rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 1.</u>** <u>Key macroeconomic indicators (in percent, unless otherwise specified)</u>

||**2000**|**2001**|**2002**|**2003**|
|---|---|---|---|---|
|Real GDPper capitagrowth|5.2|5.1|4.5|2.4|
|Agriculturalgrowth|-13.0|17.4|-3.2|-7.0|
|Inflation,GDP deflator|75.0|89.8|24.1|16.3|
|Inflation,foodprices|105.3|87.6|6.8|1.0|
|Agriculture, producers’ price<br>inflation|158.0|70.0|1.0|1.0|
|FDI(% of GDP)|0.3|1.6|3.3|7.1|

Source: International Financial Statistics, Serbia Statistical Office, and World Development Report 2003.

However, the economic growth and macroeconomic stability have not been translated into significant poverty reduction, according to the 2002 and 2003 SLS.<sup>1</sup> Serbia wide, headcount poverty declined by only about 5 percent between 2002 and 2003 (Table 2). Urban poverty had declined from about 8.3 percent in 2002 to 7.1 percent in 2003, about 15 percent decrease. On the other hand, rural poverty, which was already higher than urban poverty by more than 87 percent in 2002, was further worsened by 6.4 percent in 2003. In 2003, the likelihood of poverty in rural areas is more twice that in urban areas in 2003. While the rural population makes up a little over 40 percent of the total Serbia’s population, Serbia’s poor residing in rural areas now account for close to three-fourths of the national poverty. Understanding the nature, extent and causes of rural vulnerability is thus a key to poverty and inequality reduction in the Serbian Republic.

**Table 2.** Despite decent economic growth, national poverty has stagnated between 2002 and 2003

||**Serbia**|**Urban**|**Rural**|
|---|---|---|---|
|2002|11.5|8.3|15.6|
|2003|10.9|7.1|16.6|
|% Change|-5.2|-14.5|6.4|

Source: Serbia LSMS, 2002 and 2003; Statistical Office of Republic of Serbia. The poverty rates are based on per consumer unit consumption expenditure adjusted for regional price variations. The poverty line of 4487 and 4987 dinars were used for 2002 and 2003, respectively.

# **The Serbia Living Standard Survey (SLS) Data**

Two major data sources are used: the 2002 and 2003 panel Surveys on the Living Standard of the Population of Serbia (SLS). The surveys were based on a two-stage stratified sample, with primary sample units being the census districts and secondary units being the households. The census districts are selected with probability, which is proportional to the number of households, so census districts with more households will be more likely to be selected. The surveys are representative of all six major regions and urban and rural areas in each region. The survey instrument includes a few modules relevant for rural poverty study, including demographic

> 1 As the data are only a two year panel, the poverty dynamics presented here are just indicative of the trend between 2002 and 2003, and it is not intended to describe the overall trend of poverty in Serbia in the early 2000s.

2
effects for indoor temperatures above 28Â°C. Rode et al. (2022) use data from several large economies to show that increasing temperature impacts on workers disutility. And Park, N. Pankratz, and Behrer (2024) find that temperatures above 32<sup>_◦_</sup> C in California, U.S., have increased workplace injuries in construction, indoor manufacturing, and warehousing by 9%.

Rising temperatures have also been estimated to raise firms’ input costs. N. M. C. Pankratz and Schiller (2022) find that exposures to high temperatures at supplier locations in a sample of large multinational firms reduce downstream firms operating income over assets by 0.6%. Using plant-level data from the U.S. Census Bureau, Ponticelli, Xu, and Zeume (2023) find that warmer than average temperatures increase energy costs and decrease productivity in small manufacturing plants (see also Birkie, Trucco, and Campos 2017; Cevik and Miryugin 2022). Moreover, the World Bank (2004) estimates that rising energy demand from additional cooling needs in commercial buildings rises 11-fold by 2037 implying surging energy costs for firms.

Our work also links to studies investigating strategies to adapt/respond to climate risks (Burke, Zahid, et al. 2024). Risk reduction is generally pursued through technical measures such as maintenance, materials, facilities, assets and process engineering, risk monitoring and assessment (Berkhout 2012; Hertin et al. 2003; Weinhofer and Busch 2013), or by adopting establishment-level climate controls (Gasbarro and Pinkse 2016; Somanathan et al. 2021; Zhao et al., 2024). Moreover, infrastructure investments such as new roads, sea walls, bridges, water treatment facilities, or power generation plants support firms to cope with climate risks (Hallegatte, Rentschler, and Rozenberg 2019; Hsiao 2023; He et al. 2021). Bettarelli et al. (2024) show that firms facing financial constraints are more exposed to climate uncertainty.

