rafmacalaba/datause-encoder-data huggingface dataset:
Table 3. Intergenerational mobility among the rural population of PROSPERA: change in relative <u>position</u>

||(1)|(2)|
|---|---|---|
|VARIABLES|Height|Years of schooling|
|Provider’s relative position (Rank)|0.354***<br>(0.018)|0.239***<br>(0.018)|
|Constant|0.323***|0.286***|
||(0.012)|(0.010)|
|Observations|4,502|4,425|
|R-squared|0.125|0.066|

Notes: Standard clustered errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations performed based on data from the Evaluation of Rural Households Survey (ENCEL) 2017.

Table 4. Intergenerational mobility height and schooling estimated simultaneously

||(1)|(2)|
|---|---|---|
|VARIABLES|Height|Education|
|Provider’s height|0.317***<br>(0.016)||
|Provider ́s education||0.221***<br>(0.015)|
|Individual’s education|0.298***<br>(0.061)||
|Individual’s height||0.161***<br>(0.044)|
|Constant|0.230***<br>(0.023)|0.211***<br>(0.021)|
|Observations|4,093|4,093|
|Adjusted R-squared|0.114|0.084|

Notes: Controlling by level of schooling in the estimation of height, and by height in the estimation of schooling. Standard clustered errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations performed with data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the ENCEL panel 1997-2000.

The potential mobility results confirm that, although an important persistence in the determination of human capital between generations exists, a vast potential for intergenerational mobility exists among the rural population of PROSPERA. That is, independence between the result observed for youth and the result observed for providers implies that such results are modifiable depending on external factors, and that they are not entirely genetically predetermined.

