rafmacalaba/datause-encoder-data huggingface dataset:
Missing weather data combined with the way in which treatment variable is defined results in a fuzzy RD, so the treatment effect is estimated via two-stage least squares. Specifically, we estimate the following equations: Equation 1 estimates the first stage by regressing treatment, _Tmct_ , on _Zmct_ = **1** _{Xmct <_ 0 _}_ , an indicator for falling below the threshold amount of rainfall. Equation 2 is the reduced form, which estimates the effect of falling below the threshold on the outcome of interest in the following year, _ymct_ +1. Thus, the coefficient of interest is _β_ 2 _SLS_ =<sup>_<u>β</u>_1</sup> . For regressions where we have observations _β_ 0 at the crop level, we include crop fixed effects. The analysis is restricted to insurable crops (i.e., maize, beans, sorghum and barley) unless we explicitly note otherwise. Household level regressions using ENIGH data are estimated with a similar set of regressions, where observations are no longer indexed by crop _c_ , but are now indexed by _h_ to indicate an individual household. In keeping with the regression discontinuity design, these regressions are restricted to a sample of data within a narrow bandwidth of the normalized threshold of zero.

# **4 Results**

## **4.1 Main results**

We begin by looking at some descriptive statistics for the analysis sample in table 1. The analysis sample for the agricultural outcomes is composed of 976 unique municipalities over 8 years with 4,311 total observations. As mentioned before, the way in which the ENIGH sample is constructed results in a smaller number of unique municipalities for our economic regressions

> (192)<sup>5</sup> but multiple observations per municipality for a total sample size of 5,879. In panel A, we

> 5Given the large number of municipalities that get dropped from the ENIGH panel, we present summary statistics for the two different samples in Appendix Table 9. While the municipalities in the ENIGH sample have much larger populations, they are very similar along other dimensions with only the education variables showing even marginally significant differences.

5
reduces the competition posed by incumbents to younger and new entrants. Consequently, a larger flow of entrants may bring in marginal entrants who are less productive than inframarginal entrants, leading to a vicious cycle of “low productivity – small firm size – less job creation”. All these in turn imply that the general equilibrium effect of business entry reform, without necessary improvements in other dimensions of investment climate that are conducive to boost aggregate total factor productivity, may make things worse despite a short-term or onetime spike in job creation. Future analysis of entry reforms needs to broaden its focus from partial equilibrium analysis of the impact of the reform to general equilibrium effects to provide a comprehensive understanding of various channels through which IC reforms can affect firm productivity and job creation.

While Hsieh and Klenow (2014) make the case for how IC related factors can lead to distortions, which in turn induce firm size to be smaller in developing countries, market failures or policy distortions can create fixed costs in the operation of businesses and thus lead to cost disadvantages for smaller firms (see Tybout 2000 for a review of issues). For instance, the burden of a complex and nontransparent regulatory environment may fall disproportionately on the smaller firms with fewer resources to deal with such a regime and might be subject to more frequent harassment or rent-seeking. Large firms may have more political influence and even can shape regulations in their favor. Another possibility (in line with De Soto view) is that it could be easier for smaller firms to dodge enforcement of regulations, as they could operate under the radar of the regulators, while large firms may suffer from higher tax burden and rent-seeking from corrupt officials because they are more easily identified and targeted. Thus, it is ultimately an empirical question if and how different aspects of IC differentially or non-linearly effect the employment growth and other outcomes of interest, such as profitability and productivity, of firms of different size in different countries.

