rafmacalaba/data-use-mentions-extended huggingface dataset:
any access to some significant water or sanitation delivery contract with the private sector. The country is considered to have some form of public‐private partnership (PPP) in 2015 if during that year there is an ongoing divestiture, greenfield or brownfield PPP in the water sector of the country other than a management or lease contract (since these do not involve any private investment), or if these investment contracts had been closed and concluded within the previous 10 years. We also ran robustness checks changing this time limit to 5 and 2 years. This information is collected from the World Bank PPP database and is also coded as a dummy variable with a value of 1 when a PPP has been awarded. The detailed country specific coding used in this paper is also reported in Annex 1.

The way we have decided to account for these PPPs implies that the IRA may have been put in place after the adoption of a PPP in the sector, which imposes some limit on any causal interpretation of the results. In other words, we can only hope for a sense of correlation or of odds of a correlation as discussed in more detail later. We do not correct for the size of the deals with the private sector (i.e. large and small projects both count as a PPP in the same way). While the main focus of the paper is the experience of developing countries, we collected the basic institutional information also on the developed economies. This allows us to identify any significant core difference between the two country groups. As discussed next, however, the stylized facts suggest that the differences in the distribution of basic governance characteristics are actually not very significant across country groups.

> 3 123 developing countries and 54 developed countries.

> 4 Updates will be available on the Bernard Vanommeslaghe Chair website <u>http://ecares.ulb.ac.be/index.php?option=com_content&task=view&id=168&Itemid=367</u>

5
heterogeneities in the impact of informal competition on formal firms’ employment growth are consistent with the “parasite” view of informality and the general findings in the related literature. Several issues remain to be explored. We provide a few examples to illustrate the point. First, our results are restricted to employment growth. It would be interesting to analyze how informal competition affects other aspects of firm performance such as sales growth, innovation, investment in physical capital, tendency to export, level and growth of factor (labor, capital and total) productivity, and so on. Second, the firm-level data that we use cover formal firms with 5 or more employees. Thus, micro firms with fewer than 5 employees are excluded. This is an important exclusion given that informal firms are very small and therefore likely to a be bigger challenge for micro firms than the SMEs. Extending the empirical exercise of this paper to micro firms is a fruitful area for future research. Third, while there are several papers that analyze the extent of informal activity in a country, the size of the spillover effect from informal to formal sector firms has not been properly explored. Thus, exploring the factors that cause informal firms to compete more aggressively against formal firms rather than operate independently is another avenue for future research. Last, we found above that the impact of informal competition varies depending on exporting activity and financial constraints faced by the formal firms. Our motivation for exploring these was to raise our confidence against endogeneity concerns and not to explore heterogeneity per se. However, heterogeneity in the impact of informal competition across industry groups, firmsize groups within SMEs, gender of the top manager, and so on can be explored.

31
- f) Excessive influence activities in the agricultural sector to induce (possibly, bribe) power utility officials to increase agricultural supply, and to reduce illicit use (including direct power losses). Such activities are socially costly, and could in addition involve increased externality costs as noted under point d). Similar influence activities can be exerted by illicit electricity consumers, to increase illicit use. Such activities are directly countervailing to the former. A “zero-sum game” situation may easily arise, where different types of influence activities have impacts in opposite directions, with no overall major change in outcome.

- g) Inefficient incentives of the power utility to stem losses, when neither the overall utility (it bottom line) nor its employees are directly rewarded for this; see also d) and e).

- h) Insufficient incentives of the power utility to provide good and reliable service to the agricultural sector. Such lack of incentives is exacerbated by the lack of payments for electricity delivery (as also stressed by Strand (2011)).<sup>10</sup> Above, this loss takes the form of directly reduced utility of agricultural users, for a given power supplied.

In sum, these various misallocations make up a major, and somewhat confusing, set of overall distortions, when the entire situation for this system is compared to a (hypothetical) “firstbest” situation. Not all distortions go in the same direction; and some of the distortions tend to partly eliminate the adverse effects of other distortions.

An important next step in this analysis would be to obtain a better empirical understanding of the quantitative distortions caused by inefficient electricity pricing and supply, in situations such as those facing Indian farmers. Initiating that empirical work in turn requires additional effort to gather the disaggregated empirical data necessary for estimating the various relationships, which is an important issue for future research. This requires additional surveys to be done of farmers’ water use and pumping practices; willingness to pay e.g. in terms of
findings in Kusek and Silva (2018), where advocacy for improved business environment and business setup assistance ranked among the most valued IPA services.<sup>7</sup>

**Figure 4. Importance of IPA services**

<!-- Start of picture text --> How important are the following services offered by Investment Promotion Agencies to your<br>company in this country?<br>Most valued IPA service (composite) 61% 28%<br>Efforts to improve the business environment<br>35% 40%<br>in the country<br>Pre-investment information 30% 41%<br>Assistance in setting up 31% 39%<br>Assistance with operational issues and<br>29% 38%<br>grievances<br>Location marketing 29% 37%<br>Pre-investment assistance 25% 40%<br>Personalized contact/response to your<br>24% 40%<br>company<br>0% 20% 40% 60% 80% 100%<br>Critically important Important<br><!-- End of picture text --> _Source_ : Computation based on the 2019 GIC Survey.

