rafmacalaba/datause-encoder-data huggingface dataset:
natural experiments (see, for example, Black et al. (2005), Currie and Moretti (2003)). We are not aware of any data on twins or adoptees that span the post‐reform period in rural China. Developing instrumental variables strategy is also daunting in our case, as it is almost impossible to find credible and comparable natural experiments for two years almost one and a half decades apart.

To understand possible causal roles played by parental education and occupation, we take advantage of a battery of recent econometric advances that do not rely on the standard exclusion restrictions required in an instrumental variables approach. First, we use the approach developed by Altonji, Elder and Taber (2005) (henceforth AET (2005)) and provide evidence on the sensitivity of the estimated intergenerational persistence with respect to different degrees of correlation between unobserved characteristics of children and parents.<sup>14</sup> Second, we utilize the approach developed by Klein and Vella (2009) that exploits restrictions on second moments for identification in the absence of any credible instruments. For recent applications of identification based on restrictions on second moment (heteroskedasticity), see, for example, Schroeder (2010), Emran and Hou (2013), Emran and Shilpi (2012).

The central conclusion from our empirical analysis is that intergenerational occupational mobility increased dramatically from 1988 to 2002 for both sons and daughters, but educational mobility remained largely static for daughters, and it worsened significantly for sons within a span of 14 years. The evidence from conditional correlations and multivariate OLS regressions indicates 14 The AET (2005) approach can also be used to estimate lower bounds on intergenerational links that cannot be driven by unobservable common characteristics across generations. The lower bound estimate, however, requires strong observables so that it is plausible to impose the restriction that selection on observables is equal to the selection on unobservables. For a recent application of AET (2005) sensitivity analysis and lower bounds estimates to intergenerational economic mobility, see the analysis of occupational mobility in rural Nepal and Vietnam by Emran and Shilpi (2011). The AET lower bound estimates in our application, however, may not to be very informative, because the observables are not powerful enough to make the assumption of equality of selection on observables and unoservables meaningful. I, however, note that an earlier version of the paper contained the AET (2005) lower bound estimates, and the conclusions from the lower bound estimates are same as the conclusions reported here.

7
**Figure 10: Skill group requirements of the SDS’s 30 occupations by skill level** _Share of workers reporting how frequently the skill group is used (%)_

<!-- Start of picture text --> a. Reading b. Writing c. Math<br>0 50 100 0 50 100 0 50 100<br>Professionals<br>Technicians and associate<br>professionals<br>Service and sales workers<br>Craft and related trades<br>workers<br>Plant and machine operators,<br>and assemblers<br>Elementary occupations<br>d. Social e. Problem solving f. Technical<br>0 50 100 0 50 100 0 50 100<br>Professionals<br>Technicians and associate<br>professionals<br>Service and sales workers<br>Craft and related trades<br>workers<br>Plant and machine operators,<br>and assemblers<br>Elementary occupations<br>g. Physical h. Management i. Digital<br>0 50 100 0 50 100 0 50 100<br>Professionals<br>Technicians and associate<br>professionals<br>Service and sales workers<br>Craft and related trades<br>workers<br>Plant and machine operators,<br>and assemblers<br>Elementary occupations<br>High<br>Semi<br>Low<br>High<br>Semi<br>Low<br>High<br>Semi<br>Low<br><!-- End of picture text --> Note: Rarely is never or less than once a month. Sporadically is at least once a week to at least once a month. Daily is every day from once a day to hourly. Source: SDS.

17
# **6.5  Accounting for Forest Clearing**

The composite biodiversity indicator in Figure 7 incorporates range and ecoregion maps that have not been explicitly adjusted for forest clearing. To provide a more accurate view of current biodiversity status, we combine the information sets reflected in Figures 3 and 7. Within each 500 m Hansen cell, we compute the product of the biodiversity indicator value and the estimated percent that remains forested in 2016. We aggregate to 0.1-degree grid cells by summing products within each aggregate cell and normalizing results so that the maximum cell value is 100. Comparison of the result in Figure 8 with Figure 7 shows that incorporation of forest clearing has a very significant effect: The previous, simple “wedge” geometry is replaced by a complex pattern with a broad distribution of the highest values. The highest-value areas in Figure 7 retain importance, but they are joined in Figure 8 by high-value clusters in Houaphan, Vientiane and Xaisômboun. In summary, the effect of incorporating forest clearing is wide dispersal of the remaining high-priority biodiversity conservation areas. In the clearing-adjusted map displayed by Figure 8, every Laotian province has clusters that rank among the highest-priority areas in Lao PDR.

