rafmacalaba/datause-encoder-data huggingface dataset:
Asian countries, such as Malaysia, Singapore, Taiwan and Hong Kong. The trend is the opposite for men. Although the majority of male migrants find work in Malaysia, they are now increasingly shifting to Saudi Arabia.

About 4 to 5 percent of all sample households in the IFLS survey reported having international migrants. The migrant sending households, however, are concentrated in some provinces such as West Nusa Tenggara, West Java and East Java. In these provinces, the share of sample households with migrants in 2007 was as high as 17 percent (Nusa Tenggara) and 6 percent (West and East Java). While female migrants are predominantly from West Java, male migrants mostly come from West Nusa Tenggara. The shares of migrants from these two main regions are increasing over time.

Although Indonesia migrant workers are increasingly coming from urban areas, the majority of them still come from rural areas. More than 60 percent of migrant workers, during the 2000-2007 period, still came from rural households. The new urbanizing trend is continuing possibly as the Indonesian population is urbanizing, and increasingly more workers from urban areas are finding work abroad. The share of households having migrant workers from urban areas increased from 34 percent in 2000 to 37 percent in 2007. The urbanizing trend is observed stronger for households with only male migrants, with the share from urban area rising from 31 percent in 2000 to 41 percent in 2007.

Households with migrants have, on average, significantly less per capita total expenditure than those without migrants. From 1993 until 2000, almost equal shares of migrant workers came from quintiles 1 to 4. Recently, the highest share of migrant workers came from the poorest quintile of households. Migrant workers from these households look for work outside of Indonesia in order to better financially support their families back home. Households having only male migrants are generally worse off but, interestingly, on average, have higher food expenditure than female migrant households. Meanwhile, households with female migrants tend to have higher expenditure on nonfood items, particularly housing, education and health.

Interestingly, when household welfare is assessed using assets owned by the household, the proportion of households owning house and land is higher among households sending migrants than among non-migrant households. Similarly, the proportion of migrant households that borrowed money in last 12 months is less than that 9
The team responsible for macroprudential analysis needs access to a broad range of data. Macroprudential analysis is a very data-intensive exercise, and data availability is often a constraining factor in strengthening macroprudential analysis. A thorough stocktaking of data availability is therefore a useful starting point for establishing a framework for macroprudential analysis. This can be helpful in identifying gaps and prioritizing measures to address them. An area where data gaps often occur is the NBFI sector, where the frequency of prudential reporting tends to be lower and the lags in producing data longer than for the banking sector. In addition, the analysis of the financial position of the household and enterprise sector is often impeded by lack of available data. For these sectors, it is likely that external sources will need to be used. A potential data source is the credit reference bureau, which may contain relevant information about the leverage, indebtedness, assets, income, and liabilities of households. Even when data are available, adequate access needs to be ensured. It may be particularly challenging to obtain access to confidential microprudential data, particularly if the central bank does not undertake supervisory responsibilities.

Another decision relates to the periodicity of macroprudential analysis, recognizing that the components presented in the previous section serve different monitoring purposes. The analysis of the time series dimension of systemic risk needs to be conducted most frequently. In practice, most countries undertake the assessment twice a year and report on the outcomes in an FSR at least once per year. Updates of the list of systemic banks and of the contagion matrix can be undertaken less often—typically once per year—since structural changes in the financial system usually take longer to materialize.

Elements of the framework for identifying systemic banks and the contagion matrix can be usefully included when conducting the periodic assessment of the time series dimension of systemic risk. The contagion matrix can be useful in thinking through the systemic impact of a particular stress scenario, because it helps in analyzing the likely direction and intensity of any contagion effects. Quantitative tools such as stress tests can be used to further substantiate the likely impact of stress scenarios on individual financial institutions.

Overall, the staffing and resource implications of putting in place a robust framework for macroprudential analysis are considerable, but still worthwhile. The significant investment requires a strong commitment on behalf of the implementing country. It is important that the implementation costs are seen against the background of the potential costs of financial crises, which can undo years of economic growth and progress in fighting poverty. This implies that any policy that contributes to reducing the likelihood of a crisis and enhancing the quality of the crisis response is likely to be cost-efficient.

