rafmacalaba/datause-encoder-data huggingface dataset:
sector choice to earnings gaps is estimated as a parameter of a dynamic structural sector choice model.<sup>12</sup> Our estimates imply large earnings differentials between the public and the private sectors of wage employment. In the absence of direct evidence on impediments to mobility, these differentials would not necessarily imply segmentation. Still, they should make the hypothesis of segmentation more credible in the Ethiopian context than it would otherwise be. At the same time, the sensitivity of sector choice to earnings gaps seems to have gone up in more recent waves of the survey, not only between the private and the public sectors of wage employment, but also between formal sector wage employment and the informal sector. In particular, the role of relative earnings in selection into the informal sector seems to have gone up substantially.

These results suggest that, if Ethiopia’s urban labor market is indeed segmented along the public vs. private or formal vs. informal sector dichotomies, it has got less and less so since 1994. The raw transition matrices we compute from the survey sample support this conclusion as do the parameters of the dynamic sector choice models that we have estimated on the same dataset. We see from the raw transition matrices that, although there was little mobility across sectors prior to the 1997 wave of the survey, mobility rates increased considerably between 1997 and 2000. Mobility rates of the 2004 wave were also at least as high as those of the 2000 wave. The increase in the rates of mobility across sectors of employment was accompanied by a small but persistent decline in the sample rate of open unemployment.

The increase in sample transition rates across survey waves is consistent with changes in state dependence parameters of the dynamic choice model that we estimate. The parameters are all positive and statistically significant in the equation for public sector employment, in the equation for informal sector employment and in the unemployment equation. At the same time, they have become smaller in more recent survey waves in each case.

> 12 Our approach here is similar to that of Gong and van Soest (2002), who estimate a dynamic structural sector choice model by using predicted wages (though not premiums) as one of the potential determinants of mobility between the formal and informal sectors.

5
and Shifting Canadian Immigration Policy.” _Journal of Immigrant & Refugee Studies_ 14(1): 46–65.

- Bratsberg, Bernt, Jeremy Ferwerda, Henning Finseraas, and Andreas Kotsadam (2020), “How Settlement Locations and Local Networks Influence Immigrant Political Integration.” _American Journal of Political Science_ : ajps.12532.

- Campo, Francesco, Sara Giunti, and Mariapia Mendola (2021), _The Refugee Crisis and Right-Wing Populism: Evidence from the Italian Dispersal Policy *_ . https://www.iza.org/publications/dp/14084/the-refugee-crisis-and-right-wing-populismevidence-from-the-italian-dispersal-policy (May 26, 2021).

- Cholewinski, Ryszard, (2004), “Family Reunification and Conditions Placed on Family Members: Dismantling a Fundamental Human Right.” _European Journal of Migration and Law_ 4(3): 271–90.

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- Clayton, Katherine, Jeremy Ferwerda, and Yusaku Horiuchi (2021), “Exposure to Immigration and Admission Preferences: Evidence from France.” _Political Behavior_ 43(1): 175–200.

- Connor, Phillip (2018), “A Majority of Europeans Favor Taking in Refugees, but Most Disapprove of EU’s Handling of the Issue.” _Pew Research Center_ .

- Dahl, Malte (2018), _Social Desirability Bias in Candidate Conjoint Experiments: What Is the Optimal Design When Studying Sensitive Topics?_

- Departamento Nacional de Estadística (DANE) (2021), https://www.dane.gov.co/files/investigaciones/boletines/ech/ech_informalidad/bol_ech_i nformalidad_jun20.pdf _Boletín Técnico Gran Encuesta Integrada de Hogares (GEIH). Noviembre 2020 - Enero 2021. Medición de Empleo Informal y Seguridad Social_ .

- Dinas, Elias, Konstantinos Matakos, Dimitrios Xefteris, and Dominik Hangartner (2019), “Waking Up the Golden Dawn: Does Exposure to the Refugee Crisis Increase Support for Extreme-Right Parties?” _Political Analysis_ 27(2): 244–54.

