rafmacalaba/datause-encoder-data huggingface dataset:
the end of 2014. In order to bolster our sample size, in early 2015 we fielded a shortened survey with no health measurements and no lab-in-field games. The interviewing for this survey was typically by telephone, and was mainly designed to reach those who had on-migrated beyond New Zealand or were located in parts of New Zealand that were impractical for face-to-face interviewing. But since surveying often uncovers details for other people (through snowball effects) we also gave this short survey to some recently identified migrants in areas where the longer survey had already been fielded. In total this yielded an additional 61 ballot winners who had ever migrated to New Zealand, of which 11 of these people had now on-migrated to Australia (10) and the UK (1).

In total this gives data on 194 households with principal applicants who ever migrated to New Zealand after winning the ballot (Figure 1), which is 64 percent of the population of 307 such individuals.

We had even less information available for the ballot losers and non-compliers, since these individuals had not filled out residency applications. We therefore used the same surveying approach for these groups as we had for the short-term impact work, which was to use the village of origin of the migrants as a way to sample, locating these households when tracking details for the migrants in Tonga. This yielded a sample of 143 ballot losers administered the long form survey, and 39 who received the short survey (of which 9 had subsequently moved to New Zealand through alternative pathways, including through winning a later round of the PAC). In addition we have a sample of 9 non-compliers, consisting of 6 who received the long survey and 3 the short survey. Figure 1 shows these groups.

We weight the ballot winner sample to reflect the population proportions of ever migrating to New Zealand versus non-compliers. We also examine robustness to two alternative weighting schemes. The first uses a snapshot of data from late 2012 on cross-border movements coming from passport scans, which revealed that 265 of the 307 ballot winners 9
**<u>Appendix table 1: Descriptive Statistics at Household Level for Pairs of Rental Partners</u>**

||**Total **<br>|**Landlord**|**Tenant**|
|---|---|---|---|
|**Household characteristics**||||
|Household size|5.37|4.49|6.24|
|Number of dependents|2.44|2.05|2.83|
|Number of male adults|1.41|1.03|1.79|
|Number of female adults|1.46|1.35|1.57|
|Female head dummy|0.25|0.48|0.02|
|Head literate|0.35|0.24|0.46|
|Farming is head's primary activity|0.70|0.45|0.95|
|Dwelling has corrugated iron roof|0.77|0.70|0.84|
|Number of rooms in dwellings|2.08|1.89|2.26|
|Age of head (years)|48.79|55.34|42.30|
|**Agricultural endowments**||||
|Total value of livestock owned (birr)|5511.98|2397.64|8602.83|
|Value of bulls and oxen (birr)|2356.21|720.91|3979.16|
|Value of other livestock (birr)|3155.78|1676.73|4623.67|
|Number of bulls and oxen owned|1.22|0.40|2.03|
|Value of farm assets (birr)|159.81|72.79|246.07|
|Total owned area (ha)|1.30|1.41|1.18|
|Share of good quality land|0.60|0.67|0.52|
|**Agricultural production**||||
|Cultivates any land|0.81|0.62|1.00|
|Owned cultivated area (ha)|0.81|0.44|1.18|
|Area of sharecropped out land (ha)|0.46|0.93|0.00|
|Area of fixed rented out land (ha)|0.02|0.05|0.00|
|Area of sharecropped in land (ha)|0.44|0.00|0.87|
|Area of fixed rented in land (ha)|0.03|0.00|0.07|
|Area of leased out land (ha)|0.49|0.97|0.01|
|Area of leased in land (ha)|0.47|0.00|0.93|
|**Assets and social capital**||||
|Value of household assets (birr)|351.40|248.27|453.76|
|House value (birr)|6494.70|6108.19|6878.30|
|No. of male adult children living in the village|0.43|0.64|0.21|
|<br>No. of female adult children living in the village|0.35|0.50|0.19|
|<br>No. of head's brothers living in the village|1.04|0.81|1.27|
|No. of spouse‟s brothers living in the village|0.65|0.54|0.77|
|<br>No. head's sisters living in the village|0.67|0.54|0.81|
|No. spouse's sisters living in the village|0.50|0.41|0.59|
|<br>No. of adult children living in the village|0.77|1.14|0.40|
|No. of brothers and sisters livingin the village|2.87|2.29|3.44|
|Number of observations|1584|789|795|

26
across countries).<sup>17</sup> But similar to EAP, within-country inequality still explains most of overall Africa-wide inequality (around 60%).

