rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix 2. Data sources and definitions**

## A.2.1 Data used for the estimation of eq. (5) in paragraph 3

Data for exports and imports grouped according to Pavitt (1984) in nominal terms were partly provided by the Italian Institute for Foreign Trade (ICE) and partly elaborated by the author on the NIMEXE data base (Eurostat). Bilateral trade flows were elaborated by the author starting from IMF Direction of Trade Statistics data. The "world" aggregate includes: United States, Japan, Germany, France, Italy, United Kingdom. A detailed classification is available on request from the author. They have been transformed in constant dollar values at 1985 GDP price indices and 1985 PPP dollar exchange rates.

The stock of domestic knowledge T is the fractional patent count taken from the US Patent and Trademark Office cumulated on a benchmark initial value.

The stock of foreign knowledge Tw is defined as _T_ but it includes the sum of patent counts for the countries entering the group defined above less the patent counts for the domestic economy. When the European stock of knowledge is used data for the US and Japan are excluded.

_P_ is the real effective exchange rate (source: Bank of Italy) _F_ is the amount of private R&D expenditure transformed in constant dollar values at 1985 GDP price indices and 1985 PPP dollar exchange rates, cumulated on a benchmark initial value and depreciated at the rate of 15 percent a year. (source CNR 1992) The sample period (annual observations) covers 1970-1991. All stocks were measured at the end of period while prices are period averages . All series measured at the end of period were adjusted in order to be consistent with flow data (Gandolfo 1981, equations (30) and (31) of chapter 3). This allows to consider variables which contain both stocks and flows in their definition. The approximate discrete analogue to the continuous model used for the estimation carried out in paragraph 3 was obtained as expounded in Gandolfo (1981) , chapter 3 paragraph 3.2.2

## A.2.2 Data used for the estimation of model (2.1)-(2.3)

The continuous time model (2.1)-(2.3) was also estimated (FIML) taking its approximate discrete analogue and data were treated as explained in Appendix 1. Following Fagerberg (1988), we introduce a measure of innovative activity including both inputs and outputs of innovative activity. We have computed a weighted average of two specialization indicators: one based on business enterprise R&D expenditures (BERD) and one based on patent counts. These two indicators are known as Revealed Technology Advantage (RTA) indicators, which correspond to the well known Balassa index used with trade data. This indicator, the index of "Industrial Technological Specialization" (ITS): is defined as follows 34
the slope of _F(z)_ at that point).<sup>5</sup> In the case of Indonesia, inequality is relatively low and the poverty line is relatively near the mode, so the sensitivity of the headcount poverty rate to the poverty lirne is quite high.

For a given percentage rise in the poverty line, how many percentage points does poverty change? Table :3 gives the answer. For both the 1996 and 1999 data, a poverty line is chosen that produces a 9.75 percent poverty rate<sup>6</sup> . We then increase these poverty lines by 5, 10, 15, 20, and 25 percent and calculate the respective poverty rates. Based on this ve estimate the (semi-)elasticity of the poverty rate as _the percentage point changes_ in the poverty rate with respect to _percentage changes_ in the poverty line. The results suggest that for every one percent fall in real expenditures the poverty rate rises by one half of a percentage point, if the poverty rate is around 15 percent. However, the sensitivity to poverty increases with the poverty line, so that at 25 percent above the poverty line a one percent change in expenditures produces around 0.6 - 0.7 percentage point change in poverty, as one moves into a range with a higher values of the pdf (steeper cdf).

The combination of the changes in real expenditures in table 2 and the responsiveness of poverty to expenditures in table 3 indicate some rough orders of magnitude of how much we should expect poverty to increase. For instance, from table 2 the median "real" expenditures of the second quintile (which is the change in expenditures at the 30k percentile) fell by 8.58 percent. This means that, absent S Just imagine the special case in which everyone has exactly the same expenditures, then either everyone is in poverty or no one is, and the cdf is discontinuous (has essentially infinite slope) at that point.

