rafmacalaba/datause-encoder-data huggingface dataset:
_Data Sources and Initial Analysis_ We construct a comprehensive dataset that includes data on bilateral preferential tariffs, external (MFN) tariffs, and trade for ten Latin American Countries—Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, Paraguay, Uruguay, and Venezuela—from 1990 to 2001. Data on preferential and multilateral tariffs come from individual country sources. The main novelty of our dataset is the compilation of the bilateral preferential tariff rates. The compilation required consultation of the official texts of the agreements (sometimes available only in hard copies), which formed the basis for the conversion of the tariff reduction programs of each agreement into yearly preferential tariffs. Appendix A1 describes the methodological details for compiling the data and a detailed description of country sources. Data were supplemented with multilateral tariffs and trade data from the World Integrated Trade System. All data are aggregated as simple averages into ISIC 4-digit industries.

Annual tariff increases make up 22% of the sample, annual tariff declines 39%, while tariffs were unchanged the rest of the time. Given the significant number of tariff increases, we disregard any potential institutional constraint on tariff increases implied in GATT’s Article XXIV in our estimations.<sup>22</sup> Declines in MFN tariffs are also larger on average than increases, resulting in considerable liberalization in aggregate.

Table I reports summary statistics of the key variables in the study. Overall, the average MFN tariff is 15.2% over the whole sample. The average annual external tariff liberalization was over 1 percentage point. The preferential tariff averages 4.7% over the period when preferences

> 22 In addition to requiring that internal trade within RTAs should be liberalized “substantially,” Article XXIV of the GATT also requires that restrictions on imports from third countries should not be higher “on the whole” than they were before the establishment of the agreement. This constraint would suggest a censored estimation of the changes in external tariffs, to rule out positive values. As noted above, however, GATT’s Article XXIV is very poorly enforced, and our sample involves developing countries, which can form agreements under the much less restrictive Enabling Clause and tend to apply MFN tariffs well below their bound levels.

22
# Table 4 (Cont'd)

|Equation|(4.1)|(4.2)|(4.3)|(4.4)|_(4.5)_|_(4.6)_|
|---|---|---|---|---|---|---|
|_Onhkth1Te_||||**1.03**<br>(1.47)||**_1.05_**<br>**(1.27)**|
|_HkhCap_||||**-0.16**<br>**(-1.96)**|||
|_EdCap_|||||-0.025<br>**_(-2.05)_**||
|_Pvlnv/Gdp_|**-0.038**<br>**(-1.17)**|**-0.037**<br>(-1.14)|||||
|_Black_|**-0.013**|-0.014|**-0.010**|**0.003**|**-0.010**|**-0.009**|
||**(-3.95)**|(-4.17)|**(-2.92)**|(0.12)|_(-1.54)_|**(-0.31)**|
|_Shock_|**-0.05**|**-0.06**|**0.008**|**_0.005_**|**-0.01**|**_-0.051_**|
||(-1.48)|**(-1.70)**|(0.22)|(0.04)|**(-0.13)**|**(-0.33)**|
|**_Adj._**_R-sq._|**0.32**|**0.30**|**0.37**|**0.81**|**_0.53_**|**0.79**|
|<br>_Obs._|**286**|**297**|**266**|54|121|_54_|
|_DW_|**_0.56_**|**0.56**|**0.66**|**0.92**|0.84|**0.83**|