_B. Effects of PROSPERA: What is the contribution of PROSPERA to the health and education of the population who received the intervention during critical development moments?_ We expect that absolute intergenerational mobility is positive, reflecting an upward movement associated with greater welfare for younger generations, and that such mobility is of greater magnitude when the exposure to the program is higher in critical periods for development. Table 26
|**Table A2: Tanzanian Exports by Secto**|**r and Partn**|**er of the**|**Model in 2**<br>Partner|**007 (in tho**|**usands of US do**|**llars and share**|**s by partn**|**er)**<br>export sh|ares||
|---|---|---|---|---|---|---|---|---|---|---|
|Product|EAC|EU27|SADC|ROW|WLD|EAC|EU27|SADC|ROW|WLD|
|All goods|258,044|421,282|368,109|1,091,912|2,139,347||||||
|Maize|4,270|0|1,193|6,491|11,954|0.36|0.00|0.10|0.54|1|
|Rice|2,500|0|68|1,406|3,973|0.63|0.00|0.02|0.35|1|
|Sorghum & millets/buckwheat|100|40|159|338|638|0.16|0.06|0.25|0.53|1|
|Wheat/meslin|19,832|3|161|10,959|30,956|0.64|0.00|0.01|0.35|1|
|Beans, Soya beans|0|129|1|34|164|0.00|0.79|0.00|0.21|1|
|Cassava/crude anim/veg matl nes|1,099|32,390|767|7,572|41,829|0.03|0.77|0.02|0.18|1|
|Other cereals (barley,rye, oats)|0|0|4|0|4|0.00|0.00|1.00|0.00|1|
|Oil seeds excl soya beans|578|1,087|271|31,491|33,426|0.02|0.03|0.01|0.94|1|
|Veg root/tuber prep/pres|0|6|79|25|109|0.00|0.05|0.72|0.22|1|
|Cotton|1,076|5,137|1,498|51,661|59,372|0.02|0.09|0.03|0.87|1|
|Coffee|572|86,326|900|29,801|117,599|0.00|0.73|0.01|0.25|1|
|Tobacco, raw and wastes|434|72,477|1,485|20,425|94,822|0.00|0.76|0.02|0.22|1|
|Tea|22,635|10,200|2,603|4,528|39,966|0.57|0.26|0.07|0.11|1|
|Cashew nuts, fresh/dried|355|8,243|2,703|16,129|27,431|0.01|0.30|0.10|0.59|1|
|Sisal etc unspun/tow/waste|75|1,637|82|4,239|6,033|0.01|0.27|0.01|0.70|1|
|Sugar/mollasses/honey|173|21,438|2,041|123|23,776|0.01|0.90|0.09|0.01|1|
|Veg & fruits, excl nuts|1,744|3,925|217|55,018|60,904|0.03|0.06|0.00|0.90|1|
|Other crops (cocoa, spices, nuts, exc|202|12,312|17|12,683|25,215|0.01|0.49|0.00|0.50|1|
|Poultry & livestock|35|147|234|841|1,258|0.03|0.12|0.19|0.67|1|
|<br>Fish/shellfish/etc.|11,037|107,050|1,604|45,889|165,580|0.07|0.65|0.01|0.28|1|
|Hunting & forestry|2,239|1,205|729|19,516|23,689|0.09|0.05|0.03|0.82|1|
|Mining & quarrying|25,702|451|4,009|220,122|250,284|0.10|0.00|0.02|0.88|1|
|<br>Meats & dairy products|271|11|311|1,913|2,505|0.11|0.00|0.12|0.76|1|
|Grain milling products|12,346|78|29,440|1,493|43,357|0.28|0.00|0.68|0.03|1|
|Processed food|10,846|776|9,663|12,129|33,413|0.32|0.02|0.29|0.36|1|
|Beverages & tobacco products|2,906|429|4,735|591|8,661|0.34|0.05|0.55|0.07|1|
|Textiles & leather products|18,820|10,640|20,595|29,200|79,256|0.24|0.13|0.26|0.37|1|
|Wood & paper products|14,322|595|943|8,081|23,941|0.60|0.02|0.04|0.34|1|
|Chemicals, excl. fertilizer|6,481|814|19,939|6,726|33,960|0.19|0.02|0.59|0.20|1|
|Manufactured fertilizers|15,599|0|9,750|2|25,351|0.62|0.00|0.38|0.00|1|
|Petroleum and products|10,512|110|1,889|1,301|13,812|0.76|0.01|0.14|0.09|1|
|<br>Rubber & plastic products|515|20|7,947|957|9,439|0.05|0.00|0.84|0.10|1|
|Glass & cement|3,344|17,199|31,558|26,702|78,802|0.04|0.22|0.40|0.34|1|
|Iron & metal products|5,982|5,387|7,872|10,123|29,365|0.20|0.18|0.27|0.34|1|
|Machinery & transport equipment|25,503|17,369|31,523|21,171|95,565|0.27|0.18|0.33|0.22|1|
|**Source: SITC Revision 3 data from UN**|**COMTRAD**|**E Statistic**|**s.**||||||||

101
**Table 4. Land Reallocation Timing and the Probability that a Village Head will Lose an Election**

||(1)|(2)|(3)|
|---|---|---|---|
|Land Reallocated in the Year of an Election|0.013|-0.035|-0.053|
||(0.100)|(0.090)|(0.091)|
|Land Reallocated in Year Before an Election|0.014|0.001|0.047***|
||(0.024)|(0.215)|(0.007)|
|Share of Households in the Largest Patrilineal Clan||0.193|0.142|
|||(0.216)|(0.220)|
|Number of Deaths in the Election Year (per 100 households)||0.003|0.008|
|||(0.005)|(0.005)|
|Share of Households in the Largest Patrilineal Clan|||0.109|
|× Land Reallocated in the Election Year|||(0.388)|
|Number of Deaths in the Election Year (per 100 households)|||-0.007|
|× Land Reallocated in the Election Year|||(0.009)|
|Share of Households in the Largest Patrilineal Clan|||0.117***|
|× Land Reallocated One Year Before the Election|||(0.024)|
|Number of Deaths in the Election Year (per 100 households)|||-0.026***|
|× Land Reallocated One Year Before the Election|||(0.006)|
|Constant|0.158***|0.128*|0.143|
||(0.059)|(0.067)|(0.068)***|
|Province-specific yeareffects|included|included|included|
|Number of observations|99|99|99|

_Land Tenure Insecurity and Migration, Page 46_
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32
These changes may have significant implications for the forest-based livelihoods of millions of poor inhabitants in the Sundarbans and adjacent areas. Therefore, understanding the physical and economic effects of salinity diffusion and planning for appropriate adaptation will be critical for management of the Sundarbans, as well as for long-term development and poverty alleviation in adjacent areas. This paper attempts to contribute by assessing the impact of aquatic salinization on the spatial distribution of mangrove species, with particular attention to the potential consequences for poor communities in the Sundarbans region.