32
**Table** **<u>3.</u>** Reported <u>Collective Action Behaviors Benefitting Community Forests</u> **<u>.</u>** n= 327

|**Variable**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|
|Number of Trees Planted in Community Forests in Previous Month<br>(MEETING_DUM)|0.455|0.499|0|1|
|Biogas Use Dummy (BIOGAS_DUM)|0.141|0.348|0|1|
|PlantedAnyTreesinCommunityForestDummy (ANYTREE_CF)|0.116|0.321|0|1|
|Number of Trees Planted on Own Land during Previous 5 Years<br>(NUMTREES_FARM)|13.77|43.292|0|500|
|Number of Hours Spent During Previous Month Monitoring<br>CommunityForests (MONITORING)|2.413|6.640|0|60|
|Number of Trees Planted in Community Forests during Previous<br>Month(NUMTREES_CF)|1.269|0.841|1|5|

30
responses to questionnaires administered in survey setting (for a review, see Das et al., 2007, 2009). These survey instruments share some common features. They typically ask survey respondents about their internal states (“feeling sad”, “feeling worthless”, etc.) and associated behaviors (“crying for no reason”, “having nightmares”, etc.). The answers are subjective assessments of the frequency or salience of a given internal state or behavior, on a 3-5 category Likert (1932) scale, such as “often”, “very often”, “sometimes”, “never”, or “always.” Appendix 1 shows the complete list of questions asked to survey respondents in Bosnia and Herzegovina in 2001.

The survey questionnaires are generally adapted to various cultural settings, and several researchers have made efforts to check for internal consistency of the survey instruments (see Wittchen, 1994, and Smith et al., 2007). When warranted by the context, Post-Traumatic Syndrome Disorders (PTSD) items have been more systematically added as a complement to existing depression or anxiety scales.<sup>6</sup> The Harvard Trauma Questionnaire (Dubois et al., 2004, Lopes Cardozo et al., 2000, and Mollica et al., 1999, among others) and the PTSD Checklists (Terhakopian et al., 2008, Pham et al., 2004) are the main survey instruments used to assess the prevalence of PTSD in the population following tragic events such as personal victimization, wars, natural disasters or economic crises. Similarly to other mental health scales, these scales are self-reported measures.

Given that almost all mental health survey data are based on subjective assessments, there is a concern that answers to these questions may not be perfectly comparable across individuals. Whether an event is perceived as “frequent”, or a statement perceived as “mostly true,” can depend on the individual’s internal reference points, which are arguably correlated with socioeconomic status, occupation, ethnic or cultural identity, or other independent variables in the analysis. Cultural adaptation of the survey instruments is aimed at tailoring a questionnaire to a specific country rather than accounting for local cultural differences within a country. This caveat

> 6 Depression and anxiety are common psychological morbidities not necessarily due to a specific event but related to genetic predisposition and environmental factors. There is some overlap in their expression but also divergence. For instance, anxiety disorder often involves the avoidance of anxiety inducing situations (agoraphobia for example), while depression often involves low motivation. PTSD is event-related and involves in some way rumination or avoidance of the traumatic event, and the triggering of key symptoms/morbidities when reminded of the event. It often resolves naturally over time but not for everyone, particular those predisposed to anxiety. Symptoms of PTSD may be congruent with depression or anxiety so a general mental health survey module can pick up PTSD morbidities that overlap with depression or anxiety.

5
to 15.7% in the control group, followed by a larger participation in household’s non-agricultural businesses by 2.3 percentage points (p-value = 0.15), from 8.4% in the control group, although the latter is measured with less precision. There are two important results to highlight here. First, labor supply for control women is increasing considerably between midline and endline, with market work participation and job search increasing by 18 and 32 percentage points, respectively (see Table A16 in the Online Appendix for results at midline). In a context with previously low women’s labor participation, these increases are remarkable but could also reflect the effects of the economic crisis, leading more household members to look for jobs. Second, these figures indicate that a large proportion of treatment women doing market work are also searching for a job, suggesting high levels of underemployment. Treatment women are either working less than they want to and/or looking for better job opportunities, which is consistent with the results from midline (Bedoya et al., 2019).

