rafmacalaba/datause-encoder-data huggingface dataset:
10 being largely consumer articles. Imports of this category decrease by 7.6% in response to the 15 % nominal devaluatica. The effect is not higher since the import price increase (in domestic currency) pushes up **the** GDP-price deflator (Py) by an average of 4.4% and the gross output price by **5.1%,** so **that** the 15% nominal devaluation is translated into a **10%** 'real' devaluation.'<sup>3</sup>

**Table 3: Simulation results for domestic GDP** growth of **3%**

|L.ime~~~~~~~~~~~~~~~<br>;arable-: hbase:|~~~~~~~~~~~~~~~~~~~.<br>devaluation|tot decline - OECD growth.|
|---|---|---|
|1.000Q|1.044.|1.058-:100|
|PQ:<br>1.000|1.051.<br>|;1i.068-.<br>1.000|
|IxY<br>~<br>LO00<br>'tQ461<br>000:<br>100<br>XW.LOO....4..0<br>XV;<br>260<br>MV jsg<br>- 0t-i.<br>- -<br>1940<br>TB-<br>;;<br>6Z607|1.0 9<br>9<br>0 .946<br>-<br>-<br>60<br>-<br>1836-<br>-|062a<br>994<br>~~931<br>10<br>1000<br>2610<br>276<br>22167<br>1940<br>443 -82|

I Indices (solution of the 'base simulation is set to 1); for the value of exports (XV), the value of imports (MV), and the trade balance (TB) current US dollars. Entries represent five-period average values (the year in which the variation is introduced is not taken into account).

Export supply is affected only marginally due to its sole dependence on gross output of the economy. Since GDP is assumed to grow as in the base case and imported intermediate goods decline, gross output of the economy falls margially 13 The term 'real' devaluation only refers to the net impact of the nominal devaluation and dte domestic price rise (PQ). It does not take inflation abroad into account.
**_<u>________________________________________________________</u>_**

- Starting a Business

- Dealing with Licenses

- Hiring & Firing Workers

- Registering Property

- Getting Credit

- Protecting Investors

- Paying Taxes

- Trading Across Borders

- Enforcing Contracts

- Closing a Business

# _<u>Investment Climate Surveys (ICS)</u>_

Investment Climate Surveys (ICS) provide both quantitative and qualitative information on a wide range of investment climate conditions and links them to their impact on firm productivity, investment and employment. The ICS -- including the Business Environment and Enterprise Surveys (BEEPS) joint with the EBRD -- report results from surveys of over 30,000 entrepreneurs in over 50 countries.

Investment climate surveys (ICS) measure entrepreneurs' perceptions of the investment climate in their country, and provide indicators of firm productivity and performance. All investment climate surveys are grounded in a <u>standard core module</u> (PDF, 180KB) that covers management’s perceptions of the severity of obstacles to operation and growth of the business. The standard core investment climate survey module also collects information on firm performance.

In addition to the core module, the investment climate surveys may be supplemented with questions from other detailed modules covering finance, labor, small and medium enterprises, and logistics, to name a few. These supplemental modules are used to explore in greater depth issues that may be topical or particularly relevant in any given country. Standard investment climate surveys are administered to the manufacturing sector in urban areas. Other sector-specific investment climate survey instruments are also being developed, including questionnaires for the hotel and tourism industry, retail-trade sector, rural manufacturing, information technology, and informal firms.

About 20 firm level surveys are being carried out each year, with a 4-5 year cycle. The firm-level survey is complemented with analysis from other studies, as wells as discussions and meetings with key stakeholder groups. For example, indicators on entry regulation estimate the procedures, time, cost and minimum capital requirements to register a business formally, thus highlighting the microeconomic and institutional conditions inhibiting productive investment. These assessments will ultimately feed into operations and technical assistance.

