rafmacalaba/datause-encoder-data huggingface dataset:
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Policy Research Working Paper 10991

# **Abstract**

This study documents short-term economic mobility in Malaysia over the first two decades of the twenty-first century, at the population level and for various subgroups. The findings show broad and steady improvements in well-being, as evidenced by large decreases in chronic poverty and significant increases in persistent economic security. The study employs a synthetic panel approach based on nationally representative micro-level data for 2004–22, with a refinement that allows presenting bootstrap point estimates and standard deviations. In addition, the study investigates several poverty and vulnerability scenarios, as well as relative mobility. First, the results indicate that chronic poverty has decreased to 2–3 percent of the population. Nevertheless, progress is not uniform: around 15 percent of the population in rural East Malaysia lives in chronic poverty. Second, the study finds considerable increases in sustained economic security—the extent of improvement, however, depends on the approach and income thresholds that are used to define security. Moreover, ethnic and regional differences in secure status are sizable at higher income class thresholds. The largest differences are of a regional dimension: an individual in urban Peninsular Malaysia is more than three times more likely to live in economic security than someone in rural East Malaysia. Altogether, the study observes upward movement across the board but little evidence of dramatic changes in the relative positions of societal groups.

This paper is a product of the Poverty Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at a.g.m.j.rongen@vu.nl.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Our estimations reveal no clear cross-country relationship between the rates of inflation and household composition in terms of age, gender of household head, or employment status. However, a clear correlate of inflation is the consumption decile of the household. The magnitude and significance of the coefficients on the consumption decile dummies indicate that poor households consistently face higher inflation than households in the top decile. The median estimates suggest that a household in the poorest decile of the consumption distribution faces an inflation rate that is 1.5 percentage points higher than a household in the top decile with the same characteristics in terms of size, age, gender of the household head, employment, and location.

The regression analysis identifies consumption deciles and household size as characteristics that can be useful for calculating aggregate, group-specific inflation rates. These group-specific inflation rates illustrate the evolution of COL inflation and may be relevant for policy purposes, as household-specific indexes cannot be routinely estimated outside specific surveys.

The group-level COL indexes are calculated by aggregating household-specific indexes (4). The D,t “democratic” index LG,t−1 (7) assigns equal weights to every household (or group) in calculating the aggregate inflation index.<sup>13</sup> where group-level shares are derived by averaging the shares of all households _i_ in group _G_ : P,t The “plutocratic” index LG,t−1 (8) adopts a weighting scheme in which the importance of households is proportional to their expenditure share in aggregate expenditures: <u>Figure 8</u> shows the distribution of the COL inflation rates relative to the mean inflation by household per capita consumption deciles or quintiles. For all EMDE ECA countries, except Belarus, households from the lowest deciles experience the highest inflation (Figure 8, panel a). This correlation reveals the regressive effect of inflation. Households in the lowest decile of the consumption distribution face inflation rates that are, on average, 1.2 percentage points higher than

> 13 As we are using household sample surveys, wi sampling weights are of course needed for the aggregation.

13
# **Annex 5 Uncommon Transport Questions in the Sample Surveys**

## **1. Iraq**

- Do the household members suffer from transportation problems? What are the problems?

- Bad condition of roads

- Traffic jams and condensations

- Scarcity or distant

- Hard to reach the transport means

- Personal security

- Other

## **2. Nepal**

- What problems, if any, do you have in running your business?

- Transport problems

- (Community – local markets and prices) How much does it cost to transport goods from the market center to the local shop/shops?

- (Community - Adequacy of consumption and government facilities) Now, I would like to ask your opinion about the condition of government facilities your household consuming. How do you take the road facilities consuming by your household?

## **3. Niger**

- In your opinion, what are the three principal causes of household poverty?

- Lack of roads

## **4. Tanzania (LSMS+, 2018/2019)**

- How satisfied or dissatisfied would you say you are with your safety in transportation /on roads?

- (Agricultural questionnaire) Generally, did you transport [CROP] for sale?

- What is the average distance you transported [CROP] for sale?

How many times did you transport [CROP] for sale during the long rainy season?

## **5. Afghanistan (Male questionnaire)**

- How long did it take to reach the nearest permanent food market (one-way travel) by foot/animal, shared public transport/bus, private vehicle/taxi, bicycle in the past month?

