rafmacalaba/datause-encoder-data huggingface dataset:
from rationalization. The creation of the single market in the European Union led to innovative analysis that required the use of multi-region models with imperfect competition or dynamic effects in order to capture the impacts of the key features of the single market (Harrison, Rutherford and Tarr, 1996; Smith and Venables, 1988; Baldwin, Forslid and Haarland, 2000). The North American Free Trade Agreement (NAFTA) led to a large number of CGE studies summarized in the Francois and Shiells (1994) volume. Among these, Levy and van Wijnbergen (1995) use their dynamic CGE model to argue that dynamic incentive problems in adjustment policies for Mexican agriculture imply that adjustment policies should focus on increasing the value of the assets of poor farmers, not their incomes. Preferential arrangements of the European Union with its Mediterranean neighbors led to policy-maker requests for CGE analysis. `Using small open economy models of the developing country under perfect competition (Harrison, Rutherford and Tarr (1997a) for Turkey; Rutherford, Rutstrom and Tarr (1993) for Morocco; and Rutherford, Rutstrom and Tarr (1995) for Tunisia), these North-South arrangements were estimated to beneficial to the developing country due to the introduction of competition into the Southern markets. Finally, Chile has adopted a strategy of negotiating preferential arrangements with all potential partners (called “additive regionalism” or “competitive regionalism”). This strategy has been controversial within Chile regarding preferential arrangements with its Southern neighbors. Using a multi-region perfect competition model, Harrison, Rutherford and Tarr (2002) estimated that Chile would lose from individual preferential arrangements with Southern neighbors unless it lowered its then 11 percent uniform tariff. But these authors show that the agreements with Southern partners are beneficial to Chile in the context of Chile’s additive regionalism strategy due to substantial estimated terms of trade gains to Chile in partner markets and the reduction of trade diversion costs if the Northern partners are included in the 8
support and stockholding, land tenure and water rights, and the ability of stakeholders to participate in political processes (W. N Adger et al. 2007; Kelly and W. N. Adger 2000; Smit et al. 2001; Smit and Wandel 2006). The net result of these complicating factors makes it challenging to observe adaptation empirically and to forecast the potential for such adaptation to mitigate adverse impacts from future climate change. However, the historical record of adaptation to climate events can give us some bounds on expectations.

Several recent and comprehensive surveys of the adaptation literature exist, including the second working group’s contribution to the IPCC report _Climate Change 2007_ (W. N Adger et al. 2007), Smit and Wandel’s (2006) review of adaptation and vulnerability, Adger’s (2003) synthesis of the literature on social capital and adaption, Agrawal’s (2008) report on institutional adaptation, and the work by Raleigh and co-authors (2008) on migration and conflict induced by climate change. For this paper, we focus solely on climate change adaptation strategies which bear on agriculture and natural resources, examining their potential impacts on rural household wealth and economic growth. This allows us to tease out the factors that constrain these adaptation strategies and the potential for policy to address these constraints.

_Climate Risk and On-farm Decision-Making_ Climate risks are an important determinant of fluctuations in farm household income. Dercon (2005) found that drought was the predominant source of serious shocks leading to loss of assets, income and consumption for rural households in Ethiopia. Sampled households which experienced drought in the last two years showed an average consumption reduction of 16% relative to households which did not experience drought, households which experienced a drought two years before that still showed a 14% consumption reduction, and the overall poverty 26
# 2 Post-conflict poverty projections: Review of existing estimates

## 2.1 Post-conflict estimates based on nowcasting methodologies

Nowcasting methodologies are typically used to fill data gaps in countries that lack up-to-date surveybased poverty estimates. Nowcasting approaches vary in terms of both methodological assumptions as well as in terms of data sources used in the analysis.