The GE(0) country-decomposition is the sum of the within and between contributions of every country, so we can analyze these contributions separately, as done in Figure 5. The total level of inequality, which has increased over time (also see Table 2, Panel A), is given by the total width of the bars. For some of the smaller countries, we have combined the contributions together. The within component is always positive, but the between component is negative when the country’s mean exceeds the overall EAP-wide mean.

It is clear that within-country inequality has increased in China and Indonesia, while it is harder to tell for smaller countries. 77% (13%) of the increase in within-country inequality from 0.17 in 1988 to 0.29 in 2012 (Table 2, panel B) was driven by China (Indonesia). At the same time, due to its growth in average consumption, China’s contribution to differences between countries has declined strongly (Figure 5, orange bars) – so much that it became negative in the most recent year. Both Other East Asia (Figure 5, green bars) and China have moved closer to regional average consumption, but from opposite directions – the excess of Other East Asia in terms of average consumption has declined.<sup>18</sup> By contrast, Indonesia’s gap to the regional mean has increased (Figure 5, blue bars), because it could not keep up with the growth in the regional average.

Taken together, it is clear that the growth in average consumption in China has had an equalizing effect on EAP-wide inequality, while the accompanying increase in inequality in China had the opposite effect. Specifically, China’s within-component increased from 0.13 to 0.22, while its between component declined from 0.14 to -0.04. Therefore, China’s contribution to total GE(0) inequality actually declined (from 0.26 to 0.18, or from 104% to 58% of total regional inequality), which is entirely driven by its growth in the mean. This equalizing effect from China was more than counteracted by the other countries, resulting in an increasing GE(0) overall.

These results, specifically on the direction of regional inequality as well as its country decomposition, are robust to a number of methodological checks. In the Appendix, we explain and estimate four robustness checks – (1) within-country price differences for a larger set of countries, (2) 2005 PPPs, (3) a balanced sample of countries, and (4) the raw data without a parametric Lorenz curve.

> 17 As explained in Jirasavetakul and Lakner (2016), this comparison of the between-country contribution is robust to using the method introduced by Elbers et al. (2008), which is particularly useful in settings where the number of groups and their relative sizes vary across comparators.

> 18 Of course, this does not imply that average consumption of Other East Asia has actually fallen. However, as Table 2, Panel H, shows, average consumption in Other East Asia has grown slower than the EAP-wide average – growth of 43%, compared with 265% for EAP.

10
Firstly, what does this finding imply for Brazil‘s innovation and growth policies? Instead of focusing on traditional instruments like the R&D tax credit, should Brazil consider widening this instrument to an ̳innovation tax credit‘ that covers a wider range of measured intangible assets where there are sufficient market failures to warrant public support, or consider a broader mix of policies targeting different types of intangible expenditures? If we were to extrapolate from growth analytics conducted for the US and UK using methodologically comparable estimates of intangible assets, then our results suggest that intangible investment has become a significant source of output growth in Brazil, implying that such polices should at least be considered. But it is possible that results from advanced economies do not translate readily to middle and low income countries. As a first step, it is therefore necessary to test the basic premise of this exercise more directly; that is, to see if measured ̳intangible investment‘ does have a statistically significant and quantifiably important impact on future productivity. We presented some positive preliminary evidence on this question in our paper. With better data, it should be possible to see how the relationship with productivity varies by type of intangible asset, which is important for policy specificity – together with considerations such as the extent of positive spillovers for different types of intangibles, and implementation challenges of specific policies, including focus, design and governance structures to mitigate rent-seeking.

The second main message of our findings is that if intangibles are so sizable in Brazil (and in China, as another recent study suggests), then their measurement should get more attention in other developing countries as well. Our work has identified two challenges in this area. The first is data availability. For instance, finding primary survey data on worker training is far more difficult in Brazil than in the US. The second issue is whether it is correct to make the same assumptions as CHS do about expenditures not observed directly. For example, new product development in finance is a major component of our intangible estimates, but what if this is an overestimate due to the crude assumption that 20 percent of costs in this industry go towards product development? Interestingly, a recent firm-level survey conducted in the UK (Marrano and Haskel 2010) attempts to measure this item directly, and the results suggest that the 20 percent assumption is optimistic even for the UK.