We contribute to the literature by combining highly granular climate data with firm-level data from nearly 160,000 firms in 134 countries from 2006 to 2023 to estimate the economic costs of increasing temperatures at the global level. It is thus one of the first studies to use, at a global scale, granular Satellite weather data combined with rich firm-level data, which is key to address endogeneity concerns. The paper most similar to ours is Kassa and 7
23

**Table 3: OLS regression on the economic determinants of official international remittances**

|Constant|-27.276|-44.726|-19.243|-28.420|-40.640|-18.374|
|---|---|---|---|---|---|---|
||(-2.49)*|(-3.81)**|(-1.45)|(-3.46)**|(-4.51)**|(-1.90)|
|N|55|45|44|109|88|84|
|Adj R<sup>2</sup>|0.271|0.367|0.572|0.282|0.313|0.538|
|F-Statistic|5.02|4.65|5.42|9.48|6.67|8.43|

Notes: Estimates obtained using ordinary least squares. All variables expressed in logs; t-ratios shown in parenthesis. Number of observations reduced for certain equations because of missing values.

*  Significant at the 0.05 level   ** Significant at the 0.01 level

Sources: Per capita GDP data from World Bank, World Development Indicators (2004); inequality and poverty data from World Bank, PovcalNet (2004); education data from Barro and Lee (2001); population data from World Bank, Development Data Platform (2005); and country credit ratings from Institutional Investor.
**Table A3.3. Government budget in 2012 and for base simulation by** **<u>period (% of nominal GDP)</u>**

|||**2012**|**2017**|**2030**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|---|---|---|
|**Receipts**|**Domestic transfers**|1.5|1.5|1.5|1.5|1.5|1.5|
||**Foreign transfers**|24.3|18.9|12.8|22.1|15.2|17.1|
||**Direct taxes**|3.9|8.5|12.8|5.4|13.9|11.6|
||**Import tariffs**|6.4|6.3|7.0|6.7|6.5|6.6|
||**Other indirect taxes**|3.8|3.5|2.8|3.7|3.0|3.2|
||**Domestic borrowing**|-0.2|1.0|1.0|1.0|1.0|1.0|
||**Foreign borrowing**|2.2|2.5|1.2|4.3|1.3|2.1|
||**Total**|42.0|42.2|39.2|44.8|42.5|43.1|
|**Spending **|**Education**|5.3|5.7|4.8|5.9|5.4|5.5|
||**Health**|4.8|5.0|4.5|5.4|5.0|5.1|
||**Water-sanitation**|1.7|1.7|1.3|1.9|1.6|1.7|
||**Energy**|3.9|4.0|4.3|4.4|4.4|4.4|
||**Roads**|5.1|5.1|5.3|5.8|5.6|5.6|
||**Other infrastructure**|2.1|2.1|2.1|2.4|2.2|2.3|
||**Other**|15.6|15.1|13.2|15.3|14.6|14.8|
||**Domestic transfers**|2.9|2.9|2.9|2.9|2.9|2.9|
||**Domestic interest**|0.4|0.4|0.5|0.5|0.5|0.5|
||**Foreign interest**|0.1|0.3|0.4|0.3|0.3|0.3|
||**Total**|42.0|42.2|39.2|44.8|42.5|43.1|

# **Note:**

**Columns show values for single years or averages for multiple years (as indicated).**

34
The GHG mitigation induced by the carbon tax _tH_ is the last term in the equation to the right in (5), and equals _tH/γ_ . The per-unit support paid to the host country per mitigated unit, for imposing a carbon tax _tH_ , is what we call the “incremental cost price” (ICP). We denote this by _α1 tH_ , where _α1_ is a fixed coefficient (the “price point” parameter related to ICP). The total support, _Q1(tH)_ , is given by this rate times the amount of mitigation induced by this tax:<sup>6</sup> We will here study what value _α1_ needs to take to make implementing this carbon tax attractive to the L country.

The support payment _Q1_ is given from (6). Emissions are in this case assumed to be reduced to fulfill the country’s unconditional NDC before (or jointly with) implementing the tax.