't-statistics in parentheses

**29**
**Table 11: Firm-level Innovation and the Business Environment in Sri Lanka**

||**(1)**|**(2)**<br>**In the last thre**|**(3)**<br>**e years, has this**<br>**introduced new**|**(4)**<br>**establishment:**|**(5)**<br>**introduced a new**|
|---|---|---|---|---|---|
||**introduced new**<br>**products or**|**significantly**<br>**improved an**|**or improved**<br>**business**|**changed the way**<br>**work is**|<br>**method of pricing**<br>**goods or**|
|**Dependent variable--In thepast 3years, has the firm:**|**services?**|**existing product?**|**processes?**|**organized ?**|**services?**|
|How many visits from officials from the Inland revenue department in past year|0.076|-0.145|-0.398|-0.648+++|-0.048|
||(0.236)|(0.184)|(0.255)|(0.227)|(0.171)|
|What is the highest level of education that you have completed?|0.107+++|0.086+++|0.111+++|0.026|-0.016|
||(0.032)|(0.026)|(0.032)|(0.027)|(0.024)|
|Have you ever taken part in any training to improve your business skills?|0.083|0.291|0.853+++|0.279|0.447++|
||(0.236)|(0.193)|(0.222)|(0.201)|(0.189)|
|Does your firm use the internet?|0.613+++|0.198|0.053|0.471++|0.361+|
||(0.230)|(0.191)|(0.239)|(0.204)|(0.188)|
|Has the firm used a consulting service in the last 3 years?|0.940+++|0.766+++|1.236+++|0.663+++|0.137|
||(0.275)|(0.239)|(0.275)|(0.249)|(0.241)|
|What percentage of your sales are made to the following: {Foreign firms}|0.019++|0.005|0.011|0.003|0.003|
||(0.008)|(0.008)|(0.010)|(0.008)|(0.008)|
|What percentage of the start-up capital came from : {Local Banks}|0.003|0.002|-0.003|-0.002|0.004+|
||(0.004)|(0.003)|(0.004)|(0.003)|(0.003)|
|What percentage of your sales are made on credit?|0.005|0.005+|0.004|0.010+++|0.009+++|
||(0.004)|(0.003)|(0.004)|(0.003)|(0.003)|
|Constant|-6.315|-7.257|-8.122|-5.155|0.694|
||(134.040)|(666.629)|(173.728)|(210.547)|(1.054)|
|Observations|529|528|497|543|585|
|Pseudo R-squared|0.299|0.165|0.302|0.179|0.180|
|Province fixed effects|Y|Y|Y|Y|Y|
|Sector fixed effects|Y|Y|Y|Y|Y|

Notes: Standard errors in parentheses. + p<.1 ++ p<.05 +++ p<.01. Source: SLLSE (2008 Booster Sample) 42
24 early 1980's. We had access only to 1987 and 1986 data, while the values for other years were obtained by deflating 1987 data by the retail price index. In order to obtain the subsistence minimum, two categories (clothing and consumer durables) out of seven categories of commodities of which the social minimum Is composed were excluded.

Finally, it should be noted that the total percentage of rpeople In poverty is obtained as the sum of people DelonIging to different social (or regional) groups who are poor, where to each group we apply a different poverty line (minimum). For example, In Poland there are four such minima: for workers', mixed, farmers' and pensioners' households; In Hungary, there are separate minima for each type of household (active and nonactive) and for each household size; In Yugoslavia there are eight minima (for each republic or province). Consequently, the use of the national minimum to the combined set of data for all households will not In general yield the same result as the one presented here.

APPENDIX 2 - SOURCES OF DATA FOR HOUSEHOLD INCOMES Polish results are based on annual household surveys conducted by the Central Statistical Office, and covering between 8500, in the early years, and more than 20,000 households later. Households are divided Into four social groups (workers, mixed, farmers and pensioners), and 7 to 8 Income groups ranked by per capita household Income. Surveys are representative of about 90 percent of the population (not Included are Army, police and private non-agricultural sector).

Yugoslav data come from five-year household surveys (conducted in 1978 and 1983). Data for other years are calculated from smaller-sample annual surveys (6000 households vs. 19,000 In five-year surveys). All households are divided Into three social groups (non-agricultural, mixed, and farmers), and between 10 and 11 Income groups (ranked by per capita household income). Surveys are conducted by the Federal Statistical Office.

Hungarian data come from biannual household surveys conducted
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- Mayer, Rebecca, Ken Figueredo, Mike Jensen, Tim Kelly, Richard Green, and Alvaro Federico Barra. 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ AICD Background Paper 3, Africa Region, World Bank, Washington, DC.

Wireless Intelligence, https://www.wirelessintelligence.com/.

# Irrigation

Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

- You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

- Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

# Transport

Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

- Carruthers, Robin, Ranga Rajan Krishnamani, and Siobhan Murray. 2009. ―Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa.‖ AICD Background Paper 7, Africa Region, World Bank, Washington, DC.

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Heinrich C. Bofinger. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

39
**Figure 5: Impact of U.S. interest rate shocks on EMDE economic activity** A.  Impact of 25 basis point shock on real GDP growth and components after one quarter price inflation after one quarter.

