rafmacalaba/datause-encoder-data huggingface dataset:
as much as Kerala. Within the perception variables Kerala performs above average for all indicators as does Karnataka, Andhra Pradesh, Tamil Nadu, and Orissa.

If we compare objective and perception data on the same investment climate dimensions in Kerala we notice a relative overlap from what the objective and the perception measures indicate.<sup>11</sup> Only in one of the indicators objective and perception data contradict themselves: transport. The reason of such apparent contradiction lies in the fact that 40% of firms in Kerala have their own transport - compared to 11% in the rest of India, so they perceive it less of a problem simply because they are not relying on outside transport. On the other hand objective data show that firms in Kerala suffer losses due to international transport that are 3 times higher than the other firms in India. So in the objective rating Kerala rightly performs worse.<sup>12</sup> On institutions the picture is the same. In a number of indicators (e.g. corruption, crime) both perceptions and objective measures show that Kerala performs much better than average. There is no instance of contradiction between objective and perception data since a number of perceptions questions measures dimension not measured by objective indicators.

In conclusion, it seems that firms in Kerala tend to complain less about constraints than in other countries. However Kerala is not the only state with above average perception on all indicators. Furthermore the perception and objective data do not contradict themselves. So, it is both perception and objective data that make Kerala second in the ranking.<sup>13</sup> West Bengal on the other hand ranks higher than many might expect thanks to its high performance on objective indicators of the business environment. As a matter of fact,

> 11 As a matter of fact for power where in the objective data Kerala performs worst the perception is the lowest of all

> 12 One could argue that there is no contradiction because they measure two separate conditions. 13 See figures A3-A9 in the Appendix for a detailed representation of the variables employed in the construction of the ICI.

11
**Table 2A. Effects of Market Access on Land Gini (IV Estimates)**

|Dep. var.|(1)|(2)|(3)<br>**ln(Landgini 2012)**|(4)|(5)|
|---|---|---|---|---|---|
|Estimator|2SLS|2SLS|2SLS|Lasso-IV|2SLS|
|**ln(MA1996)**|**0.244*****|**0.232****|**0.241*****|**0.255*****|**0.197*****|
||**(0.07)**|**(0.09)**|**(0.06)**|**(0.06)**|**(0.05)**|
|ln(Slope)|-0.044|-0.043|-0.044|-0.044|-0.041|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|
|Elevation|-0.191|-0.186|-0.190|-0.195|-0.172|
||(0.15)|(0.15)|(0.15)|(0.16)|(0.14)|
|Crop Suitability|-0.003**|-0.003**|-0.003**|-0.003**|-0.003**|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|ln(Rain)|0.053|0.052|0.053|0.054|0.048|
||(0.06)|(0.06)|(0.06)|(0.06)|(0.05)|
|ln(Temperature)|-0.428|-0.406|-0.421|-0.447|-0.343|
||(0.42)|(0.44)|(0.41)|(0.42)|(0.37)|
|ln(Rain CV)|0.138|0.134|0.137|0.142|0.121|
||(0.14)|(0.14)|(0.13)|(0.14)|(0.13)|
|ln(Temp. seasonality)|-0.024|-0.027|-0.025|-0.021|-0.035|
||(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|
|Pop. density, 1961|0.015**|0.016*|0.016**|0.015**|0.018***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Constant|-1.908|-1.806|-1.877|-2.271|-1.506|
||(2.45)|(2.42)|(2.40)|(2.47)|(2.22)|
|First Stage||||||
|<br>ln(km of railroad)|0.117***||0.114***||0.192***|
||(0.03)||(0.03)||(0.07)|
|ln(dist. to GQ)||-0.128***|-0.120**||-0.015|
|||(0.05)|(0.05)||(0.08)|
|ln(dist. to GQ) x||||-0.131***|-0.112*|
|ln(slope)||||(0.04)|(0.06)|
|ln(dist. to GQ) x||||0.152|0.106|
|<br>Elevation||||(0.18)|(0.19)|
|ln(km of railroad) x||||0.002***|-0.001|
|<br>Crop suitability||||(0.00)|(0.00)|
|<br>Weak identification|18.94|6.25|21.93|9.19|34.23|
||0.0000|0.0134|0.0000|0.0000|0.0000|
|Observations|200|200|200|200|200|
|R-squared|0.239|0.263|0.247|0.216|0.321|