The focus of our analysis is on the Bangladesh Sundarbans,<sup>4</sup> as the potential impact of salinity on the region has become a major concern for the Government of Bangladesh and affiliated research institutions. Recently the Bangladesh Climate Change Resilience Fund (BCCRF) Management Committee has highlighted salinity intrusion as a critical part of adaptation to climate change. Prior research on this issue has been conducted or co-sponsored by the Ministry of Environment and Forests (World Bank 2000) and two affiliated institutions: the Center for Geographic and Environmental Information Services (Hassan and Shah 2006) and the Institute of Water Modeling (IWM 2003; UK DEFR 2007).

Various projections of soil and water salinity for the Bangladesh Sundarbans have suggested significant risks for residents’ welfare.<sup>5</sup> Sundarbans communities are among the poorest of the poor in Bangladesh, and their livelihoods will be significantly affected by spreading and

> 4 Approximately, 60 percent of the Sundarbans is in Bangladesh, covering about 6,000 sq. km in certain districts of Khulna, Satkhira and Bagerhat districts.

> 5 Previous research on salinization in coastal Bangladesh has employed a variety of methods (see for example Nobi and Das Gupta 1997; Aerts et al., 2000; IWM 2003; CEGIS 2006 and Bhuiyan and Dutta 2011).  Many of these studies have simulated salinity change in rivers and estuaries using hydraulic engineering models and have compared the results with actual measures.  In the most comprehensive study to date, Dasgupta et al. (2015a) have used 27 alternative climate change scenarios to project salinity trends in coastal rivers to 2050, with a model that links the spread and intensity of salinity to changes in the sea level, temperature, rainfall, and altered riverine flows from the Himalayas. The study provides new estimates of location-specific river salinity through 2050.

4
11

**Table 3. Long-Run Price Elasticities of Motor Vehicle Ownership, Usage, and Fuel Efficiency**

|_Study_|_Vehicle_<br>_ownership_|_Vehicle_<br>_usage_|_Fuel_<br>_efficiency_|_Vehicle_<br>_depreciation_|
|---|---|---|---|---|
|_Vehicle_**_price_**_elasticities_|||||
|Pindyck (1979)|-0.78|n.a.|-|-0.71|
|**_Gasoline price_**_elasticities_|||||
|Wildhorn et al. (1974)|-0.25|-0.36|0.17||
|Sweeney (1978)|-|-|0.72||
|Pindyck (1979)|-0.64|-|1.43|-|
|Wheaton (1982)|..|-0.48 - -0.55|0.09 0.14|-|
|Train (1986)|-0.11|-0.27|-||
|Hensher et al. (1990)|n.a.|-0.22 -0.39|-||
|Johansson and Schipper (1997)|-0.02 0.00|-0.35 - -0.05|0.45- 0.35|-|
|**_Operating cost elasticities_**|||||
|Mannering and Winston (1985)<br>one car household - short run<br>one car household - long run<br>two car household - short run<br>two car household - long run|-0.23<br>-0.28<br>-0.06<br>-0.10||||

-Not available

Vehicle prices have increased faster than income in the 1 990s, but this reflects mainly increases in vehicle quality. Fuel prices have varied but have not grown as fast as income, and have actually declined from 1980 to 1995 after increasing in the 1970s. Per capita income has grown in both developing and high-income countries and is expected to continue to do so. In the future, average vehicle prices may decline as a result of lower production costs, or they may increase at a rate similar to that of income. If the past magnitudes of income and price elasticities hold for the future, global motorization can be expected to grow unless there are strong increases in the prices of-or the taxes and fees on-vehicles, fuels, and vehicle use.