- Dinesen, Peter Thisted, and Frederik Hjorth (2020), “Attitudes toward Immigration: Theories, Settings, and Approaches.” In _The Oxford Handbook of Behavioral Political Science_ , Oxford University Press.

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- Enos, Ryan D. (2014), “Causal Effect of Intergroup Contact on Exclusionary Attitudes.” _Proceedings of the National Academy of Sciences of the United States of America_ 111(10): 3699–3704.

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- Gillespie, Brian Joseph, Clara H. Mulder, and Michael J. Thomas (2020), “Migration for

28
|**Variables**|**Program**|**Standard**<br>**Error**|**Observations**|
|---|---|---|---|
|Number of Members Migrated to Urban Areas for<br>Employment or Search of Employment|-0.013|0.053|4,316|
|Number of Members Migrated to Rural Areas for<br>Employment or Search of Employment|0.074*|0.039|4,316|
|Total Yearly Cash & In-kind Income from all sources<br>in Rs.-winsorized-1 percent|46.663|13619.58|4,316|
|Total Yearly Cash & In-kind Income from Migration<br>in Rs. (Cash and Kind)-winsorized|1,593.749|1211.525|4,316|
|<br>Total Yearly Cash & In-kind Income from Livestock<br>in Rs.-winsorized-1 percent|2,471.463|2647.996|4,316|
|Total Yearly Cash & In-kind Income from Casual<br>Labor in Rs. (Cash and Kind)-wins|5,586.228|5347.75|4,316|
|Total Yearly Cash Income from Salary in Rs. -<br>winsorized-1 percent|1,450.220|7396.362|4,316|
|<br>Total Yearly Cash & In-kind Income from<br>Agriculture in Rs.-winsorized-1 percent|7,685.463|6186.849|4,316|
|Total Yearly Income from Land (cash and kind)-<br>winsorized-1 percent|-328.243|1727.323|4,316|
|Total Yearly Cash & In-kind Income from Fisheries<br>in Rs.-winsorized-1 percent|-726.672|629.181|4,316|
|Total Yearly Cash & In-kind Income from<br>Enterprises in Rs.-winsorized-1 percent|-<br>15,524.562*|8858.266|4,316|
|Total Yearly Income from Public Transfers<br>MGNREGS and other Government Programs|-107.385|2225.988|4,316|
|Total Yearly Cash & In-kind Income from Private<br>Transfer in Rs. (Cash and Kind)|-1,694.819|1215.663|4,316|
|<br>Total Yearly Cash Income from Other Sources in Rs.<br>-winsorized-1 percent|-1,348.990|1219.37|4,316|
|Total Number of Village Level GroupParticipation|0.026|0.046|4,316|
|Total Number of Different Programs|0.221|0.194|4,316|
|Total Number of Enterprises in Household|-0.139***|0.046|4,316|
|Does the Household own anyEnterprise?(Y/N)|-0.131***|0.044|4,316|
|Total Number of employees (Permanent and<br>Temporary)|-0.161*|0.087|4,316|
|Total Number of external employees (Permanent<br>and Temporary)|-0.029|0.036|4,316|
|Anyexternal Fundingfor enterprises|-0.026|0.02|4,316|
|Households cultivating HVA crop (Non Paddy-<br>Wheat-and-Millets)-(Y/N)|0.219*|0.123|4,316|
|Does anybody in the Household have a NREGS card?<br>(Y/N)|0.039|0.154|4,316|
|<br>Total Paid days for all Household Members under<br>MGNREGS work (in a Year)|20.697|266.267|4,316|