Notes: Columns (1) and (2) are estimated by two-stage least squares (2SLS) using railroad length and distance to the Golden Quadrilateral as instrumental variables, respectively. Column (3) is estimated using predicted Market Access as an instrumental variable. The predicted Market Access are fitted values from a zero stage with both railroad length and distance to GQ. The Lasso in column (4) is estimated using a parsimonious set of instruments chosen by Lasso from a broad set that include railroad length, distance to GQ, and each of their interactions with the exogenous control variables as instrumental variables. Lasso selects three instruments which are reported in the first stage in column (4). Column (5) is estimated using predicted Market Access as an instrumental variable. The predicted Market Access are fitted values from a zero stage including railroad length, distance to GQ, and all the instruments chosen by Lasso. In columns (3) and (5), under the first stage, we report the zero stage estimated coefficients of the instrumental variables. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
-6-

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**Figure** 1: **Th Impact of** ubeldLzd Loan **X St MMCC - k** on **Corpcorut** Investmnts In **Productve Aset:** X **I***

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**Figure** 2: The **Impac of** Subsldlud Loan X2 at MCC **-** k on **Corporate** Invesments In **Produtve Asset: X2** > **1r**
- Behrman J., H. Alderman, and J, Hoddinott, (2004), “Hunger and Malnutrition”. In Bjorn Lomborg (ed.) _Global Crises, Global Solutions._ Cambridge (UK): Cambridge University Press.

- Behrman J., Cheng Y. and P.E. Todd (2004), “Evaluating Preschool Programs when Length of Exposure to the Program Varies: A Non Parametric Approach”, The Review of Economics and Statistics, vol.86, no1: 108-132

- Breusch, T. S. and Pagan, A. R., (1980), The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics, _Review of Economic Studies,_ vol. 47, n1, pp. 23953

- Christiaensen L. and H. Alderman, (2004), “Child Malnutrition in Ethiopia: Can Maternal Knowledge augment the Role of Income?”, _Economic Development and Cultural Change_ , vol. 52, n.2, pp. 287-312

- Deaton, A. and M. Grosh (2000); “Designing household survey questionnaires for developing countries: Lessons from fifteen years of the Living Standards Measurement Study”. Volume 1, pp. 91-133, Washington, D.C.: World Bank

- EDI 2004. Kagera Rural CWIQ. Baseline Survey on Poverty, Welfare and Services in Kagera Rural Districts.

- Demombynes G. and J. G. Hoogeveen 2004. Growth, Inequality and Simulated Poverty Paths for Tanzania, 1992-2002, World Bank Policy Research Paper No. 3432 _._ World Bank. Washington DC.

de Onis M, Blössner M, Borghi E, Frongillo EA, Morris R. 2004. “Estimates of global prevalence of childhood underweight in 1990 and 2015”, _Journal of the American Medical Association_ : no.291, pp.2600-6.

- Gillespie and Haddad 2001, “Effective Food and Nutrition Policy Responses to HIV/AIDS: What We Know and What We Need to Know”, _Journal of International Development_ v13, n4, pp. 487-511

- Glewwe P., S. Koch and Nguyen B. L., (2004), “Child Nutrition, Economic Growth and the Provision of Health Care Services in Vietnam”, in _Economic Growth, Poverty, and Household Welfare in Vietnam_ , ed. Glewwe P., N. Agrawal and D. Dollar, World Bank.

- Haddad, Lawrence., Harold Alderman, Simon Appleton, Lina Song, and Yisehac Yohannes. (2003), “Reducing Child Malnutrition: How Far Does Income Growth Take Us?” _World Bank Economic Review,_ 17(1): 107-131

- Heckman J., Lalonde, R., and Smith, J. (1999). “The Economics and Econometrics of Active Labor Market Programs”.  In O. Ashenfelter, & D. Card (Eds.), _Handbook of Labor Economics_ (Vol. 3A, pp. 1865-2097).  Amsterdam: Elsevier Science.