One important way forward is to start incorporating questions on different types of intangible expenditures in firm surveys already conducted by official agencies. A desirable end-goal would be to have all firms report their investment in specific categories of globally-recognized intangible assets as 34
**Figure 1.** Decomposition of vulnerability into poverty and risk in rural Serbia

<!-- Start of picture text --> Poverty Risk<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Rural Vojvodina West Central East Southeast<br>Serbia Serbia Serbia Serbia<br><!-- End of picture text --> Source: Author’s calculations based on Serbia SLS 2002 and 2003.

High level of human capital such as education decreases vulnerability and poverty. Available evidence related to education elsewhere generally supports this finding (see, for instance, Adam and Jane (1995) for Pakistan; and Campa and Webb (1999) for Peru). Households with maximum education higher than secondary face significantly lower vulnerability than those with lower educational attainment. Jalan and Ravallion (1999) and (2000), for Pakistan and China, respectively, find similar evidence that higher level of education is what that matters. Our findings also imply that household demographic composition matters: households with bigger and more elderly members are more vulnerable and are more likely to be in poverty. All things being equal, increased household size is found to place extra burden on a household’s asset/resource base and positively correlated with vulnerability and poverty. McCulloch and Baulch (2000), Jalan and Ravallion (1999 and 2001), and Aliber (2001) bring similar evidence from Pakistan, rural China and South Africa, respectively.

With regard to occupational variables, interestingly employment in the informal sector leads to less vulnerability and poverty, as does the number of household gainfully employed. On the other hand, households with greater dependence on agricultural income are at a significant disadvantage of vulnerability and poverty. Location is also an important correlate of vulnerability. Controlling for other factors, rural household residing in east and southeast Serbia are 22 and 13 percent, respectively, more vulnerable than those living in other parts of rural Serbia.

One of the most critical determinants of rural vulnerability and poverty is household durable asset ownership. Families with higher value of durable assets are significantly less vulnerable. Assets determine not only future income potential, but also the possibility of bouncing back from crisis situation as they affect household responses to shocks and their ability to cope with vulnerability. Studies elsewhere show that higher vulnerability may reflect low levels of asset holdings or persistently low returns on assets and (Gaiha and Deolaiker, 1993; Jalan and 9
Food prices and production come from a global agricultural model (Havlík et al. 2015). They are different for each climate model considered, region, and global socio‐economic scenario. We assume that food prices and productions follow the SSP5 path in our _prosperity_ scenario and the SSP4 path in our _poverty_ scenario. Then, for each region and in each scenario, we take the minimum and maximum price increase across all climate models (Global Climate Models or GCMs) and use them as boundaries to create an ensemble of scenarios (Table 3). We use the corresponding production variations to calculate the impact on farmers’ income (ensuring that our price and production inputs are fully consistent).

_Table 3 Low‐impact and high‐impact changes in the agriculture sector due to climate change (RCP8.5) in 2030. Source: GLOBIOM model (IIASA). Bold numbers are positive ones._

# (a) Low‐impact climate change

|World Bank region|SSP|Socio‐economic<br>scenario|Price difference<br>(minimum<br>across all GCMs)|Corresponding<br>production<br>difference|Corresponding<br>revenue<br>difference|
|---|---|---|---|---|---|
|EAP|SSP4|Poverty|**0.37%**|‐0.74%|‐0.38%|
|EAP|SSP5|Prosperity|‐0.13%|‐0.75%|‐0.88%|
|ECA|SSP4|Poverty|‐2.62%|**2.66%**|‐0.024%|
|ECA|SSP5|Prosperity|‐3.27%|**12.00%**|**8.34%**|
|LAC|SSP4|Poverty|**0.39%**|‐0.21%|**0.18%**|
|LAC|SSP5|Prosperity|‐0.01%|‐0.18%|‐0.19%|
|MNA|SSP4|Poverty|‐2.03%|**4.25%**|**2.14%**|
|MNA|SSP5|Prosperity|‐1.49%|**1.70%**|**0.18%**|
|SAS|SSP4|Poverty|**3.29%**|‐1.23%|**2.02%**|
|SAS|SSP5|Prosperity|**1.46%**|‐1.34%|**0.10%**|
|SSA|SSP4|Poverty|**0.74%**|‐1.39%|‐0.66%|
|SSA|SSP5|Prosperity|**0.23%**|‐1.33%|‐1.10%|