Several of the investment climate surveys also contain detailed information on transportation constraints including trade facilitation programs. There are data on questions such as the number of competitors, suppliers, customers, and percentage of firms that find transportation and customs and trade regulations to be “major” or “very severe” constraints, infrastructure 6 __________________________________________________________________________________________________________
**Table 2 Determinants of an individual's likelihood to be an internal migrant**

|VARIABLES|(1)|(2)|(3)|
|---|---|---|---|
|age|0.0223***|0.0203***|0.0205***|
|age<sup>2</sup>|-0.000312***|-0.000271***|<br>-0.000275***|
|male|0.0158|0.00503|0.0099|
|married|0.0939***|0.0532***|0.0560***|
|prim|0.0836***|0.0986***|0.0965***|
|seci|0.0683***|0.0818***|0.0731***|
|secc|0.173***|0.131***|0.113***|
|terc|0.287***|0.202***|0.167***|
|urb|-0.0305|-0.0354|-0.0523**|
|male_head|-0.102***|||
|age_head|0.00132***|||
|educ_head|-0.0145***|||
|mfrat|0.0670***|||
|hhsizem|0.0161***|||
|dep_child|-0.116***|||
|dep_elder|0.333***|||
|ownhouse|-0.0344***|||
|lit_clusH|-0.151***|-0.189***||
|pay_clusH|-0.118*|-0.157*||
|sni_clusH|-0.00989|-0.0349|-0.0813*|
|wtr_clusH|-0.0153|-0.0221|-0.0487*|
|Western|0.0625|0.0812*|0.103**|
|Central|0.137***|0.235***|0.284***|
|Volta|0.130***|0.203***|0.259***|
|Eastern|0.0522|0.130***|0.159***|
|Ashanti|0.149***|0.255***|0.290***|
|Brong_Ahafo|0.0692*|0.158***|0.195***|
|Northern|0.0648|0.131**|0.235***|
|Upper_East|0.00507|0.0397|0.125*|
|Upper_West|0.0973**|0.244***|0.346***|
|Observations|7677|7677|7677|
|Pseudo R<sup>2</sup>|0.319|0.148|0.139|

*** p<0.01, ** p<0.05, * p<0.1.

_Note_ : Estimated coefficients are reported as marginal probabilities. Observations are weighted by sample weights and standard errors are clustered at the PSU level. Stratification at the region level is also taken into account in the estimates.

17
- Brodmann, S., Pouget, Y., & Gatti, R. (2010). Labor Mobility in the Middle East and North Africa: Challenges and Opportunities. _World Bank, Washington, DC_ .

- Carletto, C., & Kilic, T. (2011). Moving up the ladder? The impact of migration experience on occupational mobility in Albania. _Journal of Development Studies_ , _47_ (6), 846–869.

- Co, C. Y., Gang, I. N., & Yun, M.-S. (2000). Returns to returning. _Journal of Population Economics_ , _13_ (1), 57–79.

- Coniglio, N. D., De Arcangelis, G., & Serlenga, L. (2009). Intentions to return of clandestine migrants: The perverse effect of illegality on skills. _Review of Development Economics_ , _13_ (4), 641–657.

- De Bel-Air, F. (2016). Migration profile: Egypt.

- De Coulon, A., & Piracha, M. (2005). Self-selection and the performance of return migrants: the source country perspective. _Journal of Population Economics_ , _18_ (4), 779–807.

- Devillanova, C., Fasani, F., & Frattini, T. (2018). Employment of undocumented immigrants and the prospect of legal status: evidence from an amnesty program. _ILR Review_ , _71_ (4), 853–881.

- De Vreyer, P., Gubert, F., & Robilliard, A.-S. (2010). Are there returns to migration experience? An empirical analysis using data on return migrants and non-migrants in West Africa. _Annals of Economics and Statistics/Annales d’ ́economie et de statistique_ , 307–328.

- Dustmann, C., Fasani, F., & Speciale, B. (2017). Illegal migration and consumption behavior of immigrant households. _Journal of the European Economic Association_ , _15_ (3), 654–691.

- El-Mallakh, N., & Wahba, J. (2021). Upward or downward: Occupational mobility and return migration. _World Development_ , _137_ .

