rafmacalaba/datause-encoder-data huggingface dataset:
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Rochegude, A. and C. Plançon. 2010. “Décentralisation, acteurs locaux et foncier en Afrique. Mise en perspective juridique des textes sur la décentralisation et le foncier”. Fiche pays 15 - Mali.

République du Mali. 2012. Bureau du Vérificateur général du Mali. Rapport 2011 République du Mali. 2009a. Rapports de Synthèse des Concertations des Etats Généraux du Foncier. Ministère du Logement, des Affaires Foncières et de l’Urbanisme, Commission Nationale d’Organisation des Etats Généraux du Foncier.

République du Mali. 2009b. “Actes de la rentrée judiciaire. Bulletin d’information de la Cour suprême du Mali.”, N° 10 République du Mali. 2009c. “Stratégie de Développement des Villes du Mali – SDVM.” République du Mali. 2009d. “La Gestion du patrimoine foncier des Collectivités Territoriales.” Ministère de l’Administration territoriale et des Collectivités Locales. Centre de formation des collectivités locales.

République du Mali. 2008. “Feuille de route validée pour la mise en œuvre du volet foncier de la LOA.” Ministère de l’Agriculture.

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République du Mali. 2004. “Document cadre de politique nationale de décentralisation (2005-2014).” Ministère de l’Administration Territoriale et des Collectivités Locales.

République du Mali. 2000. “Stratégie Nationale d’accès à un habitat décent pour les populations à faible revenu.” Ministère de l’Equipement de l’Aménagement du Territoire de l’Environnement et de l’Urbanisme.

République du Mali. 1995. “Stratégie Nationale du Logement.” Ministère de l’Urbanisme et de l’Habitat, Bamako. Selod, H., and L. Tobin. 2013. “City structure and informal property rights in West Africa”, World Bank mimeo. Tacoli, C. 2002. “Changing rural-urban interactions in sub-Saharian Africa and their impact on livelihoods : a summary.” IIED. Working paper series on Rural-Urban interactions and livehood stategies. Working paper 7.

Toulmin, C., and J. Quan, J. (Eds). 2000. _Evolving land rights, Policy and Tenure in Africa_ . IIED and NRI.

UN-Habitat. 2012. _Handling Land. Innovative tools for land governance and secure tenure_ . UN-Habitat : Nairobi. UN-Habitat. 2010. _The State of African Cities. Governance, Inequality and Urban Land Markets_ . UN-Habitat : Nairobi. Ville de Bamako. 2012. _Bamako 2030 : croissance et développement - Imaginer des stratégies urbaines pour un avenir maîtrisé et partagé_ .”.

Wehrmann, B. 2008. “The dynamics of peri-urban land markets in sub-Saharan Africa : Adherence to the virtue of common property Vs. quest for individual gain” _Erkunde_ 62 (1): 75-88.

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World Bank. 2002. “Upgrading of Low Income Urban settlements”. Country assessment Report. Mali. Africa Region, AFTU1 & AFTU2, Washington, DC.

39
otherwise identical national competitors because they have higher global profits, which they share with their workforce in both countries. Thus, the FDI wage premium exists only in less advanced countries.

Examining the causal effect of foreign ownership on wages is quite challenging due to the demanding data requirements. Ideally, to establish the causal effect of foreign acquisitions on wages, one would like to trace the pay of individual workers who are continuously employed in firms that change ownership and control for unobservable worker heterogeneity as well as firm heterogeneity.<sup>4</sup> The recent availability of linked employer-employee data has allowed researchers to make progress in this area. The broad message that emerges from these studies is that although the FDI wage premium appears to be small or even negative in industrialized countries, it is positive in emerging markets.

One of the first studies of this type is by Heyman, Sjöholm, and Tingvall (2007), who use matched employer-employee data from Sweden for the second half of the 1990s. In their most demanding specification, they use propensity score matching to create a control group for the sample of Swedish firms that underwent foreign acquisitions.<sup>5</sup> Then, they employ the difference-in-difference estimator to examine whether the wages of individual workers have changed as a result of foreign acquisitions. Most importantly, they are able to control for individual fixed effects. Because they restrict their sample to workers who remain in the same firm for the entire period of observation of the firm, they obtain within-individual and 5
68

