rafmacalaba/datause-encoder-data huggingface dataset:
the level of protection because of the large weight attached by the government to aggregate welfare. If we instead allow for our more general setting, the reason for which the government does not react is not that it cares mainly about social welfare, but rather that there is no lobbying pressure when imported goods are very imperfect substitutes for domestically produced goods.

# **References**

- [1] Calder ́on, C., 2006. Trade, Specialization and Cycle Synchronization: Explaining Output Comovement between Latin America, China and India. mimeo, The World Bank.

- [2] Chang, P.L., 2005. Protection for sale under monopolistic competition. Journal of International Economics 66 (2), 509-526.

- [3] Davidson, R. and Mackinnon, J. G., 1981. Several tests for model specification in the presence of alternative hypotheses. Econometrica 49(3), 781-793.

- [4] Facchini G., Willmann G. and J. Van Biesebroeck, 2006 Protection for sale with imperfect rent capturing. Canadian Journal of Economics 39(3), 845–873.

- [5] Gawande, K., 1997. Generated regressors in linear and non linear models. Economic Letters 54(2), 119-126.

- [6] Gawande, K., Bandhopadhyay, U., 2000. Is protection for sale? A test of the GrossmanHelpman theory of endogenous protection. Review of Economics and Statistics 89(1), 139-152.

- [7] Gawande, K., Krishna, P., 2004. The political economy of trade policy: empirical approaches. In: Choi, K., Harrigan, J. (Eds.), Handbook of International Trade. New York: Basil Blackwell.

- [8] Goldberg, P., Maggi, G., 1999. Protection for sale: an empirical investigation. American Economic Review 89(5), 1135-1155.

22
22 Mexico MFA

**Table 4. Coefricients** on **the** Regressicns Testing **for** the Composition **Effect**

|Group 1|-4.412<br>**(4.0 61)**|1.178**a**<br>**(0.3018)**|0.638'<br>**(0.160)**|-0.537**&**<br>**(0.1536)**|0.579c<br>**(0.3496)**|0.409<br>**(1.0833)**|
|---|---|---|---|---|---|---|
|Group 2|1.131<br>(1.8777)|-0.428<br>**(0.4093)**|-0.011<br>(0.0201)|0.053<br>(0.0355)|0<sup>.</sup>88 8 <sup>**b**</sup><br>**(0.4239)**<br>_|___|
|Group 3<br>___________|2 3 <sup>.</sup>0 59 <sup>b</sup><br>**(8.3124)**<br>|-1.5 54 b<br>1**(0.8387)**|0.010<br>**(0.0484)**|||____.__|
|Group 4|22,570 A<br>**(4.1507)**<br>|4 5 <sup>72b </sup><br>**(0.2117)**<br>|0..129<br>**(0.1340)**||||
|Group 5|5.896 <br>**(3.4188)**_|0.076<br>**(0,2967)_**|-0.498<br>**(0.3835)**|-0.039<br>**(0.0299)**|0.011<br>**(0.0483)**|-0.065<br>**(0.4308)**|
|Group 6<br>____<br>|0.173<br>_<br>___(0.1608)i<br>|0.27A<br>(0.2363)<br>|-0.082<br>(0.1146)||||
|System wei<br>Standard er<br>' significan|ghted**R**<sup>**2 **</sup>**=**0.8<br>rors in parenth<br>t at the one per|142.<br>^":.<br>ce:rt level.|||||

b significant at the five percent level.

**C** significant at the ten rercent level.

Table S. F-Tests for Composition Effect

|Ho:|Ho=°<br>0ij|0|yj+<br>aO|
|---|---|---|---|
|_ _ _ _ _<br>_ _ _ _|No Marginal Effect<br>_ _ _ _ _ _<br>_ _<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _E|No CoEDosition<br>ff'e ct_|No Rent Sharing<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|
|Group I|F(1,34) = 3.559<br>cannot reject|F(5,34) = 15.71<br>reject|F(6,34)=22.25<br>reject|
|Group 2|F(1,34)=2895<br>reject|(?)<br>__|(?)|
|Group 3|F(1,34)=3.860<br>cannot reject|F(2.34)=2.194<br>cannot reject|F(3,34)=25.111<br>reject|
|C.roup 4<br>_______|F(1.34)=7.287<br>_______________<br>reject|(?)|(7)|
|Group 5|F(1.34)=0.638<br>cannot reject|F(5,34)= 1.721<br>cannot reject|F(6,34)=40.774<br>reiect|
|Group 6|F(1,34)= 1.550<br>canmot reject|F(2,34)=0.824<br>cannot reiect|F(3,34)= 13.534<br>reject|