27
information coming from statistical households’ surveys. Labor growth is exogenous. Capital accumulation derives from savings/investment decisions. The model has a vintage structure for capital that allows for putty/semi-putty assumptions with sluggish mobility of installed capital.

The model allows for a wide range of productivity assumptions that include autonomous improvements in energy efficiency that can differ across agents and energy carriers. The model can be calibrated to different SAMs that follow a standard set of conventions in representing the economic structure. The model is implemented in the General Algebraic Modeling System (GAMS) software and an aggregation facility is used as a front-end to the model to allow for full aggregation flexibility.

# **_Model Description_**

The IRENCGE-DF model is a recursive dynamic computable general equilibrium (CGE) model. Each year, a scenario is solved as a static equilibrium, with dynamic equations linking exogenous factors (such as population growth and capital accumulation) across years with, in addition, update equations for productivity factors. Each static equilibrium relies on a relatively standard set of equation specifications.

Production is modeled using a series of nested constant-elasticity-of-substitution (CES) functions designed to capture the substitutions and complements across the different inputs, notably capital and labor, but also with a focus on energy as energy policies are one of the key objectives of the IRENCGE-DF model. Energy is assumed to be a near-complement with capital in the short-run, but a substitute in the long-run. Thus, rising energy prices tend to lead to rising production costs in the short-run when substitution is low, but a long-run response would lead to energy-saving technologies that dampen the cost-push factor. This feature of the model is embodied in a vintage capital structure that captures the semi-putty/putty relations across inputs with more elastic long-run behavior as compared to the short-run.

The model allows for both multi-input and multi-output production. The former, for example, would allow for electricity supply to be produced by multiple activities—thermal, hydro, solar and other renewable forms of electricity production. The latter allows for a single activity to produce more than one product—for example, oil seed crushing produces both vegetable oils and oil cakes (for feed).

Labor and capital income are largely allocated to households with pass-through accounts to enterprises. Government revenue is derived from both direct and indirect taxes.

Households are disaggregated into ten income groups and their demand is modeled using the constant-differences-in-elasticity (CDE) demand function that is the standard utility function used in the Global Trade Analysis Project (GTAP) model. The model allows for a different specification of 7
37 Appendix Table 2. Distribution of Remittance-Receiving Households by Decile Group, Ranked by Observed Per Capita Household Expenditure, Excluding Remittances

|**Rank**|**Households**<br>**receiving**<br>**internal**<br>**remittances**<br>**(from Ghana)**|**Internal**<br>**remittances as**<br>**percent of total**<br>**per capita**<br>**household**<br>**expenditure**<br>**(including**<br>**remittances)**|**Households**<br>**receiving**<br>**international**<br>**remittances**<br>**(from African**<br>**or other**<br>**countries)**|**International**<br>**remittances as**<br>**percent of total**<br>**per capita**<br>**household**<br>**expenditure**<br>**(including**<br>**remittances)**|
|---|---|---|---|---|

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|(decile)|(percent)|(percent)|(percent)|(percent)|
|Lowest 10|9.4|10.8|8.1|10.5|
|Second 10|11.1|11.7|6.0|11.8|
|Third 10|9.7|8.4|4.7|18.1|
|Fourth 10|11.5|7.1|6.1|11.4|
|Fifth 10|10.5|9.2|8.7|13.9|
|Sixth 10|10.0|7.3|7.2|9.9|
|Seventh 10|9.7|12.3|13.1|20.7|
|Eighth 10|9.1|10.2|10.5|18.8|
|<br>Ninth 10|9.2|8.8|13.9|12.5|
|Top10|9.8|14.8|21.7|20.4|
||100.0||100.0||

Notes: All values based on observed – not predicted – data. Households ranked into decile groups on the basis of observed per capita household expenditure (excluding remittances). For those households receiving internal remittances (from Ghana), column (2) shows the percent of total per capita household expenditure (including remittances) coming from internal remittances. For those households receiving international remittances (from African or other countries), column (4) shows the percent of total per capita household expenditure (including remittances) coming from international remittances.