# _Heterogeneity across investor types_

On average, importing a higher share of inputs is associated with placing greater importance on IPA services. When the importance of affiliates’ most-valued IPA service is used as the dependent variable, the coefficient on import share of inputs is positive and significant at the p<0.10 level in our preferred ordered logistic specification with country and subsector fixed effects (Table 41). This result is also robust to the exclusion of fixed effects and alternate specifications using OLS. Looking across specific services, we find similar results with respect to IPA assistance with business setup (Table 46, p<0.05) and with operational issues and grievances (Table 47, p<0.10). Both of these results are also robust to alternative specifications.
We dene the labor status of the family members by introducing a discrete variable as follows : Then, we can express the number of hours worked by household member _i_ as a function of her own labor income, household income, labor status of the other members, and a xed cost of working.

Note that this equation only holds whenever the wage income is high enough to cover the xed cost of working. In other words, the number of hours is left-censored, so in the data we observe: In this model, the heterogeneity summarized in _fi_ makes the less productive individuals move in and out of the labor force or supply more or fewer hours. In addition, when a shock such as a terrorist attack hits a household, women face two choices: (i) Supply more hours of work to compensate for income lost by the main earner (because he eventually joins one of the bands in conict or dies as a consequence of it, switching _uj_ ), which is known as "the added worker eect" or (ii) Supply fewer hours of work, eventually leaving work for safety reasons (by changing _fi_ ).

# 3 Data

We combine several sources of data, including the Global Terrorism Database and national labor force surveys. Each are described in turn in this section.

## 3.1 Terrorism in South Asia

The Global Terrorism Database (GTD, START [2018]) systematically records all attacks since 1972 around the world. For an incident to be included in the data set three conditions are necessary:

- i. It must be intentional;

- ii. It must entail some level of violence or immediate threat of violence and

iii. Its perpetrators must be sub-national actors.

6
.032|.022|
|Most difficult questions, average ICC|.097|.207|.186|.174|

# _Design effects_

We use these ICCs to calculate interviewer design effects using an average interviewer workload of 46.5 interviews. To calculate interviewer design effects, we use the ICC results from Specification II. Using the design effect formula from Section 1, we find a mean design effect of 7.2. In other words, clustering of responses by interviewers results in survey data with average variances that are 7.2 times higher that they would be without interviewer effects. Design effects range from 1.6 for number of people in the household to 16.2 for hours per day worked in primary occupation by the household head. By comparison, there were 10 interviews per area cluster which together with the mean ICC of 0.099 results in a mean design effect of 1.9.

The design effects found here are high compared to O’Muircheartaigh and Campanelli (1998) who found a mean interviewer design effect of 1.8 with a high of 5. However, this was based on a smaller survey cluster size of around 24 respondents per interviewer and the highest ICC

> 7 Calculated as the arithmetic mean of the ten ICCs.

9
in commodity prices during the first decade of the 2000s or the 2008 global financial crisis, or they may be due to domestic shocks such as those induced by variations in local climatic conditions. Price changes may also occur as a result of economic policies such as changes in taxes, wages, subsidies or social transfers. In all these cases, the policy maker may want to estimate the impact on wellbeing _ex-post_ (for example in the case of economic shocks) or _ex-ante_ (for example in the case of economic policies). The work proposed applies to both cases and provides guidelines for macro or micro economists or for macro or micro simulation exercises of economic shocks or policy reforms.