Note: All rejections are at the one percent level. Failure to reject **is at** the five percent level.
97 and attractive to users. Since public sector ability to corntinue to provide free contraceptives Is questionable over the longer term, these NGOs may vell be the major alternative source for current users who cannot offer unsubsidized commercial prices and who must rely on public or quasi-public service programs. Thus, reducing overall costs of family planning provision even for NGOs is In the long term interests of family planning availability.

Enterprile Program's Feasibility Study of Commercial Clinics Providing Sterilization in Jakarta is also an interesting method of encouraging priv.-. sector act'uity in an area that has an uncertain demand, and therefore ma. constitute i risky investment. Gathering and analyzing information on the nature and extent of the family planning market is an appropriate nd useful use of public funds, especially vhere the social benefits are high and individual risks are high.

These experiments are altering the definition of private sector activities. They are not only persuading firms to shoulder the cost of family planning provision, but they do not alvays provide financing for services. These nev initiatives offer services that can help initiate or enhance a family planning service delivery operation, provide seed money for family planning investments and feasibility studies, or demonstrate the financial benefits of such investments. It is a different and much more market oriented approach than those applied in the past and it bodes vell for sustained service availability vithout long-term donor involvement.

**al.tp:Pa1ly** Planning **faper** g(wiol
<!-- Start of picture text --> \ 0.1% p Corporate & multinationals Banks ,<br>primary<br>f : target<br>0.9% Large enterprises<br>10%> || Medium enterprises The SME<br>Finance<br>' GAP<br>20% y| Small enterprises<br>65- ai . Micro<br>75% icro-enterprises finance<br>Percentages represent the number of companies<br>Source: SME Banking Knowledge Guide, /FC, 2009<br><!-- End of picture text -->
**_<u>________________________________________________________</u>_**

Global Logistics and Port indicators include:

- Customs Performance (e.g., customs clearance time and cost; percentage of consignments inspected)

- Bureaucracy/red-tape associated with typical trade transactions (e.g. number of signatures; time and costs for processes from order to delivery)

- Port performance (e.g., container waiting time at ports, time to enter port, vessel turn – around time)

# _<u>International Exhibition Logistics Associates (IELA)</u>_

IELA, a trade association has prepared average clearance time through customs for 67 countries. The data on customs clearance is collected from local agents in different countries, who report these times to the association based on their experience. The clearance is related to the exhibition in a particular country, and differs from commercial clearance. The procedures followed for clearance of cargo for an exhibition are normally longer, since the entry is on a temporary basis. Also, there is very little information on developing countries.

# _<u>Trade and Transport Facilitation in Southeast Europe Program (TTFSE)</u>_

The TTFSE regional program was established in 1998 with support from the World Bank, the EU, and bilateral partners. Based on face-to-face interviews and discussions with representatives of companies, the TTFSE is a continuation of the monitoring work during 2001-03 to ascertain whether improvements for customs users are possible or not. Its methodology is participatory in nature – seeking to give ownership to the different stakeholders. Operating in eight southeast European countries<sup>4</sup> , it aims to create a framework for facilitating trade by encouraging more efficient and less costly trade flows, and to modernize customs administrations and other border control agencies. It seeks to reduce non-tariff costs to trade and transport (processing time for traders and transporters, “facilitation payments”, smuggling and corruption at border crossings, reduced waiting times), as well as increase the efficacy of anti-smuggling methods.

TTFSE consists of the following project components: Customs Services Procedures Reform, Trade Facilitation Development, Support to Integrated Customs Information System (ICIS), Improvement of Roads and Border Crossing Facilities, and Program and Project Implementation.

As part of the TTFSE Program, all countries involved agreed to measure performance indicators of two types :

- Data on the performance and efficiency of Customs Administrations in Southeast Europe since 1999

- Data on the border crossing times and clearance times, as experienced by users at the TTFSE pilot sites.

> 4 Albania, Bosnia-Herzegovina, Bulgaria, Croatia, Romania, Serbia, Moldova, FYR Macedonia.