- What was the cost of return transport to the permanent food market, including overnight accommodation if needed, in the past month?

- What is the cost of transporting 50 kg of wheat from the permanent food market to the community in the past month?

45
- 2) Overall, age appears to be related to exposure to AI occupations only in lower-middle and high-income countries. This finding is consistent with Cazzaniga et al. (2024), who find for a smaller set of five advanced and emerging countries that age differences between workers do not follow a consistent pattern in their relative exposure. At the same time, workers with post-secondary education are more exposed to AI than uneducated workers in low- and upper-middle-income countries. Note that this is different from the bivariate analysis (Figure 7), where post-secondary education was strongly associated with high exposure to AI occupations. Thus, the introduction of other variables into the regression framework suggests that there are other, likely correlated, variables that influence AI occupation exposure. For example, better educated workers are also more likely to work as professionals and therefore have a higher AIOE, but it is not education per se that is driving high AI occupation exposure (see Table 3 in the appendix).

- 3) In terms of employment status, self-employment and unpaid work are significantly associated with higher exposure to AI compared to paid work in low, lower-middle and upper-middle income countries, according to the multivariate regression results (Table 3, Appendix). This suggests a large difference in the task structure of self-employed compared to paid workers, with the former having a higher potential for adopting AI technologies. Gender and location differences play a smaller role in the multivariate results. Living in an urban area is only significantly associated with higher AIOE in high-income and lowermiddle-income countries.

# 3.5. Lack of electricity infrastructure as a barrier to AI applications

In the previous analysis, we assumed that AI is ready for use in occupations. This assumption is in line with previous analyses such as Felten et al. (2021, 2023). However, almost all of these analyses are conducted in high-income countries, mostly in the US, where the infrastructural prerequisites for AI, namely access to electricity, access to the internet and ownership of a smartphone/laptop, are in place. This is not always the case in the developing world, where access to electricity, Sustainable Development Goal 7, is lacking for around 760 million people, most of whom live in Sub-Saharan Africa (IEA 2023). This figure rises to 1.2 billion if households with theoretical access to electricity are included, meaning that the electricity is there but often not working, unreliable or too expensive to use (Min et al. 2024). In terms of internet users, the World Bank (2024) reports 5.3 billion internet users in 2022, with a world population of 8 billion. High- and middle-income countries have made significant progress in recent years, but low-income countries are lagging behind, with only one in four using the internet.

To shed light on access to electricity we included access to electricity at the household level in the analysis, assuming that access to electricity at the household level is similar to access to electricity 21
Policy Research Working Paper 6355

# **Abstract**

This paper uses small area estimation techniques to update Vietnam’s province and district-level poverty map to 2009. It finds that poverty rates continue to be highest in the northern and central mountainous regions, where ethnic minorities make up a large fraction of the population. Poverty has fallen in most provinces and districts over this decade, but the pace of poverty reduction has been least pronounced in those localities with high initial poverty or inequality levels. As a result, poverty rates have become more spatially concentrated over time, which is consistent with widely observed growth processes linked to agglomeration. The authors hypothesize that this makes geographic targeting of the poor more relevant as a means to re-balance growing welfare disparities between geographic areas. Simulations indicate that in both 1999 and 2009, geographic targeting for poverty alleviation improves upon a uniform lump-sum transfer and this becomes more evident the more spatially disaggregated the target populations. The analysis further indicates that the gains from geographic targeting have become more pronounced over time in Vietnam. Although poverty reduction in Vietnam has been impressive, further progress may thus warrant increased attention to geographic targeting.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at planjouw@worldbank.org, mmarra1@worldbank.org, and c_nguyenviet@yahoo.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Using Social Media to Change Gender Norms: An Experiment within Facebook Messenger in India<sup>∗</sup> Dante Donati<sup>†</sup> Victor Orozco-Olvera<sup>‡</sup> Nandan Rao<sup>§</sup> _Keywords:_ Edutainment; Gender norms; RCT; Social media; Violence against women _JEL Codes:_ C93; D90; J16; L82

