rafmacalaba/datause-encoder-data huggingface dataset:
- Gopalan, C., B. V. R. Sastri, and S. C. Balasubramanian. 2004. _The Nutritive Value of Indian Foods_ . Indian Council of Medical Research, Hyderabad: National Institute of Nutrition.

- Gupta, A. D. 2013. “Veblen preferences and falling calorie consumpton in india: Theory and evidence.” _Working Paper_ .

- Henry, C. 2005. “Basal metabolic rate studies in humans: measurement and development of new equations.” _Public Health Nutrition_ .

- Indian Council of Medical Research. 2009. “Nutrient requirements and recommended dietary allowances for indians.” _National Institute of Nutrition_ .

- Krishnaveni, G., S. Veena, R. Kuriyan, R. Kishore, A. Wills, M. Nalinakshi, S. Kehoe, C. Fall, and A. Kurpad. 2009. “Relationship between physical activity measured using accelerometers and energy expenditure measured using doubly labelled water in indian children.” _European Journal of Clinical Nutrition_ .

- National Sample Survey Organization. 2000. “Choice of reference period for consumption data.” _NSSO Report 447_ .

- NSSO. 2013. “Nutritional intake in india, 2011-2012.” _Report No. 560_ .

- Patnaik, U. 2010. “On some fatal fallacies.” _Economic and Political Weekly_ 45(47): 81–87.

- Shetty, P., M. Soares, and M. Sheela. 1986. “Basal metabolic rates of south indian males.” _Report of FAO_ .

- Smith, L. C. 2015. “The great indian calorie debate: Explaining rising undernourishment during india’s rapid economic growth.” _Food Policy_ 50: 53–67.

- Smith, L. C., O. Dupriez, and N. Troubat. 2014. “Assessment of the reliability and relevance of the food data collected in national household consumption and expenditure surveys.” _International Household Survey Network Working Paper 8_ .

- St. Jeor, S. T., and P. J. Stumbo. 1999. “Energy needs and weight maintenance in controlled feeding studies.” _Well-Controlled Diet Studies in Humans, A Practical Guide to Design and Management._ .

26
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 12. infrastructure spending needs in Sierra Leone for 2006 to 2015**

<u>US$ million per year</u>

|Sector<br>New<br>investment<br>Rehabilitation|Total capital<br>expenditure|Operations<br>and<br>maintenance|Total needs|
|---|---|---|---|
|ICT<br>29<br>0|29|10|39|
|Power<br>84<br>2|86|31|117|
|Transport<br>21<br>68|89|31|120|
|Water supply and sanitation<br>118<br>31|149|52|202|
|Total<br>252<br>102|354|125|478|
|Sources: Mayer and other 2008; Rosnes and Vennemo 2009; Carruthers and others<br>Derived from models that are available on-line at http://www.infrastructureafrica.org/ai<br>**Figure 20. Sierra Leone’s infrastructure spending needs are high relativ**<br>Estimated infrastructure spending needed to meet targets, as percentage of GDP<br>SSA|2009; You and oth<br>cd/tools/models.<br>**e to GDP**|ers 2009.||
|MIC<br>Resource-Rich<br>ECOWAS<br>LIC-NoFragile<br>LIC-Fragile||||
|Liberia||||
|Zimbabwe<br>Congo, Dem. Rep.||||
|Ethiopia||||
|Sierra Leone||||
|Mali<br>Kenya||||
|Benin<br>Ghana||||
|Cote d'Ivoire<br>Uganda<br>Capex|O&M|||
|Nigeria||||
|South Africa<br>||||
|Cape Verde||||
|0<br>10<br>20<br>30<br>40<br>50<br>% of GDP|60<br>70|80||

Legend: LIC = low-income countries, MIC = middle-income countries, COMESA = Common Market for Eastern and Southern Africa Source: Foster and Briceño-Garmendia 2009.

Sierra Leone already spends a sizable amount ($13 million per year) to meet its infrastructure needs (table 13). Around 70 percent of the total is allocated to capital expenditure and the remaining 30 percent to operations and maintenance (O&M). Operating expenditure is entirely covered by budgetary resources, resources from state-owned enterprises, and payments by infrastructure users. Capital expenditure is funded by ODA (47 percent), the public sector (19 percent), the private sector (18 percent), and nonOECD financiers (16 percent).