Welfare for the L country, including tax implementation support payments, is: The second term in the square bracket represents the pure allocation loss, or “deadweight loss” (DWL), associated with implementing the comprehensive carbon tax _tH_ in the host country. This is the net welfare loss that follows from the activity-reducing impact of the carbon tax on the host economy. Note that (7) does not include any environmental costs or benefits related to imposing this carbon tax.

We seek the value that _α1_ must take for the host country to be willing to adopt a comprehensive carbon tax _tH_ , when GHG mitigation induced by this tax can be applied toward fulfilling the host country’s unconditional NDC. The net gain for the host country is

> 6 The result that the required ICP compensation is proportional to the square of _tH_ follows from the quadratic specification of (1), and (4). The” true” compensation formula may have other shapes; for example when using the Enerdata simulations as done in section 5, the compensation increases less rapidly in _tH_ .

7
100 must be selectod and the program and control groups should be iarge enough and diverse enough to confidently assess whether tho program was effective. Because there are .auy constraints on the family's decisionmaking that will also influence women's productivity, and these constraints may differ in unmeasured ways across communities, **a** dispersed sample of comunities for the pilot programs and for **the** control **cox unities** Is desirable. **A** theoretical nodel of family behavior should prescribe **the** primry factors systematically influencing the indicators of program success. **Tnese** control factors would be monitorod in the base-line and follow-up<sup>surveys ofthepilot and control</sup> community households. Evidence on the prograu's impact will be sore convincing if a rindom design of the counity sample **is** adopted.

Pilot programs might fir5t be designed around the objective of Increasing school enrollment and graduation rates for girls. A second series of monitored pilot programs could **assess** the effect of equalizing access to agricultural extension services by male and female farmers in Africa, South-East Asia and Latin America. Measures-of "farmer contacts"<sup>as an indicator</sup> of extension output has serious problems (Birkhaeuser i".jJ , 1988), and therefore measures of productivity of men and women across distinct program<sup>regions should be</sup> analyzed. A third set of pilot programs might explore alternative configurations of child and maternal health progrims, where the mix of staff by level and gender would vary. Finally, program innovations in the delivery of credit, such as the Grameen Bank in Bangladesh, should be evaluated. Not only in terms of the gender of the loar recipient, but also the program's consequences for community-wide productivity of womev, and men. Data should also be sought on investments of famil'es in the education and health of males and females in the program and control coomunities. An intergenerational consequence of
4. Assessing (a few) variations in the assumptions of the CBN methodology Without a doubt, the application of the CBN methodology relies not only on the availability of very detailed information on households’ consumption and expenditure patterns, but also on a series of decisions that can potentially affect the ultimate result. In the section above, we showed that our results are robust to the expansion of the calorie-food mapping between POF and TBCA. We also find that applying a parametric or nonparametric approach to the estimation of the lines does not affect the results greatly.

Following, we continue exploring the potential effects of changing five different assumptions of our main specification: energy requirement, treatment of food away from home, reference group, incorporating implicit rent, and the treatment of lumpy items.

Energy requirement The energy requirement of an individual has been defined by WHO (1985) as that level of energy intake from food that will balance energy expenditure when the individual has a body size and composition consistent with long-term good health and that will allow for the maintenance of economically necessary and socially desirable physical activity. Our main specification uses a 2,100 calorie intake because we believe it to be a good benchmark. It is also placed roughly in the middle of several sources consulted (Table 1).

We explore how changing such a benchmark could influence our results. The US National Research Council (1989) estimated energy requirements by age group and sex. Based on this, and using the detailed data on Brazil’s demographic profile from the POF 2017/18 data, we calculate a population-weighted average required calorie intake. Moreover, this requirement could be estimated for the bottom 40 percent of the population – in other words, our reference group. As shown in Table 8, an estimate for the required calorie intake of a demographic population that resembles the bottom 40 percent in Brazil would be 2,268 kcal.<sup>16</sup> We round the caloric needs benchmark down to 2,250 kcal per day and re-run our CBN specifications. Table 9 shows the results. The table shows that, while the food poverty line increases proportionally with the required calorie intake (+7.1%), as expected the lower poverty line changes from R$455 to R$487.

> 16 An alternative method could also be to directly use the information from the POF 2017/18 and the consumption patterns of the consumption per capita distribution to estimate the actual kcal consumed and apply those to estimate the food poverty line. However, as has been documented in many other studies (for instance World Bank, 2019; Myanmar Ministry of Planning and Finance and WBG, 2017), the relatively poor households do not typically consume enough to cover their caloric needs. In POF 2017/18, a simple extrapolation of the average calories consumed in the bottom 40 percent is about 1,190. Thus, the definition of a food poverty line is normative and uses a benchmark instead of actual caloric consumption.

