rafmacalaba/data-use-mentions-extended huggingface dataset:
promotion program was indeed successful in advancing women’s careers. As column (1) in Panel A of Table 3 shows, the women selected for the program were 24 percentage points more likely than the nominated group to have been officially promoted to supervisor after the conclusion of the promotion program. This result remains significant after accounting for multiple hypothesis testing, as the sharpened q-values in squared brackets show. The women also saw some increase in their personal and household income. The women’s personal income rose by about 689 Taka, equivalent to about 7% of the income in the comparison group, as per column (2), and total household income increased by about 84 Taka (about 0.4% of the comparison group mean, see column 3).

However, for the women who did receive an official promotion, income increased more. When I employ an instrumental strategy and use the selected variable as an instrumental variable for being a supervisor in column (4),<sup>45</sup> I find that the personal income of officially promoted women increased by about 17% compared to the comparison group mean, though the estimate is noisy and remains insignificant. Recall, however, that all selected women were assigned to trial as supervisors for at least two months, and therefore had experience in a leadership position. A number of them were also still waiting to be officially promoted at the time of the household surveys. Of course, the official promotion is an endogenous outcome, and I therefore focus on the intent-totreat effects of being selected for the promotion program for the main results.

# **4.2 Decision-making**

The second step of the analysis suggests that the selected women’s power in intra-household decision-making increases, at least initially. As column (2) in Panel B of Table 3 shows, the impact on the decision-making index is large at 0.42 SD in wave 1 of the household survey and significant at the 10% level. However, the effect
prices that are more than double that of standard coffee. Finally, reducing transport costs and the introduction of quality-enhancing techniques will increase the incentives for rural households to participate in the production of exportable products. We assess the impact on incomes and poverty of successful switching of the poorest farmers into higher return economic activities.

Our analysis, based on information from the 2001 household survey, suggests that all three mechanisms have a very strong potential for reducing poverty in Rwanda, with 2
deputy-provincial level with provincial PSC membership, we regard this rank change as a promotion during his tenure. In contrast, if he loses the PSC membership during his term as the city secretary, this is a demotion.<sup>56</sup> Apart from this additional check of determining promotions and demotions for PSs who have a rank change during their term, we reclassify all the 7 PS groups into 3 classes based on their hierarchical levels when they leave office. The three groups are labeled as: class-A consisting of PSs of deputy-provincial level with the membership of the provincial PSC when leaving office; class-B consisting of PSs of the deputy-provincial level without membership of the provincial PSC when leaving office; and class-C consisting of PSs of the prefecture level when leaving office.

Next, we manually identify destination positions for all city party secretaries by exploring the rich information from the CPED data set. We categorize all these new jobs into 45 mutually exclusive and jointly exhaustive groups. These 45 groups are:

- (1) Investigated for corruption or other illegal activities during the PS term, and it is categorized as a demotion for all PSs of class-A, B, and C.

- (2) Suspended or dismissed during the PS term. PSs in this group are usually those who make serious mistakes (from the upper government’s point of view) and take major responsibilities in public affairs. This case is also classified as a demotion for all three classes of PSs.

- (3) Died during the PS term: some city secretaries died of disease or accidents during their term while some PSs committed suicide in fear of investigation. Since public announcements rarely mention the latter case, we practically treat this case as a demotion.

- (4) Full and direct retirement after the PS term. Typically, local leaders do not retire in the full sense immediately after leaving office (Li and Zhou 2005). A city leader who approaches the retirement age will be placed in some honorary position in PC or PPCC (
for Scientific and Industrial Research. Data were collected on the basis of a 20% Census sample in 12 states and using the complete enumeration samples in other states and union territories.