Furthermore, we also analyze the Average Treatment Effect on the Treated(ATT) estimates on the program participants in treatment areas. For this estimation, we define the treatment as only the participant households in the treated areas but drop the non21
**Table A1.** Differences in perceptions of gender roles by all men and women surveyed ( _weighted_ )

|**Gender role attitudes (Proportion Agreeing with Statements)**|**Women**<br>**Mean**|**SD**|**Men**<br>**Mean**|**SD**|**T-test of**<br>**Differences**|
|---|---|---|---|---|---|
|Only men should work outside the home|0.246|0.43|0.215|0.41|0.03|
|It is not okay for women to work outside of the home, even if she has young<br>children less than 5 years|0.174|0.38|0.257|0.44|-0.07**|
|Men should not perform household chores|0.289|0.45|0.328|0.47|-0.05|
|Women should perform household chores|0.977|0.15|0.98|0.14|-0.01|
|Men should not care for children/dependents|0.097|0.3|0.141|0.35|-0.03|
|Women should care for children/dependents|0.968|0.18|0.927|0.26|0.04***|
|Women are not as capable as men to manage workers.|0.06|0.24|0.137|0.34|-0.07***|
|Women are not as capable (intellectually) as men of being successful|0.066|0.25|0.099|0.3|-0.03*|
|Men should make all the important business and financial decisions in the<br>family|0.375|0.48|0.467|0.5|-0.09**|
|Women farmers should focus on growing food for the family|0.319|0.47|0.289|0.45|0.031|
|It is not okay for a woman to grow crops for sale in the market|0.054|0.23|0.073|0.26|-0.02|
|Only men should grow crops for sale in the market.|0.239|0.43|0.178|0.38|0.05|
|It is not okay for a woman to manage men working on her land|0.041|0.2|0.087|0.28|-0.04**|
|It is okay for a man to manage women working on his land|0.948|0.22|0.971|0.17|-0.02|
|Observations|585||276|||

T-statistics- * p<0.10, ** p<0.05, *** p<0.01 47
to guide spending ministry costing of programs and more specific mandated provisions to guide MTEF budget and forward year cost estimate practices. The method is to compare and contrast program specification and costing practices and to identify instructive examples of costing methods and their application. Table 4.1 sets out the program costing practices. Program specification and costing guidelines were not available in the cases of Austria and the Netherlands.

# **4.1 Requirements and guidance for program specification**

As previously noted, all jurisdictions use a programmatic structure for the budget estimates; however, program specification and their level of disaggregation and standardization vary significantly. Of the four jurisdictions, only Australia and Canada appear to issue formal policies on program budget reporting.

The Australian programs policy is mandatory. All central general government sector entities are required to budget, measure performance and report on an ‘outcomes and programs’ basis. Budget reporting in Australia comprises three levels: ministerial portfolios (of which there are 21), entities (departments and agencies, of which there are 111) and programs (of which there are approximately 300). Each entity is required to report budgeted expenses and performance using an ‘outcomes statement’ that specifies the intended high level outcomes for the entity and the ‘programs’ (activities) that contribute to achievement of those outcomes. For each ministerial portfolio, detailed information about programs and budget allocations for relevant entities are grouped together in a Portfolio Budget Statement (PBS).

A program is the minimum level of reporting required for budget documentation, and is recorded in both the estimates information management system – the Central Budget Management System (CBMS) – and the PBS; program data must be consistent between CBMS and PBS. The program guidelines stipulate four fundamental criteria for specifying programs:

- Programs must be activities or groups of activities that contribute to the intended results of each outcome statement.

- Programs must be ongoing in nature (minimum duration of 5 years).

- Programs must be material in size of annual expenditure (greater than $50m-$100m).

- Programs must map to a single Government Financial Statistic (GFS) sub-function and single government outcomes statement.

It should be noted, however, that in the Australian case ‘programs’ defined for CBMS and PBS reporting may not necessarily map either to the organizational structure of entities or the 18
and informal access to credit, and remittances.<sup>9</sup> In common with the National Bureau of Statistics (NBS) Rural Household Survey, respondent households keep daily diaries of income and expenditure, and a resident administrator living in the county seat visits with households once a month to collect information from the diaries.

Our measure of consumption includes nondurable goods expenditure plus an imputed ‡ow of services from household durable goods and housing. In order to convert the stock of durables into a ‡ow of consumption services, we assume that current and past investments in housing are “consumed”over a 20-year period and that investments in durable goods are consumed over a period of 7 years.<sup>10</sup> We also annually “in‡ate” the value of the stock of durables to re‡ect the increase in durable goods’prices over the period. Finally, we de‡ate all income and expenditure data to 1986 prices using the NBS rural consumer price index for each province.