9
In addition to the coefficients reported, the Engel curve regression includes as covariates household size; four demographic ratios (for children, youth, elderly, and migrants); the gender, age, sector of activity, and education of the household head; and prices for two types of street meals, which are a close substitute for food at home—the numerator of the food share in the dependent variable. Including these prices of close substitutes (and the relative price of food, whose coefficient is reported in table 1) favors the Engel curve method; typically these would be unobserved absent a price survey because unit values are unavailable for street meals and nonfoods given that household surveys usually just obtain reports on the quantities of well-defined food groups. Since the WCPD models include many more covariates (the fixed effects for each item) the summary statistics reported are adjusted- _R_<sup>2</sup> , which range from 0.57 to 0.61 for the allconsumption aggregate and from 0.66-0.67 when housing and durables are excluded. The adjusted- _R_<sup>2</sup> is lower for the food Engel curve, at 0.58 (and 0.45 without housing and durables).<sup>16</sup> The spatial variation in the cost-of-living can be observed from the size and significance of the dummy variable coefficients for each region and sector. According to the WCPD results (regardless of weights), the only area in 2010 without a significantly lower cost of living than the base region of urban Red River (Hanoi) is the urban Southeast, which has Ho Chi Minh City. The region and sector with the lowest cost-of-living is the rural Mekong Delta, which is Vietnam’s rice bowl. Except for Red River and Southeast, the between-region differences in the cost-of-living exceed the urban-rural differences within region; apart from Hanoi and Ho Chi Minh City, most cities are small and not highly differentiated from their rural hinterland. These patterns are quite similar to those found in 1993 with the VLSS, which is consistent with the regional variations in

> 16. This exceeds the average _R_<sup>2</sup> in the Engel curves of Almås and coauthors (0.44), so any poor performance of the Engel curve deflators here should not be due to a poorly specified regression model.

23
insurance products need initial subsidies to be taken up, careful RCT experimentation on how best to use subsidies to induce stable demand at fair price is an important issue. Similar games can be organized to reveal how agent behavior will adjust to protection against extreme events, and willingness to co-pay for such protection in a public-private partnership.

Given the extent of behavioral inconsistencies in the decision to invest in riskreduction and to insure, and the need to anticipate agent responses to design publicprivate partnerships in SDRFI, we believe that conducting such experiments is a necessary first step.

# **2. Is there political support for DRFI?**

It is well known that disasters are politically risky. Inadequate response to shocks has cost their tenure to many governments. At the same time, governments will only devote fiscal resources to DRFI if they receive electoral support for this initiative. Assessment of electoral support to DRFI can be done ex-post through the analysis of natural experiments with existing schemes of protection against natural disasters, or ex-ante in insurance games eliciting responses on political support.

Mexico offers a unique opportunity to analyze ex-post electoral responses to disaster protection. As we describe it in the country case study for Mexico, there are three schemes that can be analyzed as natural experiments in that perspective: FONDEN at the federal level, CADENA at the state level, and Fondos de Aseguramiento at the local institutional level.

CADENA provides insurance against drought for farmers through a state-level insurance that is free to farmers. It has been shown to induce positive risk management responses as observed through higher yields where coverage is available (Fuchs and Wolff, 2011). Fuchs and Rodriguez-Chamussy (2011) have analyzed the impact of insurance payouts on voter behavior in the 2006 presidential election. The unit of analysis is the electoral section, and the question is whether payments received by farmers in the electoral section in 2005 (by which time Agroasemex insured 15 states) affected voting behavior toward the incumbent political party in the 2006 election. The identification strategy is a regression discontinuity design based on the index insurance threshold rainfall levels to trigger payments. They find that disaster relief buys votes. The incumbent party is estimated to have garnered 8% more votes where indemnity payments had been made prior to the election, a gain attributed to voter switching political party rather than to increased electoral turnout.

We propose to analyze these three schemes in a political economy perspective. This is important to assure governments that there is political support for DRFI and to help determine how political support can be enhanced without falling into the temptations of populism. FONDEN started operating with a combination of risk-retention and risktransfer instruments in 2006 (World Bank, 2012). Data on payments can be obtained and matched onto electoral outcomes at the presidential, governor, and municipal levels.