Aterido et al. (2011) attempt to shed light on this issue by measuring the regulatory environment, prevalence of corruption, access to finance, and access to infrastructure on employment growth of firms of varying size. They use firm level data of 56,000 enterprises in 85 21
Figure 10: Productivity Impact from Changing Distortions to the US 2010s Levels <!-- Start of picture text --> Correlation = −0.289***<br>PRI<br>GIN<br>CHN<br>ZMB<br>LAO INDLKANAM AZESUR<br>UGA WSM<br>MN E IRN<br>TZA PAK HNDM AR IDNJOR GTM THA CUBKAZ SAU<br>MLT ISL<br>MWI PHLMNG EGY TUNECUCOLZAFMKDJAMVENMUSTURBGRMEXLVABLRHRVRUSHUNPOLESTCYPSVKPRTCZESVNGRCKORESPNZLITAFJPN FINRAD DEU G IRLBNKSWEBRCANELAUTCHEAUS NOR LUX<br>LTU<br>SLV<br>KGZ PRY ARM UKR URYMYS<br>KHM<br>1000 4000 16000 64000<br>GDP per Capita: 2010 (2005 I$)<br>EAP ECA LAC MENA NAM SAS SSA<br>125<br>25<br>5<br>0<br>Implied Growth: Country Level 2010s − US 2010s −5<br><!-- End of picture text --> Both variables are plotted using the log scale. As the implied growth for some countries lies in negative territory, a constant was added to the series so that the minimum value corresponded to unity. EAP: East Asia and Pacific, ECA: Europe and Central Asia, LAC: Latin America and Carribean, MENA: Middle East and North Africa, NAM: North America, SAS: South Asia, SSA: Sub-Saharan Africa.

34
factors.<sup>61</sup> This suggests that a non-negligible share of the idiosyncrasy in farmers’ self-reports are due to differences in perception rather than actual exposure to drought and casts doubt on the reliability of self-reported shock identification in order to attribute losses to shocks in years without a severe drought.

## <u>What do we conclude and still need to know?</u>

Farmers’ self-reported drought exposure features considerable idiosyncrasy that is unexplainable by observable factors such as rainfall intensity or SLM practices. Therefore, differences in perception between farmers, rather than differences in actual exposure to drought, seem to drive the considerable variation in drought reports within the same EA that we observed earlier. This is somewhat attenuated in years with severe drought where local rainfall accounts for a substantial share of the variation in self-reported drought exposure.

Therefore, for the attribution of losses to disasters and with rainfall data of sufficient resolution available, the “basis risk” of overlooking some truly idiosyncratic drought crop losses should be less severe than the measurement error incurred by relying on self-reports alone. One direction for future research could thus be the use of weather stations at a high density, e.g. the community level, or microsensors at even higher resolution to accurately contextualize self-reports and calibrate accurate measures of local shock exposure.

# **5. Discussion and Takeaways**

Every year, disasters threaten millions of lives and livelihoods depending on agriculture in LMICs. What is more, this burden will likely increase in the coming decades due to the effects of climate change (Agnolucci _et al._ , 2020). In order to inform robust disaster risk reduction policies, accurate measurement of crop losses is of paramount importance and an important component of SDG 1.5.2 and Sendai indicator C-2.

In recent years, advances in the availability of and methodological innovations in farm surveys have made them rich and reliable resources of primary data in agriculture (Carletto and Gourlay, 2019). In the coming years, the _50x2030 initiative to close the agricultural data gap_ will furthermore provide an unprecedented opportunity to measure crop losses at a highly disaggregated level, in large samples across 50 LMICs, and over time. Survey data thus have the potential to complement existing approaches to measure disaster crop losses that come with demanding data requirements in the context of LMICs by providing data that is

- (i) high in spatial resolution and density

- (ii) scalable to collection and trialed and tested for the specifications of smallholder agriculture

> 61 Our results are qualitatively robust to the use of a drought dummy (based on rainfall below the 14-year average) instead of absolute rainfall and for the use of a logit model instead of a linear probability model. Furthermore, they are robust to the use of a dummy whether there is any self-reported drought based on damage on the crop _or_ area loss.