17 ____________________________________________________________________________________________________________
As mentioned earlier, the Afores at this time are unable to purchase equity. However, foreigners are free to buy shares in the Mexican stock market, and Mexico represents over a 7% share in the S&P/IFC global composite emerging market equity index. However, this diverse potential investor base contributes to an increased cost of placement for new issues.

**Table 5:  Costs of an Ordinary IPO (in Mexican Pesos)**

||**Face Value**|**Issued - 1000s**|**Mexican Pesos, (**|**approx US$100**|**0s below)**|
|---|---|---|---|---|---|
||200,000|500,000|1,000,000|2,000,000|4,000,000|
|**Cost Category:**|(18,180)|(45,500)|(91,000)|(181,800)|(363,640)|
|Investment Banking Fees|7,000,000|17,500,000|35,000,000|70,000,000|140,000,000|
|CNBV|360,000|900,000|1,558,288|2,458,288|4,258,288|
|technical study|14,456|14,456|14,456|14,456|14,456|
|Stock exchange registration|232,500|500,000|500,000|500,000|500,000|
|Indeval (depository)|625|625|625|625|625|
|Legal Fees|800,000|800,000|800,000|800,000|800,000|
|Prospectus|280,000|280,000|280,000|280,000|280,000|
|Road Show|200,000|200,000|200,000|200,000|200,000|
|Publications and Press|71,500|71,500|71,500|71,500|71,500|
|Rating Agency|504,850|642,850|872,850|1,150,000|1,150,000|
|**Total Costs**|**9,463,931**|**20,909,431**|**39,297,719**|**75,474,869**|**147,274,869**|
|**% of Issue Size**|**4.73%**|**4.18%**|**3.93%**|**3.77%**|**3.68%**|

# **V.  Brazil**

# **A. Primary Markets Characteristics**

While the size of the Brazilian markets is not too different from those of Mexico and Chile, the security markets have not been a significant source of corporate finance for Brazilian firms. Through the past 5 years, the total value of new equity issues on an annual basis has been below US$1 billion, while the highest value of bond issues over this period annually has been just under US$7 billion. For comparison purposes, the development bank BNDES lent over this amount alone in the year 2001.

That said, despite global events in 1998-1999, and election worries within the country in 2002, four to ten equity IPOs have taken place each year, along with 25-42 corporate bond issues. As the following chart shows, activity slowed substantially in 2003. While Brazil has substantial market capitalization in its equity market, it suffers from low free float, and substantial de-listings. As a result, the number of registered companies on the stock exchange has been falling. However, unlike Chile and Mexico, institutional investors (including pension funds, insurance companies and various investment funds) are more involved with equity than private company debt. On the aggregate, equity has comprised 13% of these investor’s portfolios, in contrast to the 4% held in private debentures.

14
# **Refier!vnces**

Besley, T. and R. Kanbur (1988): "Food Subsidies and Poverty Alleviation," EconomiJcournal Boateng, E., K. Ewusi, R. Kanbur and A. McKay (1992): "A Poverty Profile for Ghana: 1987-1988," Journal **of African** Economies Corden, W. M. and **J.** P. Neary (1982): "Booming Sector and Deindustrialization in a Small Open Economy," Economic Journal Foster, E., **J.** Greer and E. Thorbecke (1984): "A Class of Decomposable Poverty Measures," Econometrica Hoff K. and **J.** E. Stiglitz (1990): "Imperfect Information and Rural Credit Markets - Puzzles and Policy Perspectives, The World Bank Economic Review Hughes, G. (1987): "A New Method for Estimating the Effects of Fuel Taxes: An Application to Thailand", The World Bank Economic Review Younger, S. D. (1992): "Aid and the Dutch Disease: Macroeconomic Management When Everybody Loves You," World Develonment 15
|**Federal District**|**Subject of the Russian**<br>**Federation**|**WTO 10-**<br>**reg code**|**WTO subj**<br>**of RF code**|**Russian Name**|**SR FD**<br>**code**|
|---|---|---|---|---|---|
|Far East Federal District|Sakhalin Region|far|sao|Сахалинская область|reg7|
|Far East Federal District|Jewish Autonomous Region|far|eao|Еврейская автономная область|reg7|

_Source: Authors’ estimations_ Table A20. Total emissions in 2001 and 2006, by type of pollutant from industrial stationary (thousands tons)

|**Pollutant**|**Abbreviation**|**2001**|**2006**|
|---|---|---|---|
|Particulate matter|PM|2 973,20|2 836,60|
|Sulphur dioxide|SO2|5 254,00|4 764,70|
|Carbon monoxide|CO|5 148,00|6 285,30|
|Nitrogen oxide|NOx|1 678,90|1 701,50|
|Hydrocarbons|CnHm|2 723,60|2 826,60|
|Volatile organic components|VOC|1 130,80|1 745,30|

_Source:_ Ministry of Natural Resources and Environment of the Russian Federation (2007).