The simplest nowcasting approach – which is used by the World Bank in the production of global poverty estimates – relies on information from national accounts (per capita GDP or household final consumption expenditure) to predict poverty. More specifically, this nowcasting methodology assumes that growth (or shrinkage) in GDP per capita registered since the country’s last poverty estimate is fully passed through to the consumption vector that is measured in household surveys (Prydz et al. 2019). Poverty is then estimated using this predicted consumption vector under a “distribution neutral” assumption i.e., assuming that inequality does not change at that each individual is equally affected by the growth/contraction in welfare. While the impact of growth on poverty reduction is well-known (Kraay, 2006; Ferreira and Ravallion, 2009), concerns have emerged related to possible un-reliability of GDP measures in developing countries (Angrist, 2022), as well as the extent to which growth is passed through to welfare in fragile and conflict affected countries (Box 2).

_Box 2: Growth rate pass-through in FCS Economies_ The assumption of full pass-through works fairly well in economies that are not in Fragile and Conflict-Affected Situations (FCS). However, available evidence suggests that a full pass through assumption is likely inappropriate in case of conflict.

In order to estimate any difference in pass-through in FCS settings, Corral et al. (2020) fitted a regression model on a sample including all countries for which survey consumption estimates are available in PIP over the period 2000 to 2019. In particular, the annualized growth rate in the survey per capita consumption is regressed on annualized growth in GDP per capita, together with an interaction of annualized growth in GDP per capita and the share of time (if any) that each country spent as FCS between 2000 and 2019. G G s s s = 0.964 ∗G G G G s −0.530 ∗G G G G s mmssm GGmmG GGmm GGmmG GGmm According to this model, for economies that have never been FCS, 96 percent of GDP growth is passed through GGh GGh GGh ∗SShaaGGa aaSS to welfare. However, for economies that have been in FCS throughout the entire period, only 43 percent (0. 64 - 0. 30) is passed through.

The reduced pass-through rate indicates that in economies experiencing conflict, violence, or fragility, changes in national accounts are larger than changes in welfare. In particular, for economies in FCS where GDP per capita declined, this implies that the poverty rates estimated using this adjusted pass-through would be lower compared to those estimated under a full pass-through assumption.
# Table 7:  Estimations of changes in employment shares in unorganized sector by state-industry

||DV: Change in|unorganized sector's|employment share for|state-industry|
|---|---|---|---|---|
||Manufacturing<br>1989-1994|Manufacturing<br>1994-2000|Manufacturing<br>2000-2005|Services<br>2001-2006|
||(1)|(2)|(3)|(4)|
|Employment growth in organized sector for state-industry|-0.074+++|-0.084+++|-0.120+++|-0.159+++|
|across the time period relative to starting total employment|(0.028)|(0.032)|(0.040)|(0.015)|
|Indicator for employment growth in organized sector being|0.022|-0.027|-0.003|-0.049+++|
|greatest in small establishments (39 or fewer employees)|(0.021)|(0.020)|(0.020)|(0.019)|
|Indicator for employment growth in organized sector being|-0.047++|-0.035+|-0.072+++|-0.003|
|greatest in large establishments (100 or more employees)|(0.020)|(0.020)|(0.019)|(0.033)|
|Indicator for entrepreneurship level in organized sector in|-0.010|-0.005|-0.043++|0.008|
|state-industry at the start of period being above median|(0.019)|(0.020)|(0.018)|(0.019)|
|Observations|563|563|563|525|
|Adjusted R-squared|0.148|0.187|0.217|0.368|
|State and industry fixed effects|Yes|Yes|Yes|Yes|

Notes: Estimations consider changes in state-industry employment shares in the unorganized sector, with industry group and time period indicated by column headers. Negative values indicate a decline across the period in the employment share of the unorganized sector for the state-industry. Explanatory variables are changes in industrial conditions of the organized sector. Employment growth in the organized sector is measured relative to the total employment in the state-industry at the start of the period. Indicator variables model whether employment growth in the organized sector is greatest among small or large establishments, with employment growth being greatest among establishments with 40-99 workers being the baseline. An indicator variable models whether initial entrepreneurship rates in the organized sector are above the median for state-industries in the survey year. Estimations report robust standard errors, include state and industry fixed effects, and weight observations by the log initial employment for the state-industry combining the unorganized and organized sectors. + significant at 10% level; ++ significant at 5% level; +++ significant at 1% level.
# 3. Empirical Implementation