<!-- Start of picture text --> A. Impact of 25 basis point shock on real GDP B. Impact of 25 basis point shock on consumer<br>growth and components after one quarter price inflation after one quarter.<br>Percent Percent<br>1.6 0.8<br>1.2<br>0.8 0.6<br>0.4<br>0.4<br>0.0<br>-0.4<br>0.2<br>-0.8<br>-1.2 0.0<br>-1.6<br>-0.2<br>-0.4<br>-0.6<br>Real Inflation Reaction Real Inflation Reaction<br>Source: World Bank<br>Notes: Panel local projection model with fixed effects and robust standard errors. See table A3 for details. Models estimated<br>over periods as long as 1997Q2-2019Q4; they exclude observations during global financial crisis (2008Q4-2009Q4) and the<br>COVID-19 pandemic. GDP = Gross domestic product; GFCF = Gross fixed capital formation; Pvt Cons = Private<br>Consumption. Impact on first quarter ( ). Orange whiskers reflect 90 percent confidence intervals.<br>tt+1<br>yy<br>GDP Pvt Cons. GFCF Exports GDP Pvt Cons. GFCF Exports GDP Pvt Cons. GFCF Exports<br><!-- End of picture text --> 26
We also estimate the labor supply curve of days worked per month. The supply curve is hypothesized to be a function of the logarithm of daily wages, age, and OSD status. Or, _In S = b, In W* + b2 0 + b3_ **_X_** _+ e_

- where, S: Average number of days worked per month in the past year.

- _W._ Daily wages earned.

- **_O:._** Severe OSD = 1. X: Vector of control variable such as age, gender, other income, and household characteristics.

Starred variables represent the fact that they are endogenous: we have<sup>a two-equation model with</sup> daily wages being endogenous. The wage equation is estimated<sup>using ordinary least squares and</sup> the predicted value of the wage rate obtained. This predicted value is then<sup>used to estimate the</sup> labor supply equation.

# **Results**

## **_Sociodemographic Characteristics of Study Participants_**

Data were collected from plantation records for a sample of 425 plantation workers.'<sup>0Of those</sup> sampled, 235 (55.3%) were permanent employees. The remaining (44.7%/O) were temporary (daily and contract) workers. 1 "Most sampled (82.1 %) were male, this being representative of the gender composition on the plantation in general. Table 1 disaggregates<sup>the sample in terms of</sup> employment type (i.e;, permanent or temporary), gender, and age.

- 10 It must be noted at the outset that the data are available for _currently employed_ workers only. Those<sup>plantation</sup> workers who are too incapacitated to work -- due to extremely severe manifestations of OSD,<sup>for instance --</sup> are not included in the sample. This may create a sample selection problem typically referred to as<sup>the "healthy</sup> worker effect". In effect, this means the estimates produced may be somewhat biased: the true magnitude<sup>of</sup> the impact of OSD on economic productivity and capacity, in all likelihood, being higher than what is observed in our sample. Nevertheless, this possibility of sample bias ought not confound the<sup>hypothesized</sup> negative relationship between severity of OSD and economic productivity and labor supply, even though the observed magnitude may be somewhat on the low side.

- l Daily and contract workers are guaranteed employment for a limited period of time only:<sup>usually during the</sup> harvest season, when labor demand is highest.

4
inventory of village populations and facilities. The 2006 PODES in Aceh was conducted in the Spring 2005. For eight villages, the PODES post-tsunami count grossly undercounts households, where for example the number of households in our 2009 survey is more than fivefold the PODES count. The ratios are 11, 7.5, 312, 23, 50, 21, 5.2 and 6.9. The absolute counts are just too low. In two of the eight cases where we have 2005 data, our survey counts versus the PODES are 212 versus 80 and 136 versus 20. Not surprisingly, we get slightly sharper size and survival results if we drop these observations, but they do not affect election results.

For house and public building counts before and after the tsunami, we use our survey numbers (and there are not reliable government numbers); what was destroyed is well recorded by the remaining foundations, as well as village mapping exercises conducted soon after the tsunami. For survival rate of the mullah and village head, we use 2005 numbers with 2007 numbers for our added villages. Given that the deaths of prominent figures are known to all villagers, we are not so worried about recall here.

