rafmacalaba/datause-encoder-data huggingface dataset:
to comprehend the implications of a hypothetical situation which they have not actually experienced themselves and,<sup>4</sup> as a consequence, exhibit a strong bias towards the status quo.

# 3. **Data sources and descriptive evidence**

This section describes the sample and nature of the data used **in** subsequent analysis by presenting descriptive statistics on household characteristics and changes in the inequality of endowments, investment, shocks, and coping strategies, and preferences regarding land rights. We find that, despite differences in sources of income, and especially the extent of migration, means of most household characteristics such as income levels are quite similar across provinces **in** the sample. Investment in enterpnse assets and consumer goods was the most prevalent, and even though a large number of households reported exposure to shocks, these did not contribute to an appreciable increase in the inequality of land endowment. Also, we find that support for the policy of no redistribution is high, especially in the provinces where this policy had been implemented before.

# 3.1 **Characteristics of the sample**

The data used in the study are from a combined village and household survey conducted by the Rural Survey Team from China's State Statistical Bureau (SSB) in May and June of 2001. The survey covered 1001 households from 10 villages in three of China's poorest provinces with significant differences in land tenure arrangements, namely Guizhou, western Hunan, and Yunnan provinces. Guizhou province was chosen because **it** had implemented the "two nos" policy early on while western Hunan and Yunnan were chosen because of their proximity and their climatic and geographic similarity to Guizhou. Thus, the provinces have simnilar geographic and other characteristics but differ significantly in their land tenure arrangements which were changed by provincial decree in Guizhou. We thus expected to find that farmers outside this province would not enjoy the same level of tenure security than those within Guizhou.

In addition to most of the vanables included in standard multi-purpose surveys such as household characteristics, expenditure, assets, different income sources, and agricultural production, the survey contained a number of modules that aimed to re-construct movements in population and key assets. To do so, information on individuals' employment history, their investment **in** productive agricultural assets, non-farm enterprises, consumer durables (including housmg), and changes in land endowments

> 4In many situations, households opted for administrative redistnbution of land as a means to shift land that had been left uncultivated because of out-migration to other users (Kung and Liu 1997). The fact that administrative redistnbutnon, rather than a decentralized market mechanism, was quoted as the mechanism of choice could be a reflection of respondents' lack of famihanty with the scope for operation of markets and the way they work

8
the global CO2 emissions would be 52 percent higher from the 1990 level and 24 percent lower from the BAU level in 2030.

If intensity based targets instead of absolute emission targets were to use to achieve the same levels of emission reduction, our study finds that the former would be significantly higher than the latter. In order to reverse the global trend of CO2 emissions by 2020 and maintaining it 42 percent above the 1990 level in 2030, average CO2 intensities at global, non-Annex I and Annex I levels would be cut by respectively, 68, 63 and 72 percent from the respective values in 1990. In 2030, the percentage cut in average emission intensity of Annex I countries is 2.6 times as high as the percentage cut in their total emissions. This study does not investigate whether or not such cuts in CO2 intensities are feasible. Further studies are therefore needed to examine the viability of intensity based targets to stabilize atmospheric stabilization of GHG concentrations at the level implied by the United Nations Framework Convention on Climate Change.

# **Reference**

Argentine Republic (1999), Revisions of the First National Communication, Secretariat for Natural Resources and Sustainable Development, <u>http://unfccc.int/resource/doc/natc/argnc1e.pdf</u> Baumert, Kevin A., Timothy Herzog and Jonathan Pershing (2004), Navigating the Numbers: Greenhouse Gas Data and International Climate Policy, World Resources Institute, Washington, DC.