127
# **Table 7: Firm Investment amount and Mobile Money Use**

|**Dependent variable: Log of Purchase of Fixed**<br>**Assets (amount inUSD)**|||**OLS**|||
|---|---|---|---|---|---|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Mobile money for any financial transactions Y:1 N:0|1.013**|1.247***||1.344***|0.486***|
||(0.434)|(0.432)||(0.460)|(0.158)|
|Establishment uses cell phone for operations Y:1<br>N:0|||-0.437|-0.866||
||||(0.875)|(0.849)||
|Firms has ISO certification ownership Y/N|||||0.394*|
||||||(0.231)|
|Senior management time spent in dealing<br>government regulations|||||0.006|
||||||(0.009)|
|Log of size||1.150***|1.067***|1.137***|0.226**|
|||(0.271)|(0.287)|(0.274)|(0.098)|
|Top manager experience in sector (years)||-0.038*|-0.032|-0.038*|-0.007|
|||(0.022)|(0.022)|(0.022)|(0.009)|
|Log of sales per worker (outliers removed)||0.091|0.099|0.099|-0.032|
|||(0.130)|(0.131)|(0.128)|(0.042)|
|Log of age of firm||-0.338|-0.403|-0.326|-0.103|
|||(0.396)|(0.423)|(0.395)|(0.121)|
|Firm is part of a larger firm Y:1 N:0||-0.197|-0.409|-0.226|-0.139|
|||(0.872)|(0.857)|(0.821)|(0.258)|
|Exports 10% or more of sales Y:1 N:0||-0.051|0.092|-0.034|-0.300|
|||(0.828)|(0.864)|(0.806)|(0.238)|
|Foreign ownership Y:1 N:0||0.485|0.381|0.461|0.233|
|||(0.938)|(0.936)|(0.940)|(0.289)|
|Firm with unfavorable credit record Y:1 N:0||-0.068|0.036|-0.158|0.248|
|||(0.499)|(0.517)|(0.519)|(0.191)|
|Establishment has a line of credit or loan at this time<br>Y:1 N:0||1.523***|1.587***|1.594***|0.240|
|||(0.560)|(0.569)|(0.559)|(0.180)|
|Firms use email to interact with clients/suppliers Y:1<br>N:0||0.868*|0.962*|0.959*|0.109|
|||(0.518)|(0.537)|(0.514)|(0.208)|
|Establishment has checking or savings account at<br>this time Y:1 N:0||-0.339|-0.387|-0.341|-0.171|
|||(0.604)|(0.629)|(0.609)|(0.314)|
|Log of mean distance - bank (km)||-2.268***|-2.204***|-2.170***|-0.784***|

28
Davila, Alberto and Rogelio Saenz. 1990. “The Effect of Maquiladora Employment on the Monthly Flow of Mexican Undocumented Immigration to the U.S., 1978-1982.” _International Migration Review_ 24(1): 96-107.

Davies, P.S., M.J. Greenwood and H. Li. 2001. “A Conditional Logit Approach to U.S. State-To-State Migration.” _Journal of Regional Science_ 41(2): 337-360.

Domencich, T.A., and D. McFadden. 1975. _Urban Travel Demand. A Behavioral Analysis_ . North Holland Publishing Co., NY, USA.

Donato, Katharine M., Jorge Druand and Douglas S. Massey. 1992. “Stemming the Tide? Assessing the Deterrent Effects of the Immigration Reform and Control Act.” _Demography_ 29: 139-157.

Durand, Jorge and Douglas S. Massey. 1992. “Mexican Migration to the United States: A Critical Review.” _Latin American Research Review_ 27(2): 3-42.

Esquivel, Gerardo Hernandez. 1997. “Essays on Convergence, Migration and Growth.” Unpublished PhD. Dissertation. MIT, MA, USA.