Source: Calculated from 1998/99 Ghana Living Standards Survey (GLSS 4).
# 5. PROVINCE LEVEL EMPIRICAL EVIDENCE

# Evidence from provincial data confirms the findings of the

previous section. Because literacy and income data are based on the census, the results do not reflect any sampling bias in the PLSS. In the last section the focus was on household level variables (the subset Z2 ). In this section I experiment with various infrastructural parameters (Z 1), indicating the degree of development of transport and communications in each province or department.

Using data from the department level is tantamount to assuming that households within a department are identical with respect to the parameters they face: income (Y), the rates of return to schooling in the three sectors (a, 6, -, and 6), and the time-allocation parameters (t ). However, each department has two regions, rural and urban. As in the previous section I assume that the price of schooling (PS) is lower in urban than in rural regions.

# Definitions of Dependent Variable: Illiteracy Rates

Sr : The census data do not provide gender-specific schooling attainment or enrollment by age group for each department. Since illiteracy rates are available at the required level of disaggregation, I use cohortspecific illiteracy rates as a proxy for investment in schooling in the department. Clearly, literacy rates are a gross approximation for schooling levels. There are two defenses for this procedure: First, the study focuses on the _differences_ in human capital between males and females. Even though illiteracy rates do not reflect absolute 35
To summarize, despite investing significant effort in cleaning the data, we did not always receive clear responses from the authorities and are concerned that they suffer from survey fatigue. We therefore recommend ongoing efforts to clean (and update) the data. It might also be noted that some countries chose not to respond to any surveys, not to respond to some surveys but to others, and not to answer some questions but others, which raises the question as to whether this was a strategic decision or simply survey fatigue.

We will not go into detail about the specific contents of the survey here given the earlier explanations provided in BCL (2006, 2004, and 2001). The latest survey continues to group the survey questions and responses into the same twelve sections as previously, namely,

- Entry into banking

- Ownership

- Capital

- Activities

- External auditing requirements

- Internal management/organizational requirements

- Liquidity and diversification requirements

- Depositor (savings) protection schemes

- Provisioning requirements

- Accounting/information disclosure requirements

- Discipline/problem institutions/exit, and

- Supervision.

Also, as is evident in the survey, the majority of questions are structured to be in a yes/no format, or otherwise require a precise, often quantitative, response. Experience suggests that simple and precise questions increase the response rate and reduce the potential for mis-interpretation.

With the third survey, we now have data spanning almost a decade, as the first responses to the initial survey were recorded in 1998. Since Survey I was the initial launch of the survey, and as internet penetration in a number of developing countries was still on the increase, many of the responses came in gradually during 1998-99, but a number of them were received in 2000 as well. The second survey (Survey II) was conducted in early 2003, assessing the state of regulation as of the end of 2002. Survey III, the latest update, sought a characterization of the environment as of the end of 2005. However, it took considerable time to clean the data, which involved going back to country authorities for clarifications, and process it for presentation on the World Bank website. Thus the data from Survey III was only available in early July of 2007 and it is perhaps accurate to interpret the responses as describing the situation in 2006.

5
<mark>participation divides t</mark> o <mark>translate into inequalities</mark> in who <mark>benefits, t</mark> here are <mark>two key steps: 1) inequalities i</mark> n <mark>participati</mark> on <mark>must translate into inequalities</mark> i <mark>n what i</mark> s <mark>demande</mark> d <mark>via the participati</mark> on, and <mark>2) inequalities</mark> in <mark>what</mark> is <mark>demande</mark> d <mark>must translate into inequalities</mark> in <mark>policy outcomes. Even if both links</mark> exis <mark>t, each link’s strength will determine the exte</mark> nt to which <mark>inequalities i</mark> n <mark>participati</mark> on <mark>map onto inequalities in policy outcomes. W</mark> hile these links are plausible, we argue they are contingent on the institutional design decisions embedded in the online platforms and the surrounding political context.

