rafmacalaba/datause-encoder-data huggingface dataset:
into account the potential spill-over effects of the increase in food prices for the food items included in the analysis on the prices for items not included.

A difficult question is whether increases in consumer prices do translate into increases in producer prices. At least two factors may dilute the impact of rising food prices on the incomes of farmers. First, production costs for farmers as well as transport costs are likely to be rising due to higher costs for oil-related products. Second, market intermediaries may be able in some cases to keep a large share of the increase in consumer prices for themselves without paying farmers much more for their crops. Because it is difficult to assess whether producers will benefit substantially from higher food prices, especially in the short term, we could consider our estimates obtained when considering only the impact on consumers as an upper bound of the impact of the rise in prices on poverty, and interpret the results obtained when factoring in a proportional increase in incomes for net sellers or producers as a lower bound of the impact.

The rest of the paper is structured as follows. Section 2 presents basic data on cereals production and consumption in Ghana based on an analysis of food consumption and production using the 2005-06 GLSS survey for a number of food categories. In section 3, we provide estimates of the overall impact of higher food prices on poverty. A brief conclusion follows.

# **2. Patterns of food consumption and production**

# **_2.1. Food consumption_**

Cereals prices are the focus of this paper, and within cereals, we focus further on rice, maize, bread and flour as these goods have experienced sharp increases in prices in 5
operating costs, and sales and other income. The reference period for the data was the 2013 calendar year. See GSS (2015) and GSS (2016a) for further information on the design and implementation of the IBES.

We measure labor productivity at the enterprise level, as the enterprise’s value-added per worker (in thousand cedis). In the IBES, the GSS defines a formal enterprise as one that is registered with the Registrar-General’s Department (RGD) and professionally maintains its financial accounting records (GSS 2015). We adopt the same definition in this study.

Our main analysis focuses on nonfarm establishments, toward examining the effects of the regional disparity in intersectoral misallocation on national nonfarm productivity. For some ancillary results that connect the regional disparity in intersectoral misallocation to national aggregate productivity, we estimate productivity based on the small sample of farm enterprises available in the IBES.<sup>8</sup> From here on in, we refer to nonfarm establishments as enterprises unless otherwise specified; in addition, we frequently refer to labor productivity as simply productivity, and employment in enterprises as simply employment.

B. _Informal and formal enterprises and employment by region and sector_ Figure 6 plots the distribution of enterprises by formality status and region. The number of informal enterprises exceeds that of formal enterprises in each region.<sup>9</sup> Richer regions have more informal and formal enterprises than poorer regions.<sup>10</sup> Figure 7 plots the distribution of employment by formality status and region. Except in Greater Accra, the number of informal workers exceeds that of formal workers in each region.<sup>11</sup> Richer regions have greater numbers of informal and formal workers than poorer regions. As the information in Figures 6 and 7 collectively show, while Greater Accra dominates other regions in terms of the numbers of enterprises and workers (both informal and formal), its dominance is stronger with respect to workers than enterprises.

C. _Estimates of regional intersectoral productivity gaps_ Figure 8 plots average productivity per worker by formality status and by region (panel a) and the gap in average productivity per worker between the informal and formal sectors by region

> 8 Phases I and II of the IBES surveyed 2,831 and 572 farm establishments, respectively (or 0.4 percent or 2.4 percent of all establishments surveyed under those phases, respectively).

> 9 See Figure A1 for the share of enterprises that is informal in each region and at the national level.

> 10 The pattern also holds on a per capita basis when we account for regional population numbers based on the 2010 Ghana Population and Household Census (GSS 2012).

> 11 See Figure A1 for the share of informal employment in each region and at the national level.