17
last sub-period are computed on the basis of more detailed information from two household surveys, known as SUSENAS (1999, 2002) produced by Statistics Indonesia (BPS)<sup>19</sup> . This information helps us demonstrate the decomposability of our measure of pro-poorness. To ensure consistency with available aggregate data, we convert the household survey data from rupiah into 1993 PPP dollars.

Figure 4.1 shows three growth patterns for Indonesia for the 1996-1999 period – the observed pattern, the benchmark and a counterfactual pattern (see on for details of this). The curve representing the actual growth pattern lies entirely below the benchmark for all percentiles way past the headcount ratio (50.49 percent). In fact, the growth patterns for all other sub-periods (not shown here) have this type of configuration too. Thus, _theorem 5 leads us to believe that economic growth in Indonesia in the 1990s has not been pro-poor in the sense defined in this paper_ . In other terms, the process of economic growth has not delivered a significant reduction in poverty, where the level of significance is set at the amount of poverty reduction that would have been achieved under distributional neutrality.

To further illustrate this point empirically, we computed our measure of propoorness according to the formulae for the Watts index, normalized poverty deficit and squared poverty gap given in footnote 7. The estimates, presented in table 4.3, are calculated using the growth patterns already described.

> 19 SUSENAS stands for Survei Sosial Ekonomi Nasional (National Socio-Economic Household Survey), and BPS stands for Badan Pusat Statistik (Central Bureau of Statistics).

20
# **1.  Introduction**

Despite considerable debate over definitions, measurement, and methodology, it is widely-accepted among academics and policymakers that well-functioning public institutions play an important role in economic development. In turn, a key ingredient in the effectiveness of public institutions is the confidence that they inspire among those whom they serve. For example, households or firms who do not have confidence in the police or the courts are unlikely to avail themselves of their services, and may resort to less efficient means of property protection or dispute resolution. Similarly, if individuals lack confidence in the honesty of the electoral process they are unlikely to vote, leading to low turnout rates that cast doubt on elected officials‟ popular mandates and their ability to carry out their agendas.<sup>2</sup> In this paper we empirically investigate the role of corruption in undermining confidence in public institutions. We document a quantitatively large and statistically significant partial correlation between measures of corruption and confidence in public institutions using a unique dataset. The Gallup World Poll (GWP) is a new and very large cross-country household survey, interviewing more than 100,000 households in over 150 countries, annually or biennially in most countries since 2006. We use questions from the 2008 wave of the GWP, covering over 78,000 respondents in 90 countries to study the links between corruption and confidence in public institutions in both developed and developing countries. Not surprisingly, in countries where respondents report a high incidence of personal experiences with corruption, and in which perceived corruption is widespread, confidence in public institutions is also low. Much more interestingly, we show that this pattern also holds across individuals _within_ countries: individuals who experience corruption and who report that corruption is widespread also tend to have lower confidence in public institutions. We show that this correlation is robust to the inclusion of a large set of variables to control for respondentlevel characteristics, including a number of proxies intended to capture the respondent‟s

> 2 These effects of corruption on confidence have not been lost on policymakers.  A recent quotation from Kai Eide, UN Special Representative of the Secretary-General for Afghanistan, neatly encapsulates this view, _“..[Corruption] pushes people away from the state and undermines our joint efforts to build peace, stability and progress for Afghanistan's peoples.”_ UNAMA Press Release, United Nations Assistance Mission, August 20, 2008.