# _4.3 The role ofpopulation density_

At both national and urban levels, population density is a crude proxy for the spatial distribution of economic activities. Everything else being equal, low overall population density should increase average trip lengths and spur motorization. In high-density cities, congestion caused by density should impose higher costs on motoring and may reduce auto ownership and use. Empirical studies find that population density is negatively related to motor vehicle ownership both at national and urban levels, but the elasticity of motor
on the unilateral depth variable interacted with the same-country dummy turns positive in column (3) of Table 3, where we control for the influence of GDP and GDP per capita of the importer (as proxies for country size and economic development) on the border effect. This seemingly surprising result is confirmed in column (5), where we include interactions with a measure of the quality of institutions (the average across the six categories of the World Bank’s World Governance Indicators (WGI) database, which is found to decrease the border effect, consistent with the evidence from Beverelli et al. (2018)), and with other TA – variables that can have non-discriminatory effect the EU and GPA dummies equal one if the importer is a member of these TAs. The positive and significant interaction effect found also for trade in private markets, where unilateral depth in procurement provisions should not matter, casts doubts on the reliability of these findings. All in all, the results indicate that domestic firms might take advantage of more transparent procedures and open data on government procurement in goods, a the expenses of foreign firms.

Different than for goods, results in services, where the border effect is found to be significantly larger in public than private markets (see columns (1) and (2) of Table 2), suggest that countries with deep provisions on government procurement are more open to international trade. The interaction effect in Table 4 is consistently negative and significant across specifications for public markets, and the effect becomes weaker as we control for size and economic development. As an indication that the unilateral depth measures captures policies relevant to government procurement in services, the interaction effect is not significant for private markets. We thus find that deep non-discriminatory provisions on government procurement in TAs have been instrumental in promoting cross-border procurement flows in services, where governments have been buying significantly more locally than firms.

An important result from Tables 3 and 4 is that the bilateral trade policy effects become much lower and lose significance as we control for the unilateral components of TAs. One explanation for this intriguing result is that part of the bilateral component of the TA variables is actually unilateral, and hence not identified. Another way to read the finding is that most of the trade effect documented in the baseline specifications is actually non-discriminatory. Importantly, this is the case also for the EU dummy, which was the trade policy variable with the strongest effect in Tables 1 and 2. The negative coefficient on the interaction between the same-country dummy and the EU indicator suggests that a substantial part of this effect comes from trade with both EU and non-EU countries.

27
21 17. On the role of informal markets, see Yung Chul Park, The Unorganized Financial Sector in Korea. 1945-75, Domestic Finance Studies 28 (Washington, D. C.: World Bank, 1980); T. A. Timberg and C. V. Aiyar, Informal Credit Markets in Domestic Finance Studies 62 (Washington, D. C.: World Bank, 1980); and Juergen U. Holst, "The Role of Informal Financial Institutions in the Mobilization of Saving," in Savings and Development, ed. Denis Kessler and Pierre-Antoine Ullmo Economica (Paris, 19Pe).

18. See J. C. Qandesara, Efficacy of Incentives for Small Industry (Bombay: Industrial Development Bank of India, 1982), Shahid Yusef, The Commercial Banking Industry In Brazil 1964-74, Domestic Finance Studies 39 (Washington, D. C.: World Bank, 1977); and Undermining Rural Development with Cheap Credit, ed. Dale W. Adams, Douglas H. Graham, and J.B. von Pischke (London: Westview Press, 1984).

19. On the noninterest cost of borrowing, see Jerry R. Ladman, "Loan-Transaction Costs, Credit Rationing, and Market Structure," in Undermining Rural Development with Cheap Credit, chap. 8; and T. Riley, Non-interest Costs of Lending and Borrowing Domestic Finance Studies 65 (Washington, D.C.: World Bank, 1980).

