rafmacalaba/data-use-mentions-extended huggingface dataset:
country.

<mark>Tajikistan is a low income country and the poorest country in the ECA region. Much of the population and the economy of Tajikistan are in the two major valleys which are the loci of irrigated agriculture and where water availability is a major climate changerelated concern. The remainder inhabits the mountainous areas of varying elevation where mudslides and other natural disasters are major risks. Adaptation strategies need to determine where to invest in planned adaptation. Such strategies face an apparent dilemma between protecting the core agricultural economy by investing in the most productive areas or to invest in disaster risk management in more remote mountainous zones.</mark> <mark>Our assessment of vulnerability considers a range of factors beyond the geo-physical impacts of climate change and draws on diverse data sources including household surveys and weather station records. These factors include the extent to which assets and livelihoods are sensitive to impacts of climate change as well as the social, economic, and institutional factors that are likely to shape adaptive capacity. In Tajikistan, migration and elevation are two critical factors to consider. A common household livelihood strategy is overseas migration for work, mostly to Russia. Tajikistan received remittances equivalent to 40% of its GDP in 2008, almost all from male migration to Russia. Households in the rural highlands have on average almost 50%</mark> <mark>3</mark>
The focus of the paper will be addressing the second type of selection so as to be able to compare households with migrant mothers to households with migrant fathers. On the other hand, the results of proposition 1 will provide a guide for interpreting the direction of the bias that may arise from conditioning on _selection of households into migration_ . I thus build on the intuition of the theoretical model introduced in section I to derive a set of instrumental variables that may influence the decision of migrant households about which of the spouses to send out for migration but will not have any direct effect on the outcome variables of equation (14). The model of section I is translated to the data by first assigning “counterfactual” gender specific destinations to each household. To this purpose I identify, for each household, the year in which the migration decision has been taken as that in which the migrant has departed; I then consider the destinations chosen by previous migrants from the same village and take the destination that was most common among female migrants as counterfactual destination for women and the one that was most common among male migrants as counterfactual destination for men. Counterfactual destinations will vary by gender, time of departure, and village. The choice of assigning the most common destination of migrants from the same village to perspective migrants is supported by the evidence reported in table 5, which shows that over 60% of migrants from the same village had chosen the same destinations. This is in line with the literature that has documented the formation of _networks of migrants_ (Bartel 1989; Altonji and Card 1991; Lafortune and Tessada 2012; Patel and Vella 2013) but can also, in this particular context, be explained by the widespread use in South Asia of recruiting agencies that are connected to other agencies in a foreign country and, therefore, typically send all of the people of the village they visit to the same destination (Parrenas 2001; Suradji 2004). Table 6 shows the gender specific destinations assigned to each household for the whole IFLS sample
# **3. Data**

**This paper presents new global evidence on the key determinants of public-private partnership (PPP) investment in electricity transmission and distribution based on a panel data analysis  for 105 developing countries over a 16 years period from 1993 to 2008.**

**Our study aims to identify the key factors affecting the private investor’s decision to enter electricity transmission and distribution and  the amount of investment sunk in this market segment.**

**The analysis of the key determinants of the private investor’s decision to enter is done using the probit model** , where the dependent variable is a dummy equal to 1 if PPPs were introduced in electricity transmission and distribution and 0 if not.

