Likewise, the share of firms that have at least some foreign ownership ranges from 7 percent in Ghana to 50 percent in Botswana. If we look at the size distribution of firms, we find similar patterns to other countries. On average over 50 percent of firms are small (less than 20 employees), but there is a large variance in size across countries.<sup>18</sup>

> 18 This is a much smaller share than would be found in most business registers or censuses in African countries. Here the composition of the sample matters particularly: since it is restricted to formal firms, large firms tend to be overrepresented.

8