33
table 2 <!-- Start of picture text --> . * * _ *.<br>VFuip~ bsem ad E6iflS hMU<br>IX Mn M~ 13 X 13 13 1S<br>bnemdlb _w 22tJ tM 0W 40.1 M.$ 9.1 130 MA<br>Tell bamR d 13 I no W 03.0 3,9. 1244.1 15. 1W.? IOWA<br>6uwwS u_ to 152.1 *.7w.g *1c.2 w3t4 -MA -am<br><!-- End of picture text --> Wm 10 hfI TrAI, 3W1W little, Pp6*0S _-_ vtlf _dl d got's_ l _1s_ d.t is Slie w plid1y _9_<sup>_w&% ML_Fr4F,. nuptai</sup> bw,ruq _ami is tglioblb.
# **Bibiliography**

- Alexander, I., A. Estache and A. Oliveri (2001), _“A few things transport regulators should know about risk and the cost of capital”_ (2001), **Utilities Policy** , Volume 9, 1-13

- Athias, L. and S. Saussier (2007), Contractual Flexibility or Rigidity for Public Private Partnerships? Theory and Evidence from Infrastructure Concession Contracts, mimeo, ATOM, Paris

- Brown, C. (2005), “ _Financing Transport Infrastructure: For Whom the Road Tolls_ ”, **The Australian Economic Review** , vo. 38, 4, 431-438

- Correia, L., A. Estache and S. Jarvela (2006), _“Is Debt Replacing Equity in Regulated Privatized Infrastructure in Developing Countries?,_ **Utilities Policy,** Vol. 14 (2), June , Pages 90-102

- Dutz, M.,C. Harris, I. Dhingra and C. Shugar (2006): “ _PPP Unites, , What Are They, and What Do They Do”_ Public Policy for the Private Sector, Policy Note no. 311, September

- Engel, E., R. D. Fischer and A. Galetovic (2007), “ _The basic public finance of publicprivate partnerships”,_ mimeo, Yale University

- Engel, E., R. D. Fischer and A. Galetovic (2001), “ _Least-present-value-of-revenue auctions and highway franchising”_ , **Journal of Political Economy,** 109, 993-1020

- Estache, A. and L. Trujillo (2007), Global economic changes and the future of port authorities”, mimeo, The World Bank

- Estache, A. and L. Trujillo (2007), Transport cost levels,  productivity and efficiency measures: Some theory and main policy uses” in **OECD-ECMT Round Table 132** ,

- Estache, A. (2006), “ _PPI divorces vs. PPI partnerships in Infrastructure”,_ **Review of Industrial Organization**

- Estache and Serebrisky (2004) _“Where do We Stand on Transport Infrastructure Deregulation and Public-Private Partnerships?”_ **OECD-ECMT Round Table 129**

- Flyvbjerg; B., M. Skamris Holm and S. L. Buhl. (2005) " _How (In)accurate Are Demand Forecasts in Public Works Projects? The Case of Transportation._ " **Journal of the American Planning Association,** vol. 71, no. 2, Spring , pp. 131-146.

- Flyvbjerg;B., N. Bruzelius and W. Rothengatter (2003). _Megaprojects and Risk: An Anatomy of Ambition._ Cambridge University Press

- Grimsey, D. andM. Lewis (2005), _“Are Public Private Partnerships value for money? Evaluating alternative approaches and comparing academic and practioners views”_ **_,_ Accounting Forum** 29, 345-378

- Guasch, J.L. (2004), _Granting and Renegotiating Infrastructure Concessions: Doing it right,_ Washington, DC. The World Bank

25
Figure 2c: Poverty Dynamics - Synthetic vs. Actual Panel Data by Sub-population Groups, Peru 2008 and 2009 <!-- Start of picture text --> Poor in 2008 and Poor in 2009 Poor in 2008 and Non-poor in 2009<br>100 30<br>90<br>80<br>70 20<br>60<br>50<br>40<br>30 10<br>20<br>10<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 5 10 15 20 25 30<br>Actual Actual<br>Non-poor in 2008 and Poor in 2009 Non-poor in 2008 and Non-poor in 2009<br>30 100<br>90<br>80<br>20 70<br>60<br>50<br>40<br>10 30<br>20<br>10<br>0 0<br>0 5 10 15 20 25 30 0 10 20 30 40 50 60 70 80 90 100<br>Actual Actual<br>Lower Bound Upper Bound<br>Estimation Estimation<br>Estimation Estimation<br><!-- End of picture text --> Data source: INEI - Peruvian National Household Survey (ENAHO), 2008-2009. Note: Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. The 45 degree line shows actual panel mobility. Upper bound estimations are based on 50 repetitions.