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<!-- Start of picture text --> Uncertainty in the surroundings of the Argentinian crisis<br>2001<br>1 Announcement of defaultannouncement of default 3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0 0<br>crisis in Arg Argentina Brazil Mexico<br>Chile Columbia United Kingdom Peru<br>uncertainty<br>incidences of crisis<br>2001:06 2001:07 2001:08 2001:09 2001:10 2001:11 2001:12 2002:01 2002:02 2002:03 2002:04 2002:05 2002:06<br><!-- End of picture text --> Figure 2: Uncertainty around the period of the Argentinean crisis firm operates. The spread between the price of the security and its eventual payout is a measure of the funding costs for the firm. Investors, however, are imperfectly informed regarding the state of fundamentals, but can choose to acquire a form of information-gathering technology that improves the precision of their information. The crucial assumption of the model is that investors are unsure as to how precise the information-gathering technology actually is. Instead, they have a belief regarding its precision, which they update by observing developments in other countries. If, for example, a surprise crisis happens in another country, investors will feel less confident about their information-gathering technology, and consequently, ascribe a lower belief regarding its precision. In equilibrium, this change in beliefs will lower the proportion of investors who adopt the information-gathering technology and, hence, increase the aggregate uncertainty regarding fundamentals in the country the investor is invested in. As this uncertainty increases, so does the spreads faced by the firm, which in turn increases the probability of a crisis in the country. In other words, surprise crises in one country lead investors to doubt the accuracy of their information and lead them to rationally make decisions that increase the probability of a crisis in a second country in which they have investments.

The model, as described above, yields two testable predictions. First, surprise (anticipated) crises result in an increase (decrease) in uncertainty, as measured by the dispersion of beliefs regarding fundamentals, in other countries. Second, an increase in uncertainty is associated with a higher probability of the incidence of a financial crisis. The empirical analysis likewise proceeds in two distinct steps. First, we use fixed-effects panel estimations to establish the link between the crisis in an initial crisis country and uncertainty regarding fundamentals in other countries. Second, we validate the importance of uncertainty by quantifying the effect of an increase in uncertainty on the probability of a crisis in an 4
- Sala-i-Martin, Xavier X. (1997), “I Just Ran Two Million Regressions,” _American Economic Review_ , 87: 178–183.

- Sala-i-Martin, Xavier X., Gernot Doppelhofer and Ronald I. Miller (2004), “Determinants of long-term growth: A Bayesian averaging of classical estimates (BACE) approach.” _American Economic Review_ , 94: 813–835.

- Tsangarides, Charalambos G. (2005) “Growth Empirics under Model Uncertainty: Is Africa Different?,” unpublished (Washington DC: IMF Working Paper: 05/18).

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in Table 9 implies that the complementarities tend to weaken the reallocation of labor towards these sectors induced by both infrastructure investments.

# **6 Political Preferences and Infrastructure Investment**

We have found evidence of misallocation of infrastructure investment. In this section, we focus on one possible source—increased political support— which has recently received substantial interest in the quantitative spatial economics literature (Glaeser and Ponzetto, 2018; Bordeu, 2023; Fajgelbaum et al., 2023). It is well established in the political economy literature that government preferences may diverge from the welfare maximization principle and may reflect the electoral incentives of the incumbent politicians. Furthermore, this is especially relevant for Brazil, where the importance of clientelist politics in securing local government transfers is well established (Brollo and Nannicini, 2012; Brollo et al., 2013, 2020). This section seeks to determine how significant these political effects are in the spatial misallocation of public infrastructure investment.

Specifically, we regress the log changes between data and model in infrastructure ( _Gi_ or _I_<sup> ̄</sup> _i_ ) on the change in votes between 2010 and 2002, controlling for the population change. To take into account the spatial structure of both votes (see Figure 10) and infrastructure, we control for latitude and longitude through _S_ (lat _i_ , lon _i_ ). In particular, we estimate the equation where _Xi_<sup>_Data_</sup> denotes _Gi_ or _Ri_ in the data while _Xi_<sup>_Model_</sup> denotes the model counterpart; ∆Vote10 _−_ 02 denotes the difference in vote shares between 2010 and 2002 for the winner of the presidential elections. To control for the spatial structure, we first let _S_ (lat _i_ , lon _i_ ) = lat _i_ + lon _i_ , and later, also be a thin-plate spline. Finally, _νi_ denotes the error term.