42
Contents

# Executive Summary

|1.|Introduction|1|
|---|---|---|
|2.|How Is Education Different In Rich and Poor Countries?|2|
|2.1|<br>Overall sector context|3|
|2.2|<br>Production of education services|6|
|2.3|<br>Education outcomes in quantity and quality|11|
|2.4|<br>Efficiency of the education system|13|
|2.5|<br>Equity in access and resource allocation|16|
|2.6|<br>Summary of the relation between country wealth and education|19|
|3.|What Are the Sources of Rich Countries' Advantage in Education?|20|
|3.1|<br>A framework for the analysis|21|
|3.2|<br>Rich countries' advantage in overall educational development|22|
|3.3|<br>Rich countries' advantage in primary education|27|
|3.4|<br>Summary of structural differences in education between rich and poor countries|36|
|4.|What Are The Policy Issues?|37|
|4.1|<br>Expansion of coverage versus lowering pupil-teacher ratios|37|
|4.2|<br>The scope for policy choice at the country-level|40|
|5.|Conclusion|42|
|Ap|pendix A: Comparing Education in Rich and Poor Countries in 1993 and 1975|44|
70 Table A5: Summary Statistics

|**Panel A: Intern**|**ational Trade**<br>||
|---|---|---|
|**Firm-level variables**|Pre-Ban<br>(1)|Post-Ban<br>(2)|
||2,036,434|491,025.4|
|Fertilizer Imports (USD)|(7,267,773)<br>[_N_=106]|(1,378,846)<br>[_N_=106]|
||1,619,553|1,531,465|
|Agricultural Exports (USD)|(6,206,280)<br>[_N_=1,565]|(6,030,005)<br>[_N_=1,562]|
|**Panel B: Cr**<br>|**op Yields**||
|**Divisional Secretariat-**|Pre-Ban|Post-Ban|
|**level variables**|(1)|(2)|
||4,236.585|2,881.95|
|Rice Yields (kg/ha)|(1,013.066)<br>[_N_=323]|(680.9113)<br>[_N_=323]|
||1,644.278|1,403.798|
|Tea Yields (kg/ha)|(1,165.624)<br>[_N_=134]|(971.3812)<br>[_N_=134]|

_Notes_ : The table shows summary statistics for key variables of interest, separately for pre-ban and post-ban periods. In addition to each variable’s average, the table displays its standard deviation (in parentheses) and sample size _N_ (in square brackets). The unit of observation is the firm in Panel A and the DS in Panel B. The data comes from the S&P Global Market Intelligence’s Panjiva data platform in Panel A and from our remote sensing estimates in Panel B. For each variable and period, the statistics are computed using all available units for which that variable was positive in that period. In Panel A, we define fertilizers as products listed in Chapter 31 of the HS system, and agricultural goods as products listed between Chapters 06 and 24. Imports and exports are measured in US dollars (USD), and crop yields are measured in kg/ha. In the case of tea yields, we use a DS-level confidence index that reflects the precision of the remote sensing estimates as a weight to compute weighted averages and standard deviations. Our definitions of pre-ban and post-ban periods differ across variables: for fertilizer imports, the pre-ban period is May 2020-April 2021 and the post-ban period is May 2021-April 2022; for agricultural exports, the pre-ban period is November 2020-October 2021 and the post-ban period is November 2021-October 2022; in Panel B, the pre-ban period is 2021 and the post-ban period is 2022.
Q = ∫(X1, X2 X3,......., Xn) where Q is the quantity of output X1 to Xn are quantities of variable and fixed factors of production (labor, land, capital, electricity, etc.).

The firm’s technological set includes all the technical knowledge available at a particular point in time about the combinations of inputs necessary for the production of its output. The process of discovery may result in new and more efficient combinations of inputs. As these combinations become available to the firm, its technological set changes. This constitutes technological change.

The supply curve for an individual firm under perfect competition is defined as the firm’s marginal cost curve above average cost in the short run. The firm’s long-run supply curve would be the portion of its long-run marginal cost curve for which marginal cost exceeds average cost. Fixed factors of production form the basis of the distinction between short run and long run. When fixed factors are present and the firm is operating in the range where marginal cost equals marginal revenue, the firm is said to be in a short-run equilibrium. In the short run, the firm will remain in business as long as it is able to cover its variable cost. Consequently, the marginal cost curve above the average cost is considered to be the supply function for the firm. The horizontal aggregation of the individual firms’ supply functions gives the industry’s supply function. Some economists, Lee and Keen (2003), for example, have argued that the conditions for consistent and representational aggregation of the supply curves may not hold if the production functions underlying the various firms’ supply curves are different and the input prices for the same factor input are different. However, such situations are more likely to arise in more complex industries where the range of technologies is much wider than in simple industries like mining where the range of technology is narrower.