**The treatment of the modeling of the key factors affecting the private investor’s amount of investment sunk in the renewable energy market is based on Heckman’s approach to sample selection** . This distinguishes between (1) the decision on whether to enter the generation segment of the electricity market (selection equation), and (2) the decision on how much investment to commit to (investment equation).<sup>1</sup> Unlike the Tobit model, the factors that affect the two decisions need not be identical and, where identical, could even be different in the sign of their effect on the decisions. The selection equation relates the choice of whether to attract PPPs (via a zero/one dummy variable) as a function of the governance variables and the price of oil is estimated over the complete set of countries using a probit model. The second-stage equation, relating PPP investment to the full set of short run and long run variables described in Table 1, is estimated over the sub-set of countries. Because the group has been selected by the first-stage equation, the possibility of selection bias would be introduced if a standard regression were used at the second stage. This bias is related to the magnitude of the correlation between the errors (
with the self-reported ethnicity data from the 2006 DHS (Samak ́e, Traor ́e, Ba, Demb ́el ́e, & Diop, 2006) as an inverted Herfindahl-Hirschman Index (HHI). The HHI, first developed to study levels of market competition, is commonly used in the literature to measure ethnic fractionalization indices. It is simply an inverse of the sum of the squares of the share of each ethnic group per population in each commune.<sup>7</sup> Figure 2 shows the distribution of the index across the country for communes where we have DHS survey data. In the figure a greater index value and darker color indicates a greater level of ethnic diversity. Additionally, we gather the 2009 commune population from the Malian census reported on the Wikipedia pages (Wikipedia, n.d.).<sup>8</sup> Lastly, all maps are made with the administrative geographical boundaries made available by the Database of Global Administrative Areas (University of California Berkely, n.d.).

For outcomes measures, we use per-capita household non-agricultural income and consumption from EMOP and then calculate commune-level poverty measures and the Gini coefficient as a measure of inequality from the consumption data. One important caveat on the EMOP income data is that it captures non-agricultural household income rather than total household income. The EMOP questionnaires are structured in a way that is more suitable to collect data on income with stable flows. We believe that this likely ignores most agricultural income. In fact, about a quarter of observations on household income is reported to be zero, while there is no report on zero consumption. Therefore, we interpret the EMOP income data to be reflective of non-agricultural household income, rather than total household income.

> 7Please refer to Equation 5 in the online appendix for details on the creation of the index.

> 8The Wikipedia page offer both circle- and commune-level population counts. We checked its internal consistency by comparing each circle-level value to a sum of commune-level values for each
# **Acknowledgments**

Our most sincere thanks to Maria Teresa Correa who for eight months relentlessly collected the vast amount of information necessary to conduct the current analysis. We would also like to thank the following individuals for their collaboration and support: in Argentina, Hugo Medina and Osvaldo Mignini; in Chile, Carlos Parra; in Mexico, Alejandro Ritch and Ricardo Rivera; in the Philippines, Sergio Marquez and Socorro Clemente for allowing us access to the appropriate stock market data and answering our numerous questions. We also thank Miodrag Deric and Craig Meisner for their research assistance. Finally, we thank the participants at the Canadian Environmental and Resource Economists Workshop (October 1997), Randy Wigle, and David Wheeler, for their generous comments.

2
With nine million hectares planted to wheat at a seed rate of about 100 kg/ha, total planted seed would come to about one million tons. Private sales of 4,000 tons of seed of new varieties would plant only 0.4 percent of total wheat area. TIGEM sales of seed for new varieties might supply another 3 percent of planted area.

However, the share of wheat cropped area reached by these new varieties could be much larger than 0.4 or 3 percent. An average farmer buying commercial seed of a new variety keeps his own seed for several years before buying new seed; also, many multiply seed for sale to neighbors. If farmers buy new seed every four years and do not sell seed to neighbors, then annual sales of seed for x percent of wheat area would deliver a new variety to 4x percent of area in four years. If farmers buying commercial seed also sell seed to one or two neighbors in the first year, then annual sales of seed for x percent of wheat area would deliver a new variety to 7x- I Ox percent of area in four years. With these estimates, new private varieties may be reaching 1.6-4.0 percent of wheat area, while new TIGEM varieties could reach 12-30 percent of planted area.

The above estimates agree with some other available information on wheat varieties. CIMMYT estimates that share of wheat area planted to improved varieties increased from 50 percent in 1984 to 71 percent in 1990. In that year, almost 30 percent of wheat area was planted to varieties from CIMMYT, Italy, US, and Eastern Europe, some of which were introduced after 1982.