Columns 4-6 in Table 5 show that treatment primary man’s labor participation is also higher, although more moderately at 4.9 percentage points (p-value = 0.01), from 84% in the control group. Market work participation is larger by 5.8 percentage points (p-value = 0.022), compared to 72% in the control group, while job search is higher by 2.7 percentage points (p-value = 0.268), compared to 74% for their control counterparts, although this impact is not statistically significant. The impacts on market work participation for men are mostly driven by increases in participation in all types of household businesses: participation in livestock rearing increases by 6.1 percentage points (p-value = 0.002), compared to 12.1% in the control group; participation in household agriculture increases by 4.7 percentage points (p-value = 0.026), compared to 14% in the control group; and participation in non-agricultural businesses increases by 2.8 percentage points (p-value = 0.034), compared to 4.8% for their control counterparts. Similar to treatment women, we also find indications of high levels of underemployment with most primary men doing market work also looking for a job.<sup>25</sup> Overall, in a context where economic conditions are driving high levels of under-employment, these results suggest that the program contributed to sustained market work participation–primarily driven by entrepreneurial activities in livestock–and diversification into household productive activities for both the primary man and the primary woman, compared to their counterparts in the control group.

> 25We could not reliably measure time spent working in endline, but the shorter-term impacts indicated that both treatment women and men doing market work have idle capacity, therefore, an important part of underemployment is likely stemming from working less time than they are willing to work.

24
fossil fuels, and raised the price of electricity, leading to a rise in the prices of energy intensive goods relative to the base case as shown in Figure 7 for 2030. Coal price at the mine-mouth rises by 6.2% while crude oil price rises by 1.2% inclusive of the tax, leading to a 1.4% rise in electricity prices.

# **Table 4. The effects of achieving higher reductions with higher carbon price case**

(R1CUT vs R2CUT; rising carbon prices with offsetting cuts in VAT)

|||2015<br>R1CUT|R2CUT||2030<br>R1CUT|R2CUT|
|---|---|---|---|---|---|---|
||Base case|:<br>Low C<br>price<br>|:<br>High C<br>price<br>|Base<br>case|:<br>Low C<br>price<br>|:<br>High C<br>price<br>|
|Variable|values|% chang<br>ba|e from<br>se<br>|values|% chan<br>ba|ge from<br>se|
|GDP (billion yuan 2010)|58,956|-0.004|-0.027|126,235|<br>-0.11|<br>-0.74|
|Consumption (bil yuan 2010)|25,290|-0.003|-0.017|<br>67,264|<br>-0.11|<br>-0.71|
|Investment (bil yuan 2010)|25,374|-0.007|-0.045|<br>43,747|<br>-0.13|<br>-0.86|
|Government consumption (bil yuan 2010)|6,973|0.000|0.000|<br>12,785|<br>0.00|<br>0.00|
|Energy use (million tons of sce)|4,005|-0.21|-1.21|<br>5,958|<br>-2.6|<br>-13.1|
|Coal use (million tons)|3,666|-0.29|-1.71|<br>4,610|<br>-4.1|<br>-20.0|
|Oil use (million tons)|537|-0.04|-0.27|<br>835|<br>-0.7|<br>-4.0|
|Gas use (billion cubic meters)|166,354|-0.09|-0.53|381,163|<br>-1.8|<br>-9.4|
|Electricity (billion kWh)|5,554|-0.11|-0.65|<br>8,951|<br>-1.5|<br>-7.9|
|CO2 emissions(inc cement; mil tons)|9,819|-0.23|-1.33|12,797|<br>-3.3|<br>-16.0|
|Cumulative CO2 (2015-2030)||||<br>166,675|<br>-1.9|<br>-9.8|
|Carbon price yuan/ton CO2||¥1.6|¥9.8||¥26.2|<br>¥157.1|
|Carbonprice as a share of total revenue||0.11%|0.66%||1.1%|5.8%|
|Electricity generation|values|% ch|ange<br>|values|% ch|ange|
|Coal|3967|-0.18|-1.04|<br>5782|<br>-2.7|<br>-14.2|
|Gas|152|0.02|0.11|<br>459|<br>-0.3|<br>-2.2|
|Nuclear|190|0.06|0.35|<br>678|<br>1.0|<br>5.4|
|Hydro|902|0.06|0.35|<br>1132|<br>0.9|<br>5.2|
|Other|133|0.11|0.67|<br>271|<br>1.6|<br>8.5|
|Wind|191|0.002|0.01|<br>498|<br>0.01|<br>0.12|
|Solar|19|0.004|0.02|<br>130|0.04|<br>0.25|

