rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure A4:  Number of Refugees hosted in Tanzania from 1990 to 2005**

<!-- Start of picture text --> Refugees Hosted in Tanzania from 1990 to 2005<br>1000000<br>883250<br>900000<br>829671<br>800000<br>680862 689373<br>700000 646900 649770<br>622203<br>602088<br>600000 564520 570367543881 548824<br>498732<br>500000<br>400000 292148<br>288103<br>300000 265184<br>200000<br>100000<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text -->

**<mark>Source:</mark>** <mark>UNHCR Population Statistics.</mark>

38
production processes among young entrepreneurs in Bosnia and Herzegovina; and Drexler, Fischer, and Schoar (2010) find that a rule-of-thumb training for micro-entrepreneurs in the Dominican Republic increases the likelihood that they keep accounting records and calculate monthly revenues.

This compendium of studies suggests that while financial literacy programs may be effective to some extent, measuring the impact of such programs is quite challenging. By and large, the findings mentioned above are based on behavioral outcomes such as opening a savings account, keeping financial records, investing in a business, or computing business earnings. Yet, if financial literacy is a precursor to financial behavior, then determining improvements in the former is necessary to understand the channels leading to changes in the latter. Furthermore, if improvements in financial literacy are required to change financial behavior, then it would be difficult to detect behavioral outcomes, since these would be situated at the end of the causal chain. Overall, the literature has overlooked a critical component in evaluating financial education programs: the intermediary impact on financial literacy levels following the intervention.

Many financial literacy surveys in high-income countries exhibit two approaches to measuring financial literacy (OECD, 2005). The first measures respondents’ understanding of financial terms and their ability to apply financial concepts to particular situations, and the second asks respondents for a self-assessment of their financial understanding and knowledge, as well as their perceptions and attitudes towards financial instruments and decision. The survey questions that have by now become the standard measure of financial literacy, however, are those developed by Lusardi and Mitchell (2009) on numeracy, inflation, and diversification.<sup>2</sup> These questions have been included in various surveys to evaluate financial literacy in many corners of the world, including the US, the Netherlands, Italy, Germany, and New Zealand. They have likewise been

> 2 The financial literacy questions developed by Lusardi and Mitchell (2009) are the following: (1) Suppose you had $100 in a savings account and the interest rate was 2 percent per year. After 5 years, how much do you think you would have in the account if you left the money to grow: more than $102, exactly $102, or less than $102?, (2) Imagine that the interest rate on your savings account was 1 percent per year and inflation was 2 percent per year. After 1 year, would you be able to buy more than, exactly the same as, or less than today with the money in this account?, (3) Do you think that the following statement is true or false? “Buying a single company stock usually provides a safer return than a stock mutual fund.”

7
**Appendix Table 1: Regression results from estimation of the propensity score.**

||Logit (raw coefficients)<br>(1)|Probit (marginal effects)<br>(2)|
|---|---|---|
|Age of interviewee|0.090***|0.049***|
||(0.026)|(0.015)|
|Age respondent squared|-0.001***|-0.001***|
||(0.000)|(0.000)|
|Number of 18-59 year olds in HH|-0.401*<br>|-0.239**<br>|
||(0.161)|(0.093)|
|Number of 18-59yo adults squared|0.041*<br>|0.025*<br>|
||(0.018)|(0.010)|
|Number of 60 years and older in HH|-0.159|-0.075|
||(0.150)|(0.091)|
|Number of 60+yo adults squared|0.111<br>|0.055<br>|
||(0.085)|(0.052)|
|Dependent ratio>=1.5|1.589***|0.956***|
||(0.083)|(0.045)|
|4+ children household|<br>1.275***|<br>0.755***|
||(0.107)|(0.059)|
|Elderly household head|1.868**|1.004**|
||(0.657)|(0.332)|
|Single parent household|0.528**|0.285*|
||(0.199)|(0.112)|
|Number of 0-5 aged children in HH|<br>0.375***|<br>0.198***|
||(0.059)|(0.033)|
|Number of 6-17 age old children in HH|0.177***|0.091***|
||(0.041)|(0.021)|
|Number of 0-5 year old children squared|-0.046***<br>|-0.024**<br>|
||(0.013)|(0.008)|
|Number of school aged 6-17yo children squared|<br>0.023|<br>0.007|
||(0.029)|(0.015)|
|Number of school-aged children attending school|0.175|0.118*|
||(0.092)|(0.048)|
|Female household head|<br>0.936***<br>|<br>0.529***<br>|
||(0.156)|(0.086)|
|Elderly respondent (65+ year old)|1.397***|0.783***|
||(0.392)|(0.224)|
|Per capita HH expenditure aggregate per month|-0.001**|-0.000***|
||(0.000)|(0.000)|
|Food Consumption Score|0.003<br>|0.001<br>|
||(0.001)|(0.001)|
|Reduced Coping Strategies Index|<br>0.005*|<br>0.003**|
||(0.002)|(0.001)|
|Livelihoods Coping Strategy Index|0.005|0.002|
||(0.016)|(0.010)|
|At least 1 HH member can speak Turkish|-0.167*|-0.099*|
||(0.082)|(0.047)|
|At least 1 HH member can read Turkish|-0.058|-0.036|
||(0.086)|(0.048)|
|Does anyone in the HH read or write in Arabic|<br>-0.104|<br>-0.066|
||(0.136)|(0.078)|
|Main income from skilled labor|-0.357|-0.215|
||(0.237)|(0.133)|
|Main income from unskilled labor|<br>-0.315**|<br>-0.186**|
||(0.103)|(0.058)|
|Debt as share of total expenditure (among those with debt)|<br>-0.005|<br>-0.003|
||(0.013)|(0.008)|
|Percent with some amount of debt|<br>0.055|<br>0.032|
||(0.082)|(0.046)|
|Strata dummies|Yes|Yes|
|Number of observations|8690|8690|