# **Table A1**

## **Dependent Variable: Growth Rate of Real GDP Educational Pillar Indicators**

|**Years : 1960 - 2000**|**Reg A1.1**|**Reg A1.2 **|**Reg A1.3 **|**Reg A1.4 **|**Reg A1.5 **|**Reg A1.6 **|**Reg A1.7 **|**Reg A1.8 **|**Reg A1.9 **|**Reg A1.10 **|**Reg A1.11**|**Reg A1.12**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**|**0.2875*****|**0.2308*** **|**0.2053*** **|**0.2128*** **|**0.2886*** **|**0.2519*** **|**0.2266*** **|**0.2339*** **|**0.2077*****|**0.1966*****|**0.2259*****|**0.2166*****|
|**Growth of Labor**|**0.5356*****|**0.7011*** **|**0.5463*** **|**0.5694*****|**0.2605***|**0.3057****|**0.2852****|**0.7035*** **|**0.5711*****|**0.5301*****|**0.7204*****|**0.6831*****|
|**Initial GDP (1960)**|**-2.2136*** **|**-3.8888*** **|**-4.3984*** **|**-3.8747*** **|**-3.5343*** **|**-3.5656*** **|**-3.9854*** **|**-3.6189*** **|**-3.9607*** **|**-4.2980*** **|**-3.5562*** **|**-3.8616*****|
|**HC15**||**0.6736**|**0.6887**|**4.3613****|||||||||
|**HC15 with**<br>**educational returns**|||||**-0.0402**|**-0.1326**|**1.3566***||||||
|**HC15 with extended**<br>**educational returns**||||||||**0.2384**|**0.0447**|**1.4302***|**0.1817**|**1.6161***|
|**Sq of HC15**||||**-1.0208****|||||||||
|**Sq of (HC15 with**<br>**educational returns)**|||||||**-0.2610****||||||
|**Sq (HC15 with**<br>**Extended**<br>**Educational Returns)**||||||||||**-0.2546****||**-0.2688****|
|**Log (Educational**<br>**Quality)**|||**0.5051**|**0.2933**||**1.0499**|**1.1254**||**0.6900**|**0.5596**|||
|**Log (Educational**<br>**Quality Extended)**|||||||||||**0.4594**|**0.3256**|
|**Log (Patents)**|**0.1479****|**0.1894****|**0.1790****|**0.1909*****|**0.1194**|**0.1095**|**0.0812**|**0.2059*** **|**0.1981*****|**0.1823****|**0.2030*****|**0.1838****|
|**Phones**|**0.0034*****|**0.0038*** **|**0.0031*** **|**0.0033*** **|**0.0032*****|**0.0023****|**0.0026***|**0.0045*** **|**0.0036*****|**0.0038*****|**0.0040*****|**0.0041*****|
|**Constant**|**0.9257***|**-0.0184**|**-1.0921**|**-3.6660***|**2.3902*****|**-1.0898**|**-2.7500**|**0.4861**|**-0.9909**|**-1.7828**|**-1.0755**|**-1.8984**|
|**R squared**|**0.6194**|**0.5809**|**0.5987**|**0.6137**|**0.6941**|**0.7100**|**0.7251**|**0.5784**|**0.5932**|**0.6071**|**0.5818**|**0.5967**|
|**Number of Countries**<br>*, **, *** denotes significance<br>Robust standard errors obtai|**107**<br>at the 90, 95<br>ned using Whi|**92**<br>and 99 percen<br>te's correction|**85**<br>t confidence l<br>for heterosce|**85**<br>evel respective<br>dasticity.|**53**<br>ly|**53**|**53**|**92**|**85**|**85**|**92**|**92**|
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for mining in the south than in the north. Input downstreamness is measured as the average distance from primary inputs suppliers (Antras et al. 2012). Overall, we find stronger intersectoral linkages with mining in the south than in the north.

For the second prediction, i.e., whether regional differences in intersectoral linkages led to regional differences in sectoral composition of regional output between 2003 and 2013, to identify the counterfactual, we distinguish between heavy manufacturing industries (chemicals, machinery, metals, and nonmetallic minerals) and light manufacturing industries (such as food, textiles, paper, and wood). Based on world input-output data for 43 countries, we find that heavy manufacturing and mining show strong intersectoral linkages, as more than 85 percent of mining intermediate inputs in manufacturing are used in heavy manufacturing. We test if employment growth in heavy manufacturing industries occurs in mining districts (districts with at least one mining firm). The elasticity of employment for heavy manufacturing with respect to employment in mining (an elasticity of 0.31) is almost 50 percent larger than the corresponding elasticity for light manufacturing (0.21).