There has been some debate over the representativeness of both the RCRE and NBS surveys, and concern over di¤erences between trends in poverty and inequality in the NBS and RCRE surveys. These issues are reviewed extensively in Appendix B of Benjamin et al (2005), but it is worth summarizing some of their ...ndings here. First, when comparing cross sections of the NBS and RCRE surveys with overlapping years from cross sectional surveys not using a diary method, it is apparent that some high and low income households are under-represented.<sup>11</sup> Poorer illiterate households are likely to be under-represented because enumerators ...nd it di¢ cult to implement and monitor the diary-based survey, and refusal rates are likely to be high among a- uent households who ...nd the diary reporting method a costly use of their time. Second, much of the di¤erence between levels and trends from the NBS and RCRE surveys can be explained by di¤erences in the valuation of home-produced grain and treatment of taxes and fees.

> 9One shortcoming of the survey is the lack of individual-level information. However, we know the numbers of working-age adults and dependents, as well as the gender composition of household members.

> 10Our approach to valuing consumption follows the suggestions of Chen and Ravallion (1996) for the NBS Rural Household Survey, and is explained in more detail in Appendix A of Benjamin et al. (2005).

> 11The cross-sections used were the rural samples of the 1993, 1997 and 2000 China Health and Nutrition Survey (CHNS) and a survey conducted in 2000 by the Center for Chinese Agricultural Policy (CCAP) with Scott Rozelle (UC Davis) and Loren Brandt (University of Toronto).

16
China provided by the National Bureau of Statistics (NBS), based on their household surveys, and our interviews with staff of NBS.

# **The 2005 PPP rate for China**

The 2005 ICP is clearly the most complete assessment to date of how the cost of living varies across the countries of the world. The ICP collected primary data on the prices of a list of internationally comparable goods and services from each of a large number of outlets within 146 countries. The 2005 ICP is a clear improvement over the ICP effort for 1993, which was the last base-year used for our global poverty measures. The number of countries participating in the price survey is larger (146, as compared to 117 in 1993) and the surveys have been implemented in a more scientific basis. New methods were used for measuring government compensation and housing. Ring comparisons (linking regional PPP estimates) were also done for a much larger set of countries (18 in all), which priced global goods. The 2005 data were also subjected to more rigorous validation methods. Otherwise, the PPPs calculated from the ICP data (and presented in World Bank, 2008) follow standard methods; as in the past, the Bank uses a multilateral extension of the bilateral Fisher price index.<sup>6</sup> Given that this is the first time that China has participated in the ICP, one can hardly be very surprised to find that the new PPP differs from the old one. But the difference is large indeed. The new estimate of the consumption PPP for China is 3.46 Yuan to the $ (4.09 if one excludes government consumption), as compared to a PPP rate of 1.42 for 1993. The corresponding “price level indices” (PPP divided by market exchange rate) are 0.52 in 2005 versus 0.25 in 1993.

This paper focuses on the implications of this major data revision for our knowledge about poverty in China, which is not only the world’s most populous country, but also the country that has made the greatest progress against poverty since about 1980s. (In due course we will also produce new estimates for the rest of the developing world.) However, while China’s participation in the 2005 ICP is clearly an important step, that participation was partial in that the government only agreed to implement the ICP price survey in

> 6 As was argued in Ravallion et al. (1991) and reiterated in subsequent papers, the weights attached to different commodities in the conventional PPP rate may not be appropriate for the poor.  An effort is underway to address these concerns in the future. Preliminary results reported in Deaton and Dupriez (2007) do not suggest that the re-weighting needed to derive a “PPP for the poor” will make an appreciable change to the aggregate consumption PPP.

3
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# Brazil

Brazil has a very extensive system of economic censuses which are the main basis fo. national accounts estimates for benchmark years, but its input-output table for 1975 is not yet available.