> 2 The 1999-2000 Annual Survey of Industries data on wages has not been released as yet. Various media sources state that the high differential between public and private workers in the factory sector remained. In particular “According to the Annual Survey of Industries for 1999-2000, in the top ten industrialized states, the average annual wage difference between public and private sector industrial workers varies between Rs 12,000 and Rs 90,000. Industrial workers in the public sector are earning 15 to 80% more than the wages earned by workers in the private sector... In Haryana, the wage differential is around 36%, while in Uttar Pradesh it is 45%.” by Mamata Singh at: http://www.rediff.com/money/2002/may/22psu.htm).

8
# **1 Introduction**

This paper uses open access, daily, global NASA satellite measurements of nitrogen dioxide (NO2) to provide estimates of business cycle movements that redress long recognized shortfalls in national accounts and other satellite estimates such as night light measures. Over the last decade, global satellite readings of night lights have revolutionized our ability to estimate economic activity in situations where government data is unreliable, or where subnational data is simply unavailable (Henderson et al., 2012). However, these readings only offer information content beyond GDP in countries with the most inaccurate statistical systems (Chen and Nordhaus, 2011), they collect data at night when most economic activity is minimal, and have notoriously misestimated important cyclical movements. For instance, during the financial crisis when on the ground data estimated GDP in the US to fall 8.9% below trend, night lights were 1% _above_ trend.<sup>1</sup> In contrast, US NO2 was 7 _._ 6% below trend at the end of 2009, clearly reflecting the recession.

In fact, we will show that NO2, a direct byproduct of combustion that stays near its source for relative long periods and is always measured during the daytime, when most economic activities occur, has explanatory power for annual economic fluctuations in addition to both lights and all of the most prominent ground level signals available. Moreover, while each of these signals and GDP are only available at a lag, NO2, is published online just a few days after measurement. Finally, we show that NO2’s uniquely explanatory power applies to all countries, including advanced economies.

Our methodological contribution is an objective, data-driven identification strategy for the error-measurement framework utilized by Chen and Nordhaus (2011) and Henderson et al. (2012). Error-measurement frameworks typically require the analyst to impose prior beliefs concerning data accuracy to overcome bias from errors-in-variables. We exploit the fact that NO2 and night lights are measured at different times to achieve identification through exogeneity in
2008). There may also be political pressures to inflate or reduce numbers.

# **_Population movement tracking systems_**

In situations where the movement of displaced populations is fluid or continuous, a movement tracking system can be a useful tool for providing rough estimates of population flows, including recurrent displacements. Movement tracking systems are useful for monitoring fluid population movements (including spontaneous and organized, internal and cross-border, and returns and resettlement) in remote or inaccessible routes and locations (including displacement sites, places of origin, and places of return and resettlement). UNHCR, IOM and other organizations have developed methods for tracking and monitoring movements of IDPs in over 30 countries, particularly in cases of disaster-induced displacement, but also in some cases of conflict-induced displacement (UNSD 2014). These systems employ a combination of data collection techniques including key respondent interviews, focus group discussions, registration, observations and physical counts, samplings and other statistical methodologies. For examples, UNHCR’s population tracking systems identifies and trains local NGOs to monitor key locations such as IDP settlements, bus stations and roads to report on movements.

The accuracy of data from movement tracking systems is subject to several caveats. These include: limited access to locations and routes due to insecurity; vast geographical areas to monitor; mixed population flows that include refugees, IDPs, pastoral and seasonal movements and economic migrants; massive population flows that overwhelm monitoring capacity; disinclination of individuals to provide information when there is no assistance being offered; pressures from communities to inflate figures to maximize future assistance; and political pressures to suppress accurate reporting on IDP movements. Additionally, due to the fluid nature of displacement in many contexts and the likelihood of recurring displacements, it is not possible to use movement data to provide estimates of population stocks.