Coe and Helpman (1995) estimate the following equation based on Grossman and Helpman's (41991) theoretical work on endogenous growth in the open economy:

> _lnTFP,, =aC, +/dlnRDd+13f_ lnRDf _+_ **_c,;/_**<sup>**_3_**</sup> **_d IJf_** >0, (1)

where RD" _(RDf)_ is the domestic (foreign) R&D stock, _£_ is an error term, and c (t) denotes country (year). Due to lack of data for Mexico (and for developing countries in general)--and as in Coe et al. (1997) and Schiff et al. (2002)--the estimation in this paper does not include domestic R&D. This is unlikely to be a problem because most of the world's R&D is performed in developed countries.<sup>5</sup> We estimate TFP equations with pooled data for a panel of industries. We define the stock of foreign R&D available in industry i, _NRDj,_ as: where k indexes OECD countries, j indexes industries, M (VA) (RD) denotes imports (value added) (R&D stock), and _a,,_ is the import input-output coefficient (which measures the share of imports of industry j that is sold to industry i).

The first part of equation (2) says that foreign R&D in industry i, _NRDj,_ is the sum, over all industries j, of _RDj,_ the industry-j R&D obtained through imports from

> _5_ In 1990 (1995), 96% (94.5%) of the world's R&D expenditures took place in industrial countries. Moreover, recent empirical work has shown that much of the technical change in OECD countries is based on the international diffusion of technology among OECD countries (Eaton and Kortum, 1999;. Kellir 2002a). For instance, Eaton and Kortum (1999) estimate that 87% of French growth is based on-foreign R&D. Since developing countries invest much fewer resources in R&D than OECD countries, foreign R&D must be even more important for developing countries as a source of growth.

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a contractionary imr :t, resulting in a fall in the consumer price index rAd an improvement in the trade deficit. Columns (4) and (5) report the resulting changes in excise revenue and revenue from customs duties and sales taxes (CDST) on imports respectively.

The relative real ir.comes in the base year of the model (1984/85) of the socioeconomic groups identified by the Household Expenditure Survey a-.e shown in Table 2. The numbers are presented as ratios to the economywide average, where the latter is weighted by the population in each group. The distributional impact of the excise tax changes of Table 1 are shown in detail in Table 3 which reports the impact on real income, i.e., income deflated by the consumer price index, for each socioeconomic group. An informal examination of those numbers is used to generate Column (6) of Table 1. Thus a plus (+) means progressive, zero (0) indicates neutrality, whereas a minus (-) indicates regressive incidence. A question mark (?) signifies that the outcome is unclear. While this information can (and will) be more formally presented, the detailed results of Table 3 are frequently of considerable interest to analysts and policy makers in identifying the main elements of a tax package. Such a package, once identified, can then be simulated using the model and its revenue and incidence impact examined in detail. Colum-n (7), which may be disregarded Table 2. Bangladesh: Base Per Capita Relative Real Income by Socioeconomic Group (Relative to Weighted Average Across Groups)

|Landless Rural|0.915|
|---|---|
|Small Farmers|1.018|
|Medium Farmers (Tenants)|1.015|
|Medium Farmers (Owners)|1.079|
|Large Farmers|1.109|
|Largest Farmers|1.204|
|Rural Informal Sector|0.903|
|Rural Formal Sector|1.097|
|Urban Informal Sector|0.766|
|Urban Formal Sector|1.064|
and still allows us to estimate treatment impacts on worker physical activity, but it does reduce the power of the study from our initial expectations. It also limits the time window over which we can investigate the effects of treatment.