- Gleditsch, N. P., Wallensteen, P., Eriksson, M., Sollenberg, M., & Strand, H. (2002). Armed conflict 1946-2001: A new dataset. _Journal of Peace Research_ , _39_ (5), 615–637.

- Gleeson, S., & Gonzales, R. G. (2012). When do papers matter? An institutional analysis of undocumented life in the United States. _International Migration_ , _50_ (4), 1–19.

- Gonzales, R. G. (2011). Learning to be illegal: Undocumented youth and shifting legal contexts in the transition to adulthood. _American Sociological Review_ , _76_ (4), 602–619.

- Gonzalez-Barrera, A. (2015). _More Mexicans leaving than coming to the US._ Washington.

- Hall, M., & Greenman, E. (2015). The occupational cost of being illegal in the United States: Legal status, job hazards, and compensating differentials. _International Migration Review_ , _49_ (2), 406–442.

30
- <mark>Nollenberger, Natalia, and Núria Rodríguez-Planas. "Full-time universal childcare in a context of low maternal employment: Quasi-experimental evidence from Spain."</mark> _<mark>Labour Economics</mark>_ <mark>36 (2015): 124-136.</mark>

- <mark>Padilla‐Romo, María, and Francisco Cabrera‐Hernández. "Easing the constraints of motherhood: the effects of all‐day schools on mothers' labor supply."</mark> _<mark>Economic Inquiry</mark>_ <mark>(2018).</mark>

- <mark>Pitt, Mark M., Mark R. Rosenzweig, and Donna M. Gibbons. "The determinants and consequences of the placement of government programs in Indonesia."</mark> _<mark>The World Bank Economic Review</mark>_ <mark>7, no. 3 (1993): 319-348.</mark>

- <mark>Schlosser, Analia. "Public preschool and the labor supply of Arab mothers: Evidence from a natural experiment."</mark> _<mark>Manuscript, The Hebrew University of Jerusalem</mark>_ <mark>(2011).</mark>

- <mark>Strauss, John, Firman Witoelar, and Bondan Sikoki. "The fifth wave of the Indonesia family life survey: overview and field report."</mark> _<mark>RAND: Santa Monica, CA, USA</mark>_ <mark>(2016).</mark>

- <mark>Strauss, John, Firman Witoelar, Bondan Sikoki, and Anna Marie Wattie. "The fourth wave of the Indonesian Family Life Survey (IFLS4): overview and field report."</mark> _<mark>RAND Corporation</mark>_ <mark>(2009).</mark>

- <mark>Strauss, John, Kathleen Beegle, Bondan Sikoki, Agus Dwiyanto, Yulia Herawati, and Firman Witoelar. "The third wave of the Indonesia Family Life Survey (IFLS3): Overview and field report."</mark> _<mark>NIA/NICHD</mark>_ <mark>(2004).</mark>

- <mark>Thomas, Duncan, Kathleen Beegle, and E. Frankenberg.</mark> _<mark>Labor Market Transitions of Men and Women During an Economic Crisis: Evidence from Indonesia</mark>_ <mark>. RAND-Labor and Population Program, 2000.</mark>

- <mark>World Bank. 2018. World Development Indicators. Washington, D.C.: The World Bank.</mark> <u><mark>http://databank.worldbank.org/data/source/world-development-indicators#</mark></u> <mark>(accessed December 15, 2018)</mark>

- <mark>World Bank.</mark> _<mark>Live Long and Prosper: Aging in East Asia and Pacific</mark>_ <mark>. World Bank Publications, 2016.</mark>

33
significance test—suggest that once we control for observable covariates, assignment into the treatment is as good as random. Putting it differently, covariates do not predict treatment after weighting. This message is consistent with the results we presented in Table 5—treated and untreated firms are very similar in terms of the observable characteristics we use in the estimation of the propensity score after weighting.