> ∗This research is part of the entertainment-education program of the World Bank’s Development Impact Evaluation Department (DIME). Computational replicability was verified by DIME Analytics. We are grateful to seminar attendees at UPF, DIME and LEAP Bocconi, and to Abhijit Banerjee, Ruben Durante, Ruben Enikolopov, Matthew Gentzkow, Eliana La Ferrara, Gaël Le Mens, Gianmarco León-Ciliotta, Haaya Naushan, Maria Petrova, Marta Reynal-Querol and Alessandro Tarozzi for their comments and useful insights. We thank Maria Correia, Sampreet Goraya, Poonam Muttreja, Alok Vajpeyi, the Population Foundation of India and Bollywood celebrity Farhan Akhtar for their inputs and insights. We also thank Niyati Malhotra for great research assistance as well as Isabella Chen, Berta Cumella, Leonardo Di Vittorio and Marco Antonio Ghiani for assistance on the Virtual Lab platform. This study was supported with grants from the World Bank’s i2i and Umbrella Facility for Gender Equality trust funds. The views expressed herein are those of the authors and do not necessarily reflect the views of the World Bank. The research received ethical clearance from Solutions IRB (IORG0007116). All errors are the authors’ sole responsibility.

> †Columbia Business School and CESifo. dd3137@gsb.columbia.edu

> ‡DIME, The World Bank. vorozco@worldbank.org

- §Universitat Autonoma Barcelona and BSE. nandanmarkrao@gmail.com
Labor Regulations 22 16 Inadequately Educated Labor Force 31 11 Source: Kenya ICA, 2003 and 2007.

Similar hard data for transport are not available but the government has been allocating increasing amounts for road rehabilitation since 2005/06. A more likely possibility is that the increased dissatisfaction with infrastructure reflects a resurgence in investment. Between 2003 and 2006, expenditure on gross fixed capital formation expanded at an average of 16 percent per year (Economic Survey 2007, Kenya National Bureau of Statistics). This evidence is consistent with the idea that after 2003, infrastructure services _replaced_ political and macroeconomic risk as the binding constraint to private investment.<sup>36</sup> A last piece of persuasive evidence supporting a sharp improvement in the investment climate is provided by import trends. Figure 9 shows a marked increase in the imports of machinery and industrial transport equipment after 2003. These imports averaged 5.6 percent of gross domestic expenditure (GDE, equal to consumption plus investment plus government spending) over 1996-2002. This went up to 9.3 percent of GDE over 2003-2006.

Figure 9 Imports of Machinery and Transport Equipment (% of Gross Domestic Expenditure) <!-- Start of picture text --> 16.0<br>12.0<br>machinery and<br>industrial transport machinery<br>8.0<br>industrial<br>4.0<br>transport<br>equipment<br>0.0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br>year<br>expenditure<br>% of gross domestic<br><!-- End of picture text --> 36 For the binding constraint approach to growth diagnostics, see Hausmann, Rodrik and Velasco (2005).

27
29 Table 12. Departures of the 1980 Cohort of Non-Refugee Settlers in Australia by Birthplace

|Birthpl.-£|DeRarture Rate|BirthDlace|Departure Rate|
|---|---|---|---|
||(%)||(%)|
|UK and Ireland|12.6|Anglo America|23.3|
|Northern Europe|16.0|Canada|24.6|
|Germany|12.6|United States|22.8|
|Netherlands|13.6|Latin America|11.7|
|Poland|7.5|Africa|5.6|
|Other N. Europe|20.6|South Africa|5.3|
|Southern Europe|16.3|Zimbabwe|6.3|
|Greece|25.6|Other Africa|6.2|
|Italy|17.3|Asia|8.8|
|Malta|11.2|Hong Kong|7.2|
|Yugoslavia|14.0|India|13.0|
|Other S. Europe|12.5|Malaysia|9.9|
|Middle East|11.3|Philippines|8.8|
|Lebanon|10.6|Vietnam|3.0|
|Turkey|11.1|Other Asia|7.7|
|Other Middle East|<br>11.9|Oceania (excl. N.Z.)|<br>11.0|

Source: Lukomskyj and Richards (1986:625) Table 13. Net Overseas Migration, Australia 1982-1987, Year ending 30 June

|1982|128,100|
|---|---|
|1983|73,300|
|1984|49,100|
|1985|73,700|
|1986|100,400|
|1987|103,700|

Source: Australian Bureau of Statistics, 1987.