-32-
# **IV. Empirical Evidence for the Slow Pace of Technology Adoption in MENA**

In this section, we show that MENA’s technology adoption in general purpose industries (GPT) has been poor. We do so within a framework of cross-country panel regressions, specifically contrasting technology adoption between MENA and EAP. The specification is as follows: i i −1 0 1 2M M 3M 4M M ×<sup>yyi</sup> 5M<sup>yyi</sup> + f i Uxx U U U<sup>−1−1</sup> ttfftth yy xx ii ,ii = ββ + ββ ,ii−1 + ββ + ββ EE+ ββ ,ii−1 + ββ EE× ,ii−1 + εε where ttfftth i captures technology adoption of technology yy in county yy at time ; henceyy Uixx captures xx iieecchxx technology adoption relative to the U.S. Technology adoption depends on the country’s development ttfftth xx ii tt iieecch ,ii level, proxied by lagged per capita income relative to the U.S. ~~(~~ <u>yi</u> −1 ), time fixed effects f , region U fixed effects, and the interaction of the region fixed effects and per capita income relative to the United yy ,ii−1 ii States. The EAP and MENA fixed effects capture region-specific difference in technology adoption _relative to the rest of the world_ . The interactions capture the speed of technology adoption in MENA and EAP, relative to other countries with the same level of income, _as income rises._ Technology adoption is proxied by (1) bandwidth per internet user (bits per second), (2) number of selfcontained computers designed for use by one person, (3) internet users in percentage of population, (4) number of ATMs per million capita (5) number of payments by credit and debit cards per million capita, (6) tractors used in agriculture per million capita, and (7) gross output of electric energy per million capita. Data are mainly from the CHAT database (Comin and Hobjin, 2010), except that bandwidth and internet users in percentage of population are from World Telecommunication Database (ITU).

For MENA, the pace of technology adoption for all technologies as income rises is slower _compared to other countries with the same income_ . The results are shown in Table 3 and illustrated graphically in Figure 6. The coefficients associated with the interaction between the MENA regional dummy and relative income are all negative and statistically significant, which translates into downward-sloping lines for MENA (in red) in Figure 6. For EAP, the coefficients associated with the interaction between the EAP regional dummy and relative income are also negative and significant, but the magnitudes are much smaller than those for MENA. That implies the speed of technology adoption as income rises in EAP is larger than that in MENA. This is shown by the gaps between the blue lines (EAP) and the red lines (MENA) in Figure 6.

It is noteworthy that both MENA and EAP have positive and significant coefficients for the region fixed effects (translating into positive intercepts of the blue and red lines in Figure 7). That suggests that at (very) low levels of income, EAP and MENA countries have a faster pace of technology adoption relative 14
decisions. If there is little or no perceived return to education (that is, no jobs at the end of the road), it is much harder to get families in poor countries to send their children to school. Where children have decent access to education, a higher skill premium stimulates a shift of the labor force from low-skill to higher-skill occupations.

# Figure 15. Child labor and household consumption levels in Vietnam

Share of 6-15 year olds working <!-- Start of picture text --> 40%<br>30% 1993<br>20% 1998<br>6.54 6.64 6.74 6.84 6.94 7.04 7.14 7.24 7.34 7.44 7.54<br>Per capita household consumption 1993 (log scale)<br><!-- End of picture text --> It should also be noted that there has been a large decline in child labor in Vietnam since the country started integrating with the global market. There is ample evidence that child labor is primarily driven by poverty and educational opportunities. Figure 15 shows the share of 6-to15 year-olds working for different households ranked in terms of their 1993 level of income.

Child labor is more prevalent in poor households. The figure also shows the share of children working in the same families five years later; child labor has declined for all income groups. The change results from the fact that everyone is richer than they were five years before as well as from the expansion of schooling opportunities.