The downward-sloping or perverse supply curve is less well-known and discussed in the theoretical literature. One theoretically intuitive example comes from the livestock industry. Since female calves can either be maintained for consumption or kept as part of the breeding stock, ranchers must regularly make the related decisions. If the price of beef is rising, rational profit-maximizing ranchers may take the decision to retain more female calves to expand the breeding stock and supply less for consumption. If this is replicated across the beef industry, in the short run, the supply of beef could be falling even as the price of beef is increasing. For an example, see Jarvis (1974). It has also been argued that risk and uncertainty may result in a negatively-sloped supply curve that violates the neoclassical assumptions of the law of supply. In fact, Just and Zilberman (1986) have drawn on data on rice risk in the Philippines reported by 8
|Social Investment Fund III<br>Ivan Drabek|
|---|
|Social Investment Fund III<br>Ivan Drabek|
|Social Investment Fund III<br>Ivan Drabek|
|Social Investment Fund III<br>Ivan Drabek|
|Social Investment Fund III<br>Ivan Drabek|
|Social Investment Fund III<br>Ivan Drabek|
|Social Investment Fund III<br>Ivan Drabek|
|Social Investment Fund III<br>Ivan Drabek|
|EGYPT-ALEXANDRIA DEVELOPMENT PROJECT<br>Ahmed A. R. Eiweida|
|EGYPT-ALEXANDRIA DEVELOPMENT PROJECT<br>Ahmed A. R. Eiweida|
|Espirito Santo Biodiversityand Watershed Conservation and RestorGunars H. Platais|
|Espirito Santo Biodiversityand Watershed Conservation and RestorGunars H. Platais|
|Espirito Santo Biodiversityand Watershed Conservation and RestorGunars H. Platais|
|Espirito Santo Biodiversityand Watershed Conservation and RestorGunars H. Platais|
|Espirito Santo Biodiversityand Watershed Conservation and RestorGunars H. Platais|
|Espirito Santo Biodiversityand Watershed Conservation and RestorGunars H. Platais|
|Espirito Santo Biodiversityand Watershed Conservation and RestorGunars H. Platais|
|Espirito Santo Biodiversityand Watershed Conservation and RestorGunars H. Platais|
|Espirito Santo Biodiversityand Watershed Conservation and RestorGunars H. Platais|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Health and Nutrition Support Project<br>Ibrahim Magazi|
|Reform Implementation Development PolicyLoan(RIDPL)<br>Husam Mohamed Beides|
|Reform Implementation Development PolicyLoan(RIDPL)<br>Husam Mohamed Beides|
|Reform Implementation Development PolicyLoan(RIDPL)<br>Husam Mohamed Beides|
|1st Business Productivityand EfficiencyDPL<br>Eva M. Gutierrez|
|1st Business Productivityand EfficiencyDPL<br>Eva M. Gutierrez|
|JO - Amman East Power Plant<br>Ferhat Esen|
|JO - Amman East Power Plant<br>Ferhat Esen|
|JO - Amman East Power Plant<br>Ferhat Esen|
|JO - Amman East Power Plant<br>Ferhat Esen|
|JO - Amman East Power Plant<br>Ferhat Esen|
|JO - Amman East Power Plant<br>Ferhat Esen|
|SL-GEF BiodiversityConservation Project<br>George Campos Ledec|
programs in 80 countries. The second database is NASDAQ, which covers data on foreign companies listed on that stock exchange since 1989. In addition, a third dataset is from NYSE and has data on foreign companies cross-listed on NYSE. The fourth source for the U.S. is Euromoney, which covers all operations of capital raised in international markets by firms. This database provides a comprehensive account of capital raised, because it includes DR programs, cross-border listings, and capital raised in equity markets around the world. The database we obtained reports 8,795 operations from 5,665 firms in 86 countries during January 1983 - April 2001.

