rafmacalaba/datause-encoder-data huggingface dataset:
or erroneous information (with costs as much as P30,000). Approximately 11.6% of respondents initiated the passport process but were not able to complete it.

Because respondents may have obtained passports without directly interacting with our staff, these administrative records are not sufficient to test the impact of receiving passport information. Table A4 reports the impact our assigned treatments on whether respondents reported in the endline survey that they currently had a valid passport. All treatments that include passport assistance [T5] have positive effects on passport ownership that are statistically significant at the 5% level or less. Effect sizes for these treatments range from 7.3 to 12.7 percentage points, which are large compared with the control group rate of 4.5%. In addition, the most comprehensive treatment that does not include passport assistance [T5], “All information + Website” ([T1]+[T2]+[T3]+[T4]), also increases passport ownership by 5.1 percentage points (statistically significant at the 5% level).

# **A4. Reported reasons for not migrating**

Table A5 presents data from our endline survey on reported reasons for not migrating, for those individuals who did receive an overseas job offer. We discuss this table in the main text.

# **A5. Impacts on migration, including endline data from log surveys**

All estimation results presented in the main text and here so far use data from respondent or proxy (other household member) surveys, which account for 91% of endline surveys. As noted above in column 1 of Table A2, there is no systematic pattern of differential inclusion in the respondent or proxy surveys related to treatment status.

36
of migrants have left Libya, and there has been a large shift in the composition of migrants according to nationality. The percentage of migrants from the Arab Republic of Egypt, Chad, Niger, Nigeria, and Sudan has significantly increased since 2015, and the percentage from Palestine, Iraq, and Somalia has decreased significantly.

International and domestic migration flows, internal displacement, political instability, and conflict interact in complex ways in Libya. Libya has the largest number of international migrants in the subregion, at more than 770,000 (UN DESA 2016) (table 1).

Migrants headed to Europe often settle in Libya for some time before starting their second migration journey (World Bank 2018). Others—including refugees—remain stranded in Libya or other countries in the Maghreb (IOM 2015). Data from the Euro Asylum Seekers’ Survey 2017 indicate that a substantial share of asylum seekers who migrate through Libya previously worked for a prolonged period in the country. For those coming from Sub-Saharan Africa, it is likely that the conflict transformed Libya from a destination into a transit country.

# **4. Data**

Data used in this analysis come from the Flow Monitoring component of IOM’s Displacement Tracking Matrix (DTM). The DTM tracks movements of migrants, IDPs, and refugee individuals and groups through the data collected at the Flow Monitoring Points (FMPs) (IOM 2017a). The FFMP tracking system consists of two data collection layers: 1) the Baseline Assessment Surveys identify the frequency and volume of migrants in and crossing through a specific point (FMPs), and 2) the Profile Surveys gather information about migrant profiles, including age, gender, area of origin, level of education, key transit points on their route, cost of the journey, motives, and intentions.

In our analysis, we use the data from the Baseline Assessment Surveys for the period January 2016 to April 2018.<sup>5</sup> Data are collected daily and quantified and reported monthly. The Baseline Assessment Survey collects information on number of migrants residing in, arriving at, and leaving from a specific FMP.<sup>6</sup> For those already present at the FMP, it records the nationality, planned destination, and length of stay. According to the DTM methodology (IOM 2016a), the primary method of data collection is

> laborers fled Libya (World Bank, 2015). Foreign workers are employed in both high skilled and qualified jobs, for which domestic workers are in short supply, and low skilled and manual jobs, that Libyans are culturally reluctant to take (Expertise France, 2016).

> 5 DTM data are considered in general to be accurate, although it is difficult to assess their absolute quality, as the DTM data are the only existing comprehensive data set on migration flows in Libya.

> 6 As reported in the official DTM documentation (IOM 2016a), before the first round of DTM data collection, the team sought to identify transit. The FMPs identified in this period (November-December 2015) have been regularly updated, as new transit points have emerged or disappeared.