37
# **Table 5: Non-Linear Effects of Insurance Variables**

_Sample of 56 countries, 1976-2004 (5-year period observations) Dependent Variable: Growth in real GDP per capita Estimation Method: GMM-IV System Estimator_

||**[1]**<br>**Life insurance**|**[2]**<br>**Non-Life insurance**|
|---|---|---|
|Constant|0.129 ***|0.098 ***|
||_0.000_|_0.000_|
|Private Credit to GDP|-0.005<br>|0.008<br>|
||_0.527_|_0.254_|
|Stock Market Turnover|0.024 ***|0.023 ***|
||_0.000_|_0.000_|
|Insurance|0.009|-0.985|
||_0.947_|_0.164_|
|Insurance<sup>2</sup>|1.423|9.911|
||_0.113_|_0.386_|
|**_Control Variables_**|||
|Log. Initial GDP per capita|-0.013 ***|-0.010 ***|
||_0.000_|_0.000_|
|Degree of openess|0.934 ***|0.540|
||_0.007_|_0.117_|
|Governement consumption|-1.779 ***|-0.894 **|
||_0.000_|_0.054_|
|Inflation|-0.003 ***|-0.003 ***|
||_0.000_|_0.000_|
|Terms of trade|0.050 ***|0.051 ***|
||_0.000_|_0.000_|
|Human Capital Investment|0.046 ***|0.044 ***|
||_0.000_|_0.000_|
|**_Period shifts_**|||
|Period 81-85|-0.014 ***|-0.016 ***|
|Period 86-90|-0.010 ***|-0.010 ***|
|Period 91-95|-0.021 ***|-0.019 ***|
|Period 96-00|-0.025 ***|-0.023 ***|
|Period 01-04|-0.036 ***|-0.035 ***|
|Countries|56|56|
|Observations|263|263|
|Specification tests (p-values)|||
|-Sargan test|0.615|0.604|
|-2nd. Order Correlation|0.642|0.783|

P-values in Italics. ***, **, * denote statistical significance at the 1, 5, and 10 percent level.

12
# **7. Conclusion**

Malnutrition is a pervasive problem in Ethiopia and it is no doubt a direct consequence of widespread poverty. Analyzing the impact of different factors on children’s malnutrition is further complicated by Ethiopia’s vulnerability to frequent and severe droughts. Using demographic and health survey data, this study examines on the impact of access to basic environmental services, such as water and sanitation, on the probability children are stunted and underweight. The focus is on the impact of externalities associated with access to these services. Therefore, controlling for household access to these services, we look at how the proportion of households with access to these services in the community affects children’s nutritional status.

We find that child’s age, mother’s height, household wealth and mother’s education are important determinants of a child’s nutritional status, which is consistent with the findings of most studies in this literature. The impact of mother’s education, however, is small and probably mostly a reflection of the very low level of female education in Ethiopia. With respect to the environmental variables, we find that there are indeed significant externalities associated with access to water and sanitation at the community level. The external impacts of the community level of access to these services are an important determinant of the probability a child is underweight. Our results also show that the impact of access to water is larger for children living in rural areas.

As the proportion of households in the community with access to water and sanitation increases, the external impact on children’s nutritional outcomes diminishes, as expected. The external impact of community access to water and sanitation on the probability a child is stunted is only significant when the proportion of households with access to these services is low. As the level of access increases, however, the coefficients on these variables become small and no longer significant. Stunting is a chronic condition and is likely to persist even after elimination of conditions that contribute to it—these results are likely then a reflection of these circumstances.

It is possible that unobserved variables, which are correlated with the community’s level of access to water and sanitation, are partly driving these results. However, in the absence of available data to control for community’s wealth, there is little we can do to 27