40
valuation of attributes of cookstoves, including durability, fuel reduction, smoke reduction, and amount of time saved by using new clean, cooking technology. The overall goal of the survey was to determine features of clean cookstoves that were well-liked by communities in order to help implement sustainable cooking practices in rural Ethiopia, with the hopes of reducing the harmful health and environmental impacts of traditional cooking methods. We use the choice experiment survey on preferences for attributes of clean cookstoves conducted in Ethiopia in 2013 and repeated in 2016 to analyze the temporal stability of preferences.

The unique setting of having a repeated choice experiment and an RCT allows us to also study technology adoption and how preferences and willingness-to-pay for improved cookstoves evolve with use and experience. There is a large economics literature on technology adoption (see Marra et al. 2003 and Conley and Udry 2010) and a smaller but growing literature on adoption and learning with regard to cookstoves (see Ruiz-Mercado 2011 and Miller and Mobarak 2014).

At the same time, it has been found that the Mirt stoves save fuelwood (Gebreegziabher et al., 2018) and that it may reduce household air pollution (Bluffstone et al. 2018). Therefore, the second focus of this work is to evaluate the degree to which such experience with such outcomes causes users to shift their preferences and change their demand for attributes.

Therefore, our research questions become _Q1: Are the preferences for cookstoves elicited from choice experiments stable over time?_ _Q2: How do users’ preferences and willingness to pay for cookstove attributes evolve after extensive experience with the Mirt stoves?_ The combined RCT and repeated choice experiment setting allows us to use a difference-indifference framework to analyze the results from the choice experiment, the first instance of a difference-in-difference choice experiment analysis that we are aware of. We use the control group to evaluate stability of preferences and the treatment group to evaluate the degree to which experience with the Mirt stove shifted user preferences and attribute values.

Results show that the willingness to pay for stove attributes of the control group are stable over the three-year period from 2013 to 2016 and that the preferences of the treatment group have evolved over this time period. To better understand how long-term stove use impacts preferences, we then refine the treatment group to only include those currently using the stoves and we find that, compared to the control group, long-term stove use increases the WTP for most of the stove attributes.

3
# **The Foreign-born Population in the European Union and Its Contribution to National Tax and Benefit Systems**

**Some Insights from Recent Household Survey Data**

**Luca Barbone, Misha Bontch-Osmolovsky, and Salman Zaidi***

> * World Bank, Washington DC. The paper was commissioned by the Czech Ministry of Labor and Social Welfare, and was presented at the EU Ministerial Conference “Strengthening EC Competitiveness – Potential of Migrants on the Labour Market” organized by the Czech Presidency of the Council of the EU in Prague, February 26th-27th, 2009. We would like to thank in particular Ms. Martina Michalcová and Ms. Martina Tomková, and the participants of the above-mentioned conference as well as numerous colleagues at the World Bank for helpful comments on earlier drafts. Nonetheless, the opinions and views presented here are those of the authors alone. Correspondence: Salman Zaidi, World Bank, 1818 H Street NW, Washington DC 20433, USA. E. mail: <u>szaidi5@worldbank.org. Phone: (202) 473 1976. Fax: (202) 522 2753.</u>
Finally, for the other variables not modeled explicitly, as anticipated, we get negative effects for the existence of a regulator, while income guarantees and price cap regulation have positive effects.

# 4 Conclusion: Policy Implications

We now discuss in some more details the principal results, in particular with regards to their practical policy implications.

## **Regulatory environment, experience and contracts' incompleteness.**

The first feature of the environment having an impact on the probability of renegotiation is the existence of a regulatory body at the time the concession was awarded. This aspect significantly reduces the occurrence of subsequent renegotiations, as can be seen from the random probit panel. The effect remains unaltered when controlling for the whole range of characteristics, shocks, as well as with sector and country dummies with some slight exceptions in Tables 7 and 8.

The pre-existence of a regulator in the field where a concession is awarded can first be related to the simple fact that a better designed regulation from the start will reduce the scope for obvious mistakes and lessen the need for later disruptive modifications. Instead, it can be expected that contingencies occurring during the life of the project could be dealt with through a normal revision process inside the existing regulatory framework. Furthermore the pre-existence of a regulator increases the quality of enforcement by better commitment.

Moreover, this aspect can be related to the deeper issue of contract incompleteness. It is sometimes argued that concession contracts should be made as complete as possible, i.e. trying to include every possible contingency to avoid leaving room for ex post renegotiations<sup>3</sup> '. However, there are limits to this approach. First, in a very complex world describing infinite contingencies might prove impossible, and so contracts are bound to be incomplete. Second, imperfect enforcement limits the effectiveness of these contracts. Finally, complex contracts might be counter-productive if they lack transparency, contain contradictory requirements and lend themselves to opportunistic revision claims. These problems favor an alternative approach which relies on short concession-specific documents, while general rules regarding concessions would be found in laws and the relevant jurisprudence. With this type of contract, previous experience in dealing with the design of concessions should have an important role in limiting the risk of later renegotiations, and this is precisely what we should expect from a specialized and experienced regulator.