7
agricultural sector in 1980 in low-income developing countries, and almost 45 percent in middle income countries.<sup>8</sup> In a formal sense, there are some similarities between household production models and the basic model of SSS, in that they all proceed by introducing a neoclassical type of production function containing household time, bought-in market goods and physical capital goods or land,<sup>9</sup> and they have time spent on household or on-farm production in addition to market labor supply and leisure in the time constraint. The key difference under the household production approach, however, is the insistence that the relevant market goods are not perfect substitutes for domestically produced goods, and so the endogeneity and household specificity of the implicit prices of the latter remain.

In the developing economy literature the household production approach has been applied to analyze household decisions on specific goods such as nutrition and health (see for example Pitt and Rosenzweig 1986), on human capital formation (see Section 3 of Strauss and Thomas 1995), and on water collection (Ilahi and Grimard 2000). However, the approach has not been applied to modeling household production activities more generally, as is evident in the surveys by Behrman (1997) and Strauss and Thomas (1995) and the overview of studies on the intra-household allocation of resources by Haddad, Haddinott and Alderman (1997c).<sup>10</sup> This mirrors the failure in the developed economy literature to integrate the theory of household production into the mainstream theoretical and empirical work on modeling household behavior. The vast body of work in the developed economy literature on the microeconometric modeling of family labor supply,<sup>11</sup> consumption demands, life cycle consumption and saving decisions,<sup>12</sup> and

> 8  Low-income economies are defined as those with a 1981 per capita income of less the US$410 and middle income economies as those with a 1981 per capita income greater that US$410 (World Bank 1983).

> 9 Though where nothing essential is lost thereby only time may appear explicitly.

> 10 There are several studies that estimate reduced form time allocation models but these do not represent applications of the household production approach described here because they treat domestic work as a perfect substitute for leisure, by specifying the net wage as the price of both.  Examples include the Skoufias (1993) model and the treatment of women’s work at home (in contrast to their time spent on water collection) in Ilahi and Grimard (2000).

> 11 For a relatively recent survey, see Blundell and Macurdy (1999).

> 12 For a survey, see Browning and Lusardi (1996).

7
- 29 ,

<!-- Start of picture text --> FIGURE 6:. GOVERNMENT REVENUES AND EXPENDITURES<br>(In billlons of Kuwalti dinars)<br>6<br>4<br>3-<br>2-<br>0-<br>1974 1975 1976 1977 1975 1979 1950 1981 1952 1983 1984 1983 1986<br><!-- End of picture text --> <!-- Start of picture text --> El Budget Revenues + Investment Revenues v Total Revenues<br>. Expenditure<br><!-- End of picture text --> Sources: Central Bank of Kuwait.

<!-- Start of picture text --> Government Finance Statistics.Yearbook.<br><!-- End of picture text -->
some extent the low power of the Hausman test (Holly 1982). Failure to take this into account may lead to underestimation of the number of countries for which secular and cyclical income elasticities differ.<sup>7</sup> These results also illustrate how the traditional model may overestimate the secular response and underestimate the cyclical one, which has important implications for forecasting short-run import flows in developing countries. Thus, for example, estimates of income elasticity using a traditional import model developed by Pritchett (1987) and BahmaniOskooee (1986), average 1.4 and 1.2, respectively. The results presented here, however, suggest a cyclical elasticity averaging 2.6. It is not surprising therefore that imports are generally underestimated in shortterm projections of cyclical upturns in developing countries.