7
potential effects assuming that subsidy savings are used (a) only for budget consolidation, (b) for budget consolidation and direct transfers to households, and (c) for budget consolidation and direct transfers plus productivity-enhancing investments. Results of these six scenarios are reported as relative changes to the baseline scenario. In scenarios 1C and 2C, we assume investment-growth elasticity of 0.5; that is, a 1 percent increase in investment leads to 0.5 percent growth in these sectors. Table 6 summarizes the assumptions of the scenarios.

# **4. Impact of Petroleum Subsidy Reform Options**

## **Accelerated versus Gradual Reform**

The first set of simulations (1A and 2A) look at a situation where the government reduces petroleum subsidies without taking further action. Lower petroleum subsidies have essentially two effects. At the sector level, higher prices for petrol and diesel increase intermediate input cost and reduce sectoral profitability and production, particularly in energy-intensive sectors. At the macro level, they lead to a redistribution of income from the private sector to the government, thereby reducing the government’s deficit, crowding-in public investment, and increasing the economy’s overall capital stock. Thus, this scenario can also be broadly interpreted as a budget-consolidation scenario.<sup>12</sup> While the cost-push effects are felt immediately, the capacity effect sets in with a one period time lag. In a first-round effect, energy price increases lead to rising domestic prices, lowering real household incomes and appreciating the real exchange rate (assuming constant world market prices). Results from the DCGE model show that overall growth drops in the initial year of reform under scenario 1A and is close to zero under 1B but recovers quickly in subsequent years (Figure 1).

> 12 It is important to note that if the savings would be used for deficit reduction and budget consolidation, positive medium- to long-term effects can be expected, such as higher credit ratings and lower interest rates (see also section with model description). However, capturing these effects is beyond the scope of this paper.

12
At _t_ = 0, all firms are constrained to produce a basic product with the basic input bundle. Without loss of generality, we normalize the cost of the basic input bundle _c_ 0( _k_ ) to 1, and the quantity shifter _q_ 0( _k_ ) of the basic product to 1 as well. The firm profits when selling basic product are: where the subscript _b_ stands for “basic.” For _t_ = 1, firms have a choice over product quality that is subject to uncertainty. In particular, each firm has the option to keep producing under basic quality. This choice carries no additional costs and no uncertainty.

Alternatively, firms can invest in risky quality upgrading. They pay a fixed cost _f_ before realization of uncertainty. This fixed cost can be anything that has an impact on the consumer valuation of the product: product development, market research to determine the optimal product design, advertising expenditure, etc. With probability _φ_ the investment is successful, and consumers like the product. In that case, the firm’s quality becomes _q_ 1( _k_ ) = _q >_ 1. At the same time, in order to produce a higher quality product, the firms need better inputs. These could be higher-end material inputs, or higher-skilled workers.<sup>8</sup> To produce higher quality final product, the cost of the required input bundle is _c_ 1( _k_ ) = _c >_ 1. When the quality-upgrading investment is successful, the firm thus earns profits of: However, with probability 1 _− φ_ the quality-upgrading investment is not successful, and consumers are not convinced that the firm’s product is of higher quality. In that case, the firm reverts back to the basic product, and its profits are: which is of course simply equal _πb_ ( _k_ ) _− f_ .

To make the analysis interesting and obtain the main result in the simplest way, we make the following assumption.

This assumption states that expected profits from quality upgrading are greater than basic profits, for all firms. Of course, if this were not the case, then the safe project has a higher

> 8Kugler and Verhoogen (2012) document the positive relationship between firms’ product quality and unit costs.