In the most recent set of official population pro'jections for Australia, the Australian Bureau of Statistics (ABS) used a singl. assumption for net migration during the period 1987/1988 to 1992/1993 and -wo assumptions for the following period. It was assumed that net immigration would rise from 140,100 in the year ending June 30, 1988 to 142,600 for the year ending June 30, 1993 (Australian Bureau of Statistics, 1988). For the period 1993/1994 to 2030/2031, Assumption I is a constant level of net migration of 140,000 per year and Assumption II is a linear decline from 142,600 in 1992/1993 to 80,000 in 1997/1998 (remaining constant at the level of 80,000 annually through 2030/2031).
# Table 17. NUTS2 Regional Groupings

|||1=West, 0=East|1= Rest (East), 2= Anatolian Tigers,<br>3=Developed West|
|---|---|---|---|
|TR10|Istanbul|0|3|
|TR21|Tekirdag Edirne, Kirklareli|0|3|
|TR22|Balikesir, Canakkale|0|3|
|TR31|Izmir|0|3|
|TR32|Aydin, Denizli, Mugla|0|2|
|TR33|Manisa, Afyonkarahisar, Ktahya, Usak|0|3|
|TR41|Bursa, Eskisehir, Bilecik|0|3|
|TR42|Kocaeli, Sakarya, Dzce, Bolu, Yalo|0|3|
|TR51|Ankara|0|3|
|TR52|Konya, Karaman|0|2|
|TR61|Antalya, Isparta, Burdur|0|3|
|TR62|Adana, Mersin|0|1|
|TR63|Hatay, Kahramanmaras Osmaniye|0|2|
|TR71|Kirikkale, Aksaray, Nigde, Nevsehir|0|1|
|TR72|Kayseri, Sivas, Yozgat|0|2|
|TR81|Zonguldak, Karabk, Bartin|0|1|
|TR82|Kastamonu, ǡnkiri, Sinop|0|1|
|TR83|Samsun, Tokat, ǯrum, Amasya|0|1|
|TR90|Trabzon, Ordu, Giresun, Rize, Artvi|0|1|
|TRA1|Erzurum, Erzincan, Bayburt|1|1|
|TRA2|Agri, Kars, Igdir, Ardahan|1|1|
|TRB1|Malatya, Elazig, BingolTunceli|1|1|
|TRB2|Van, Mus, Bitlis, Hakkari|1|1|
|TRC1|Gaziantep, Adiyaman, Kilis|0|2|
|TRC2|Sanliurfa, Diyarbakir|0|1|
|TRC3|Mardin,Batman, Sirnak, Siirt|0|1|

_Notes:_ The East/West groupings are based on altitude, TRA and TRB are high altitude regions are also known as the roof of Turkey. The three-region grouping is based on OECD (2014).

46
in the treatment forest experiencing 0.054 fewer days on fire than the synthetic control unit), they are also measured with less precision, and the effect is fully extinguished by 2019. A similar pattern emerges when using the 50% confidence criterion (see Column (2) of Table 1). There are no discernible effects in any of the other months of the year except for March, where the program seems to have resulted in a _reduction_ in forest fire occurrences in 2017 and in an _increase_ thereof in 2019. However, when using the 30% confidence criterion, these impacts are small (a decrease or increase of less than 0.025 days of fire on the average grid cell; see Column (1)), and virtually zero (and highly insignificant) when using the 50% criterion (in Column (2)). That means that the March results are not likely to be real (and definitely not economically significant), and hence November is the only month in which the FIP managed to change forest fire occurrences.

Based on Figure 4 and Table 1, we conclude that the program was not effective in reducing forest fire occurrences throughout the year; it only managed to reduce the number of forest fire occurrences in the first month after the harvest has been completed. The next question is whether this sizeable yet relatively short-lived impact of the FIP program on the frequency of November forest fires resulted in an increase in forest cover. We use the annual forest level tree cover data from the Global Forest Change dataset of Hansen et al. (2013), which combines Landsat images and MODIS data with groundtruth verification (Hansen et al., 2010). We apply the SCM estimator on the annual forest-level tree cover measure; the results are presented in Figure 5.

Two features of Figure 5 are most notable. First, while average tree cover in the treatment forests is systematically larger than that in their synthetic controls, the differences are small in the pre-intervention period (about 0.01 percentage points on average), and the synthetic control also closely traces the observed tree cover in the treatment forests over time. Second, the post-intervention difference continues to be small, although the figure also seems to suggest that the reduction in tree cover is slowed down (if not stopped) in the treated forests, whereas the decline in the synthetic control seems to 21