In our subsequent empirical model, the intensity of intersectoral networks is identified jointly by the location of a mining firm and the year in which it was established. We interpret the effect of the mining shock on intersectoral linkages as causal if the contemporaneous and lagged effects are positive and strong, and lead effects (placebo) are weak and negative. Constructing a district-year panel with 119 districts and 24 years (1990–2013) based on data on the location and the year of establishment of a firm collected in the 2014 IBES, we find stronger intersectoral linkages between mining and heavy manufacturing in the south than in the north. The relationship between mining and heavy manufacturing firms in the south appears to be causal, based on the placebo tests.

Finally, we test if average firm-level employment (in different sectors) is higher in mining districts in the south and if average employment in a manufacturing firm is higher in 2013 (compared to 2003) if it is located in a mining district in the south. On average, employment size in heavy manufacturing located in a mining district in the south (compared to the rest) is higher by 23.2 percent. We also find that average employment size of heavy manufacturing firms grew by more than three times in mining districts in the south between 2003 and 2013 (compared to light manufacturing firms). Overall, the variation in the mining employment shock across districts explains employment growth particularly in heavy manufacturing in the south. The evidence strongly suggests that this process of structural transformation was facilitated by the entry of new firms in heavy manufacturing in mining 4
Table 14: Typology **of** land tenancy **contracts in different parts of Orissa**

|**Form**|**Terms of contract**|**Comm**|**on among:**|**Geographical**<br>**location**|**Whether**<br>**limited to**<br>**particular**<br>**crops**|
|---|---|---|---|---|---|
|||**Lessors**|**Lessees**|||
|Sharecropping|Gross output divided<br>equally (1:1) between<br>landlord and tenant<br>after deduction of<br>expenditure on<br>chemical fertilisers or<br>HYV seeds<br>Gross output divided<br>equally (1:1) between<br>landlord and tenant<br>Gross output divided<br>either (1:2) or (1:3)<br>between landlord and<br>tenant respectively|Small and<br>marginal<br>farmers<br>Small and<br>marginal<br>farmers<br>Small and<br>marginal<br>farmers|Landless, and<br>small and<br>marginal<br>farmers<br>Landless, and<br>small and<br>marginal<br>farmers<br>Small and<br>marginal<br>farmers|Sambalpur<br>Cuttack, Khurda,<br>Gopalpur<br>Cuttack|No<br>Paddy, gram,<br>lentils and jute<br>Potato|
|Fixed tenancy<br>in kind|Agreed amount (8-10<br>bags/acre/crop) to be<br>paid after harvest;<br>tenant responsible for<br>cultivation|Medium<br>farmers|Small and<br>marginal<br>farmers|Sambalpur|Paddy|
|Fixed tenancy<br>in cash|Agreed amount of<br>cash paid to landlord<br>before getting access<br>to land|Medium<br>farmers|Small and<br>marginal<br>farmers|Sambalpur|Paddy|

Source: Sarap (1998), Swain (1998), and field notes.

Table **15: Common reasons for leasing land in Orissa**

|**Leasing-out**|**Leasing-in**|
|---|---|
|* Lack of supervisory labour|*Limited non-farm employment opportunities (i.e.|
|* Lack of bullock power|excess labour-land ratio)|
|* Small (uneconomic) holding|*Availability of bullocks|
|* Land distant or of poor quality|*Consolidate and increase operational holding|

Source: Sarap (1998), Swain (1998), and field notes.

59
insurgency which occurred between 1981 and 1993 as a case study. In districts affected by terroristic attacks, the long-term yearly investments in agricultural technology is reduced by around 17 percent. This contraction turns in to an average reduction of farmers’ income of 4 percent.

Besley and Mueller (2012) study the effect of violence on house prices in Northern Ireland. They show that the peace process and its corresponding decline in violence led to an increase of house prices in the most affected regions compared to other regions. Their paper offers a way to understand the heterogeneity of these changes across regions which directly link to the role of expectations. They find that house prices in Belfast increased by 6 to 16 percent due to changes in expected violence in the peace process.