42
|**Table**|**1. SDG indi**|**cators**|**related to gender (N=181 countries)**||**Sex**||
|---|---|---|---|---|---|---|
|**Goal**|**Indicator**|**Tier**|**Description**|**Sub-Indicator (if any)**|**Disaggregation**<br>**Available**|**Any Data**<br>**Available**|
||5.3.2|1|Proportion of girls and women aged 15-49 years<br>who have undergone female genital<br>mutilation/cutting, by age||NA|11.0%|
||5.4.1|2|Proportion of time spent on unpaid domestic and<br>care work, by sex, age and location||17.1%|17.7%|
||5.5.1|1|Proportion of seats held by women in<br>(a) national parliaments and (b) local<br>governments|Any data: Proportion of seats held by women in<br>national parliaments (% of total number of<br>seats); AND/OR Proportion of elected seats<br>held by women in deliberative bodies of local<br>government (%)|NA|98.3%|
||5.5.2|1|Proportion of women in managerial positions|Proportion of women in senior and middle<br>management positions (%)|NA|50.3%|
||5.6.1|2|Proportion of women aged 15-49 years who<br>make their own informed decisions regarding<br>sexual relations, contraceptive use and<br>reproductive health care||NA|21.0%|
||5.6.2|2|Number of countries with laws and regulations<br>that guarantee full and equal access to women<br>and men aged 15 years and older to sexual and<br>reproductive health care, information and<br>education||NA|40.9%|
||5.a.1|2|(a) Proportion of total agricultural population<br>with ownership or secure rights over agricultural<br>land, by sex; and (b) share of women among<br>owners or rights-bearers of agricultural land, by<br>type of tenure|Any data: Proportion of people with ownership<br>or secure rights over agricultural land (out of<br>total agricultural population), by sex (%);<br>AND/OR Share of women among owners or<br>rights-bearers of agricultural land, by type of<br>tenure (%)|14.9%|15.5%|

17
<u>Test 3: A concern with Test 2 is that the vignette responses are strictly endogenous, given</u> that they come from the same respondent at the same time and so could be jointly influenced by some latent characteristic. One response to this concern is to instead use the vignette responses to re-calibrate the subjective welfare responses. Our rescaling method follows that developed by King et al. (2004) and King and Wand (2007) in which only the relative position of the selfreported score in relation to the vignette rankings matters to the analysis. For example, all respondents who ranked themselves below vignette 1 would have a score of 1 in the re-scaled rankings, regardless of the actual values given to either the self-reported score or the ranking of the first vignette. Similarly, all those respondents who placed themselves at the same level as the first vignette would have a re-scaled ranking of 2, those between vignettes 1 and 2 would have a 3, and so on. Rescaling therefore gives nine possible values to the dependent variable.