This <mark>paper reviews four case</mark> s o <mark>f public participation on civic technologies (civic tech) platforms where data f</mark> rom <mark>existing studies allow us t</mark> o <mark>trace the full process of inequality through 1) the initial digital divide, 2) participants’ demographics, 3) demands m</mark> ade <mark>through the process,</mark> and <mark>4) policy outcomes. We examine online voting</mark> in <mark>the Brazilian state of Rio Grande</mark> do <mark>Sul’s participatory budgeting (PB) process, the local problem reporting Fix My Street (FMS) platform</mark> i <mark>n the Unite</mark> d <mark>Kingdom, the Iceland online crowdsource</mark> d <mark>constitut</mark> ion <mark>process,</mark> and <mark>the global petitioning platform change.org.</mark> <mark>Participation on these platforms is government/civil society-led rather than citizen-led (Simonofski, Fink and Burnay, 2021) in that the platform is specifically designed for a particular type of civic engagement and there is a clear policy goal of the participati</mark> on (Peixoto and Fox, 2016) <mark>rather than political discuss</mark> ion as an end itself (e.g. on <mark>social media). W</mark> hile the design of e- participation platforms and the social contexts of these platforms vary substantially, we chose four cases that illustrate each combination of a large or small digital divide and low or high constraints on the choices participants can make through the platform.

This paper first describes what might affect links between 1) the digital divide, 2) inequalities in participation on digital platforms, 3) inequalities in demands made through the platform, and 4) inequalities in the policy response to demands made through the platform. We then analyze the four case studies through this theoretical lens, looking at whether each link holds in practice. None of the four cases fits the assumed model of digital inequality, with at least 3
countries. An origin-destination matrix of all flights in a week in East Africa shows that Uganda, after Tanzania, has the highest number of flights per week from Kenya and Ethiopia (figure 13). Uganda also benefits from frequent services provided by other international carriers such as Emirates.

**Figure 12. Evolution of seats and city pairs in Uganda (2001-2009)**

<!-- Start of picture text --> a. Seats b. City pairs<br>3,000,000 30<br>2,500,000 25<br>2,000,000 20<br>1,500,000 15<br>1,000,000 10<br>500,000 5<br>- 0<br>2001 2004 2007 2009 (Est) 2001 2004 2007 2009 (Est)<br>Total Total International<br>International<br>Intercontinental excluding flights between NA and SSA Intercontinental Domestic<br>Domestic<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source:_ Bofinger 2009.Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ As reported to international reservation systems.

NA = North Africa; SSA = Sub-Saharan Africa.

**Figure 13. All flights in one week in East Africa, 2007**

|||Burundi|**_Ethiopia_**|D<br>**_Kenya_**|estinatio<br>Rwanda|n<br>Sudan|Tanzania|Uganda|
|---|---|---|---|---|---|---|---|---|
||Burundi||_7_|_8_|20|||7|
||**_Ethiopia_**|_7_||_11_|_7_|_15_|_21_|_14_|
|n|**_Kenya_**|_7_|_11_||_15_|_34_|_87_|_39_|
|Origi<br>|Rwanda|20|_7_|_14_|||1|11|
||Sudan||_12_|_17_||||4|
|T|anzania||_7_|_48_||||6|
||Uganda|7|_14_|_39_|11|12|16||

_Source:_ Bofinger 2009.