To cover international activity in Europe, we use a dataset from the London Stock Exchange (LSE) that contains information on international activity for firms from 63 countries from January 1997 on. The second dataset comprises international activity on all German stock exchanges and is provided by the Frankfurt Stock Exchange (FSE). It covers international firms for 68 countries for 1999 and 2000.<sup>9</sup> The data from Bank of New York, Euromoney, NYSE, NASDAQ, LSE, and FSE allow us to construct the list of international companies. Precisely, we define international firms as those that cross-list directly, have their equity traded in the form of DRs, or raise capital in international stock markets. Our definition is thus more general than just listing on international exchanges, because it also captures capital raising without listing. We do not consider, however, the degree to which foreign investors hold shares or trade in local markets as an indicator of internationalization. It would not be possible to construct such a series on a consistent cross-country basis because most countries do not distinguish between local and foreign investors in their domestic markets. We also limit ourselves to only three financial centers, and thus do not consider firms that cross-list in any other (developed) country to be international. Furthermore, although there are some firms during the period that are no longer international, i.e., do no longer have an ADR program or are no longer cross-listed, we neither study this aspect (nor do we expect it to influence our results as exit is fairly limited over the period studied).

> 9 Since the time series available for the Frankfurt data are much shorter, we do not classify firms as international if they internalize only through FSE, the reason being that we cannot pinpoint the exact timing of internationalization. Instead, we use the FSE data to remove firms from the pool of domestic firms.

7
The strong growth of the EV market is in contrast to the recent decline of the overall passenger vehicle market. Nevertheless, the EV diffusion has been highly uneven across countries. Norway leads the world in EV adoption, where the EV share tripled from 18% in 2015 to 67% in 2020. Sweden and Netherlands also saw remarkable growth in the EV market share in the new vehicle market, reaching over 20% in 2020. For countries such as China, Spain, Canada and the United States, the EV share is smaller (3%-5% in 2020), but still represents significant growth over the years (Figure 3). Appendix figure S1 shows the map of the EV penetration rate in 2020 for all countries around the globe. On average, EVs have a stronger presence in richer countries: Northern Europe has generally higher EV shares while African and Asian countries have relatively low EV penetration. But it is not clear what explains the large differences among countries with similar socio-economic characteristics.

**Cross-country variation in EV choices** There is also considerable variation in the types of EVs purchased across regions. Figure 4 presents the average battery capacity, post incentive price, and vehicle size for battery EVs sold during 2013-2020 in four main regions in the estimation sample: China, Japan, Europe,<sup>1</sup> and North America.<sup>2</sup> We present the sample and sales-weighted averages in dark and light shades, respectively. Sales-weighted average battery capacity is generally higher than the sample average in all regions except Japan, indicating battery capacity is a key attribute that consumers select on. In particular, consumers in Europe and North America disproportionately prefer EVs with higher driving range. The sales-weighted average post-incentive price is lower than the sample average post incentive price in all regions, as expensive models tend to have lower market shares. Finally, in North America, the sales-weighted average size is slightly larger than the sample average, which may suggest that consumers prefer larger vehicles. Comparing across regions, EVs sold in North America have the largest salesweighted average battery capacity. EVs sold in China have lower post incentive price, battery capacity, and size on average, likely driven by both consumer preferences and purchase subsidies based on driving range (Li et al., 2021). EV models in Europe and Japan tend to have higher prices than other markets.

From 2015 to 2020, the number of EV models increased from 90 to 370 in the world, reflecting the ever growing choices for consumers.<sup>3</sup> There appears to be strong preference towards local

> 1Norway, Sweden, Netherlands, Switzerland, Germany, France, United Kingdom, Austria, Spain.

> 2United States and Canada.

> 3Source: IEA Global EV Outlook 2021.

2