> 31See the example of the Buenos Aires water concession, running hundreds of pages and several volumes, mentioned in Klein (1998).

32
- ; -

summary of the ctuntry studies and, in l1eu of<sup>having read the individual</sup> study, at least some of the clnims must be accepted on faith. Moreovcr, the range of broad generalization is almost always<sup>restricted to the ten</sup> countries included in the project.

# I. INTRODUCTION

Only a relatively modest literature exists<sup>on the subject of tax</sup> reform in developing countries. Much of that<sup>literature is descriptive in</sup> its approach and typically refrains from asking<sup>whether alternative policy</sup> meesures might have been more successful than<sup>the ones that were actually</sup> chosen. Fu.ther, as Gillis (1988) has noted,<sup>even the measurement criteria</sup> by which the success or failure of a tax<sup>refcrm are to be judged are not</sup> well established. In his own appraisal of<sup>developing country reform</sup> efforts, Gillis (1988) h.s evaluated the merits<sup>of a reform according to</sup> whether or not the announced goals of the reform<sup>have been met.</sup> Newbery and Stern (1986), on the other<sup>hand, have attemptod to</sup> immerse tax reform in the normative framework<sup>provided by the theory of</sup> optimal taxation. This theory attempts to<sup>account for the impact of a tax</sup> reform on the goals of minimizing tax induced<sup>losses in the efficiency of</sup> resource allocation and of respecting vertical<sup>equity norms. The former</sup> dimension of a reform is captured by the responsiveness of taxpayers to tax induced relative rrice changes while the<sup>latter relies upon particular</sup> specifications of a social welfare function. Optimal tax<sup>reforms in this</sup> context tend to be those which settle on a configuration<sup>of tax rates that</sup>
Table 6: DD estimates

||New frms|New foreign<br>frms|Share of<br>foreign frms|Gross profts<br>per capita|Net sales<br>per capita|
|---|---|---|---|---|---|
|Panel A: Binary||||||
|Treatment (1/0)|0.1025|0.4090|0.0266|94.4093|2166.0120|
|_Robust S.E._|(0.0673)|(0.2122)*|(0.0129)**|(83.8947)|(938.6023)**|
|_Clustered S.E._|(0.1010)|(0.3254)|(0.0213)|(111.3936)|(1531.0429)|
|Panel B: Continous||||||
|Refugees (x10,000)|0.0273|0.1153|0.0046|38.6701|545.0321|
|_Robust S.E._|(0.0063)***|(0.0160)***|(0.0015)***|(20.9750)*|(235.7229)**|
|_Clustered S.E._|(0.0079)***|(0.0228)***|(0.0023)*|(29.0360)|(335.0298)|
|Observations|145|116|116|116|116|
|Panel C: Placebo||||||
|Refugees (x10,000)|0.0017|0.0191|0.0004|15.5967|273.6829|
|_Robust S.E._|(0.0056)|(0.0138)|(0.0008)|(14.3532)|(140.9966)*|
|_Clustered S.E._|(0.0067)|(0.0197)|(0.0011)|(20.4790)|(201.1717)|
|Observations|87|58|58|58|58|

*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Source:_ Authors’ own analysis results from data described in text. _Note:_ All models include province and year fixed effects. Robust standard errors in parenthesis. Data are from 2009 to 2013 for 29 provinces in Panels A and B. 2009 data are only available for the new firms variable. The placebo treatments in Panel C are estimated by assuming that the number of refugees in 2013 had arrived in 2011 and use data from 2009 to 2011.

10 provinces that receive refugees in 2012 and 2013. The difference is that the control group is comprised now of all provinces in Turkey including western provinces, which are weighted according to their pre-treatment similarity to the treatment region. The findings appear to confirm the IV and DD estimates. The positive effect on the number of new firms with foreign owners is visible as the gap between the synthetic and treatment group lines widen after 2012. The total number of new firms in the treatment region appears to be largely unaffected though the gap between the treatment and synthetic control lines widen and close slightly in 2012 and 2013 respectively. We find positive effects for gross profits and net sales and the effect appears stronger for gross profits. In all outcomes, the positive effects are limited or absent in 2012. We might assume that the reason is that the regions near the border hosting the refugees were initially negatively affected by the civil war in Syria, but the effect turned positive in 2013 with increasing supply of unskilled labor and demand. A weaker effect in 2012 would also explain the stronger positive effects we find in the IV model for sales and profits where the treatment year is 2014 compared to the DD model where the treatment years are 2012 and 2013.