The 1975 Censo Industrial (IBGE, 1981a and 1981b), which covers mining as well as manufacturing, uses the same concept of value added as the US industrial census which we will henceforth refer to as the "US census concept". In Brazil, the latter concept is called "valor de transformacao". This value amounted to 306,893 million cruzeiros for manufacturing in 1975, as compared with 268,927 million in the national accounts estimate (Gusma Veloso, 1987) of manufacturing's contribution to GDP at factor cost. The difference between the two is due to miscellaneous costs for service inputs. These are not deducted from "valor de transformacao" but the census contains information on these cost items (there are 15 of them, as noted below). In the national accounts these items are deducted as inputs. As in the USA, Brazilian output is valued at producer prices excluding indirect taxes (IPI and ICM), but the output data refer to production including net changes in stocks, whereas US output figures refer to shipments trom the establishment, and do not take account of changes in stocks.

Thus, the Brazilian census information is reasonably congruent with the national accounts. Our adjusted estimate of gross value added in column (2) of table 2.1 is 263,269 million cruzeiros for 1975. This compares with 268,927 million in the national accounts. The remaining difference is due to the imputation for "autonomos" (i.e. non-census establishments) and to a small national accounts adjustment for differences in costs as recorded by companies and establishments.

Although we have arrived at a reasonable reconciliation of the two sources, it seems clear that the Brazilian national accounts understate industrial output by relying almost exclusively on activity as recorded in the industrial census. This understatement of output in the national accounts has been stressed by several observers, e.g. Merrick and Graham (1979), Pfeffermann and Webb (1979), and by the World Bank team who recommended changes in the national accounts (Tyler, Goldberg, Blazic-Metzner, 1984).

There is also a very big discrepancy (see table 2.2) between employment in manufacturing as recorded in the 1980 industrial census (4,839,253) and employment as recorded in the population census (6,939,421) and a similar discrepancy in 1950, 1960 and 1970 as well. As there was no population census in 1975, we cannot check for that year, but it seems clear that the national accounts adjustment for activity by "autc.nomos" is too small.

Finally, it should be noted that the Brazilian industrial census and the national accounts _reat certain primitive agricultural transformation processes (e.g. the more rudimentary kinds of flour milling) as agricultural activities, and some dressmaking activ4i-es are included in "commerce" rather than manufacturing. On the other hand, some repair work, e.g. on motor vehicles is treated as a manufacturing rather than a service activity.
the risk behavior of the group, the larger the scale of the loop, so that the group exhibits higher incidence at the early stages and higher prevalence at the later stages.

**Figure 2. HIV prevalence and infection probability for different risk groups**

<!-- Start of picture text --> 6 0% I - - -<br>50.0% m . m<br>40.0%5*<br>9 30.0% medium nsk<br>20 0%<br>10.0%<br>0,0% 16 .<br>0 0% 5.0% 10.0% 15.0% 20.0% 25.0% 30 0%<br>probability of being Infected during the next 5 years<br><!-- End of picture text --> When individuals move from one risk group to another (for example, when an adolescent girl becomes a commercial sex worker, or a commercial sex worker stops sex work), then their risk profile would move from one of these curves to another, representing a change from one type of risk behavior to a different one.

The analysis estimated the primary cases averted by simulating the evolution of prevalence and infection probability for different risk groups. This made use of the illustrative risk groups as represented in the simulation model developed by the Actuarial Society of South Africa (ASSA). The results are shown in Figure 2. Although it is no substitute for the use of real data (which are lacking). the benefit of this method is that it gives a realistic estimate of the period probability in a Southern African setting, while arriving at a "best guess" at the differences in likely risk behavior between the different risk groups.

# _Secondary infections averted_

A variety of variables is instrumental in determining the number of secondary infections averted. These include:

- the number of sexual partners the individual will have over the period for which the vaccine offers protection

- the number of sexual contacts with these partners

- their probability of being infected, per sexual contact

- whether they, or their partners, have a sexually transmitted infection (STI)

_40_