5
Table 4: Impact on rst generation adult stature by age at start of the civil war

|||Height in cm||Height|for age sd|(HAZ)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Age 22 to 29 _×_ Months of war|0.011|0.011|0.014|0.002|0.002|0.002|
||(0.033)|(0.034)|(0.035)|(0.006)|(0.006)|(0.006)|
|Age 9 to 15 _×_ Months of war|-0.026|-0.026|-0.027|-0.004|-0.004|-0.005|
||(0.026)|(0.026)|(0.025)|(0.004)|(0.004)|(0.004)|
|Age 4 to 8 _×_ Months of war|-0.068**|-0.067**|-0.071**|-0.012**|-0.011**|-0.012**|
||(0.031)|(0.031)|(0.032)|(0.005)|(0.005)|(0.005)|
|Age0 to 3 _×_ Months of war|-0.122***|-0.127***|-0.136***|-0.021***|-0.021***|-0.023***|
||(0.033)|(0.032)|(0.032)|(0.006)|(0.005)|(0.005)|
|Observations|4,421|4,421|4,421|4,418|4,418|4,418|
|Adjusted R-squared|0.031|0.031|0.043|0.031|0.031|0.043|
|Birth year and month xed eects||Yes|Yes||Yes|Yes|
|Regional trends and other controls|||Yes|||Yes|
|Village xed eects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of clusters|383|383|383|383|383|383|
|Control mean (Age 16 to 21)|151.4|151.4|151.4|151.4|151.4|151.4|

Note: *** (**) (*) indicates signicance at the 1% (5%) (10%) level. Treatment on the treated estimates are reported based on a dierence-in-dierence specication. Standard errors are clustered at village level. All ages in the table refer to age at the start of the war. Comparison cohort is age 16 to 21 and cohort 22 to 29 is a second comparison group that serves as a placebo test. Other controls include an indicator for high caste and month of interview xed-eects.

42
sri lanka country risk profile 14 precipitation flood, meaning that there is a 2% chance of flooding damage in excess of this value in any given year.

The top panel of Figure 3 on the previous page illustrates the expected impact of a 2017-like flood event on individual incomes in Rathnapura district. The black outline indicates the regional income distribution as reported in HIES, while the red histogram illustrates the expected income distribution immediately following a 50year flood in the region. This distribution shows a mode around US$450 per person, with 10% of the district below the line. The bar on the per year, living poverty large right shows the number of people with incomes higher than US$2,000 per year; the poverty and subsistence lines are indicated by the dotted lines around US$300 and US$350 per year, respectively. Income losses resulting from this 2017-like event are expected to push some 11,300 individuals into income poverty in Rathnapura, and 5,700 below the subsistence income level, meaning they cannot meet basic needs.

At the national level and for the average year, we estimate that over 5,600 Sri Lankans face income poverty or subsistence for some amount of time every year due to precipitation flooding. However, large events can impoverish broad segments of the population when they occur. For example, the expected destruction from nationwide 50-year flooding could move nearly 34,000 individuals nationwide into transient income poverty or subsistence. Of these, the model indicates that just over a thousand will still be in poverty 10 years later. This suggests that disasters may have a long-term impact on the income and consumption of some Sri Lankan households.

# _Consumption poverty_

Income losses represent a complementary perspective to asset losses, and generate new insights into the costs of disasters in Sri Lanka. However, as a metric of disaster impacts, income losses may yet underestimate the link between poverty and disasters. First, they do not account for the reconstruction costs that households incur in rebuilding their assets after a disaster. Second, while wealthy households may have access to considerable savings, credit, remittances, and insurance to finance this recovery, these resources are often available only informally, if at all, to poor households [4, 5]. This is important, because access to these tools can mean the difference
Table 12 compares the estimates for fourth graders and eighth graders using data from Armenia, Latvia, and Lithuania. First, note that although the smaller sample size greatly reduces the statistical significance of the estimates, the estimated effect of classroom standard deviation of age on fourth graders’ math achievement remains statistically significant at the 10% level (column 1) and is similar in magnitude to that using the full sample. Column (3) and column (4) show that the negative effect is much smaller in the eighth grade than in the fourth grade. Specifically, the negative effect is not statistically significant for science and only significant at the 15% level for mathematics. Based on the IV estimates, the reduction in the effect size is roughly 18% for mathematics and 38% for science. Given the many caveats highlighted above and the smaller sample size, it appears that the negative effect of classroom age heterogeneity on achievement, especially for mathematics, does persist to some extent.