Faini, R., J-M. Grether and J. De Melo. 1999. “Globalization, and migratory pressures from developing countries: a simulation analysis.” In Faini, R., J. De Melo and K. F. Zimmermann, eds. _Migration. The Controversies and the Evidence._ Cambridge University Press, Cambridge, UK.

Faini, Riccardo (2004) “Trade Liberalization in a Globalizing World” mimeo, presented at the Annual Bank Conference on Development Economics.

Feenstra, R.C. and G.H. Hanson. 1995. “Foreign Direct Investment and Relative Wages: Evidence from Mexico’s Maquiladoras.” NBER working paper 5122. Cambridge, MA, USA.

Field, G.. 1982. “Place-to-Place Migration in Colombia.” _Economic Development and Cultural Change_ 31: 538-558.

Fry, T.R.L. and M.N. Harris (1998). “Testing for Independence of Irrelevant Alternatives. Some Empirical Results.” _Sociological Methods & Research_ 26(3): 401423.

Gabriel, S.A., J. Shack-Marquez and W.L. Wascher. 1996. “Does Migration Arbitrage Regional Labor Market Differentials?” _Regional Science and Urban Economics_ 23: 211233.

Goss, E. P and N.C. Schoening. 1984. “Search time, Unemployment and the Migration Decision.” _Journal of Human Resources_ 19: 570-579.

20
# **Table 1. Low Elevation Zone Coastal Share of Coastal Population, 2000**

||**LECZ**<br>**Share (%)**<br>**of Total**<br>**Coastal**<br>**Population**|**LECZ**<br>**Share (%)**<br>**of Urban**<br>**Coastal**<br>**Population**|**LECZ**<br>**Share (%)**<br>**of Rural**<br>**Coastal**<br>**Population**|
|---|---|---|---|
|**Developing Country**|**30.9%**|**33.4%**|**28.5%**|
|_By Income:_||||
|Low Income|46.5%|43.0%|48.1%|
|Lower Middle Income|26.0%|30.3%|23.2%|
|Upper Middle Income|32.8%|34.6%|29.5%|
|_By Region:_||||
|East Asia & Pacific|40.2%|48.5%|33.0%|
|Europe & Central Asia|7.5%|8.6%|5.8%|
|Latin America & Caribbean|12.1%|13.7%|9.1%|
|Middle East & North Africa|26.9%|25.5%|28.4%|
|South Asia|35.0%|34.6%|35.2%|
|Sub-Saharan Africa|18.4%|24.4%|12.6%|
|**Developed Country**|**18.7%**|**19.8%**|**13.9%**|
|**World**|**27.5%**|**28.1%**|**26.7%**|

The low elevation coastal zone (LECZ) is the contiguous area along the coast that is less than 10 m above sea level. The estimates of LECZ populations are from Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban‐Rural Population and Land Area Estimates Version 2. Palisades, NY: ‐ NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz <u>urban‐rural‐population‐land‐area‐estimates‐v2. Population and land area estimates are subdivided by elevation</u> zone as derived from Shuttle Radar Topographic Mission (SRTM) elevation data at ~90m (3 arc seconds) resolution.

Coastal populations are those living within 100 kilometers (km) of the coast, and excluding countries without territory within 100 km of a coastline measured using an Azimuthal Equidistant (world) projection. See the appendix for details on the spatial methods in calculating these coastal population distributions.

Low‐income economies are those in which 2013 per capita income was $1,045 or less, lower‐middle‐income economies are those in which 2013 per capita income was between $1,046 and $4,125, upper‐middle‐income economies are those in which 2013 per capita income was between $4,126 and $12,745, and developed countries are those in which 2013 per capita income was more than $12,745 , based on the Atlas conversion factor instead of simple exchange rates, as defined by the World Development Indicators, available at <u>http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=world‐development‐ indicators.</u> 5
# **Appendix B** : **Returns to Household Characteristics**