# **_Population censuses_**

National population and housing censuses often provide the most comprehensive source of population data and offer the potential for estimating numbers of forcibly displaced people. To estimate the size of displaced populations a census would need to
- 21 -

# **IV. Consequences of the Liberalization of the Capital Account and the Financial Sector**

The liberalization of the capital account and the financial sector resulted in rapid build-up of vulnerability, a vulnerability with both macro and micro manifestations. On the macro side, the liberalization program resulted in a surge in private capital inilows and rapid credit growth. The increased foreign borrowing and rapid credit growth resulted in high leverage at the economy wide level as well as an asset price bubble.33 In turn, this led to a rapid increase in foreign exchange exposure and a shortening **cif** the maturity structure, rendering the economy vulnerable to reversals in capital inflows and downturns in economic activity. One micro manifestation of the economy wide borrowing binge is the rapid build-up of leverage and the increased foreign exchange exposure of the corporate sector. Similarly, as a result of the lending boom and coupled with the practice of collateral based lending, banks and finance companies became more vulnerable to economic shocks in the 1990s by: (i) lending excessively to sectors or firms whose debt service capacity was particularly susceptible to shocks; and (ii) reducing; their own capacity to absorb negative shocks, especially by exacerbating currency and maturity mismatches, by mispricing loans, and by underprovisioning for future potential losses. This build-up of vulnerability is analyzed in greater detail below.

## **1. Surge in Capital Inflows, Increased Reliance on Foreign Capital, and the Shortening of the Maturity Structure**

**_Surge in Capital Inflows._** Together with Malaysia, Thailand is one of the cotmtries that received the largest capital inflows in the East Asia region, indeed in the world, relative to GDP. Between 1988-96, according to data from the Bank of Thailand, Thailand received a staggering cumulative amount of US$ 100.3 billion, about 55 percent of 1996 GDP, or 9.4 percent of GDP on average p.a. (excluding errors and omissions). As can be seen from
of equality.<sup>10</sup> Between 2002 and 2010, there was not a marked increase in segregation. However, since 2012, there was a steady rise in the spatial inequality in the distribution of the poor. Provinces with a cumulative share of about 50% of the total population had about 30% of the poor population in 2002, whereas only about 3% of the poor population in 2020. Over the years, poor provinces such as Dien Bien and Lai Chau saw their share of the poor population increase disproportionately. Despite a rapid decline in average poverty levels, we find that the remaining poor were increasingly segregated in certain provinces in the country.

# **3. Relationship between economic growth, inequality and poverty**

# **3.1. Factors affecting provincial poverty**

We further analyze the VHLSS data to find which factors were highly correlated with provincial poverty levels. Following the existing literature that examines the effects of income and inequality on poverty (Bourguignon, 2003; Kraay, 2006; Chen and Ravallion, 2010; Ferreira, 2010; Marrero _et al._ , 2024), we estimate the following province fixed effects (FE) model 1L 2 ′ (1) In equation (1), _Pi,t_ ii,tt _i,t_ is a poverty index of province iitt iitt _i_ in year iitt tt _t_ , _Yi,ti,t_ is per capita expenditure, ii iitt _Gitit_ is the PP = αα+ ββ LL(YY ) + ββ GG + δδXX + ττ + uu + εε In equation (1), _Pi,t i t_ , _Yi,ti,t Gitit_ is the Gini index, _Xit_ is a vector of explanatory variables including high school completion rates, population density (in logarithmic form), shares of the urban population and the ethnic minority population, share of the population with a high school diploma, and different types of investments such as state spending and investment spending. These are the control variables that
the other. Unfortunately, the administrative links between the two taxes are virtually nonexistent, so that the widespread undervaluation of transactions subject to stamp duties is likely to have significant effects on capital gains revenues.