# (b) High‐impact climate change

|World Bank region SSP|Socio‐economic<br>scenario|Price difference<br>(maximum<br>across all GCMs)|Corresponding<br>production<br>difference|Corresponding<br>revenue<br>difference|
|---|---|---|---|---|
|EAP<br>SSP4|Poverty|**3.4%**|‐3.2%|**0.05%**|
|EAP<br>SSP5|Prosperity|**1.9%**|‐3.0%|‐1.17%|
|ECA<br>SSP4|Poverty|‐0.1%|**0.9%**|**0.81%**|
|ECA<br>SSP5|Prosperity|‐1.0%|**4.7%**|**3.72%**|
|LAC<br>SSP4|Poverty|**1.4%**|**0.2%**|**1.63%**|
|LAC<br>SSP5|Prosperity|**2.0%**|**2.2%**|**4.26%**|
|MNA<br>SSP4|Poverty|**2.7%**|**0.5%**|**3.18%**|
|MNA<br>SSP5|Prosperity|‐0.8%|**0.9%**|**0.14%**|
|SAS<br>SSP4|Poverty|**7.7%**|‐4.5%|**2.85%**|
|SAS<br>SSP5|Prosperity|**4.9%**|‐3.5%|**1.22%**|
|SSA<br>SSP4|Poverty|**7.1%**|‐6.1%|**0.63%**|
|SSA<br>SSP5|Prosperity|**3.1%**|‐5.8%|‐2.91%|

21
# **1 Introduction**

Despite uncertainties as to how climate change will unfold in the coming decades, what seems clear is that the brunt of its economic impact will fall on low-latitude agriculture, the dominant source of livelihood for the world’s poor. Partly based on this observation, the Stern Review (Stern, 2006) concludes that global warming will exacerbate the poverty of nations. Yet, little is known about the consequences for the poverty of people within nations. There is simply a dearth of micro-level evidence on the distributional implications of climate change, evidence showing which households will suffer the greatest income losses and how far they will fall (or possibly rise) along the economic ladder.<sup>1</sup> Such information could be useful for targeting policies that ameliorate the negative impacts of warming or that promote adaptation to it.

There are a number of reasons to believe that the costs of climate change will not be borne equally across income groups. First, regional variation is expected in future warming as well as in the climate sensitivity of agricultural production, variation which could be correlated with average regional income. Irrigated areas, for instance, may be wealthier and, at the same time, less vulnerable to rising temperatures. Secondly, households differ in their ownership of agricultural assets, notably land, as well as in their human capital and allocation of labor. The returns on land and labor (of different types) will probably respond differently to climate change. Thirdly, households will be exposed in varying degrees to changes in food prices that are likely to occur with global warming. Urban households tend to be net buyers of food to a much greater extent than rural households (as are non-farm households within rural areas), leaving them relatively worse off when prices rise.

The aim of this paper is to quantify how these sometimes conflicting forces will play out in India over the next thirty years. Viewing global warming essentially as a change in the prices of land, labor, and food, we investigate the effects on household-level welfare using a simple comparative statics framework. Our analysis takes a step beyond conventional partial equilibrium comparative statics, though, by considering the welfare impact of these three future price changes _jointly_ . The first part of the paper estimates the climate sensitivity of land and labor productivity in India. With these estimates and forecasts of future food prices changes in hand, the second part of the paper predicts the welfare consequences of climate change for households at each point along the initial welfare distribution.

Aside from its sheer size and importance as an agricultural producer, India provides an

> 1Hertel et al. (2010) study the poverty impacts of climate-induced agricultural productivity change in 15 developing countries using computable general equilibrium (CGE) modeling. Our approach is distinct in its reliance on household data, which allows us to draw out more fine-grained and country-specific distributional implications.