## **Table B1: OLS and Unconditional Quantile Regression Coefficients on Log Expenditure, 2001**

|_Eq Name:_|OLS|Quantile 10|Quantile 25|Quantile 50|Quantile 75|Quantile 90|
|---|---|---|---|---|---|---|
|_Dep. Var:_|LPCEXP|RIFQT_10|RIFQT_25|RIFQT_50|RIFQT_75|RIFQT_90|
|Constant|13.159472|12.210145|12.571805|12.924253|13.729915|13.827996|
||(0.0639)**|(0.1606)**|(0.1110)**|(0.0797)**|(0.0861)**|(0.1278)**|
|Male|-0.174076|-0.059530|-0.191985|-0.206050|-0.269449|-0.170897|
||(0.0150)**|(0.0377)|(0.0261)**|(0.0187)**|(0.0202)**|(0.0300)**|
|Age of Head|-0.011417<br>(0.0022)**|0.006544<br>(0.0055)|-0.005325<br>(0.0038)|-0.007807<br>(0.0027)**|-0.013364<br>(0.0030)**|-0.009057<br>(0.0044)*|
|Age Head|||||||
|Squared|0.000087|-0.000110|0.000011|0.000061|0.000117|0.000104|
||(0.0000)**|(0.0001)*|(0.0000)|(0.0000)*|(0.0000)**|(0.0000)*|
|Age<5 (% of|||||||
|Household)|-0.007902<br>(0.0005)**|-0.007698<br>(0.0012)**|-0.009874<br>(0.0008)**|-0.006203<br>(0.0006)**|-0.008073<br>(0.0006)**|-0.010204<br>(0.0009)**|
|Age 5 to <10|||||||
|(%HH)|-0.012690<br>(0.0004)**|-0.015400<br>(0.0011)**|-0.014570<br>(0.0008)**|-0.010822<br>(0.0006)**|-0.013067<br>(0.0006)**|-0.013608<br>(0.0009)**|
|Age 10 to < 15<br>(%HH)|-0.009995<br>(0.0005)**|-0.008390<br>(0.0012)**|-0.012397<br>(0.0008)**|-0.010170<br>(0.0006)**|-0.011347<br>(0.0006)**|-0.011913<br>(0.0010)**|
|Age 15 to <20<br>(%HH)<br>|-0.008513<br>(0.0005)**|-0.006559<br>(0.0012)**|-0.010314<br>(0.0008)**|-0.007059<br>(0.0006)**|-0.008955<br>(0.0006)**|-0.009859<br>(0.0009)**|
|Age 20 to <25|||||||
|<br>(%HH)|-0.006930<br>(0.0005)**|-0.007858<br>(0.0013)**|-0.008141<br>(0.0009)**|-0.005454<br>(0.0006)**|-0.006481<br>(0.0007)**|-0.007318<br>(0.0010)**|
|Schooling|||||||
|<br>(Years)|0.035724|0.023833|0.036002|0.036836|0.036160|0.048525|
||(0.0015)**|(0.0039)**|(0.0027)**|(0.0019)**|(0.0021)**|(0.0031)**|
|Land|0.000040<br>(0.0002)|-0.000327<br>(0.0005)|0.000266<br>(0.0003)|-0.000542<br>(0.0002)*|0.000681<br>(0.0002)**|0.002758<br>(0.0004)**|
|Access to Credit|0.272581<br>(0.0187)**|0.344990<br>(0.0469)**|0.502057<br>(0.0324)**|0.167755<br>(0.0233)**|0.174148<br>(0.0251)**|0.203044<br>(0.0373)**|
|Has Migrant (s)|-0.003390<br>(0.0110)|-0.093183<br>(0.0277)**|0.035803<br>(0.0191)|-0.008674<br>(0.0137)|-0.007870<br>(0.0148)|-0.013549<br>(0.0220)|
|Distance to|||||||
|Nearest Hospital|-0.007699<br>(0.0010)**|-0.018778<br>(0.0024)**|-0.005528<br>(0.0017)**|-0.003056<br>(0.0012)*|-0.001005<br>(0.0013)|-0.000380<br>(0.0019)|
|Distance to|||||||
|Nearest Tarred|||||||
|Road|0.000842|0.003488|-0.000158|0.001588|0.000488|0.000673|
||(0.0002)**|(0.0004)**|(0.0003)|(0.0002)**|(0.0002)*|(0.0003)|
|Public Sector|0.137185|-0.261937|0.280543|0.158812|0.164989|0.327043|
||(0.0247)**|(0.0619)**|(0.0428)**|(0.0307)**|(0.0332)**|(0.0493)**|
|Private Sector|||||||
|(Formal)|0.268188|-0.061321|0.392699|0.266786|0.271007|0.346203|

40