Measuring changes in welfare due to changes in prices is also an issue very relevant for adjusting welfare measures (such as GDP or the poverty headcount) spatially or longitudinally and therefore measuring changes over time correctly. As the latest round of the global Purchasing Parity Power (PPP) surveys has shown, changes in data on prices can change welfare measurements very significantly. Changing measure or method for estimating welfare effects of price changes can obviously amplify or reduce the effect of price changes. Practitioners as well as international organizations engaged in measuring the impact of price changes on welfare give surprisingly little weight to the choice of estimation method. For example, the World Bank and the IMF use as methods of choice for spatial and longitudinal price adjustments the Laspeyers or Paasche indexes while they almost invariably use the Laspeyers index when simulating the impact of price changes on welfare, and this often irrespective of the magnitude of the price change. Theoretical economists, on the other hand, tend to privilege the equivalent variation or consumer’s surplus measures when it comes to measure changes in welfare due to price changes. A priori, these are normative decisions and good arguments can be found to justify each of these choices. But the outcomes of these choices can be very different in terms of welfare measure6
the consumption bundle of household _h_ . Following Deaton (1989, 1997), we can safely ignore the private marginal utility of income _∂V_<sup>_h_</sup> _/∂_ ln _y_<sup>_h_</sup> in policy evaluation. In addition, letting _τi_ be the instrument of tariff protection for sector _i_ and assuming perfect price pass-through elasticities,<sup>5</sup> so that _d_ ln _pi_ = _−τi/_ (1 + _τi_ ), the estimable welfare effects are given by: The interpretation of this equation is straightforward. After a price change caused by tariff cuts _d_ ln _pi_ = _−τi/_ (1 + _τi_ ), the first-order effects on real income can be well-approximated with the corresponding income and expenditure shares. In the language of Deaton (1989), because we are working with tariff cuts and price declines, net-consumers benefit while net-producers suffer. Though this is well-known, it is important to clarify that the Deaton first-order approximation captures direct, short-term effects of tariff liberalization. In particular, it does not take into consideration second-order effects such as consumption and production adjustments (which may become progressively more important over time), labor or investment decisions, or any dynamic effect more generally.

The goal is to calculate the welfare effects generated by the _entire_ structure of tariff protection. To obtain a measure, we sum the changes in welfare in (4) over all traded goods _i_ to get: where _V_<sup>�</sup><sup>_h_</sup> is the proportional change in household real income. Next, we use this expression to assess trade policy with the HIT database.

> 5Note that we are making the assumption of perfect pass-through for convenience and ease of exposition. The data in principle accommodate richer and more realistic pass-through assumptions, see e.g. Nicita (2009), Ural-Marchand (2012), and Bergquist (2017) for pass-through estimates for selected commodities
may seem surprising as one may expect television to be a particularly effective means of communication for audiences with low literacy, but it should be noted that ours is a uniformly highly educated sample, as is the region.<sup>36</sup> So it is plausible that the education effect we uncover reflects the fact that the message of Shuga was relatively progressive.<sup>37</sup> No robust effects are found for the interaction between treatment and speaking English at home, possibly because the latter variable has very little variation in our sample.

# 8 Conclusions

In this paper we have tested the effectiveness of an entertainment education TV series, MTV Shuga, aimed at providing information and changing attitudes and behaviors related to HIV.

> 35 Note that English is the language of instruction in schools and is the only offi cial language in the country.

> 36 The high level of education is not an artifact of our sample: the average respondent in the 2008 Nigeria DHS (of similar age and living in the South West) had 11 years of education.

> 37 Note that despite the negative coeffi cient on the standalone treatment dummy, the overall effect of treatment is abundantly positive when evaluated at the mean years of education. For example, ceteris paribus the impact on knowledge is positive for any respondent with at least 9.5 years of education, which corresponds to the 4th percentile in the distribution of education.

31
environmental policy and regulations. Understanding if preferences elicited in developing country settings, especially in rural communities, are stable is important with regard to using results from non-market valuation studies over time.

Therefore, the first focus of this work is to determine if preferences elicited using choice experiment surveys in developing country settings are stable over time. We test the temporal stability of preferences for identical respondent groups over a three-year period, using identical choice experiment surveys, in a test-retest experimental setting.

The background of the study is a valuation study on preferences for clean cookstoves and a randomized controlled trial (RCT) of improved cookstoves conducted in Ethiopia. The main objective of the valuation study was to use a choice experiment survey to examine Ethiopian households’ 2
(0.00702)||(0.0138)|(0.0175)|
|_Indiv.Controls_|Yes|Yes|Yes|Yes|Yes|Yes|
|_CountryFE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Y earFE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_N_|60987|57964|57964|57964|57964|57964|
|_R_<sup>2</sup>|0.177|0.175|0.178|0.183|0.183|0.184|

Standard errors clustered at the country-year level in parentheses. Individual controls include age, gender, religion and household composition of the respondent ∗ _p_ < 0 _._ 10, ∗∗ _p_ < 0 _._ 05, ∗∗∗ _p_ < 0 _._ 01