There is little information on the magnitude of capital gains collections or the individuals who pay the tax. Nevertheless, it is possible to make some approximate estimates of the capital gains revenue loss due to undervaluation of stamp duty transactions, using the same basic approach as earlier. Consider again a “representative” transaction, in which a property with true value of Rs. 1 million is exchanged. Suppose that the property has been held longer than 3 years, so that it is subject to the long-term capital gains tax rate in the individual income tax of 20 percent. Suppose also that the individual understates the true value by various percentages: 10 percent, 25 percent, and 50 percent. The stamp duty revenue loss is indicated in Table 11; the capital gains revenue loss can be similarly estimated because, other things equal, it is only the extent of undervaluation of the final transaction that affects the capital gains collections. The simple calculations in Table 13 indicate the magnitude of the capital gains revenue loss for this representative transaction.

|**Table 13. Capital Gains R**|**evenue Loss for“Represent**|**ative” Rs. 1 Million Transaction**|
|---|---|---|
|**10% Undervaluation**|**25% Undervaluation**|**50% Undervaluation**|
|Rs.20,000|Rs. 50,000|Rs.100,000|

The potential impact on total capital gains collections can also be calculated under various assumptions. Recall that for all States (non-judicial) stamp duties collections in 20002001 totaled Rs. 100,782.3 million. Assume that the average tax rate applied to these transactions in the different States was 15 percent, so that the total
= 1076)<br>||
|---|---|---|
|No child|Has child|Do you have children?|
|(N=137; 12.7%)|(N=939; 87.3%)||
|Youngest child aged 6<br>years and older|Youngest child aged<br>0-5 years|What is the age of your<br>youngest child?|
|(N=504; 53.7%)<br>No one:|(N= 435; 46.3%)<br>HH/Non-HH members/Institutes:|Who took care of your|
|No childcare available|Childcare available|youngest child if you|
|(N=206; 47.4%)|(N=229; 52.6%)|need to go to work?|

Source: Authors.

working-age male and female members; (b) households with only working-age females; and (c) households with only workingage males.

> 7 This paper’s focus on ever-married females ages 15–49 years is consistent with the Demographic Health Survey (DHS). We determine the marital status of women by matching the marital status of women from the household roster with women’s own responses in the individual surveys.

6
in out-of-pocket tobacco-related medical expenses could represent 0.5 percent of household income for the poorest 10 percent of the population.<sup>18</sup> Figure 4. Effect of reducing medical expenses Panel A. 25% Price increase Panel B. 50% Price increase <!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile Decile<br>Medium-bound Elasticity Lower-bound Elasticity Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity Upper-bound Elasticity<br>Source: Estimation using a price shock of 25% Source: Estimation using a price shock of 50%<br>Panel C. 100% Price increase<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity<br>Source: Estimation using a price shock of 100%<br>.3 .6<br>.2 .4<br>.1 .2<br>Inconme Gains (%) Inconme Gains (%)<br>0 0<br>1.5<br>1<br>.5<br>Inconme Gains (%)<br>0<br><!-- End of picture text --> _Source:_ Author estimations, based on the IHS 2012-2016, HIES (2017), Goodchild et. al (2018) and WDI (2017). _Notes:_ Only _outof-pocket_ tobacco-related medical expenses are considered. Deciles are created based on per capita household consumption.

# (C) Change in productive Years of Life Lost (YLL)

Panels A to C of <u>Figure 5</u> show the additional share of income that households could gain as labor incomes, after the tobacco tax increase results in lower years of life lost. Compared to other country applications of the ECBA, these results seem more progressive. This
travel time from a city of 100,000. The appendix graphs this data in the the form of cumulative densities, showing for each density d, the proportion of national population (or area) with density less than or equal to d. Because we are agnostic about density thresholds, each graph is presented both on a density scale of 0 to 50 people/km2, and on one of 0 to 500 people/km2 According to the tabulations, about 13% of LAC populations live at ultra-low densities, of less than 20 per square kilometer. Essentially all these people are more than an hour distant from a large city, and more than half live more than four hours distant. This group plays a key role in environmental management, because they occupy the great bulk of the continent’s territory and forests.