28
# **São Paulo**

|**Initiatives from the city hall**|**Initiatives from the private**<br>**sector**|**University based initiatives**|**Other initiatives**|
|---|---|---|---|
|In<br>2005<br>the<br>**Department**<br>**of**<br>**the**<br>**Environment**did a study about the state<br>of the environmental in Sao Paulo. The<br>document explains how urban dynamics<br>are pressuring the environment of the Sao<br>Paulo underlying demographic impact,<br>economic<br>development,<br>provision<br>of<br>services,<br>and<br>land<br>occupation.<br>The<br>analysis<br>is<br>done<br>from<br>five<br>(5)<br>environmental indicators:<br>1.<br>quality of air,<br>2.<br>quality of water,<br>3.<br>land,<br>4.<br>biodiversity,<br>5.<br>use of urban land<br>6.<br>www.prefeitura.sp.gov.br|In 1996**Instituto Polis**created the<br>municipal management index with three<br>main variables (1) waste management, (2)<br>child education, and (3) child mortality,<br>all of which being compared with the<br>social municipal index. The key element<br>of its success was that all information was<br>available or easy to find in Brazilian<br>municipalities. During the 90s, that Index<br>became a tool for measuring performance<br>of<br>public<br>governments<br>in<br>Brazil.<br>However,<br>lack<br>of<br>resources<br>has<br>constrained production of new data based<br>on that index for further years.<br>www.polis.org.br<br>||**Seade**a public foundation of the State of<br>Sao Paulo annually produces indicators on<br>the following topics: Social Inclusion,<br>Life<br>expectancy,<br>Racial<br>inequality<br>indicators, Youth vulnerability Index,<br>Social responsibility Index , Social<br>Vulnerability Index, Information on Sao<br>Paulo’s<br>districts,<br>Development<br>of<br>Millennium Objectives Research on life<br>conditions, Job market index, Municipal<br>public finances index, Cultural Guide of<br>the State of Sao Paulo, Information for<br>Municipal<br>Plans,<br>Memory<br>of<br>Demographic Statistics, Child mortality<br>(2004), Electoral movement, Women and<br>work, Sao Paulo’s Municipal profile,<br>Internal brut product (annually and every<br>three months), Integrated system of<br>criminal information.<br>www.seade.gov.br|
|Periodically the**Planning Secretariat**<br>presented a series of social indexes<br>Human development index, Human rights<br>maps, Youth vulnerability index, Social<br>exclusion map (2002), Social vulnerability<br>map, Social vulnerability index for the<br>City of Sao Paulo.<br>www.prefeitura.sp.gov.br|||As a Brazilian municipalities network,<br>**Muninet**was created in 2002 by the<br>World Bank in partnership with more than<br>twenty (20) Brazilian institutions and<br>other<br>international<br>organizations.<br>Muninet‘s objective is to strengthen<br>municipal development in Brazil.<br>Indicators presented by Muninet in the<br>Internet:<br>Population,<br>Municipal<br>Characteristics,<br>Development in terms of: Culture, Human<br>Development, social exclusion index,<br>Education, Municipal finances, Municipal<br>management, Income<br>http://muninet.org.br/|

55
**Table 2: Summary Statistics of Firm Characteristics**

||<br>Support|El Salvador<br>No-Support|N|Support|Georgia<br>No-Support|N|
|---|---|---|---|---|---|---|
|Firm Size|||||||
|Small (5-19)|0.646|0.737|407|0.592|0.628|472|
|Medium (20-99)|0.325|0.164**|407|0.325|0.347|472|
|Large (100+)|0.029|0.099**|407|0.084|0.025**|472|
|Firm Age|||||||
|0-10 years|0.124|0.186|408|0.544|0.584|476|
|11-20 years|0.455|0.435|408|0.309|0.318|476|
|20+ years|0.421|0.379|408|0.147|0.098|476|
|Manufacturing|0.212|0.217|408|0.232|0.215|476|
|Construction|0.046|0.105|408|0.154|0.113|476|
|Transportation|0.095|0.053|408|0.084|0.025|476|
|Hotels & restaurants|0.013|0.058|408|0.175|0.073*|476|
|Retail|0.417|0.317|408|0.227|0.204|476|
|Years of the top manager's experience|22.422|22.091|408|16.991|15.932|476|
|Exporting directly 10% of sales|0.110|0.121|408|0.150|0.112|476|
|At least 10% of foreign owned|0.065|0.191**|408|0.057|0.086|476|
|Product/service or process innovation|0.454|0.475|408|0.492|0.485|476|
|Part of a larger firm|0.218|0.368*|408|0.039|0.030|476|
|Log sales per worker|9.947|9.965|408|9.817|9.826|476|
|Majority female owned|0.289|0.175|408|0.097|0.138|476|
|Fully credit constrained|0.021|0.087|408|0.013|0.158***|476|
|Bank loan/line of credit|0.600|0.343***|408|0.469|0.409|476|
|Experiencing electrical outages|0.495|0.462|408|0.555|0.506|476|
|Offering formal training|0.638|0.528|408|0.365|0.294|476|

Note: This table shows firm characteristics in El Salvador and Georgia ES datasets. Support columns show the respective statistics of firms that have received government support as reported in COV-ES R2. No-Support columns show the respective statistics of firms that have not received any government support. N denotes the number of observations. Means are weighted using the sampling weights from the ES. Asterisks in the No-Support columns are for unconditional t-tests comparing the respective statistic with the same statistic for the firms that received support. Standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 4 for more details.