‐15‐
a country to intervene, for any market failures would be _international_ in scope, and hence _national_ economy policy is clearly not the correct type of intervention. Although there is some controversy on this matter, with – for instance – Irwin and Klenow (1994) finding that learning-by-doing spillovers in the semiconductor industry are as strong internationally as domestically, our reading of the more general evidence leads us to think that domestic spillovers are stronger, since knowledge spillovers are clearly attenuated by distance (Audretsch and Feldman, 2003).

Ultimately, then, this is an empirical matter. If R&D spillovers go beyond sectors but stay mostly within borders, one cannot easily discard policies to push resources towards high R&D sectors. Of course, favoring high-R&D sectors may not be the most standard way to encourage R&D; a more conventional approach would be simply to subsidize R&D. But if for practical reasons the latter approach is not advisable, then perhaps a sectoral approach is relevant. As with any policy option, however, there are significant costs and risks that would have to be carefully considered.

Before going any further with policy discussions, however, it is necessary first to explore whether in fact there are significant systematic differences in R&D intensities across sectors, and whether this can explain a significant part of LDCs’ (and Chile’s, in particular) shortfalls in R&D.

# _A look at sector level R&D data_

We first examine how R&D investment varies across sectors for the OECD since LDC data do not yet permit this kind of exercise. Table 1 reveals several important stylized facts. First, there is a wide range of average R&D investment rates by sector from around .1% in Services, apparel or publishing to almost 30% in pharmaceuticals; office, accounting and computing equipment; and air and spacecraft. Second, there is tremendous variation of investment rates within sectors. In manufacturing in the 9
(favelas),<sup>12</sup> of which 82 were classified as risk areas by the _Polícia Militar_ (Military Police) in 2002.

The state of Ceará has one of the lowest HDI among Brazil’s 27 states. However, it is the state that has improved the most in terms of human development measured by the HDI during 19912000. Ceará’s HDI increased from 0.593 in 1991 to 0.700 in 2000 (see Table 1).

|**Table 1: Human Developm**<br>|**ent Index,**<br>|**1991-2000**<sup>**13**</sup><br>|
|---|---|---|
|**State**|**HDI 1991**|**HDI 2000**|
|Brazil|0.696|0.766|
|Pernambuco|0.620|0.705|
|Rio Grande do Norte|0.604|0.705|
|Ceará|0.593|0.700|
|Bahia|0.590|0.688|
|Sergipe|0.597|0.682|
|Paraíba|0.561|0.661|
|Piauí|0.566|0.656|
|Alagoas|0.548|0.649|
|Maranhão|0.543|0.636|
|Federal District (Brasilia)<br>Source:_Atlas do Desenvolvimento_|0.799<br>_Humano no B_|0.844<br>_rasil_,UNDP.|

During 1991-2000, the municipality of Fortaleza improved its HDI slightly less than the state as a whole; the HDI increased from 0.717 to 0.786 (see Table 2). However, it is important to note that large disparities are observed when disaggregating the HDI by neighborhood.

> 12 Source: IBGE. IBGE defines a _favela_ or abnormal housing agglomeration as at least 51 houses without access to basic services and without land title to the properties or recent documentation.

> 13 See _Atlas do Desenvolvimento Humano Do Brasil_ , 2003 that describes the calculation of the municipal HDI indicator (HDI-M).