Table 11 repeats the analysis presented in Tables 9 and 10 with a rescaled post-vignette ranking as the dependent variable. (For completeness we also estimate regressions for the rescaled responses with controls for the vignettes.) The rescaled regressions show similar relationships with the household characteristics, including significant correlations with PCE, higher education, professional jobs, larger households and the geographic variables. The vignettes, in general, lose their significance with the notable exception of vignette 3.

Tables 9-11 assess the impact of the vignettes on the economic gradient in subjective welfare under the assumption that this impact is constant across the income distribution. To allow for more flexibility in the specification we estimate non-parametric regression functions with linear controls (“partial linear regressions”).<sup>13</sup> By comparing the non-parametric results with and without vignettes (as linear controls), we can test for FORB across the income distribution. We define two alternative binary outcome variables: _SW_ being poor (steps 1-2) and _SW_ being rich (steps 4-6). Since we have both pre- and post-vignette _SW_ , we have 4 outcome variables (pre/post, poor/rich). Further, we assess the FORB from estimates with and without the nonincome traits. Thus, we have a total of eight pairs of partial linear regressions (Figures 1-4).

Figure 1 shows the results for reporting oneself as poor and as rich, with and without vignettes as controls. The “poor” curve is downward sloping: households are less likely to place themselves on the lowest two steps as _PCE_ increases. The opposite is true for those who place themselves on the upper rungs (“rich”). The economic gradient in subjective wellbeing is

> 13 We used the PLREG program for STATA written by Lokshin (2006).

13
3 Figure 2: Currency appreciation in developing countries by income between 2003 and 2011 <!-- Start of picture text --> 40 38 Low income<br>Lower-middle income<br>35<br>31 Upper-middle income<br>30<br>24 25<br>25 23 22<br>21 21<br>19 20<br>20 17 17 17<br>15<br>10<br>3<br>5 2<br>0<br>Below -20% -20 to 0% 0 to 20%* 20 to 40% Above 40%<br>Sources : IMF 2012 and author’s calculations.<br>Note : Calculations based on 29 low-income, 48 lower-middle-income, and 41 upper-middle-income countries.<br>* “0 to 20%” includes seven lower-middle- and five upper-middle-income countries in which the exchange rates did<br>not change.<br>category<br>% of countries in each income<br><!-- End of picture text --> This paper is part of a larger study assessing the implications of high oil prices and oil price volatility on fuel use, the downstream petroleum sector, and household fuel consumption in developing countries, and follows two previous publications (Bacon and Kojima 2008; Kojima 2009a). The paper first examines one measure of vulnerability to oil price increases—net oil imports relative to income—in periods of high and low oil prices, and breaks down changes in vulnerability over time into several factors that contribute to determining the magnitude of vulnerability. This allows cross-country benchmarking and helps to show how changes in such factors as the oil intensity of energy and the energy intensity of the economy can make countries more vulnerable to oil price shocks or less so. Although the effects of high oil prices on net oil exporters are also analyzed, the main focus of this paper is on vulnerability arising from oil consumption, because the latter affects every country.

The paper also investigates the changes in the retail prices of four fuels—gasoline, diesel, kerosene consumed by households, and liquefied petroleum gas (LPG) for household use— between January 2009, when crude oil prices were below US$45 a barrel, and January 2012, when they rose above US$100. By comparing the price changes at the retail level to those on the world market, the degree of passing through of fuel price increases on the world market can be estimated. Passing through less than fully might imply any combination of fuel tax reduction, greater subsidies, lower costs of supply, and lower profit margins for oil companies. Conversely, passing through more than fully might reflect higher fuel taxes, subsidy reduction, higher costs in the supply chain, or higher profit margins. Examination of the degree of pass-through helps understand how different markets have been reacting to rising oil prices in recent years.

The German development agency, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), surveys retail prices of super gasoline and diesel every other year in November, the last survey having been conducted in November 2010 (GIZ 2012). The
technology is employed in the firm, and therefore, they do not reveal whether a technology that is present is widely utilized or just marginally.

Specifically, the FAT survey comprises five sections:

- Module A– Collects general information about the characteristics of the establishment; such as sector, multi-establishment and ownership.

- Module B – Covers the technologies used in seven general business functions.

- Module C – Covers the use of technologies for functions that are specific to each of 11 agriculture, manufacturing, and services sectors.

- Module D – Includes questions about the drivers and barriers for technology adoption.