35
# **The Impact of Protracted Displacement on Syrian Refugees in Jordan: The Evolution of Household Composition and Poverty Rates** Julieth Santamaria<sup>a</sup> , Lucia Hanmer<sup>b</sup> Eliana Rubiano<sup>c</sup>

_<mark>The authors of this paper conducted their research under Gender Dimensions of Forced Displacement project. The project is coled by Lucia Hanmer and Diana Arango under the guidance of Hana Brixi, Global Director, Gender Unit, The World Bank Group.</mark>_ _a Inter-American Development Bank. bc World Bank Group_

**Keywords:** Refugees; Gender; Poverty; Vulnerability; Syria **JEL Codes:** O53, D10, I32, J16

<mark>Acknowledgements: This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership''. The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.</mark>
20

- 11/ For example, doubling (halving) the elaiticity estimates in column (3) incre4ses (decreases) our estimates of the welfare gains with the central elasticity estimates from US$20.9 billion to $21.0 ($20.8) billion, respectively, or + 0.42.

- 12/ We note that estimates of quota premia, in both textiles and automobiles, vary from year to year. We obtained estimates of quota premia for 1984, the year for which we benchmarked the model. Thus, our estimates apply to the conditions prevailing in that year. For other years, the premia rates and our cost estimates may differ.

- 13/ If the marginal supplier is more efficient than Hong Kong, our premium rate estimate is conservative and the associated welfare gain estimate is downward-biased.

- 14/ In his study of the similar arrangement for quota right distributions among cheese exporters to the US, Anderson (1985) comes to similar conclusions, i.e. that the distortionary costs coming from the bilateral quota arrangements is extremely high. Had we further disaggregated textiles and apparel as Anderson did in his study of cheese quotas, we would have found even higher welfare costs.

- _15/_ See Elliott, Sohctt and Takacs (1987) for an appraisal of such a proposal for US trade policy.

- 16/ If one interprets the individual welfare estimates in table 3, as linear approximations to the nonlinear demand and supply curves in the model, then the sum of the individual estimates will underestimate the total welfare gain. This is indeed so, but the underestimate is small: for the small country case, the underestimate from table 3 is only $30 million. This non-linearity effect is even more important for the relativeiy large welfare equivalent (to QRs) of uniform tariffs reported below.

- 17/ It should be noted that, in the presence of a balance of trade constraint, the export demand and import supply elasticities are not independent (see Whalley and Yeung, 1984) and, along the offer curve will be less in absolute value than the figures cited in the text (see Jones and Berglas, 1977). The numerically calculated value for the aggregate export demand elasticity is -4.6; the numerical value for the aggregate import supply elasticity (including intermediates with the small country assumption maintained) is 6.3.

- 18/ For example, Whalley (1985, table 10.2) estimates welfare losses from US unilateral NTB removal and US unilateral tariff removal. Deardorff and Stern (1986, table 4.6) obtain small welfare gains.

- 19/ See Tarr (1988, chapter 2) for a comparison of our results with PE estimates.

- 20/ We call our estimate a proxy for these adjustment costs because we have not incorporated into our model an endogenou **s** ector that moves resources, as suggested by the theoretical work by Mussa.
consider themselves as participants in all conservancies. On average, 26% of the households reported that they knew about conservancy constitutions.

The average household income from conservancies is derived from total income reported by household members and their corresponding occupational status related to the conservancy (Type B). Type B occupation includes direct employment as well as wage and enterprise income indirectly arising from the conservancy. Individual conservancy incomes are added to obtain household conservancy income.<sup>3</sup> Except for _Torra_ , average incomes from conservancies are lower than average household income. This shows that households who obtain income from conservancies also have other sources of income. On average only 12 percent of the households sampled reported any income from conservancies.