23
be a concern if women are less likely to accurately remember more distant births and deaths. To minimize recall errors, we do not use information on births that occurred more than 11 years prior to the date of the survey. As we discuss below, our results are robust to different cut-offs for the recall period. Second, any given survey is representative of women ages 15-49 at the time of the survey, but is not representative of all births and child deaths in earlier years. To see this, note that a woman aged 49 in a survey carried out in 2000 would have been 39 in 1990. If no surveys were carried out between 1990 and 2000 in this country, no data would be available on births to women aged 40 or older in 1990. Children born to older women may respond to economic fluctuations differently than those born to younger women. To avoid this problem, we discard from the sample births to women age 40 or older. Our analysis therefore provides meaningful estimates of the relationship between income fluctuations and infant mortality for women aged 15 to 39. (We note, however, that only 1.2 percent of births in our sample of DHS countries occur to women age 40 or older.) This retrospective construction of births and infant deaths to women aged 15-39 results in series of varying lengths and with varying start periods depending on the number and dates of DHS surveys in each country.

The DHS collect a great deal of _current_ information on mothers (for example, their education levels, whether they are employed) and children (for example, the gender and birth order and, in the most recent surveys, height and weight). Some DHS also ask respondents about their use of health services—for example prenatal check-ups and the place of delivery. However, these data are not collected in a comparable fashion in every survey, and typically are available only for the last birth. The degree to which we can analyze possible transmission mechanisms from income to infant mortality with our data is therefore limited.

6
increasing underemployment in rural areas, there is, however, rationale behind the desire to see the private sector take greater root outside its traditional strongholds. While causation has not been established, there is a strong association between high levels of private sector development and low levels of poverty in Vietnam.

50. Seen across Vietnam’s main three geographic divisions—North, Center, South— the domestic formal private sector is indeed diversifying. For the past five years, the shares of the North and Center have steadily risen. In the past two years, this trend has accelerated, with the percentage of northern firms increasing from 17 percent to 22 percent. Over the same period the Center’s share also increased three percentage points, leaving the South’s share to fall from 74 percent to 66 percent. Given the South’s enormous lead, however, one must be cautious in assuming that the beginning of a convergence of the three regions’ shares necessarily means that the degrees of business friendliness among different local policy environments are also converging. The South may, instead, have hit a point of diminishing growth due to the sheer size of its existing set of companies.

**Table 5.6 Private Companies by Region, 1998 and 2000**

|**Existing Co**|**mpanies**<br>**1**|**998-2000**|**Percentage o**<br>**Registered F**<br>**Not Account**|**f**<br>**irms**<br>**ed For**|
|---|---|---|---|---|
|**Provinces**<br>**1998**|**2000**|**Growth**|**1998**|**2000**|
|Red River Delta<br>3602|5732|59.1%|33.1%|40.5%|
|North East<br>752|1114|48.1%|30.1%|41.0%|
|North West<br>74|146|97.3%|27.5%|22.3%|
|**_The North_**<br>**_4428_**|**_6992_**|**_57.9%_**|**_32.6%_**|**_40.3%_**|
|North Central Coast<br>724|1336|84.5%|35.1%|33.9%|
|South Central Coast<br>1568|2383|52.0%|44.0%|40.8%|
|**_The Center_**<br>**_2292_**|**_3719_**|**_62.3%_**|**_52.2%_**|**_32.6%_**|
|Central Highlands<br>584|1248|113.7%|44.0%|37.1%|
|Northeast South<br>11062|12296|11.2%|16.5%|42.0%|
|Mekong River Delta<br>7655|7338|-4.1%|36.8%|48.6%|
|**_The South_**<br>**_19301_**|**_20882_**|**_8.2%_**|**_26.2%_**|**_44.7%_**|
|**Total**<br>**26021**|**31593**|**21.4%**|**29.8%**|**42.7%**|

Source: GSO, 2001 51. It is interesting to note that, while new registrations were indeed higher in the North and Center over the two-year period, it was in fact a rapidly increasing divergence between registrations and existing companies in the South that was the main reason that the other two regions were able to gain significant ground. Again, it was mostly divergence between the registration and existence of sole proprietorships, in particularly, that accounts for the overall difference. Possible reasons for this divergence have already been discussed.

52. Much of the poor performance of the private sector in the South occurred in a the northeastern most provinces of the Mekong Delta—six provinces lying between Ho Chi 25