# **6.2 Political Risk in Enterprise Surveys**

Political risk consistently features as a crucial constraint to economic activity in enterprise surveys. The Multilateral Investment Guarantee Agency (MIGA), for example, provides surveys for a number of years each of which show that multinational enterprises report political risks as a key factor for investment in developing countries. Especially for economic activity which requires a mid-term perspective of three years and more political risk is crucial. However, the term political risk captures “the probability of disruption of the operations of multinational enterprises by political forces or events” which is clearly related to political violence but not necessarily larger in conflict countries. In this regard MIGA (2011) is particularly useful as it collected data particularly for risk in Conflict-Affected and Fragile (CAF) economies. The report stresses that these countries are ranked as particularly risky. The three main channels of transmission of political risk to foreign investors in CAF countries are:

- the possible destruction of assets resulting from conflict itself;

- the unavailability of inputs and adequate human resources;

- abrupt declines in domestic demand, leading to lasting impoverishment that persists beyond the end of hostilities.

For investors in CAF states, past losses appear directly related to divestment: 22 percent of MNEs surveyed say that political risk in CAF countries has resulted in scaling back, canceling or delaying their investments.

Perhaps more surprisingly political risk and insecurity also appear to be important constraints for local firms. The enterprise survey conducted by the World Bank in a large set of countries asks whether political instability is an obstacle to the operations of the respective establishment. Only 35 percent of all establishments report that political instability is no constraint. Ten percent report that political instability is the worst constraint they face, i.e. more important than about 10 other factors including finance, corruption or regulation. Furthermore, the tendency of enterprises to report that political instability is an obstacle to their operations is positively associated with 43
Table 8: **ASP Attendance and Dropout**

||Attendance<br>(as % of sessions)<br>(1)|Dropped<br>the ASP<br>(2)|Sessions<br>scheduled<br>(3)|
|---|---|---|---|
|_Virtue_|0.195***|-0.110**|0.235|
||(0.048)|(0.046)|(3.548)|
|_Mindful_|0.064<br>(0.043)|-0.058<br>(0.040)|0.034<br>(2.871)|
|Psychology-based curricula|0.131***<br>(0.042)|-0.084**<br>(0.039)|0.137<br>(2.723)|
|P-value for_Virtue_=_Mindful_|0.0046|0.1598|0.9542|
|<br>Mean_Clubs_group|0.6142|0.2258|42.7689|
|<br>Observations|897|897|897|
|height||||

_Notes:_ This table shows the ASP attendance rates. The estimation sample includes all students treated in _Clubs_ , _Virtue_ , or _Mindful_ . It compares the attendance of participants randomly assigned to _Virtue_ or _Mindful_ to those treated in _Clubs_ . Column (1) shows the attendance rate as a percentage of the number of total sessions conducted (column (3)). Column (2) presents the share of students who dropped out of the ASP. “Mean Clubs is the mean of the outcome for the that in _Clubs_ . The co- group” group participated efficients for both the _Virtue_ and _Mindful_ groups are derived from a unified regression the on the broader of cur- analysis. Conversely, coefficient category psychology-based ricula is obtained from a distinct regression that employs a dummy variable set to 1 for students who participated in any such curriculum _(Virtue or Mindful)_ . All estimations include the relevant control variables at baseline. Bootstrapped standard errors at the course level are in parentheses.<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _,_<sup>_∗∗_</sup> _p <_ 0 _._ 05 _,_<sup>_∗_</sup> _p <_ 0 _._ 1 53
_Table 1.7: Institutional Cost to Deliver the DTS and RPs – Public Large TVI (PHP)_