The results presented in table 1 indicate that relative prices generally play an important role in determining import demand in the Latin American and Asian-Pacific countries in our sample, but appear to be of little consequence in the African and (perhaps surprisingly) Mediterranean countries of our sample. It is interesting to note that for all countries for which both cyclical and secular income elasticities are significantly different from zero, relative price coefficients are also significantly

- 7/ Results using spectral analysis as in Haynes and Stone (1982) and Marquez (1988) illustrate that the ambiguity of the interpretation of both cyclical and secular income elasticities cannot be fully overcome when using income trend and deviation from trend as proxies for secular and cyclical income. In addition, the well-known Monte Carlo experiments showed long ago that spectral analysis requires long time seiles data in order to be reliable. This sample size precludes the use of spectral analysis in the case of most developing countries because although quarterly and/or monthly trade flows are usually available, the unavailability of prices is generally a binding contraint.
goods supplied by foreign firms to Chinese companies when the equipment was needed to fulfill processing contracts.<sup>35</sup> On the eve of accession (2000), export processing trade accounted for 41 percent of imports and 55 percent of exports in China. The remaining 45 percent of exports, called ordinary exports, were produced mainly with domestic inputs and only a small portion of duty–paid imported materials.<sup>36</sup> According to the Customs authorities, in 2000, 60 percent of imports entered China duty-free, out of which 41 percentage points were imports used for export processing, 13 percentage points were capital goods, and 6 percentage points were goods that fell in special categories.<sup>37</sup>

# _4.1.1 Benefits associated with duty drawbacks in China_

Duty drawbacks in China reduced the anti-export bias of the old planned economic system and improved the competitiveness and efficiency of the economy by allowing export producers to import at international prices. In the absence of duty exemption schemes, exporters in China would have faced negative effective rates of protection (Figure 1 in Ianchovichina 2004). Duty drawbacks reduce the negative effective rates of protection (ERPs) for export competing firms, allowing many export producers to operate at world prices and compete more effectively in world markets. Duty drawbacks reduce the dispersion in ERPs (Table 2), and therefore increase economic efficiency (Bach and Martin, 2001).

> 35 China’s State Council decided to eliminate these exemptions as of April 1, 1996. However, this deadline was not strictly enforced and eventually the exemption program was formally re-established (Lardy, 2002).

> 36 According to version 4 GTAP database (McDougall _et al_ ., 1998), in 1995 China’s firms exported on average 10 percent of their output and 14 percent of Chinese imports were for final consumption. According to China’s Customs 40 percent of imports were ordinary imports that were not duty exempt. Thus, approximately 26 percent were ordinary imports used as intermediates, implying that approximately less than 3 percent of imports were used for the production of ordinary exports.

> 37 These categories included materials used by research institutions and products used by the disabled when these products were unavailable in China.

27
# **Conclusion**

The findings of this study suggest that the provision of high quality energy services to rural areas has lagged behind urban areas. It is both financially and physically more difficult to service remote and poor populations compared to those living in urban areas. However, one would expect energy poverty would be commensurate with income poverty. This pattern is confirmed for urban India but it is not the case for rural areas. This means that despite national energy programs to help bring better energy services to people in rural areas, a significant gap in services still persists. There are many programs to deal with rural electrification under the Rajiv Gandhi Grameen Vidyutikaran Yojana rural electrification scheme. Although these programs made some progress in terms of access to the grid electricity system, there are still significant challenges in improving the reliability of power supply in the country. The challenges are basically two-fold: How to improve the access of rural households to electricity beyond the current rate of 56 percent and how to ensure reliable and adequate supply of electricity (Sargsyan et al, 2010).

Besides providing electricity, improving biomass use and its efficiency is essential for reducing energy poverty. According to our findings, some 90 percent of rural households in India still use fuel wood that explains some 56 percent of household total expenditure on energy. Yet only less than 4 percent of rural households (according to the survey used in this paper) had improved stoves for biomass use. Improving efficiency of fuel wood use for cooking is extremely important. An initiative has recently been launched to reach the majority of biomass energy users via a program that aims to both reduce CO2 emissions and improve rural health (Indian Institute of Technology Delhi and The Energy and Resources Institute, New Delhi. 2010). It is expected that this initiative would improve the efficiency of biomass use in India.