- Hoddinott, J. and B. Kinsey, 2001. “Child growth in the time of drought”. _Oxford Bulletin of Economics and Statistics_ 63: 409-436

- Killewo J. and others. 1990. “Prevalence of HIV-1 Infection in the Kagera Region of Tanzania: A Population-Based Study”. _AIDS_ 4 (11): 1,081-85.

- Mkenda A.E. (2004), “The Benefits of Malnutrition Interventions: Empirical Evidence and Lessons to Tanzania”, unpublished paper, World Bank.

15
**Figure 5.** Population-adjusted incidence of public programs by type of household. Uruguay, 2017. **Panel A.** Direct taxes and transfers

<!-- Start of picture text --> 250%<br>200%<br>150%<br>100%<br>50% Direct Taxes<br>0% AFAM-PE<br>Non-Contributive pensions<br>-50%<br>Food and Other direct transfers<br>-100%<br>-150%<br>Adult Only Only Only maleOnly male Other Other<br>couple - female female adults - adults - childless types of<br>children adults - adults - children no families<br>children no children<br>children<br>Panel B. Education<br>350%<br>300%<br>250%<br>200%<br>150%<br>100% Child Care<br>Preschool and Primary<br>50%<br>Secondary<br>0%<br>Tertiary<br>-50%<br>-100%<br>-150%<br>Adult Only Only Only male Only male Other Other<br>couple - female female adults - adults - no childless types of<br>children adults - adults - no children children families<br>children children<br><!-- End of picture text --> Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology Notes: The shares of the population are as follows: Adult couple - children (29%), Only female adults – children (5%), Only female adults – no children (2%), Only male adults – children (2%), Only male adults – no children (3%), Other childless (36%), Other types of families (22%). Total does not add up to 100% due to rounding.

18
WPS3960

# **The Impact of Energy Price Changes in Moldova**

Iaroslav Baclajanschi, Lawrence Bouton, Hideki Mori, Dejan Ostojic, Taras Pushak and Erwin R. Tiongson<sup>1</sup> The World Bank, 1818 H Street, NW, Washington, DC 20433

## **Abstract**

In January 2006, the price of natural gas supplied to Moldova increased from $80 to $110 per thousand cubic meters (mcm). Prices may increase further in the near future, putting additional pressures on the economy and leading to adverse effects on the poorest households. This study examines the potential impact of higher energy prices on the economy of Moldova by simulating the likely macroeconomic consequences of recent and future price increases. Moreover, it estimates the direct impact on individual households using data drawn from the 2004 Household Budget Survey (HBS). It assesses the distributional implications of the price shock, noting how the social impact may vary depending on the intensity of energy use, geographic location, and the relative share of energy in household expenditure. The results suggest that energy price changes could dampen economic growth while putting additional strains on the current account deficit. The impact on the poorest households could be significant and protecting them may require resources in the amount of 0.7 to 1.7 percent of GDP. This study identifies possible policy responses to dampen the shock of the energy price increase and to promote the longer-term objective of reducing energy vulnerability.

JEL Classification Numbers: L95, I32, I39 Keywords: natural gas, prices, macroeconomic impact, poverty World Bank Policy Research Working Paper 3960, July 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Corresponding author: Lawrence Bouton, Europe and Central Asia Region, MSN H4-401, World Bank, 1818 H Street, NW, Washington, DC 20433 USA. Tel. (202) 473 9158. email: lbouton@worldbank.org.

1
Figures 11-12 <!-- Start of picture text --> REAL AVERAGE INDUSTRIAL. WAGES<br>rSUINtS A lCWM0 mumCC. la1. 12. WS<br>22I<br>21<br>20<br>to<br>Is<br>17<br>la J.c w-2 i. W JUM<br>PRICE INDICES<br>hCs_- UM, . 100<br>12.5el 2<br>a 1wI t flXlAL SUM 0C cpe<br><!-- End of picture text --> 19
and camp-like settings. Second, we examine how attitude formation is shaped by networks, suggesting that the ‘contact effect’ may also be shaped by dynamics at the intra-group level. It implies that one of the limiting factors underlying the positive impact of contact may be the way it is mediated through neighbourhood and household level discussions. Although it is beyond the scope of the paper to examine this intra-group attitude formation process, it is something that is touched upon within the literature on the economics of information’s discussion of, for example, ‘echo chamber effects’, which describe non-Bayesian behaviours at an intra-group level (B ́enabou 2015; B ́enabou and Tirole 2016; Levy and Razin 2019).