- Module E – Collects information on employment, financial statements and performance, which allow us to compute labor productivity and other measures at the establishment level.

# **A.1.1 The Grid**

We construct a technology grid that identifies the main business functions and the key technologies used to carry out the tasks of each business function. To design modules B and C, the survey draws upon the knowledge of experts in production and technology in various fields and sectors. These experts provided their knowledge on: i) what are the key general and sector-specific business functions, ii) what are the different technologies used to conduct the main tasks in each function, and iii) how are the different technologies related both in terms of their sophistication and the degree of substitutability between them.

First, we started with desk research revising the specialized literature identifying business functions and technologies across the value chain.<sup>27</sup> Second, for each sector, as well as for the general business functions, we hold meetings with private sector specialists at the World Bank Group to validate the initial findings and start to define the key business functions and technologies. Third, we hold meetings with Lead and Senior Economists across the World Bank Group, including the International Finance Corporation (IFC), from different fields of specialization and wide experience with sectoral projects in several countries (e.g. agriculture, manufacturing, retail, transport, health, etc.). Fourth, we hold meetings and validation exercises with external senior consultants, with wide experience on the field (e.g.

> 27This process involved the revision of peer-review journals and reports from international organizations and industry associations.

61
Table 9: **Reservation wages of workers and wages paid by firms**

||Sa<br>N|mple of workers<br>Reservation Wage|Wage paid by hiring frm<br>Experience<br>No Experience|s<br>All|
|---|---|---|---|---|
|High school only|1108|1400|1588<br>855|973|
|Vocational training|1563|1500|1900<br>1017.5|1500|
|Diploma|242|1900|3250<br>1168|2900|
|Degree|665|2500|5685<br>3500|4500|

_Notes: This table describes self-reported reservation wages (for job-seekers) and offered wages (from firms), disaggregating by educational qualification._ 35
Table 9. NTMs in Indonesia Source: Gourdon and Nicita (2012).

Japan’s NTMs are mainly concentrated in two sectors, food and chemical, but the type of NTM employed differs between them (Table 10). The food sector registers 90.6% of its product lines with an SPS measure, while the chemical sector has 81.9% of its import lines with a TBT. Protection in the textile & footwear sector is minor, 3.1% for TBTs and 2.3% for SPS measures. TBTs are mainly used in the rubber, plastics, wood & paper sector, as 39.7% of the products belonging to this category are affected by them. Barriers on the machine & equipment sector appear mostly in the form of TBTs, affecting 14.2% of the product lines corresponding to this category.

Table 10. NTMs in Japan Source: Gourdon and Nicita (2012).

To analyze the impact of NTMs on Eurasia’s exports, we construct two coverage ratio indicators. The first index for country _j_ , destination _d,_ NTM-category _c_ , and product _i_ is calculated as follows: c,d where Di i s a dummy variable reflecting the presence of one or more NTMs in category _c_ affecting product _i_ in destination _d_ , and Xj,id is the value of exports from country _j_ of product _i_ to destination _d_ . The second indicator is calculated as follows: where Xj,iW is the value of exports from country _j_ of product _i_ to the world w . Table 11 presents the results. Column I presents the value of the first index and column II presents the value of the second index.