While people shifting to new varieties are responding to economic incentives, aggregate data on wheat yields after reform do not give any indication of the effect of these incentives. Over the 1980s, Turkey's average wheat yield grew more slowly than in previous decades and did not even keep pace with increases in world average yield. Part of the explanation for slow growth in wheat
_31_

**_Invoicing method:_** Before cutover, new customers are asked to provide S.W.I.F.T. with the results of a traffic survey. On the basis of this survey and S.W.I.F.T.'s estimated costs related to the customer's country, S.W.I.F.T. informs the customers whether or not the traffic is expected to cover costs, and if not, gives an estimate of the amount which will have to recovered.

After cutover, the costs are reviewed in the light of, for example, exchange rate, contract prices, local taxes, and the amended annual amount is divided by four to give the direct quarterly costs. The costs fixed at this point remain in force for the rest of the budget year.

At the end of each quarter, the following calculation is performed by S.W.I.F.T.: A-B-C-D-E-F = R

|where|A|=|direct quarterly costs|
|---|---|---|---|
||B|=|joining fees (divided by 5)|
||C|=|quarterly revenue from traffic sent|
||D|=|quarterly revenue from traffic received|
||E|=|new joining fees, if any (divided by 4)|
||F|=|subsidy for traffic received (if any)|
|and|R|=|the amount (if any) to be recovered.|

If the direct costs are lower than the revenues (that is, R is negative), no reimbursement will be made to the user.

If the direct costs are higher than the revenues (that is, R is positive), an additional invoice will be sent to each user to recover his share, calculated according to the allocation formula defined by the User Group Chairperson.

At the beginning of each subsequent
household head attended school, a question posed similarly in both the census and EPM21-22. Thus, no significant transformation was necessary. The second candidate variable regarding education pertained to the highest level of education attained. However, there was a distinction in the response categories, with EPM21-22 offering more detailed options compared to the census. Consequently, the survey variables were adjusted to consolidate categories to match those in the census. Following this transformation process, the distribution of categories (the proportion in each category) was compared, and the variable was deemed acceptable due to its similar distribution in both the survey and census. This general process was replicated to identify the remaining variables, which were subsequently applied to the DHS dataset.

# Statistical methodology

The statistical procedure used is the empirical best predictor model developed by Molina and Rao (2010) based on the work of Battese, Harter, and Fuller (1988). Since this method requires that the error terms be distributed normally, household per capita consumption is log transformed to better approximate normality. Sampling weights are applied in the consumption prediction model following Guadarrama et al (2018), as described in Skarke and Kreutzmann (2020). As is standard, household size was used as population weights when aggregating poverty headcount estimates across households in the census data.

The model used to generate the estimates can be described as follows: 7 | P a g e
7 years.

Before providing descriptives for the outcomes of firms in each year, we note that often we observe firms in a given year in the RNE data but information on outcomes can be missing. For that reason, we first document the share of non-missing observations before providing statistics on average values of outcomes of interest.<sup>11</sup> Approximately half of the firms declare employment and wage information in each year - for those, the average number of employees is between 15 - 19 and the average wage bill is approximately 200,000 dinars. The average total revenue is in the range 1.5 - 1.8 million dinars in the

> 11When we consider aggregate outcomes at the sectoral level in the next section, we treat these missing observations as zeros. In our firm level regressions, we restrict our analysis to a balanced sample of firms for which we observe non-missing outcomes in every period.

10
GVC participants – which is proxied throughout this paper by firms that both import and export – varies across countries. Around one in four manufacturers in Morocco, Tunisia and Jordan can be classified as GVC participants, a share that is significantly higher than the averages of lower-middle-income and upper-middle income economies (Figure 2). In Egypt, this share is particularly low, at 5% and remains below the average of lower-middle income countries. Tunisia and Morocco have opted for an economic model oriented toward exports and industrialization supported by a pro-active policy of attracting FDI (Saliola and Zanfei, 2009). This has enabled transfer of technology and know-how. For example, Morocco has become an important supplier of global value chains in the clothing, automobile parts and aerospace industries (AfDB, OECD and UNDP, 2014; Amraoui et al., 2019).