Table 5: Indicators of Matching Quality

||Stan<br>Raw|dardize<br>IPW|d Difer<br>PSM|ences<br>NMM|Raw|Varian<br>IPW|ce Rati<br>PSM|o<br>NMM|
|---|---|---|---|---|---|---|---|---|
|**Panel A: EFS**|||||||||
|Log export value|1.50|-0.06|-0.01|0.00|0.75|0.81|0.96|1.00|
|Shr. of exports in negative list|0.69|0.00|0.03|0.00|1.12|1.00|0.99|1.00|
|Importer|-0.84|-0.01|-0.05|0.00|0.39|0.92|0.84|0.99|
|**Panel B: LTFF**|||||||||
|Log export value|2.65|0.08|-0.04|0.01|0.45|1.10|1.18|0.99|
|Shr. of exports in eligible list|1.77|0.02|0.14|0.00|0.12|0.91|0.51|0.12|
|Importer|-0.44|0.02|0.22|0.02|0.30|0.98|4.01|0.30|

The standardized difference for each covariate _Xk_ is given by _SDk_ “ ~~?~~ _X_ p _sk_<sup>2</sup> _k,,_ 11 ́<sup>`</sup> _X_<sup>_s_2</sup> _k,k_ 0 _<u>,</u>_ 0<sup>q{2,where</sup> _X k,_ 1 and _X k,_ 0 denote the sample mean of covariate _Xk_ in the treatment and control groups respectively and _s_<sup>2</sup> _k,_ 1<sup>and</sup><sup>_s_2</sup> _k,_ 0 are the sample variances of covariate _Xk_ in the treatment and control groups respectively. The variance ratio is defined as _V R_ “ _s_<sup>2</sup> _k,_ 1<sup>{</sup><sup>_s_2</sup> _k,_ 0<sup>.IPWstandsforinverseprobabilityweighting,PSMstandsforpropensity</sup> score matching weighting and NNM for Mahalanobis matching weighting.

19
Table 4: Eect of a direct SLM training on contact farmers' adoption and knowledge

||Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Panel A: CFs' Knowledge and Adoption, un|weighted||||
|Knowledge score|0.633<br>[0.173]|0.052<br>[0.055]|347|0.102|
|Number of techniques known by name|4.131<br>[1.626]|0.706<br>[0.546]|347|0.098|
|Number of techniques adopted on own plot|1.786<br>[1.309]|0.673**<br>[0.225]|347|0.224|
|Number of techniques adopted on any plot|3.738<br>[1.889]|0.733**<br>[0.250]|347|0.241|
|Panel B: CFs' Knowledge and Adoption, wei|ghted||||
|Knowledge score|0.646<br>[0.189]|0.059<br>[0.056]|347|0.138|
|Number of techniques known by name|0.687<br>[0.236]|0.079<br>[0.076]|347|0.071|
|Number of techniques adopted on own plot|0.314<br>[0.220]|0.113**<br>[0.031]|347|0.248|
|Number of techniques adopted on any plot|0.601<br>[0.265]|0.106**<br>[0.034]|347|0.226|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey 2012, 2013.

Notes: Regressions include the following variables: a constant, age, a completed primary school dummy, a single dummy, number of children, total landholdings, the number of rooms in the dwelling, baseline CF's number of years since formal training, a dummy for missing the baseline CF variable, district indicators, an incentive treatment dummy, and an endline dummy. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. CF=contact farmer; ITT=intent-to-treat eect.

35
**Table 11: Between-group differences in current salaries: YP and non-YP samples**

|||YP|||Non-YP||
|---|---|---|---|---|---|---|
||Total<br>Difference|Characteristics|Structure|Total<br>Difference|Characteristics|Structure|
|Ml-M2|0.07|1.75<br>(1.73)|-1.69<br>(2.06)|6.33|1.11<br>(0.741)|5.22<br>(0.965)|
|MI-Fl|17.08|13.24<br>(1.23)|3.85<br>(1.77)|13.7|8.02<br>(0.478)|5.68<br>(0.974)|
|MI-F2|20.35|15.21<br>(1.81)|_5.13_<br>(2.22)|19.41|8.67<br>(0.846)|10.75<br>(1.34)|
|M2-FI|17.02|11.48<br>(2.08)|5.53<br>(1.90)|7.37|6.91<br>(0.940)|0.46<br>(1.237)|
|M2-F2|20.28|13.46<br>(1.43)|6.82<br>(1.95)|13.08|7.56<br>(0.518)|5.53<br>(1.34)|
|FI-F2|3.27|1.98<br>(1.88)|1.29<br>(3.21)|5.72|0.64<br>(0.834)|5.07<br>(2.12)|

Notes: Standard errors are given **in** parentheses.