The Workers’ Party won the presidential elections in 2010, 2006, and 2002. Our model is calibrated to 2010, so we take the differences in votes between the 2010 and 2002 elections as a change in support for the Workers’ Party (the incumbent party). A shorter time interval may not allow infrastructure projects to be finalized. The share of people who voted for the incumbent party candidate, Luiz Inácio Lula da Silva, comes from the publicly available data provided by _Tribunal Superior Eleitoral_ (Superior Electoral Court).<sup>24</sup> Figure 10 shows the geographical distribution of votes in the 2006 elections. The incumbent party is supported by the Northeast, North, and East regions, while the opposition candidate prevails in the Central and South regions.

Table 10 gives the result of estimating equation (37). Models (1) and (4) correspond to the Fully Optimal allocations, models (2) and (5) correspond to the efficiency and coordination

> 24 The data can be accessed through their open data portal `https://dadosabertos.tse.jus.br/` . Numbers are at the municipality level, which we aggregate to the microregion level.

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# **Table 12 – Local banks: Adverse reform spillovers affecting international market conditions for domestic financial sector**

_Please indicate the regulatory reforms which contributed to a significant reduction or tightening of your institution’s activities in emerging markets and developing economies (EMDEs), particularly those that are jurisdictions not required to implement the reforms (you can select more than one reform area)._

|**N = 20**|**Risk-based**<br>**capital**<br>**reforms**|**Leverage**<br>**ratio**|**Liquidity**<br>**measures**|**Measures**<br>**dealing with**<br>**systemically**<br>**important**<br>**banks**|**Measures**<br>**dealing**<br>**with OTC**<br>**derivatives**|**Any other**<br>**G20**<br>**financial**<br>**reform**|**No**<br>**Significant**<br>**Impact of**<br>**any reform**|**Don't**<br>**know**|
|---|---|---|---|---|---|---|---|---|
|Foreign term funding specifically for long-<br>term<br>investment<br>purposes<br>(e.g.<br>infrastructure and project finance)|13|7|11|5|2|1|3|1|
|Intra-group liquidity management|5|4|12|4|2|1|5|2|
|Intra-group capital management|13|4|5|5|2|1|5|1|
|Cross-border bank credit funding|13|5|8|6|2|0|4|2|
|Other funding activities in international<br>markets|12|6|9|7|3|1|2|4|
|Trading and market making in international<br>markets of sovereign and/or corporate<br>bonds issues by your jurisdiction|11|5|8|5|3|1|4|4|
|International hedgingmarkets|8|7|8|6|7|1|3|3|
|Correspondent banking services directly<br>with large international banks overseas|11|8|11|5|4|2|5|1|
|Remittances services directly with large<br>international banks overseas|7|4|9|4|3|3|7|3|
|Access to cross-border re-insurance|4|3|4|4|1|1|4|11|

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For each group defined by these aggregation methods, we use the mean value of the variables.

# 3.5 Descriptive statistics

The final dataset contains 23,437,477 firm-level observations from 64 countries. In total, there are 3,882 economically meaningful grids where firms from our Orbis dataset are present. Table 1 presents the summary statistics for the key weather and economic variables based on grid-level observations.

The mean temperature uncertainty across all grids during the sample period from 2001 to 2019 is 2.09<sup>◦</sup> C. The average temperature (MeanT) and maximum temperature (MaxT) are 16.20<sup>◦</sup> C and 30.54<sup>◦</sup> C, respectively. The mean fixed asset growth (SizeGrowth) is -1.45%.

Further detailed descriptions can be found in the appendices, where we provide comprehensive country-level summary statistics. Table 7 in Appendix B presents these country-level statistics using country-specific firm-level observations. Table 8 in Appendix B shows the number of grids for each country for each year.

||Mean|SD|Min|Q25|Q50|Q75|Max|
|---|---|---|---|---|---|---|---|
|# of frms|302.89|1253.10|2.00|5.25|22.58|136.08|26070.63|
|Uncertainty|2.09|0.52|0.71|1.72|2.06|2.43|4.55|
|MeanT|16.20|9.94|-9.87|8.05|15.22|24.02|35.46|
|MaxT|30.54|5.97|11.96|26.42|30.51|34.73|49.27|
|SizeGrowth|-1.45|23.20|-733.03|-10.47|-0.10|7.23|277.01|
|Capintensity|0.36|0.14|0.00|0.27|0.34|0.43|0.97|
|Income|13532.81|14080.59|1061.59|5016.33|7471.71|11146.79|66870.01|

Table 1: Summary statistics. This table presents key aggregate statistics at the grid level, including the number of firms per grid, temperature uncertainty (Uncertainty) average temperature (MeanT), maximum temperature (MaxT), income, growth rate of fixed assets (SizeGrowth), and the ratio of tangible fixed assets to total assets (CapIntensity). Observations are made at the grid level using the mean values of these variables over time, resulting in 3,882 observations. For each variable, key summary statistics (mean, standard deviation, minimum, 25th percentile, median, 75th percentile, and maximum) are presented.