Fig.1 Imports and exports of goods in 2019 Fig. 2 Trading profiles in 2019 (% of firms) (% of GDP) <!-- Start of picture text --> 100<br>EGY<br>JOR<br>80<br>LBN<br>MAR<br>60<br>PSE<br>TUN<br>40<br>MENA<br>20<br>LMI<br>UMI<br>0<br>0 10 20 30 40 50 60<br>Percentage of GDP<br>Nontrader Importer Exporter GVC participant<br>Import Export<br><!-- End of picture text --> _Source:_ the authors’ calculations based on World Bank World _Source:_ the authors’ calculations based on EBRD-EIB-WB Development Indicators. _Note:_ regional share calculated as Enterprise Survey. simple average of the countries.

While the level of trade participation of firms in the MENA region is comparable to other lower-middle income (LMI) and upper-middle-income (UMI) economies, we find that innovation rates are particularly low (Figure 3). This lack of innovation,
.

# **Methodology and empirical model**

Motivated by the model of innovation proposed by de Mel et al. (2009), we test the correlation of both firm/owner-specific and business environment indicators with various measures of firmlevel innovation. Our basic empirical specification is of the form: where [Innovate]i is a measure of innovation undertaken at firm _i_ , _A_ is a vector of industry controls, Z is a vector of business environment indicators which vary at the firm level, and _K_ is a vector of objective firm- or owner-specific characteristics which may influence innovation. εi is a randomly distributed disturbance term. We estimate this equation using a probit estimator, controlling for industry and regional (provincial) fixed effects.

# **What explains innovation within firms?**

Estimates from equations exploring the relationship between available business environment variables and a core set of innovation measures are reported in Tables 10 (from the 2011 WBES) and 11 (from the 2009 SLLSE).<sup>19</sup> Across both datasets, measures of innovation are most

> 18 The sample frame for this dataset came from two sources. 400 enterprises were selected from a census of firms carried out by AC Nielsen, Lanka. The Nielsen census covered only part of the geographic area of the survey, so the sample was supplemented by asking wage workers in the representative listing for the name and size of the firms where they are employed. An additional 210 firms with 5 to 250 workers were thereby added to the sample. 19 We also have estimated the business environment correlates to a wider range of innovation measures, available from the authors upon request.

22
...es the e¤ect of a social transfer shock, it is not something that, naturally, should be commended as desirable.

# 4 The share of HtM individuals and the share of social transfer reaching them in data

Given the nature of our contribution, it is essential to be able to measure the share of HtM individuals, �, as well as the share of social transfers reaching them, �. As discussed in the introduction, no paper disciplines the share of social transfers reaching HtM agents, �, by data. It is generally assumed that social transfers solely reach HtM agents (i.e., � = 1), which is referred to as perfect social transfers targeting. Regarding the share of HtM agents, �, quantitative papers estimating STMs for the United States (Giambattista and Pennings, 2017; Pennings, 2020) discipline the share of HtM agents to one-third based on Kaplan et al. (2014). There have been two main strategies to identify HtM agents in the United States and other developed countries.

A ...rst group of studies relies on survey data on household portfolios, with an emphasis on savings, net worth, and the liquidity of assets (e.g., Kaplan and Violante, 2014; Kaplan et al., 2014; Aguiar et al. 2020). In their seminal paper, Kaplan et al. (2014) ...nd that between 25 and 40 percent of United States households are HtM, with their preferred estimate being one-third. Of all HtM households, about one-third are poor HtM (who hold little or no liquid wealth and no illiquid wealth) and two-thirds are wealthy HtM (who also hold little or no liquid wealth but have signi...cant amounts of illiquid assets on their balance sheets, led by real estate properties).