Table 12: Between-group differences in entry salaries: YP and non-YP samples

|||YP|||Non-YP||
|---|---|---|---|---|---|---|
||Total<br>Difference|Characteristics|Structure|Total<br>Difference|Characteristics|Structure|
|M1-M2|0.20|4.35|_-4.15_|10.18|0.97|9.21|
|MI-Fl|-5.21|-8.11|2.90|21.89|12.54|9.35|
|MI-F2|-6.43|-5.23|-1.20|31.99|15.16|16.82|
|M2-FI|-5.41|-12.46|7.06|11.71|_11.58_|0.14|
|M2-F2|-6.63|-9.58|2.95|21.81|14.20|7.61|
|FI-F2|-1.22|2.88|-4.10|10.10|2.62|7.47|

42
same time. A superlative index allows changes in the basket between the base period or region and the current period/region and so accounts for consumer substitution, while the Laspeyres index continues to price the base period or region basket. But using budget shares from two periods or regions has a potential problem; these may not refer to the same standard of living. In contrast, a fixed-weights Laspeyres index refers to the base period or region standard of living. The potential “income bias” in the superlative index will not happen in the special case of homothetic preferences, with budget shares not changing with income. But observed behavior, such as falling food shares as income rises, is inconsistent with homothetic preferences. The income bias of the superlative index may exceed the Laspeyres substitution bias and may be positive or negative whereas substitution bias only overstates changes in the cost of living between the base period or region and the current one (Dumagan and Mount 1997).

For time-space deflation, a multilateral index method is needed to calculate regional and temporal price levels jointly so as to ensure transitivity (Hill 2004). The two main methods used for PPPs in cross-country studies are Geary-Khamis (GK), used in the Penn World Table, and EKS (Eltetö, Köves and Szulc), used by the World Bank. The GK method lets subindexes add to a total, which is useful for deflating GDP and its components but is less needed for comparing levels of living (Deaton, Friedman, and Alatas 2004). Moreover, the GK index uses plutocratic weights and does not allow for substitution effects; these are undesirable features if comparing household living standards over space. The EKS allows substitution because it uses underlying Fisher indexes (geometric means of a Paasche and Laspeyres) which are superlative in the sense of being an exact cost-of-living index for some homothetic utility function that is a flexible functional form, 14
# **1. Labor Force Survey and Vietnam Household Living Standard Survey**

This paper uses two main sources of data: the Labor Force Surveys (LFS) of 2007 and 2009, and the Vietnam Household Living Standard Surveys (VHLSS) of 2006 and 2008.

The Labor Force Surveys of 2007 and 2009 were collected by the GSO. The 2007 LFS was conducted in the month of August among about 170,000 households using a fairly short questionnaire. The GSO implemented a newly improved questionnaire in 2009. The sample size of the 2009 survey was about 18,000 households. Households were selected randomly in two stages from the list of 15% sample enumeration areas of the 2009 Population and Housing census. All usual residents of the selected households were interviewed and enumerated. The idea is to have such LFS on a more frequent (potentially quarterly) basis. These LFS data are not directly comparable with the data from the previous period, since the methodology has changed significantly (i.e. levels cannot be compared between the two surveys; see ILO, 2010a).