The analysis for Indonesia shows that technical barriers to trade are one of the most important forms of NTMs that affect Eurasia’s exports. Countries such as Belarus, Russia, Tajikistan, Ukraine, and Uzbekistan have more than 50% of their exports to this destination affected by this type of NTM. Quantitative restrictions such as licenses, quotas, prohibitions, and other quantity control measures are also relevant, affecting exports from Armenia (69%), Georgia (47%), Kazakhstan (49%), Moldova (39%), and Ukraine (37%). SPS measures are relevant in Turkmenistan, Uzbekistan, and Kyrgyzstan, with 86%, 27%, and 18% of their exports affected by this policy barrier, respectively. Since Indonesia is not a main destination of 12
# **Appendix Table I. 12: Unorganized Sector Log (Real Output) and Spillovers from Small, Medium-sized and Large Organized Plants**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Small Organi|zed Plants|Medium-size|d Organized|Large Orga|nized Plants|
|Hil O Sill||<br>_Oth_|<br>_er-sector Emp_|<br>_loyment Spill_|<br>_overs_||
|orzonta utput povers|||||||
|from Organized Sector|0.1568***|0.0424*|0.0727***|0.0097|0.0987***|0.0372+|
||(0.0259)|(0.0236)|(0.0270)|(0.0249)|(0.0259)|(0.0237)|
|Forward Outputt Spillovers|||||||
|from Organized Sector|3.3850***|8.4633***|2.1312***|-2.8665*|1.8507***|-14.5856***|
||(0.7063)|(2.0031)|(0.6363)|(1.7243)|(0.6872)|(2.6380)|
|Backward Output Spillovers|||||||
|from Organized Sector|1.8990***|3.2306**|0.0714|3.2105**|1.8622***|3.7642***|
||(0.4786)|(1.6102)|(0.4036)|(1.4476)|(0.4192)|(1.0150)|
|||_Oth_|_er-sector Pro_|_ductivity Spill_|_overs_||
|Average TFP of Horizontal-|||||||
|linked Organized Plants|0.1471|0.4016++|-0.1178|0.2467|-0.6872|-0.1820|
||(0.2949)|(0.2848)|(0.5463)|(0.4880)|(0.5404)|(0.5008)|
|Average TFP of Forward-|||||||
|linked Organized Plants|0.3647++|4.5774***|-0.0404|0.0196|-0.1140|-0.7285|
||(0.2671)|(0.9363)|(0.3574)|(0.7767)|(0.3610)|(0.8826)|
|Average TFP of Backward-|||||||
|linked Organized Plants|-0.2302|2.4251**|2.9122***|0.4555|1.6150***|6.4698***|
||(0.2111)|(1.0978)|(0.5747)|(1.8816)|(0.4211)|(1.1590)|
|||_Ow_|_n-sector Emp_|_loyment Spill_|_overs_||
|Forward Output Spillovers|||||||
|from Unorganized Sector|0.5705**|-4.1584***|0.8264***|-0.2879|0.6361**|4.2946***|
||(0.2904)|(0.8735)|(0.2774)|(0.6469)|(0.3235)|(1.3761)|
|Backward Output Spillovers|||||||
|from Unorganized Sector|0.6202***|-1.6978*|0.8557***|-1.4178+|0.6451***|-1.9320***|
||(0.2218)|(0.9338)|(0.2374)|(0.9057)|(0.2238)|(0.5792)|
|Constant|-131.1744***|-|-80.3811***|40.4008|-107.6278***|203.2785***|
||(16.0006)|(27.2294)|(14.9490)|(39.3116)|(18.0683)|(43.4291)|
|Observations|5888|5888|5888|5888|5888|5888|
|R-squared|0.0802|0.2834|0.0552|0.2814|0.0644|0.2820|

_Note: The independent variables are constructed using the total employment by large organized plants in a stateindustry cell. Small plants are defined as those with less than 50 employees, medium-sized plants have 51-150 employees and large plants are defined as those with more than 151 employees. All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Columns 2, 4 and 6 contains state-year and industry-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 65
additional challenge is that even where firm census data are available, there is a dearth of information on the quality of electricity supply, thus constraining empirical assessment of the role of electricity shortages on (net) entry of firms.

In this section, I use firm census data from Ethiopia to show the relationship between exposure to outages and the density of (manufacturing) firms across locations in the country and also show how outages influence the operation (shutdown) of (manufacturing) firms in the country. Specifically, I estimate the following fixed effect specification where _Yit_ is a placeholder for firm outcomes in location (district or city) _i_ at time _i_ , _UnreliableSupplyit_ is a measure of unreliable electricity provision at location _i_ in time _i_ , **X** _′_ **it**<sup>is a vector of con-</sup> trols, while _θi_ and _δt_ represent location and time fixed effects respectively. The analysis is conducted at district and firm levels. In the district-level analysis, two main outcomes are explored: the number of firms in a district (Woreda) in a given year, and firm density (i.e. the number of firms per 1000 people). These outcomes represent the (net) entry of firms across various districts in Ethiopia in a given period. Thus, using these outcomes, I explore the potential effect of unreliable electricity provision on the intensity of (manufacturing) firms across locations. A priori, unreliable electricity provision in a given locality is expected to influence the exit (and entry) of firms thereby resulting in low firm density. In the firm-level analysis, the main outcome is the number of months in a year that the firm operates. Again, firms exposed to frequent outages often shut down during outage periods relative to firms with reliable supply.

Further, in the district-level analysis, _UnreliableSupplyit_ is measured by the share of firms in a district citing electricity outages as a major constraint to their activities. In the 37