On the other hand, improving access to modern fuels such as LPG for cooking and other purposes would help reduce energy poverty. However, the sale of LPG in India, for example, is 26
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10 <!-- Start of picture text --> Grid-Connected Wind Turbine Economics<br>Avg Cost: Wind vs. Fuel Oil Generation<br>cents/kWh<br>8-- 6m/sec -<br>8<br>NON-ECONOMIC<br>7 -<br>- 7m/seC --<br>6 / ~~~~ECONOMIC<br>4<br>5 15 25<br>Fuel Oil Price ($/bbl)<br>Source: See Annex<br><!-- End of picture text --> Figure 3 Wind vs. Oil-Based Generation Costs 31. With this _caveat_ in mind, a rough "order of magnitude" analysis can be conducted on electricity generation costs of wind vs. thermal. The results of such an analysis are shown in Figure 3 in which representative generation costs over a range of oil prices for a hypothetical grid, supplied exclusively by large base load fuel oil steam plants and smaller diesel oil gas turbine peaking plants, are compared to estimated wind electricity generation costs."'/ Though actual costs can vary considerably, these generic cost estimates show that lower oil prices can significantly change generation costs. A 50% drop in representative 1985 import prices of diesel oil (from 30e/l to _5l/l)_ and fuel oil (from US$ 30/bbl to US$ 15/bbl) could reduce electricity generation costs some 30%, from 8.3¢ to 5.8e/kWh. Of course, if a significant share of total demand is met by coal, gas, nuclear, or hydro capacity, the actual change in system generation costs due to lower oil prices will be less than that depicted in Figure 3.

32. The wind electricity costs presented in Figure 3 are rough estimates, based on the actual costs of 100kW wind turbines in California wind farms, for a good wind regime (annual average windspeed of 6m/sec) and two excellent regimes (7 and 8m/sec). It is evident that electricity generated with an annual average windspeed of 6m/sec, not unlike the resource in the mountain passes of California, would cost as much as electricity generated by an optimally run oilDetailed assumptions are tabulated in the Annex Wind electricity costs are based on actual costs from California

**11/** wind farms.
PPP levels from the World Development Indicators database, so that they are comparable across countries in each year.

Descriptive statistics, including sample means, medians, and indicators of the distributions for the largest possible samples for each variable are presented in Annex 1. These samples are used in the scatter plots discussed in the following section. Annex 2 presents the descriptive statistics for the common sample used in the econometric estimations.

Since inference from the econometric results requires the use of a common sample for the three models, it is worth discussing how the restricted common sample differs from the larger samples for each variable of interest. Table 2 compares the means and medians across the two samples. The restricted sample covers countries with typically lower levels of development, as both the mean and the median of the full sample are significantly higher than that of the restricted sample. An implication of this is that the incidence of bank accounts and digital payments is lower in the restricted than in the full samples. Interestingly, this is the not the case for the case of mobile money; the means and medians across the two samples are virtually identical. These descriptive statistics thus anticipate one of the main findings of the empirical results discussed in the following section.

**Table 2. Sample means and medians**

|**Variable**|**Full sample**<br>**mean**|**Full sample**<br>**median**|**Restricted**<br>**sample mean**|**Restricted**<br>**sample median**|
|---|---|---|---|---|
|Log GDP per capita|9.35|9.49|8.74|8.73|
|Bank accounts|0.557|0.523|0.389|0.340|
|Digital payments|0.423|0.336|0.267|0.231|
|Mobile money|0.106|0.045|0.105|0.045|

<u>Source: Authors’ calculations based on data from the Findex database from 2014 and 2017. See Annexes 1 and 2 for</u> details. Notes: The “full sample” contains different numbers of observations across variables. The restricted sample contains 149 observations. GDP per capita is in logs; other variables are the number of adults reporting use of each form of payment divided by adult population in each country (age 15+).

# **5. Results**

The first set of results are shown in scatter plots depicting the relationship between each of the three payments variables and the logarithm of GDP per capita. In turn, for each of the indicators, we discuss the econometric results, including the criteria for selecting the functional forms.

# **5.1. Visual evidence**

7