The VHLSS is a household survey that contains information on demographics, education, health, labor market status, consumption, assets, dwelling and non-labor income. The surveys collected information through face-to-face interviews with household heads and key commune officials. These surveys have been conducted every two years by the GSO since 2002. The sample size consists in 2006 and 2008 of about 45,000 households (about 36,000 households in the income survey and about 9,000 households surveyed on both income and expenditure) in about 3,000 communes/wards which were representative at national, regional, urban, rural and provincial levels. A subset of households that were interviewed in 2006 were re-interviewed in 2008. These are clearly identified in the 2008 dataset, and we are able to use information on 4086 such households.

The labor market information collected in LFS and VHLSS is not directly comparable. Each dataset has its own reference period and questions. For example, while it is possible to construct indicators such as unemployment and informal employment in the LFS, this is not possible in the VHLSS. On the other hand the VHLSS have valuable information regarding educational attainment. We therefore make use of both types of data.

4
Beginning with the autumn of 1989, there is period in which 6, takes on large negative values. This latter period coincides with the beginning of a series of economic reforms in Argentina.<sup>3</sup> The reforms include the State Reform Law (in September 1989) which announced, among other things, various privatizations, and the New Foreign Investment Regime (in November 1989) which **essentially** opened the Argentine capital markets to foreign investors by eliminating restrictions on foreign ownership (except in selected sectors) and by eliminating restrictions on the repatriation of capital.

The values of 6t for Brazil (Figure 5) are particularly large in the period from 1985 to 1989. The largest deviations occur in 1986. This corresponds to a period in which the govemment announced the Cruzado Plan which instituted strict price controls on goods, wages, and official exchange rates. There was a short-lived boom in the stock market in which the IFC index of stocks doubled (in $US terms) in the span of two months (from February to April). The boom was followed by a decline in the IFC index stocks to their February levels by the end of the year.

Chile (Figure 6) has extremely large values of adjusted mispricing in the 1977-1978 period. There are smaller (but still large) values of mispricing in 1981 and 1987.

Colombia (figure 7) shows a steady decline in Et for the periods ending March 1986 through March 1988. After that period O, stays relatively close to zero until 1992.

The values of O, are relatively close to zero for Greece (Figure 8) until the mid-1980's. There is also a large increase in mispricing in 1990. After several years of a socialist government (1981-1989) and a year in which two elections failed to produce a clear winning party, the Sources of information on economic and political developments as well as extant capital controls in emerging markets are Chuppe and Atkin (1992), International Finance Corporation (1993) Park and Van Agtmael (1993) and Levine and Zervos (1994).

16
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

- Briceño-Garmendia, Cecilia and Shkaratan, Maria. 2010. ―Power Tariffs: Caught between Cost-Recovery and Affordability.‖  Policy Research Working Paper forthcoming, World Bank, Washington DC

- World Bank. 2010a. _Angola: Electricity Dialogue_ . Back to the Office Report, World Bank, Washington, DC,  March 15–26.

# ———. 2010b. _Angola Enterprise Surveys 2010_ .

- www.enterprisesurveys.org/ExploreEconomies/?economyid=142&year=2009.

- ———. 2010c. ―Getting Electricity: A Pilot Indicator Set from the Doing Business Project.‖ <u>http://doingbusiness.org/data/exploretopics/getting-electricity.</u>

# Transport

- Bofinger, Heinrich C. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

- Carruthers, Robin, Ranga Rajan Krishnamani, and Siobhan Murray. 2009. ―Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa.‖ AICD Background Paper 7, Africa Region, World Bank, Washington, DC.

- Curtis, Barney. 2009. ―The Chirundu Border Post: Detailed Monitoring of Transit Times.‖ SSATP Discussion Paper No. 10, Regional Integration and Transport—RIT Series, Africa Region, World Bank, Washington, DC.

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

- Nathan Associates. 2010. _Definition and Investment Strategy for a Core Strategic Transport Network for Eastern and Southern Africa_ . Draft Report prepared for the World Bank, Washington, DC, as of October 2010.

- Ocean Shipping Consultants, Inc. 2009. ―Beyond the Bottlenecks: Ports in Africa.‖ AICD Background Paper 8, Africa Region, World Bank, Washington, DC.

46