Time series of weather variables. Figure 2 presents time series plots for three key weather variables (temperature uncertainty, mean and maximum) across all the grids considered in our study. The black line in each plot represents the annual mean value of the weather variable, accompanied by a grey shaded area representing the 95% interval for each year. Since the 95% interval is wide and the overall trend in the variables is unclear, an inset plot without the 95% interval is included.

Consistent with the existing evidence (IPCC 2023), our data shows an observable increasing trend in both the mean and maximum temperature values. However, the temperature uncertainty does not display a clear global pattern, aligning with findings from other studies on temperature variability (the standard deviation of temperature measures) (Huntingford et al. 2013, Olonscheck et al. 2021).<sup>15</sup>

> 15Figures 5, 6, and 7 in Appendix B show the country-specific time trends of these weather variables.

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example, a firm survey by Grupo de Servicios de Información (1999) indicates that for all firms the quality of skills ranks as the second most important constraint. For small firms, important for employment and income generation, the quality of skills is the main production constraint.

# **3. Measuring the Contribution of Education to Growth**

The accumulation of human capital through education has long been acknowledged to be an important factor in the development process of a nation. Education is thought to be beneficial because it decreases inequality, improves the quality of life, and in particular it is a factor in rising the income level and facilitating economic growth. This section will concentrate on the latter effect and summarize some evidence on the relationship between education and growth.

## **3.1 Augmented Solow Model and Endogenous Growth**

The existing literature contains a number of distinct conceptual rationales for the inclusion of human capital in models of economic growth. According to Sianesi and van Reenen (2003), the two main macro approaches are the augmented Solow model and the new growth theories.

## **_Augmented Solow Model_**

One way to estimate the impact of education on growth is to adapt the Solow (1956) model. The augmented version extends the basic framework to allow human capital as an extra input to enter the production function. In particular Mankiw et al. (1992) show that traditional growth theory can accommodate human capital and provide a reasonable approximation for empirical analysis. At the economy-wide level, it may also take into account human capital externalities. Still, one of the key insights is that the factor accumulation affects the _level_ of income, but per se is insufficient to achieve long-run _growth_ . Long-run growth depends rather on growth in technological progress. Human capital accumulation may therefore have only a short-term impact on the rate of growth.

However, rates of accumulation are expected to have explanatory power for growth rates during the transition to an eventual equilibrium growth path. In particular, considering the case of Guatemala? presumably far away from a balanced growth process? consideration of transition could open up the possibility of assessing the macroeconomic role of education for economic growth within this framework. In addition, since the ‘short run’ in the context of growth theory is often thought of in terms of decades, these effects can be worthwhile policy objectives. Up to now, for the reasons clarified below, this approach has remained the workhorse of applied empirical research. The model is fairly flexible and allows for alternative specifications that can be adjusted to best match the available data.

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Environment Directorate and Development Cooperation Directorate, Organisation for Economic Cooperation and Development (OECD), Paris.

Alford, D. and R. Armstrong, 2010. The Role of Glaciers in Stream Flow from the Nepal Himalaya. The Cryosphere Discuss 4:469–494.

Asia Pacific Network (APN), 2005. Enhancement of National Capacities in the Application of Simulation Models for the Assessment of Climate Change and its Impacts on Water Resources and Food and Agricultural Production. Final Report. Available at - <u>http://www.apn</u> <u>gcr.org/newAPN/resources/projectBulletinOutputs/finalProjectReports/2005/2005-CRP01CMY-Khan_CAPaBLE_FinalReport.pdf.</u> Accessed 5 Jun 2011 Archer, D.R., and H.J. Fowler, 2004. Spatial and Temporal Variations in Precipitation in the Upper Indus Basin, Global Teleconnections and Hydrological Implications. Hydrology and Earth System Sciences 8: 47-61.