||20|10|20|11|2|012|20|13|2|014|
|---|---|---|---|---|---|---|---|---|---|---|
||DTS|RP|DTS|RP|DTS|RP|DTS|RP|DTS|RP|
|Personnel|550,000|12,450,000|576,000|12,000,000|600,000|12,400,000|672,000|13,300,000|696,000|14,300,000|
|Non-personal|120,000|640,000|128,287|744,000|317,243|650,000|280,000|750,000|649,441|853,000|
|Materials and supplies|140,000|750,000|150,000|800,000|147,000|950,000|209,820|1,100,000|214,717|1,200,000|
|Maintenance|150,000|800,000|110,000|750,000|170,000|1,300,000|155,618|2,200,000|68,425|3,200,000|
|Administrative|72,000|216,000|90,000|300,000|120,000|310,000|140,000|400,000|170,000|600,000|
|Capital expenses|63,000|400,000|140,000|650,000|160,000|700,000|180,000|1,200,000|190,000|1,400,000|
|Contingencies|0|0|0|0|0|0|0|0|0|0|
|Total|1,337,050|18,942,575|1,374,005|18,009,876|1,623,599|17,833,589|1,571,339|18,544,000|1,658,112|18,216,291|
|Enrolled|499|1865|562|3,038|550|2,272|132|2,473|255|4,262|
|total per trainee|2,679|10,157|2,445|5,928|2,952|7,849|11,904|7,499|6,502|4,274|
|DTS/Non-DTS|0.3|—|0.4|—|0.4|—|1.6|—|1.5|—|
|personnel cost per trainee|1,102|6,676|1,025|3,950|1,091|5,458|5,091|5,378|2,729|3,355|
|DTS/Non-DTS|0.2|—|0.3|—|0.2|-|0.9|—|0.8|—|
|MMC per trainee|707|1,046|712|724|867|1,298|4,132|1,820|1,855|1,361|
|DTS/Non-DTS|0.7|—|1.0|—|0.7|—|2.3|—|1.4|-|
|_Source: DTS Tracking_<br>_Note:_|_Survey,_|_2015, Worl_|_d Bank. W_|_orld Bank_|_calculati_|_ons._<br>DTS<br>|RP||||
||||Ave|rage per-tra|inee cost|5,297 7,|141||||

MMC = Material/supplies, Maintenance, and Capital Expenses
existence of bias against females in the allocation of resources within the household. The idea is to formulate these questions explicitly, so that it is possible to identify whether and to what degree there is evidence of this bias. Second, to identify causes of this bias, with the objective of isolating key factors that can be used for policy.

In contrast to earlier studies that attempt to account for malefemale differences in human capital, we do not assume any discrimination against females either at home (in the parents' utility function) or in the market (in the returns to human capital). All that is assumed in this paper is that women have a comparative advantage in working in some sectors of the economy. Thus increases in the shares of these sectors will increase the demand for female human capital.5 This explicit attention to factors that can be used as policy instruments -- and the relative neglect of factors reflecting gender bias in tastes -- is the point of departure from the earlier literature such as Gertler and Alderman (1989).

The paper is organized as follows. Section 2 develops the theory. Section 3 tests these hypotheses using data from World Bank (1990), United Nations (1990), and from Summers and Heston (1988) for 1965 and 1985-87. Section 4 concludes the paper with a discussion of the policy implications.

5Differences in marginal effects of sector shares on investments in schooling and health are also indicative of segmentation of the market for labor. For example, if an increase of one percent in the share of services (at the expense of agriculture) raises the demand for schooling by more than a one percent increase in the share of industry (again at the expense of agriculture), then labor market segmentation cannot be ruled out.

4
Branches are, instead, not independently incorporated and fully owned by their foreign parent bank.

Foreign-owned banks represent about 50 percent of the total number of banks in Latin America and Eastern and Central Europe. Those included in our sample capture more than 80 percent of the assets controlled by all foreign banks, on average, across countries. Hungary, Peru and Bolivia are the exception, where the foreign banks in our sample account for less than 60 percent of the assets of the foreign banks in the system, mainly because the biggest foreign banks in these countries are owned by banks from neighboring countries that are not among the world’s top 100 banks.<sup>11</sup> Overall, we capture 247 out of 387 foreign controlled institutions or about 65 percent of the foreign operations in the countries in our sample.

Among the banks in our sample with operations in Latin America and/or in Eastern Europe, there seems to be a preference for subsidiaries (Table 2), which account for 65% of the number of foreign bank operations in Latin America and for 82% of those in Eastern Europe.

The operations of the top 100 banks by country of origin (or home-country) are shown in Table 3. It is clear that U.S., German, and Japanese banks are dominant both in terms of assets and number of institutions. There are 20 U.S. banks, 16 German banks, and 11 Japanese banks in the top 100 list. Regarding our sample of host countries, we observe that U.S. and German banks have operations in 15 out of the 20 host countries we consider. Italian and Dutch banks operate in 14 and 12 countries, respectively. The actual number of branches and subsidiaries in the hostcountries that make up our sample can also be observed in Table 2. U.S. banks have 26 subsidiaries and 24 branches, followed by German banks with 42 subsidiaries and 6 branches.

> 11 Peru's low share data is more subtle and it is due to the importance of Banco de Crédito del Peru and Interbank's shareholders which are companies from Bermudas and Bahamas respectively.

7