# _4.6 Policy Simulation: Age Grouping and Achievement Gain_

Given the significant negative effects of classroom age variance on achievement, school principals may improve student achievement by reducing the extent of age variance within the classroom through grouping students by age. The question is how much achievement gains are feasible for all ability-mixing schools to switch to age grouping, given its age distribution and the point estimates presented above? Does age grouping lead to greater or less inequality between students of different ages?

# _4.6.1 Mean Effects of Re-assignment_

The preferred point estimates for mathematics and science reported respectively in specification (5) of Table 4 and Table 5 can be used to simulate the achievement gains attainable by 21
**<u>Table 1: Relationship between historical malaria and current productivity (yield)</u>**

||Rice|Banana|Ground<br>Nut|Other<br>Oilseeds|
|---|---|---|---|---|
|Proportion of Area Under|||||
|LDO|1,589**|-1,645|843.5***|-314.1|
||(2.336)|(-1.095)|(3.007)|(-0.236)|
|R-squared|0.199|0.080|0.285|0.069|
|Historical Malaria Incidence|-2.546|-18.03|-0.00790|-15.65|
||(-0.350)|(-1.658)|(-0.00254)|(-1.432)|
|R-squared|0.170|0.088|0.100|0.088|
|Observations|118|98|57|101|

Regressions control for average rainfall, slope, current malaria incidence and dummy for within 5km of a river. Robust t statistics in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. Source: Emran and Shilpi (forthcoming)

**Table 2: Land under Restrictions, Historical Malaria and Health Status**

||Anemia among n<br>|on-pregnan<br>|t women<br>|%<br>I<br>|suffering Chro<br>llness/disabilit<br>|nic<br>y<br>|
|---|---|---|---|---|---|---|
||Mild/Moderate|Severe|Any|Male|Female|All|
|Proportion of Area Under LDO|-34.726|0.459|-34.164|-7.264|7.000|-3.421|
||(1.79)*|(0.38)|(1.81)*|(1.39)|(0.51)|(0.47)|
|Historical Malaria Incidence|-1.181|-0.045|-1.203|-0.134|0.071|-0.085|
||(1.21)|(0.77)|(1.26)|(0.51)|(0.11)|(0.24)|

Robust z statistics in parentheses. Standard errors corrected for clustering at village level * significant at 10%; ** significant at 5%; *** significant at 1%, Source: Emran and Shilpi (forthcoming) Table 3: Employment Choice and Land Restrictions: Probit and Multinomial Logit Results

||Non-Farm<br>Employment|Self-employ.<br>Non-<br>Agriculture|W<br>Agriculture|age employment i<br>Manufacturing|n<br>Services|
|---|---|---|---|---|---|
||(Marginal Effects)||(Margina|l effects)||
|Proportion of Area Under LDO|-0.527***|-0.0819|0.148*|-0.174***|-0.205*|
||(-5.174)|(-1.285)|(1.750)|(-3.460)|(-1.749)|
||(Probit Coefficients)|(|Multinomial Lo|git Coefficients)||
|Proportion of Area Under LDO|-1.451***|-3.006***|-1.592***|-3.511***|-2.937***|
||(-5.174)|(-4.342)|(-2.821)|(-6.245)|(-5.111)|
|**Controls**||||||
|Individual characteristics|Yes|Yes|Yes|Yes|Yes|
|Household characteristics|Yes|Yes|Yes|Yes|Yes|
|Area characteristics|Yes|Yes|Yes|Yes|Yes|
|Observations|8,948|8,948|8,948|8,948|8,948|

All regressions include individual's age, education, marital status, and dummies for household's religion/ethnicity and a full set of area controls including travel time to cities, disease environment, land and labor productivity indicators and ethnic composition. Robust t statistics in parentheses. Standard errors corrected for clustering at the sub-district level (DSD) * significant at 10%; ** significant at 5%; *** significant at 1%