# **4 Research Design**

Our analysis relies on quantitative data (Section 4.1) and qualitative data (Section 4.2) collected in refugee camps, settlements, and cities in Kenya, Uganda, and Ethiopia. We use OLS and IV regressions (Section 4.3) to assess the extent to which interactions between refugees and hosts affect their perceptions of the other community.

## **4.1 Quantitative Data**

Our analysis uses first-hand data collected in Kenya, Uganda, and Ethiopia between 2016 – and 2018 (n= 16,608). Our survey covered the three capital cities Nairobi, Kampala, and – Addis Ababa and three groups of camps or settlements, the Kakuma camps in Kenya, the Nakivale settlement in Uganda, and the Dollo Ado camps in Ethiopia. The first data collection took place in Kakuma in November-December 2016, followed by Nairobi in May 2017, and both Ugandan research sites in April 2018. Finally, data collections in Addis Ababa and Dollo Ado were undertaken between September and December 2018.

In each site, we surveyed representative samples of the numerically most significant refugee populations and the host nationals in the community living nearby. For camp-like contexts, this means nationals of the three countries residing in towns and villages in the immediate vicinity of the camps and, in the case of Nakivale, inside the settlement. In the capitals, we restricted the geographic scope of the research to areas with a high concentration of refugees, as such ‘hosts’ refer to host nationals living in refugee-dense neighbourhoods.<sup>4</sup> For each population in these locations we used simple random sampling or two-stage cluster sampling to obtain representative data. The sampling methodology and sample sizes of all host and refugee strata are detailed in Tables A.1 and A.2 in the Appendix.

The questionnaires included modules on a range of themes such as demographics, economic activities, income, consumption, assets, networks, mobility, health, well-being, and social cohesion. While the basis of the questionnaires remained similar in each site, we im-

> 4In Kampala, these are Kisenyi, Nsambya, and Katwe areas; in Nairobi, Eastleigh, Kayole, Umoja, Kasarani, and Githurai; and in Addis Ababa, Bole Michael, Jemmo, Gofa Mebrat Hail, Megenagna, Hayahulet, and Gerji.

12
- 15 -

On the health side, the sample data come from the two DHS data in 2011 and 2018. After data with missing values are excluded, data for about 38,000 individuals are used, of which about 5,800 people reported that they had a fever within the last two weeks. On the transport side, travel time is estimated by using spatial analysis based on the underlying road conditions. Using road condition data (i.e., road roughness), network analysis was carried out to estimate the shortest travel time required to visit a health facility. In general, it takes more time to drive on roads in poor condition. Other connectivity variables, such as estimated transport costs and road distance, which are used in the robustness discussion, are also calculated in the same manner. These variables are all related to the underlying road conditions but different. The average travel time to visit a health facility is 0.14 hour i.e., about 10 minutes ( **Table 2** ). However, there is a substantial variation from nearly zero minutes to more than 2 hours.

The information on whether a household owns transport means is also included in the model. In general, transport means are conducive to better access of households to health facilities. In our data, about 38 percent of households possess bikes or motorcycles. There are only a few households that own cars.

Whether people visit a health facility may also depend on what kind of facility exists around them. The model includes the type of health facility, following Mozambique’s health system classification: Hospital, urban and rural health centers, and health posts. Since remoteness may be a general disadvantage of health facilities in terms of medical supplies and availability of doctors and nurses, the distance from each facility to the nearest major city is calculated and included in the model. The major cities with more than100,000 population are considered, and the distance is measured by travel time by car.