1
|||Ordered Probit Est|imation(full scale|)|Probit|Estimation|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||No<br>psychological<br>distress|Mild<br>psychological<br>distress|Moderate<br>psychological<br>distress|Severe<br>psychological<br>distress|Has anxiety<br>(Subscale)|Has depression<br>(Subscale)|
|Consider oneself a member of an ethnic or racial|||||||
|minority=1|(0.012)|(0.001)|(0.004)|(0.008)|(0.014)|(0.015)|
|_Ref: Socioeconomic status: Lower_|||||||
|Lower middle|0.125***|0.044***|-0.034***|-0.134***|-0.148***|-0.167***|
||(0.014)|(0.008)|(0.003)|(0.018)|(0.025)|(0.024)|
|Upper middle|0.210***|0.044***|-0.064***|-0.190***|-0.238***|-0.283***|
||(0.014)|(0.008)|(0.004)|(0.018)|(0.024)|(0.024)|
|Upper|0.231***|0.042***|-0.072***|-0.201***|-0.236***|-0.291***|
||(0.024)|(0.008)|(0.008)|(0.021)|(0.032)|(0.033)|
|_Ref: Rural or Farm or Isolated house_|||||||
|Small city or town or suburb|0.014|0.001|-0.005|-0.010|-0.00003|-0.003|
||(0.021)|(0.001)|(0.007)|(0.015)|(0.027)|(0.028)|
|Large or capital city|0.021|0.001|-0.007|-0.015|-0.014|-0.007|
||(0.020)|(0.001)|(0.007)|(0.015)|(0.026)|(0.027)|
|Has citizenship or valid residence permit=1|-0.017*|-0.0002|0.006*|0.012*|0.015|0.049***|
||(0.010)|(0.0003)|(0.003)|(0.007)|(0.013)|(0.013)|
|Household live has outdoor spaces=1|<br>0.079***|<br>0.001|<br>-0.027***|<br>-0.053***|<br>-0.065***|<br>-0.081***|
||(0.011)|(0.001)|(0.004)|(0.007)|(0.013)|(0.014)|
|Log of GDP per capita|0.026**|0.0004|-0.009**|-0.017**|-0.024|-0.039**|
||(0.012)|(0.0004)|(0.004)|(0.008)|(0.015)|(0.016)|
|_Ref: South Asia, East Asia, and Pacific_|||||||
|<br>Europe and Central Asia|-0.176***|0.024***|0.061***|0.091***|0.129***|0.170***|
||(0.017)|(0.005)|(0.006)|(0.007)|(0.017)|(0.018)|
|Latin America and the Caribbean|-0.136***|0.024***|0.047***|0.064***|0.183***|0.095***|
||(0.022)|(0.005)|(0.008)|(0.012)|(0.025)|(0.024)|
|Middle East and North Africa|<br>-0.283***|<br>-0.001|<br>0.095***|<br>0.190***|<br>0.285***|<br>0.277***|
||(0.023)|(0.010)|(0.007)|(0.022)|(0.033)|(0.034)|
|North America|<br>-0.192***|<br>0.022***|<br>0.067***|<br>0.103***|<br>0.222***|<br>0.191***|
||(0.033)|(0.005)|(0.011)|(0.023)|(0.042)|(0.042)|
|Sub Saharan Africa|-0.256***|0.009|0.087***|0.160***|0.237***|0.238***|
||(0.039)|(0.013)|(0.012)|(0.038)|(0.058)|(0.059)|

26
**Figure 3 Sri Lanka. Remittance Flows to Urban Rural and Estate Households (percent of total)**

<!-- Start of picture text --> ,<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Urban Rural Estate<br>Total Foreign<br><!-- End of picture text --> Section 3 makes suggestions to strengthen the formal remittances’ infrastructure<sup>8</sup> to enable it to compete with informal service providers, and Section 4 summarizes policy recommendations.

# **II. Formal and Informal Infrastructure for Remittances**

_Overall, Sri Lanka has an impressive density of institutions providing financial services (state and commercial banks, postal network, and microfinance institutions), which offer a good basic infrastructure for remittances flows._ Under the Exchange Control Act, foreign exchange transactions are permitted only through licensed commercial banks appointed as fullfledged authorized dealers, unless any other person is appointed by the Central Bank of Sri Lanka (CBSL) as a restricted dealer. Accordingly, both inward and outward remittances from and to Sri Lanka are presently effected through 22 licensed commercial banks (Table 2).

**Table 2  Remittances by Licensed Commercial Banks**

||**(million R**|**upees**)||**2004**|
|---|---|---|---|---|
|**Institution**|**2002**|**2003**|**2004**|(% of total)|
|Bank of Ceylon|741.8|772.0|881.4|56.4|
|People’s Bank|183.3|208.9|232.7|19|
|Seylan Bank|102.5|129.0|147.5|9.4|
|Hatton National Bank|42.7|57.5|81.6|5.2|
|Comercial Bank|63.7|79.6|93.4|6.0|
|Sampath Bank|58.8|75.4|76.1|9|
|Others|92|91.6|51.2|3.3|
|**Total**|**1,287.0**|**1,410**|**1,563.9**|**100.0**|

_Source: Hettiarachi (2005)_ 4