2
||Sample 2|10.00|1.00|1.00|98.00|1.00|1.00|1.00|4.00|4.00|4.00|4.00|4.00|61.84|59.50|21.46|13.19|2.41|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Maximum|Sample 1|10.00|1.00|1.00|99.00|1.00|1.00|1.00|4.00|4.00|4.00|4.00|4.00|63.82|73.20|36.49|16.50|2.77|
||Full sample|10.00|1.00|1.00|101.00|1.00|1.00|1.00|4.00|4.00|4.00|4.00|4.00|na|na|na|na|na|
||Sample 2|1.00|0.00|0.00|16.00|0.00|0.00|0.00|1.00|1.00|0.25|1.00|1.00|23.67|21.45|0.01|0.01|0.15|
|Minimum|Sample 1|1.00|0.00|0.00|16.00|0.00|0.00|0.00|1.00|1.00|0.25|1.00|1.00|23.67|21.45|0.00|0.01|0.02|
||Full sample|1.00|0.00|0.00|15.00|0.00|0.00|0.00|1.00|1.00|0.25|1.00|1.00|na|na|na|na|na|
|tion|Sample 2|2.34|0.25|0.50|16.54|0.35|0.48|0.46|0.54|0.44|0.51|0.94|1.07|7.98|9.50|1.38|0.76|0.63|
|ndard Devia|Sample 1|2.44|0.27|0.50|16.09|0.38|0.48|0.45|0.57|0.45|0.58|0.96|1.07|9.34|11.35|1.42|0.83|0.69|
|Sta|Full sample|2.49|0.27|0.50|16.33|0.35|0.48|0.45|0.59|0.45|0.61|0.96|1.08|na|na|na|na|na|
||Sample 2|6.94|0.07|0.52|43.34|0.15|0.63|0.30|2.34|3.58|1.20|2.21|2.71|35.53|34.60|1.47|1.01|1.25|
|Mean|Sample 1|6.78|0.08|0.51|41.77|0.17|0.64|0.28|2.39|3.56|1.27|2.24|2.90|37.85|37.75|1.34|1.01|1.03|
||Full sample|6.62|0.08|0.52|41.24|0.15|0.63|0.28|2.42|3.56|1.28|2.27|2.90|na|na|na|na|na|
||Sample 2|66630|66630|66630|66630|66630|66630|66630|66630|66630|66630|66630|66630|66630|66630|66630|66630|66630|
|Observations|Sample 1|95612|95612|95612|95612|95612|95612|95612|95612|95612|95612|95612|95612|95612|95612|95612|95612|95612|
||Full Sample|263097|267870|267870|264839|267870|267870|267870|260301|238856|233484|234025|234563|na|na|na|na|na|
||Variable|Life satisfaction|Unemployed|Female|Age|Tertiary education|Married|Trust in people|Trust in institutions|Importance of family and friends|Importance of work|Importance of politics|Importance of religion|Gini WVS|Gini Wider|Income (000, USD, PPP)|Relative Income|GDP capita (000, USD, PPP)|

25
Available evidence indicates that Bangladesh’s enviable agricultural growth has been driven primarily by TFP growth, underpinned by a combination of technical progress and efficiency gains triggered by policy reforms in the 1980s and early 1990s. Using International Food Policy Research Institute (IFPRI) estimates, Gautam and Faruqee (2016) note that agricultural TFP in Bangladesh grew at an average annual rate of about 2.7 percent between 1995 and 2011.<sup>5</sup> During this period, Bangladesh was among the better performers in the world, comparable to China and better than the star performers in East Asia (Figure 2).<sup>6</sup> _Figure 2: Agriculture Total Factor Productivity Growth: Bangladesh and Selected Countries, 1995-2011_ <!-- Start of picture text --> 3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>CHN IDN IND LKA MYS PAK PHL PRK VNM<br>1995‐11 2005‐11<br>BGD 1995‐11 BGD 2005‐11<br>Source : Gautam and Faruqee 2016<br><!-- End of picture text --> What is remarkable about this productivity driven growth is that it has been achieved on farms that are best described as very small, especially when compared to those in other countries such as the United States. And these farms have continued to decline in size. Table 1 shows the average farm sizes in Bangladesh for 2000 and 2008, in terms of both area owned and area operated or cultivated, calculated from the rich panel survey used in this study. It is against this dynamic backdrop that this paper investigates the farm size and productivity relationship in Bangladesh’s agriculture.

The literature investigating the IR hypothesis is large, with a substantial body of evidence in support of the hypothesis, but also studies that do not find empirical support for it. The traditional approach to examining the IR was to compare yields (output per unit of land) and farm size. This partial or unconditional land productivity measure was supplanted by conditional productivity, an approach that controls for other factors using a production function (see Barrett et al. 2010; Gaurav and Mishra, 2015; and Yamauchi, 2016). While conceptually superior to a partial productivity

> 5 See Nin-Pratt (2015) for details on IFPRI’s estimation of agricultural TFP by country.

> 6 The most recent estimate from the International Agricultural Productivity database shows TFP growth in Bangladesh between 2001-2013 to have been 2.7% per year, which is in the top 20% of performers among the 173 countries in the database [data from USDA (2017); see Fuglie (2015) on the methodology used].

4