While 83 workers were allocated activity trackers at the beginning of the study, 25 accelerometers were lost over the eight-week study period, resulting in 58 devices being returned at the end of the study.<sup>18</sup> This analysis utilizes the daily data generated by all trackers, until the point that they were lost. Figure 1 presents the frequency of tracker loss by the number of days until the tracker was lost by the worker. Tracker loss does not seem to occur at any particular point in time, though loss increases in the first week after assignment and during weeks 5 and 6 of the study when the plantation was unexpectedly closed and workers were monitored less closely.

Because workers who lost their accelerometer may have different characteristics, thus possibly resulting in biased estimates, we assess the correlates of the probability of losing the tracker device.<sup>19</sup> In Table 2, worker characteristics and health status are regressed on an accelerometer loss indicator. No statistically significant relationships are found between tracker loss and any of the observed individual, household or health characteristics, including age, experience, schooling, household composition, assets, malaria test result status, BMI, and hemoglobin. All estimated coefficients are also very close to zero in magnitude.

# _Balancing tests_

The identification of the relation between health and physical activity relies on the temporally randomized exposure to a malaria testing and treatment program. The assignment of workers into treatment or comparison group is described in detail in the next section. To avoid confounding the impact of treatment with possible interactions between worker characteristics, we

> 18 We investigated and carried out qualitative interviews to assess whether these devices were stolen, but there is no reason to believe so. Given the relatively high earnings from cane cutting, workers are keen to keep a good reputation with the plantation, and they were fully aware that the device was not useful without the complementary workstation and log in password to download and read the information, which they did not have access to.

> 19 Selective loss may introduce a selection bias in later estimation results. For instance: if workers who had more physical capacity were more likely to lose their tracker due to greater physical activity, then this would likely bias activity measures downwards. If workers who were more educated or wealthier were more careful with their tracker, then this differential probability of tracker loss might also potentially bias the estimates.

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1. Not unexpectedly VR retrofitting proves more expensive than VR measures implemented at the time of original construction.

2. Caribbean _industrial_ purpose structures, as opposed to regular commercial structures, customarily have the least storm resistive designs and materials.

3. From insurers' observations, storm damage to contents characteristically runs twice the damage cost to the respective structures.

Following that, the 2.25% mean was scaled to VR cost estimates for the several construction and occupancy use classes. The methodology and judgement incorporates reasonable confidence on the potential of VR measures, at the indicated cost levels, being able to reduce hurricane damage (and PMLs) by 50%. This confidence is founded on post-hurricane surveying and information from clients who have adopted similar VR measure recommendations.

It should be made clear that estimates shown in the table are necessarily broad averages for the classes shown. Individual structures have individual structural, exposure, and location characteristics which can vary their VR performance from the averages displayed.

# **Illustration of Impact of Hurricane Vulnerability Reduction (VR) Measures on the Risk Premium : Risk Amount Ratio**

**Assumptions:**

1. Building Structure Hurricane PML:

- Before VR retrofitting 10 % After VR retrofitting 5 %

2. Policy Claim deductible is 2% x sum insured 3. Sum Insured is $ 500,000 4. Premium rates (%) are: Full rate Catastrophe (CAT) Element Without VR measures 1.00 .60 With VR measures 0.75 .45

||**Risk Premium**<br>**_Before VR Measures_**<br>$|**:**<br>**Risk Amount Ratios**<br>**_After VR Measures_**<br>$|
|---|---|---|
|Sum Insured|500,000|500,000|
|PML %|10%|5%|
|PML $|50,000|25,000|
|Claim Deductible 2 %|10,000|10,000|
|Net Insurance Liability|40,000|15,000|
included in the model. Our analysis is informed by two rounds of data, collected from a large sample of children and their caregivers in rural Cambodia.