# **3.2 Subjective ranking as a measure of** **_well-being_**

An alternative explanation to the bunching in the middle is offered by changing the interpretation of the Cantril’s ladder question. In this alternative case, the underlying variable measured by the responses is not anymore of a distributional nature, but it is of a subjective well-being nature. Individuals, rather than answering in distributional terms, i.e. attempting to figure out to which part of the distribution of consumption they belong, they interpret the question in terms of a standard well-being question. Such a question usually provides a range from a minimum to a maximum intensity that respondent can choose from. In fact, in the LiTS surveys, the question asking individuals their position on Cantril’s ladder is phrased in a way that is ambiguous about whether each step should include 10% of the population. The reference to a 10-percent-sized group is explicitly made only with respect to the top step which is said to include the 10% of the richest, and the bottom step with 10% of the poorest people. It seems quite plausible
is a sensible one under these circumstances, it is clearly strong. In a recent survey of commodity price impacts, Brambilla and Porto (2009) argue that the pass-through from international food prices is generally well short of 100 percent. In addition, spatial heterogeneity in infrastructure, transport costs, and market structures within countries often causes non-trivial regional differences in prices, even inside a given country. (We will see below that this is definitely true of Brazil in 2008.) Imperfect pass-through and regional price variation suggest the need for detailed data on changes in consumer prices _within_ developing countries during the food price shock period. Reliance on the time series of world prices for a few key commodities also

> 4 Estimates of substitution effects such as those of Friedman and Levinsohn (2002) and Robles and Torero (2010) are also possibly subject to bias, by ignoring substitution along the quality, rather than quantity, margin. See McKelvey (2011) and Gibson and Kim (2011). Implications of quality substitution for the estimation of welfare effects rather than simply quantity or calorie elasticities remains a subject for future work.
priorities are distorted in this fashion, the average quality of projects with high funding priorities is relatively closer to the average quality of projects with low funding priorities. As the aggregate marginal return to funds is equal to the average productivity of projects with marginal funding priority (equation 2), this implies compliance with Property 1.

# **7.1 Empirical interpretation**

How plausible are Assumptions 1 and 2? In this section I present empirical evidence that is consistent with Assumptions 1 and 2, and is hence supportive of Property 1 (by Theorem 1) .

As illustrated in the previous sections, there are many general equilibrium implications of Property 1 that are consistent with empirical regularities. However, there are obvious problems with testing Property 1 directly. Essentially, it would require an exogenous source of variation in the supply of funding that does not also affect the productivity of funding. This is problematic, as we would expect that periods of high productivity would imply both that the returns to funding are higher and that the supply of funding is larger. Using the standard small open economy trick and restricting attention on global variation in the supply of funding is not helpful here, as it may capture variation in global productivity that also affects the domestic marginal return to funds.

Appendix A presents an empirical investigation of Assumptions 1 and 2. I use the AMADEUS data set<sup>11</sup> , that includes balance sheet data for all formal sector firms for several European and Eastern European countries, between the years 1990-2011. For each firm, I construct a measure of the marginal productivity of funding ( _Ai_ ). In the appendix I illustrate how this measure maps into the productivity of projects ( _Ag_ ( _x_ )).

I divide the set of countries into “distorted” and “efficient” based on the crude measure of GDP per capita. I choose the cutoff to be the GDP per capita in Cyprus. After removing countries with insufficient data, the list of “efficient” economies includes Austria,
of use for some metals, such as in alloys. Third, in several cases, especially coal and tin, the results of the model improve considerably when cross‐prices are included.

As a final extension and check, the control variables were included in the com‐ modity‐specific demand equations. A key result emerging from this extension is that un‐ like those from the aggregate equations, the results for individual commodities are sen‐ sitive to the inclusion of control variables. Detailed parameter estimates are reported in Appendix E (Tables E1‐E9). Even in the cases of oil and copper (two of the four equations that performed well in the base specification), the inclusion of population density or trend in the former, and the inclusion of urbanization in the latter, weakens the performance of the model. This implies that estimating the demand functions for group aggregates may capture substitutabilities and complementarities that are not accounted for by simply in‐ cluding cross prices.

# **5. Conclusions**

This paper has examined the relationship between commodity demand and per capita income level for energy and metal commodities both at the aggregate and commodity‐ specific levels. Three energy commodities (oil, coal, and natural gas) and six metals (alu‐ minum, copper, lead, nickel, tin, and zinc) were featured in the analysis. These commod‐ ities account for more than 85 percent of global energy consumption and for nearly all base metal use as measured by volume. The analysis was based on an autoregressive distributed lag model utilizing annual data for 1965‐2018 for up to 63 countries.

Several findings emerged. First, the estimation of demand equations for individual commodities gave highly heterogeneous results, consistent with most of the literature; only four of the nine commodities showed significant results consistent with theory (i.e. that demand increases as income rises and prices decline) and _a priori_ expectations (i.e. that demand increases at a decelerating pace). Second, the group aggregates