9
devaluation in 1994. In the meantime, Ghana significantly raised its cocoa producer price so that the difference between the two countries became negligible at the end of the 1990s. Changes in cash crop production were slower, but they also occurred. Between 1994 and 2001, Mali more than doubled its cotton production in the South through the extensive cultivation of new lands; however, the cultivation of cotton had not yet reached the most southern border area (Dufumier and Bainville 2006). In contrast, the Ivorian “cocoa frontier” has moved westward and today reaches the southern Guinea border. With these developments, some of the border discontinuities that we identified for the 1990s may have changed. After 2002, the civil war in Cˆote d’Ivoire and the five-year partition between North and South may have further reduced the Ivorian advantage at the northern borders, at least until 2007. In the South, Ghana continued to catch up with its neighbor (Eberhardt and Teal 2010). Hence, although infrastructure showed greater persistence in 1993 and in 1998, border differences may have been attenuated since then. Unfortunately, the data that we gathered do not cover those more recent economic conditions.

# **V. Conclusion**

The borders between Cˆote d’Ivoire and its neighbors divide fairly comparable areas in terms of geography, anthropology, and precolonial history. At the end of the 1980s and in the 1990s, Cˆote d’Ivoire was by far the wealthiest country, particularly because of its export crops. By applying regression discontinuity designs to a set of household surveys, we show that this higher wealth was translated at the borders with Ghana and Mali in the form of large and consistent discontinu35
Table 1: The Incidence of Poverty in Rural India: 1983-2009/10 (Percent of Population Poor)

||1993 Expert Group<br>Poverty Lines|2012 Expert Group<br>Poverty Lines|Global Poverty Line<br>(PPP $1.25 per day)|
|---|---|---|---|
|1983|46.5|_na_|57.8|
|1993-94|36.8|50.1|52.3|
|2004-05|28.1|41.8|43.8|
|2009-10|_na_|33.8|34.3|

Source: Quinquennial rounds of the NSS Consumption surveys 1983-2009-10. _PovCalNet_ calculations for the global poverty line estimates.

In an earlier companion paper, Himanshu et al (2011) document that the expansion of non-farm employment in rural India has increasingly been drawing from ranks of the socially disadvantaged. This is in large part due to the fact that the sub-sector that has seen the most rapid growth in recent years has been casual rural non-farm employment. This is the segment which has the highest participation by the socially disadvantaged (lower caste) and the illiterate. Given that casual non-farm employment, though worth considerably less than regular employment, still pays considerably better than agriculture (the wage premium is around 35-50% in 2009-10) the direct impact of non-farm growth on rural poverty in India has thus been positive. Himanshu et al (2011) also point out that direct effects are likely to be reinforced by indirect effects. Notably, they find evidence from regression analysis based on a region-level panel covering the period 1983-2004-05 that expansion of the non-farm sector is accompanied by general labor market tightening. This has the effect of raising wages amongst agricultural laborers – typically the poorest of the rural poor - and thereby contributes to poverty alleviation even amongst those who are not directly employed in the non-farm sector.

# **Poverty, Mobility and Inequality in Palanpur**

# _The Village Setting_

The village of Palanpur, in Moradabad District in west Uttar Pradesh, has been the subject of study since 1957-8, when it was first surveyed by the Agricultural Economics Research Centre (AERC) of the University of Delhi. The AERC resurveyed the village in 19623. In 1974-5 Christopher Bliss and Nicholas Stern selected Palanpur as a village in which to study the functioning of rural markets and the behavior of farmers. They spent just under a year residing in the village and collecting quantitative data, based on a set of questionnaires they designed and fielded, as well as qualitative information emerging out of informal discussion and 10
<!-- Start of picture text --> 90<br>70:ae<br>ov<br>g a o==Reference<br>9V9 60 i<br>c<br>E4 a 50 J” —— International pledges<br>3 ====More ambitious proposals & national policies<br>‘= not yet pledged internationally<br>o 40<br>2 «=e lllustrative pathway likely holding warming<br>S below 2°C<br>2 30<br>iG}[°} «==—=|llustrative pathway leading to below 1.5°C by<br>2100<br>.10NS<br>(0)<br>1990 2000 2010 2020 2030 2040 2050<br><!-- End of picture text -->