19
OCEANIA

# 1. **Estimates** and Projections

|**lw**<br>|PwuSthm|**route**||**Gouttd**<br>**ot**||**W**|**Tn**|**I**|**40I**|**Im**<br>1|im|
|---|---|---|---|---|---|---|---|---|---|---|---|
|19<br>1990<br>199<br>2000<br>2005<br>2010<br>2015<br>2020<br>2025|24634<br>26651<br>28797<br>30119<br>32711<br>34420<br>35945<br>**37264**<br>38502|%. 7<br>54.9<br>55.1<br>53.5<br>52.4<br>51.5<br>52.4<br>53.7<br>5S.6|:95-89<br>)0-94<br>i995-99<br>20C-04<br>2005-09<br>2010-14<br>2014-19<br>2020-24<br>2025-49|1.57<br>1.55<br>1.36<br>1.19<br>1.02<br>0.5?<br>0.72<br>0.65<br>0.40|19.4<br>19.0<br>17.9<br>17.1<br>16.2<br>15.4<br>14.7<br>14.3<br>13.3|7.9<br>7.7<br>7.5<br>7.4<br>7.4<br>7.5<br>7.5<br>7.7<br>9.3|2.4<br>2.40<br>2.33<br>2.31<br>2.26<br>2.20<br>2.13<br>2.11<br>2.10|69.2<br>70.2<br>71.3<br>72.3<br>73.<br>73.6<br>74.6<br>75.5<br>77.4|7r.0<br>76.0<br>77.0<br>73.1<br>78.8<br>79.9<br>80.4<br>61.3<br>63.3|28<br>24<br>20<br>17<br>16<br>Is<br>14<br>13<br>9|37<br>31<br>26<br>21<br>20<br>19<br>17<br>16<br>11|
|2050<br>207<br>2100<br>2125<br>2150|42519<br>**44065**<br>4974<br>4559<br>4690|64.?<br>66.6<br>71.3<br>73.0<br>73.9|2050-.<br>20?5-,.<br>2100-24<br>2125-49|0.14<br>0.08<br>0.05<br>0.03|12.4<br>12.1<br>11.8<br>11.7|11.0<br>11.2<br>11.3<br>11.3|2.07<br>2.07<br>2.06<br>2.06|79.4<br>80.6<br>61.9<br>62.3|63.7<br>67.3<br>66.6<br>69.1|4<br>3<br>2<br>2|5<br>4<br>3<br>3|

## 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> Ea<br>e s<br>%3 2 lD lX<br>526555 _<br>10 3 6 4 2 0 2 * 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> ~~~~~70-74 (ll<br>eS~~~6.69<br>~~~~60.64 6~~~~5-ag 332:<br>55.56 E =<br>50.54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-¶4<br>542<br>O~ ~ ~~-4 1<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

## 3. Distribution of population (percent): 1990 and 2025

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- 23 -

available after 1983. We are therefore, in effect, assuming that there was no further unaccounted for quality change outside of computers and semiconductors after 1983, an unlikely story.

The conclusion here is that the estimate of long tern increases in world prices of manufactured goods derived from the UN unit value index is too high by at. least one per cent per year. Furthermore, the overestimate could be considerably greater if the quality corrections made here have counterparts in other manufactured products, even if there would be few, if any, on the scale of the corrections for computers and semiconductors.
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Figure 12. Hidden costs**

- **a. Botswana’s power utility is comparatively efficient**

<!-- Start of picture text --> DRC Congo<br>Malawi<br>Tanzania<br>Zambia<br>Mozambique<br>Botswana<br>0 100 200 300 400 500 600 700<br>Percentage of revenues<br>Losses Underpricing Collection Inefficiencies<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009) and Briceño-Garmendia and Shkaratan (2010). _Note:_ [*] Projection. DRC = Democratic Republic of Congo.

# **Challenges**

The power sector is key to Botswana’s sustainable development and currently its major bottleneck. Botswana’s energy demand was about 3,660 gigawatt-hours (GWh) in 2008 (with a peak load of 500 megawatts, MW), which is projected to grow at about 6 percent per year to reach 5,300 GWh by 2017 (peak load of 850 MW) and 6,890 GWh by 2026 (peak load of 1,130 MW). The mining sector accounts for about 50 percent of the demand, the commercial sector about 20 percent, and the residential sector about 25 percent. Botswana has been dependent on power exports from South Africa. In 2008 Botswana imported about 2,440 GWh (67 percent of its power requirements) from Eskom, the national electric utility of South Africa, while its own small 25-year-old coal power plant (Morupule A, 4 x 33 MW) provided about 22 percent.