> 23 It is worth noting that the empirical evidence obtained from properly adding interaction terms to empirical speci...cation (3) with respect to country-varying variables
.|...|0.45|0|0|0|
||**Yemen***|...|43.5|...|...|0.30|1|0|0|
|**Average**|**/Total**|**32.9**|**21.3**|**4.3**|**6.3**|**0.60**|**11**|**2**|**0**|
|_Source:_W|ealth of Nations(2011:|195),PRMPS M|TEF Databas|e,IMF(viii),W|DI|||||
|_Notes:_* C|ountries denotes net oi|l &gas exporte|rs(2008) (see T|able 1)||||||

20
|Log of dest. GDP/capita|||0.743***|0.706***|0.801***|
|---|---|---|---|---|---|
|Dest GDP growth|||0.812***|0.890***|0.927***|
|N|13986|13986|11706|11706|10706|

_* p<0.10 ** p<0.05 *** p<0.01. Source: Own calculations based on Lao PDR customs data. The time dependence of the hazard is introduced as log(time) in all models._ Finally, comparisons between the RE logit models (1)-(3) and the logit and Cox in (4) and (5), respectively, reveal small but statistically significant differences which go in the expected direction. One can observe an important effect from unobserved heterogeneity when comparing models (3) and (4): the positive (negative) coefficients are indeed underestimated (overestimated), just as expected. Generally speaking, logit and Cox models should return very similar effects – this is observed in the analysis.

Moreover, to ascertain the importance of the unobserved heterogeneity, Stata calculates a likelihood-ratio test on the hypothesis that the proportion of the total variance contributed by the panel-level variance component is zero. In all the RE models this hypothesis is strongly rejected, confirming the importance of taking frailty into account. This finding agrees with other research which has revealed significant unobserved heterogeneity in similar data (Brenton, Saborowski and Uexkull 2009, Rondinelli and Nicoletti 2009, Hess and Persson 2011b) but conflicts with the minority view that it is not a concern (Esteve-Perez, Sanchis-Llopis and Sanchis-Llopis 2010).

# 5.2 **Investigating Network Effects**

Networks create positive externalities by allowing members to share in the experiences of
a single measure is insufficient in some special circumstances. This case arises when policies that we record as separate measures are intertwined and, hence, their restrictiveness can only be assessed by considering the entire bundle of such measures. For example, foreign suppliers can in principle enter through mergers and acquisitions or via greenfield investment, and it does not make much sense to score restrictions for each route in isolation, for they represent alternative options. Rather, the difficulty of entering a market is most plausibly assessed by looking at the admissible combinations of entry options, rather than individual routes. We call such bundles ‘synthetic’ measures as they consist of two or more individual measures that are separate entries in the database, but whose impact on restrictiveness is scored jointly for the purposes of constructing the STRI. Only when the assessment of restrictiveness, i.e. scoring, is complete at the measure level including synthetic measures, does the process of aggregation start.

# **2.3  Aggregation**

In the first version of the World Bank database (2008-11), the associated STRI relied on an assessment of groups of measures, which were encapsulated in a ‘policy summary’. The aggregation methodology described here is similar in spirit in the sense that the overall restrictiveness of a group of policy measures, e.g. at the sector-mode level for a given economy, results both from the types of measures applied as well as from the number of measures applied. The severity of measures is captured directly in the scores assigned to individual measures (Table 1). In a second step, the set of individual measure scores needs to be aggregated for a given sector-mode or even an entire economy.

Aggregation of this kind is a challenge in a wide range of fields, such as in consumer or production theory. One of the most widely used mappings in economics for both utility and production functions is the ‘Constant Elasticity of Substitution’ (CES) functional form, which generically combines an array of elements according to � .