Table 2 also shows that there are non-income related benefits that accrue from conservancies. In Kunene region, a majority of the households in established conservancies considers distribution of meat as a key conservancy benefit. For example, 76% of households in Torra and 62% of households in ≠Khoadi //Hoâs indicated meat distribution to be a conservancy related gain. We were unable to include such nonincome assistance in our analyses.

Table 3 compares households in established versus comparator conservancies. The average income of households in established conservancies is higher than that of comparator conservancy households. Thus, it may appear that the differences in household income are a result of the conservancy influence. However, the average level of male and female education in the established conservancies is also higher. It is also appears that established conservancies may have access to slightly better infrastructure. A larger percent of households in established conservancies have electricity relative to the comparator group.

This table also shows the differences in the sources of livelihood in the two regions. In _Kunene,_ most households reported agriculture as the primary source of livelihood. In _Caprivi,_ households were divided between livestock, formal and informal employment.

# **3. Methodology**

The 2002 Socio-Economic Household Survey (SEHS) was designed primarily to evaluate the impact of conservancies on household incomes and assess how such impacts vary among households with different socio-economic characteristics. Based on our initial assessment of the data, we focus on three specific questions:

> 3 Conservancies provide wage employment and self-employment opportunities, income through hotel levies, meat to households and community level income through tourism related contracts and agreements (Jones 1999a and 1999b).  Unfortunately, the household survey data does not collect information on community related benefits.

6
Table 34

# Morishima Elasticities Of Substitution In The Transportation Sectors Of Upper-Middle To High-Income Countries

|||Morishi<br>|ma elasticit<br>|ies of subst<br>|itution<br>|
|---|---|---|---|---|---|
|Country|Factor i|�<sup>m</sup><br>io|�<sup>m</sup><br>ie|�<sup>m</sup><br>id|�<sup>m</sup><br>is|
|Mexico||||||
||o|||||
||e|||:503|�:166|
||d||:318||:017|
||s||:305|:030||
|Turkey||||||
||o||:002|�:515|:822|
||e|�:013||�:454|:776|
||d|:125|:110||:072|
||s|:138|:121|:048||

Table 35 Morishima Elasticities Of Substitution In The Transportation Sectors Of Lower-Middle To Low-Income Countries

|||Morishi|ma elastici|ties of subst|itution|
|---|---|---|---|---|---|
|Country|Factor i|�<sup>m</sup><br>io|�<sup>m</sup><br>ie|�<sup>m</sup><br>id|�<sup>m</sup><br>is|
|Indonesia||||||
||o|||�:504|:865|
||e|||||
||d|:079|||:281|
||s|:093||:267||
|South Africa||||||
||o|||||
||e|||�:454|1:068|
||d||:283||:330|
||s||:410|:204||
Table 4. Effects of the PEP Program on Labor Market Access

|_Dep Variable:_|Employed [=1]<br>(1)|Formal Job [=1]<br>(2)|
|---|---|---|
|_Panel A:_All sample|||
|PEP [=1]|0.032<br>(0.011)|0.075***<br>(0.037)|
|Observations|6,339|4,104|
|_Panel B:_Women|||
|PEP [=1]|0.061*<br>(0.026)|0.066***<br>(0.017)|
|Observations|3,591|1,437|
|Wave FE|Yes|Yes|
|HH FE|Yes|Yes|
|Department_×_wave|Yes|Yes|
|Geographic Sampling_×_wave|Yes|Yes|
|Pre-migration controls_×_wave|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1) using variables on labor market access as main outcomes. Panel A presents results for the whole sample and panel B for women only. Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Pre-migration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a written contract, and the time gap between the last job and the migration episode); (iii) household characteristics (number of children, household size, access to public services, owning dwelling, and having a smartphone); and (iv) networks prior to migration episode (had family and friends in Colombia, knew of job opportunities before migrating, and migrated for healthrelated reasons). Standard errors clustered at the household level in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

27