Botswana’s generation capacity is set to expand with the construction of Morupule B coal plant that will add 600 MW (4 x 150 MW) by 2012. This addition to generation capacity will make Botswana selfsufficient in power and offset South African plans to stop exporting power to SAPP as early as 2013.

Despite recent network extensions, access remains low. Power supply reaches only around 50 percent of the population, while access for an average upper MIC in Africa stands at 60 percent. While this is a comparatively small percentage, however, it represents a significant improvement from previous years, particularly in the rural areas. But further extension of the network will challenge the financial position of the BPC.

In the strict economic sense, BPC has faced sustained underpricing since 2006, with tariffs that did not allow for cost-recovery (figure 14). BPC’s cost recovery capacity deteriorated due to an increase in the South African Eskom’s power supply prices. Due to other financial resources available to the BPC, 26
bias introduced by measurement error in our proxy for human capital.<sup>13</sup> . Given that we are interested in determining the possible effect of natural resource abundance on economic growth, we extend traditional growth regressions by incorporating the share of natural resources exports in GDP and in total exports as proxies of resource abundance ( _Natural_ ).<sup>14</sup> As control variables we use human capital, measured by average years of schooling among the over-25 population ( _H_ ); government expenditure as a fraction of GDP ( _G_ ); openness, measured as exports plus imports divided by GDP ( _OPEN_ ); terms-of-trade shocks ( _TT_ );<sup>15</sup> investment as a fraction of GDP ( _I_ ); and initial income ( _y_ ). All the variables are measured at the beginning of each period of the panel. However, as a robustness test, we also estimate regressions using average values of some variables for each period. All the estimations use either period dummies and regional dummies for Africa and Latin America, or fixed effects, depending on the estimation technique ( _DREG_ ).<sup>16</sup> Our benchmark regression for the rate of growth, _γy_ , is the traditional growth equation extended by the inclusion of natural resources, as estimated by several authors and as implied by our model.<sup>17</sup> This regression can be written as where _i_ is a country index and _t_ indicates the number of the cross-section regression of the panel.

In a second stage we include an interaction effect between human capital and such as the Generalized Method of Moments, as proposed by Caselli, Esquivel, and Lefort (1996). 13For a revision of this point see, for example, Krueger and Lindahl (1999).

> 14As in most of the recent literature, we use the World Tables CD-ROM as a data source and define natural resources exports as the sum of exports of fuels and nonfuel primary products.

15We replicate the measure of terms-of-trade shock developed by Easterly, Pritchett, and Summers (1993). See appendix B.

> 16For a detailed discussion of the control variables see Sachs and Warner (1995) and Temple (1999). In our empirical specification we do not rule out the conditional convergence hypothesis; hence we include the lagged value of income per capita. Given the theoretical framework, it may be possible to recover conditional convergence to a given growth rate after including a decreasing marginal return to capital.

> 17Because in our model the economy exports all of its output, we can use either the share of natural resources exports in GDP or the share of natural resources exports in total exports as the proxy for resource abundance. See equation 30

17
# **2.1 The Case of Emerging Market Economies**

Studies in this strand of the literature suggest that contrary to industrial countries, where real factors emerge as the main determinants of inflation, nominal factors play a more important role in affecting the evolution of inflation in emerging market economies. For instance, the IMF (1996) shows that the output gap does not play an important role in explaining inflation in developing countries. Instead, changes in money growth and nominal exchange rates have higher explanatory power in explaining inflation. This finding does not suggest that inflation is not a function of excess demand in these countries; it simply implies that the contribution of excess demand is dominated by those of nominal shocks. More precisely, inflation in the medium term is viewed as the result of the government financing its deficit through the creation of money or through time inconsistent monetary policy.

The empirical evidence concerning the link between fiscal deficits and inflation has been rather elusive in spite of the theoretical links. At the level of any particular country, it may be difficult to establish a clear short-term link between fiscal deficits and inflation. In fact, the correlation may be even negative during extended periods of time. Evidence suggests that the existence of a positive correlation in the long-run is also not a clear-cut phenomenon (Agenor and Montiel, 1999). For instance, Fisher, Sahay, and Vegh (2001) find that the relationship between the fiscal deficit and inflation is only strong in high inflation countries—or during high inflation episodes—but they find no obvious relationship between fiscal deficits and inflation during low inflation episodes or for low-inflation countries.

5