Armstrong, R. L., 2010. The Glaciers of the Hindu Kush-Himalayan Region: A summary of the Science regarding glacier/melt retreat in the Himalayan, Hindu Kush, Karakoram, Pamir and Tien Shan Mountain Ranges. Kathmandu, ICIMOD. ISBN 978-92-9115-176-9.

Asian Development Bank (ADB) and International Food Policy Research Institute (IFPRI), 2009. Building climate resilience in the agriculture sector in Asia and the Pacific. Asian Development Bank. ISBN 978-971-561-827-4.

Bagla, P., 2009. No Sign yet of Himalayan Meltdown, Indian Report Finds. Science 326 (5955): 924-925.

Baidya, S.K., M.L. Shrestha, and M.M. Sheikh, 2008. Trends in Daily Climatic Extremes of Temperature and Precipitation in Nepal. Journal of Hydrology and Meteorology 5: 38-53. Bajracharya, S.R. and P. Mool, 2010. Glaciers, Glacial Lakes and Glacial Lake Outburst Floods in the Mount Everest Region, Nepal. Annals of Glaciology 50: 81-86.

Bajracharya, S.R., P. K. Mool, and B.R. Shrestha, 2007. Impact of climate change on Himalayan Glaciers and Glacial Lakes, pp. 119. ICIMOD. ISBN 978-929-115-032-8.

Bajracharya, S.R., S.B. Maharjan, and F. Shrestha, 2011. Glaciers Shrinking in Nepal Himalaya. In J. Blanco and H. Kheradmand. (Eds.) Climate Change - Geophysical Foundations and Ecological Effects. ISBN 978-953-307-419-1. pp. 445-457.

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to a 20 percent increase in area flooded in Bangladesh. Severity of extreme floods, such as the 20-year flood event, is estimated to increase marginally. Another study conducted in 1998 concluded that an increase in monsoon rainfall by 11 and 20 percent will increase surface runoff by 20 to 45 percent in Bangladesh (Ahmed and Alam, 1998).<sup>13</sup> For Bangladesh alone, the median predictions of 16 General Circulation Models for three emission scenarios considered by the IPCC AR4 point to a warming of 1.55  C and an increase in precipitation of 4% by 2050 (Yu et al. 2010).<sup>14</sup> In sum, the warmer and wetter climate predicted for the GBM basin by most of the climate-related research indicates vulnerability of Bangladesh to severe monsoon floods will increase with climate change.

# 4. Exposure to Inundation Risk

In order to assess potential change in the extent and depth of inundation in a changing climate, this research conducted flood modeling for two scenarios. The 1998 exceptional flood event (see Box 1) with a return period of 90 years has been considered as the baseline scenario. It is the counterfactual against which future climate change risk is measured. For the climate change scenario, changes in the monthly precipitation and temperature predicted by the GCM- MIROC 3.2 under the A2 emission scenario have been considered.<sup>15</sup> Since flooding in Bangladesh depends on the rainfall in the entire GBM basin, predictions of precipitation and temperature between 1998 and 2050 for each 50km x 50 km grid covering GBM basin from the MIROC 3.2 have been applied to the historical monthly averages for 1998 to approximate the monthly precipitation and temperature over the entire GBM basin in 2050.

Inundation risk exposure for each scenario is then determined in four steps.

_First_ , the Ganga Brahmaputra Meghna model in the MIKE BASIN platform, developed at the Flood Forecasting and Warning Centre (FFWC) of Bangladesh Water

> 13 An Earlier study indicates that a 10 percent increase in monsoon precipitation in Bangladesh could increase runoff depth by 18 to 22 percent, resulting in a sevenfold increase in the probability of an extremely wet year (Qureshi and Hobbie, 1994).

> 14 The resolution of the models varies with about 5 grid boxes typically covering Bangladesh. The national values are weighted averages, with the weights equal to the percentage of each grid that is within Bangladesh. 15 The MIROC 3.2 has been used in this research to approximate potential extreme effects as it results in the largest increase in runoff among the GCMs for Bangladesh.

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