# **2) Methods**

We investigated our hypotheses using data from a large panel of children in rural Cambodia. We rely on a unique panel data set of almost 7,000 children (age 2-4 in 2016) and their families, collected in rural Cambodia in 2016 and 2017. The child survey comprises a comprehensive battery of cognitive tests (executive function, language and early numeracy) as well as anthropometric measures (height and weight). The caregiver survey includes basic socioeconomic characteristics, questions about the socio-emotional development of the child (Strengths and Difficulties Questionnaire), as well as 25 questions about parental practices, measuring three key dimensions of parenting (cognitive parenting, emotional parenting, negative parenting). Thus, the caregiver survey allows for distinguishing the role of socio-economic characteristics and the role of specific parental practices in explaining children’s development.

# **Participants**

During the baseline data collection exercise in 2016, a survey firm<sup>1</sup> used an adapted version of the EPI walk to select 6,934 eligible households in 305 villages.<sup>2</sup> Each sampled household included children aged between 24 and 59 months.<sup>3</sup> Approximately 11 months later,

> 1 Angkor Research©, under the close supervision of the Principal Investigators.

> 2 EPI refers to the Expanded Programme on Immunization of the World Health Organization (see e.g. Henderson & Sundaresan (1982)).

> 3 The study was approved by the institutional review board of the Paris School of Economics and the National Ethics Committee for Health Research by the Cambodian Ministry of Health. The data were collected as part of a randomized controlled trial investigating the effects of preschools on school readiness of children in rural Cambodia. Our main outcomes for this study, collected in 2017, stem from a first follow-up on the trial intervention. Between baseline and the first follow-up, community preschools were constructed in some of the sample villages. An awareness campaign was conducted to stimulate enrollment into the new preschools. Further, a home-based program, in which trained core-mothers address a range of topics around parenting, was implemented. These

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Other assumptions of the standard competitive labor markets model could also be relaxed. In one of the few attempts to do this, Babiker and Eckaus (2007) explore the impact of constraints on real wage adjustment and on inter-sectoral flows of labor in different regions of the world and find that China and India could be the countries most adversely affected by the structural change induced by carbon pricing, largely because these are the regions that need most reallocation of labor as a result of environmental policy.<sup>3</sup> It would be very useful to consider the implications of various models of labor market functioning more specific to developing countries (e.g. as reviewed by Behrman, 1999) for the employment and wage impacts associated with a shift towards green policies.

As an example, consider the model of Satchi and Temple (2009). As they point out, “At present, we know relatively little about how changes in sectoral productivity translate into labor market outcomes.” Yet green policies appear likely initially to lead to lower productivity in the energy sector, with pervasive knock-on effects in the rest of the economy, unless at the same time other market failures, such as those involved in the provision of energy supply infrastructure, are corrected. Satchi and Temple set up a smallscale general equilibrium model, in the tradition of labor market search models, designed to characterize typical middle-income countries (their calibrations use Mexican data). There are three sectors: a formal urban manufacturing sector, an informal urban sector, and a rural sector. Underemployment – essentially, employment in the informal sector – arises because of job/worker matching frictions in the formal sector. The urban wage (in the formal sector) is endogenously determined, unlike in Harris and Todaro (1970). The rural sector is assumed to be perfectly competitive with constant returns to scale and full employment, thus ruling out the type of surplus labour considered in many low-income countries.

The authors consider the impact of productivity shocks in different parts of the economy. If green policies are thought of as having adverse impacts on energy sector productivity, it matters where energy sector jobs are located. Suppose they are in the formal urban sector, as would be the case if they are largely in regulated energy utilities. Then the size of the informal sector may increase (and definitely does not decrease) as a result. The urban

> 3 The ways in which Babiker and Eckaus model real wage rigidity and limits on inter-sectoral labour flows can be argued to be inappropriate over the very long run associated with integrated assessment models.  But their study nevertheless demonstrates the potential importance of these constraints.

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