- 20.See V. G. Patel, Innovations in Banking: The Gujerat Experiments, Domestic Finance Studies 51 (Washington, D.C.: World Bank, 1978); and V. V. Bhatt, Financial Institutions and Technical Consultancy Services: The Indian Experiment in Small Enterprise Promotion, EDI Seminar Paper 24 (Washington, D. C.: World Bank, 1981).

21. See V. V. Bhatt, DeveloRment Perspectives (London: Pergamon Press, 1980), chap. 15.

- 22.On this cost-risk reducing pact age of measures, implemented by a central bank, see C. Rangrajan, Inn.Zvations in Banking: The Indian Experience, Domestic Finance Studies 66 (Washington, D. C.: World Bank, 1980).

- 23.Mainstream economic theory and studies ignore these vital aspects of financial policies and emphasize the importance of unregulated markets and interest rate structure. This theory is congenitally incapable of analyzing the rele and functioning of credit markets. For prescriptions based on this theory, see R. I. McKinnon, Money and Capital in Economic Development (Washington D. C.: The Brookings Institution, 1973). See also Bhatt, DeveloRment Perspectives, introduction.

- 24.See Keynes, The General Theory of Emolovment Interest and Money, p. 208.

- 25.The following section is based on information specially obtained from the Syndicate Bank of India. Having been a member of the Board of Directors from 1969 to 1972, the author had the benefit of detailed discussions with the top management of the bank and access to confidential data. See also N. K. Thingalaya, Innovations in Banking: The Syndicate's Experience, Domestic Finance Studies 46 (Washington, D. C.: World Bank, 1978).

26. See Selden Menefee, The Pais of Manioal (New York: Asia Publishing House,
## **Ownership shares**

For most of the firms, the ownership data were derived from Paul Buddle Communications, “Tanzania— Telecoms Market Overview and Statistics,” dated April 5, 2008. For the five relevant companies we have the following ownership information.<sup>27</sup>

**Zanzibar Telecommunications Corporaton (Zantel)** : Etisalat of the UAE acquired a 34% stake, with the government retaining 18%. The other shareholders are Kintbury Investment of the Channel Islands (24%) and MEECO International of Tanzania (24%).

**Vodacom Tanzania** : Vodacom Tanzania is a subsidiary of South Africa-based Vodacom (Pty) Ltd, which owns 65% of the company. The remainder is held by Tanzanian companies Planetel Communications (16%) and Caspian Construction (19%).<sup>28</sup>

**MIC Tanzania (also known as TIGO)** : Luxemburg-based Millicom International Cellular (MIC) owned 84% of the company until it assumed 100% ownership in early 2006 when it bought out other minority shareholders.

**Celtel Tanzania:** Celtel Tanzania Ltd is 60% owned by Celtel International and 40% owned by the Tanzanian government. Celtel International is a subsidiary of the Zain Group of Kuwait.

**TTCL:** Although it is in the process of privatization, it is 100 percent owned by the Government of Tanzania.

## **Ownership Share Results**

The results for market share by country (in percent) are as follows: Tanzania, 31; EU, 22; EAC, 0; SADC, 27; Rest of World, 20 (see Table B1). In our model, we sum the shares of SADC and EAC for our Africa region.

# **II. Banks in Tanzania**

# **Bank Market Shares**

The data source was Bankscope, an on-line data source for about 29,000 banks worldwide.<sup>29</sup> Through Bankscope, we obtained data on total assets by bank in Tanzania, ownersshareholders of the bank and the percent of the bank owned by each owner-shareholder. Market share of each bank was defined based on the bank’s assets as a share of total bank assets in the country. We divided the regions into the European Union, East African Customs Union (EAC),

> 27 The market share of Benson Informatics, rounded to the nearest one-tenth of one percent is zero. Consequently we ignore Benson.

> 28 Planetel Communications and Caspian Construction are local TZ companies. See the Mail & Guardian Online at: <u>http://www.mg.co.za/article/2007-04-13-vodacoms-elite-network</u> 29 It combines data from the main information provider, Fitch Ratings, and nine other sources, with software for searching and analysis. Each bank report contains balance sheet and income statements with up to 200 data items.

103
# Non-Routine Cognitive Interpersonal

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# Routine Cognitive

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