About a quarter of LAC population is estimated to live at densities below 50, again essentially all of them more than an hour distant from a large city, and nearly half in very remote (> 4 hours travel to a city) conditions. While this group may include some prosperous ranchers, it certainly contains a large number of very poor people. Almost half (46%) of LAC lives at population densities below 150, and more than 90% of this group is at least an hour distant from a city, and about a third of them (18% of LAC total) are more than four hours’ distant (see table 3). Even at densities of up to 500, the great majority of 5 Population totals are reported in table 6.
is considered "disguised unemployment in the agricultural sector. Data on paid employment in non-agricultural activities are taken from Statistical Handbook 1995: States of the Former USSR and are for 1994. Central Government, Education and Health employment are taken from IMF Report No. 96/134 of June 12, 1996 and relates to 1995. State-owned enterprise employment is an estimated based on data from the Uzbekistan Economic Memorandum of June 20, 1994 and relates to 1992. Military employment data include Ministry of Defense staff and centrally controlled units, but exclude papamilitary units, such as the Internal Security troops (16,000 men under the control of the Ministry of Interior) and the National Guard (700 men under the control of the Ministry of Defense).

GDP estimate is from Statistical Handbook 1995: States of the Former USSR and is for 1994.

# Latin America and Caribbean

# Antigua & Barbuda

Population and labor force are taken from IMF Report No. SM 96/163 of July 5, 1996 and relates to 1995. Central Government, Non Central Government employment is taken from IMF Report No. 96/163 of July 5, 1996 and relates to 1994.

Data on GDP at market price as well as Central Government wage bill are from IMF Report SM 96/163 of July 5, 1996 and relate to 1995. Average Government wages and wages and salaries as multiple of per capita GDP are therefore the result of calculations emanating from this data.
where _x_ represents the amount of exports of good _i_ and _n_ is the number of products. When a country exports a single good, the value of _HHIP_ is equal to one while _HHIP_ is equal to 0 when the country exports a large variety of products (Haddad et al. 2013; Cadot et al. 2011 2013). Lower values of _HHIP_ indicate a greater extent of product diversification. COMTRADE data on exports by product at a 4-digit level from the SITC Revision 1.0 are used to compute _HHIP_ . On the other hand, the Herfindahl-Hirschman index of export markets (HHIM) is the sum of squared weights of export by destination in total trade. The formula is similar than the one above: in the case of _HHIM_ , _x_ represents the amount of exports to country _i_ and _n_ is the number of destinations. Again, lower values of _HHIM_ indicate a greater extent of market diversification. Bilateral trade data from COMTRADE are used to compute _HHIM_ .

Table 1 shows the medians for _HHIP_ and _HHIM_ across groups in Sub-Saharan Africa for the periods 1990-94 and 2010-14. In terms of export product concentration, the (median) value of _HHIP_ for SubSaharan Africa declined from 0.31 in 1990-94 to 0.21 in 2010-14 (that is, a reduction of 0.10). The reduction of _HHIP_ was sharper among resource rich countries in the region —with the median declining from 0.8 in 1990-94 to 0.54 in 2010-14. Non-resource rich countries —with greater levels of export product diversification than resource rich countries— also experienced a decline in _HHIP_ from 0.27 to 0.16.

In the case of export market concentration, the (median) value of _HHIM_ remained almost invariant in Sub-Saharan Africa over the past two decades. Export market concentration slightly fell from 0.14 in 1990-94 to 0.13 in 2010-14. The _HHIM_ also remained invariant over time for the group of non-resource rich countries (0.12 in both 1990-94 and 2010-14). However, export market concentration
jointly estimate the equations. A detailed treatment of the SUR estimator can be found in Zellner (1962, and 1963).

> 11 These findings are based on random-effects (RE) estimates using panel data of 1998/99 and 2010/11. We also attempted fixed-effects (FE) implementation of the SUR estimator. The findings of FE model does not vary much from that of the RE model except for that the magnitude of the FE estimates is higher. We decided to report the findings of RE model to avoid upward biases, if any, in the findings of FE.

16