In addition to traditional individual and household characteristics, such as household head’s age and sex, the analysis uses a set of household characteristics related to sanitary conditions and hygiene practices for the selection equation. These are particularly important in our health or malaria context. While 35 percent of households have access to electricity,
Annex 7: Mali - Detailed results for impact <u>of food price increase on headcount index in sample of West and Central African Countries</u>

||**Share in**|||**25% increase**<br>**Upper**<br>**bound**|**50% increase**<br>**Upper**<br>**bound**|**25% increase**<br>**Lower**<br>**bound**|**50% increase**<br>**Lower**<br>**bound**|
|---|---|---|---|---|---|---|---|
|**Food item**|**total**<br>**consumption**|**Proportion**<br>**of Consumers**|**Baseline**<br>**Headcount**|**Impact**<br>**(Cons. only)**|**Impact**<br>**(Cons. only)**|**Impact**<br>**(Cons.& Prod.) **|**Impact**<br>**(Cons.& Prod.) **|
|||||<br>**Mali National**||||
|Rice|7.2|95.1|**47.5**|48.9|50.2|48.3|49.1|
|Corn|4.2|91.0||48.2|49.4|47.9|48.8|
|Maize|0.6|48.1||47.5|47.6|47.4|47.4|
|Wheat|1.5|74.0||47.6|47.8|47.6|47.8|
|All|13.4|99.4||50.1|52.8|49.2|50.9|
|||||**Mali Urban**||||
|Rice|9.6|96.0|**25.5**|27.4|29.0|27.3|28.6|
|Corn|3.6|88.4||25.8|27.1|25.7|27.0|
|Maize|0.8|42.0||25.6|25.8|25.4|25.5|
|Wheat|1.9|83.6||25.7|25.9|25.7|25.9|
|All|15.9|99.4||28.8|31.3|28.4|30.7|
|||||**Mali Rural**||||
|Rice|5.7|94.6|**57.6**|58.9|60.1|58.1|58.6|
|Corn|4.5|92.7||58.6|59.7|58.2|58.9|
|Maize|0.4|51.7||57.7|57.7|57.6|57.5|
|Wheat|1.3|68.2||57.8|57.9|57.8|57.9|
|All|11.9|99.8||60.0|62.7|58.8|60.3|

Source: Authors’ estimation using country household survey data 26
implementation of multilateral trade agreements. Furthermore, it contains all support to mainstream trade policy, including technical standards, customs regimes, and tariff structure.

- 2) _Trade development_ (US$ 2910 million in 2005): This category covers business development and activities aimed at improving the business climate, access to trade finance, and trade promotion in the sectors of agriculture, forestry, fishing, industry, mining, tourism, and services.

- 3) _Economic infrastructure_ (US$ 5586 million in 2005): All aid flows directed towards the improvement of the infrastructure for transport, storage, communications and energy.

For our study, all three categories are relevant for trade facilitation. But, a key objective of our study is to investigate which type of aid-for-trade-facilitation has been particularly effective, so we consider alternative aggregations of aid flows using the raw data from the OECD-CRS. Our aggregations are chosen to better match the trade facilitation reforms in the literature typified by Wilson, Mann, Otsuki (2003, 2005). The appendix presents the correspondence between categories and codes used by the OECD-CRS and the four classifications that we use.

- 1) **_<u>Narrow</u>_** <u>trade facilitation</u> (US$ 296 million in 2005) includes all five digit codes belonging to _Trade Policy and Regulations_ (chapter 331, as explained in the Appendix).  This narrow agenda focuses on both border (customs, TBT, RTAs) and behind the border (SPS, TRIPS) reforms found by the trade facilitation research to be importantly related to trade flows.

- 2) **_<u>Broad</u>_** <u>trade facilitation (US$ 8495 million in 2005), which sums up the aid</u> flows from the other two categories, _Trade Development_ and _Economic Infrastructure._ This broad category includes both border (ports, roads) and services (telecoms, finance) infrastructures shown by the trade facilitation research to be importantly related to trade.  This category also includes sectorspecific initiatives.

- 3) **_<u>Hard</u>_** <u>trade facilitation (US$ 6086 million in 2005) includes aid directed</u> towards investment into infrastructure projects, such as upgrading of ports and construction of new roads. Most of the hard aid is also classified as broad aid, but there are some projects in the broad category that we classify as soft projects, specifically those related to the banking and financial services and training services that are sector-focused.

- 4) **_<u>Soft</u>_** <u>trade facilitation</u> (US$ 2705 million in 2005)  includes funds directed toward building institutional capacity related to trade, such as a training of customs officials, streamlining  custom procedures, projects related to banking

> 24 See WTO/OECD (2006) for definitions, and values of aid flows for 2005.

7