rafmacalaba/datause-encoder-data huggingface dataset:
of the estimated expected payoff (taken from Table 1) to the price net of the discount. Notably, in Ahmedabad, for farmers receiving the Rs. 30 discount, our estimates suggest the insurance is significantly better than actuarially fair (expected payouts are 180% of net premiums). Despite this, less than half of eligible farmers receiving this discount choose to purchase insurance.

# _C. Gujarat: Flyer experiments_

Flyer experiments involve randomizing the content of the flyer given to households along two dimensions: (i) the religious emphasis of the flyer: Muslim, Hindu or neutral (the latter is the omitted dummy), and (ii) whether the flyer emphasizes the benefits of insurance to the group rather than the individual. We are interested in how religious cues affect trust and concern for self vs. group. While in general Hindu and Muslim groups live in close proximity and harmony, Gujarat has nevertheless been subject to ethnic tension, particularly in 2002 when there was significant violence between the two communities.

As before, we estimate a linear probability model of how insurance demand depends on these treatments. Results are presented in Table 7. Even-numbered columns include village fixed effects, while odd-numbered columns exclude them.

Columns (1) and (2) study the entire sample, and include each intervention individually. The overall take-up rate is 23.8% (i.e. 23.8% of households given a flyer eventually purchase insurance), similar to the take-up rate in the villages where video treatments were used. None of the baseline treatments are statistically significant, however, and the coefficients are small.

The next two columns include the interactions of the two different treatments. Notably, the group emphasis treatment now has a significant positive effect on take-up when combined with a neutral religious setting. However, the use of a Muslim religious setting on the flyer (instead of a neutral one) reduces take-up by 9-10 percentage points, statistically significant at the 5% level in both cases.

To investigate this further, the final four columns of Table 7 repeat this analysis separately for households with characteristically Muslim names (columns (5) and (6)) and characteristically Hindu names (columns (7) and (8)), as identified by our research team after the completion of all field experiments.<sup>18</sup> We find that, amongst households receiving a group

> 18 We emphasize that treatment status was assigned randomly and was orthogonal to the religious identity of the respondent. After the marketing effort was finished, Gujarati research assistants identified the religious identity of

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|Table 5:|2SLS and|LIML regress|ions||
|---|---|---|---|---|
|Equation number|5-1|5-2|5-3|5-4|
|dependent variable|Morality|Morality-ability|Log GDP|Log GDP|
||2SLS (1<sup>st</sup>)|correlation<br>2SLS (1<sup>st</sup>)|per capita<br>2SLS (2<sup>nd</sup>)|per capita<br>LIML|
|No secondary schooling|-16.429**|-0.013|-1.018*|-0.790|
|(% of labor force)|(-2.12)|(-0.74)|(-1.62)|(-1.12)|
|Distance from equator|0.099|0.002*|0.009*|0.009|
||(1.22)|(1.81)|(1.63)|(1.47)|
|Trade share of GDP (ln)|-4.917***|-0.013|0.390**|0.444**|
||(-3.17)|(-0.74)|(2.07)|(2.01)|
|Rule of law|<br>3.593**|<br>-0.002|<br>0.192|<br>0.129|
||(2.55)|(-0.16)|(1.31)|(0.72)|
|Morality|||0.047**|0.055**|
||||(2.45)|(2.19)|
|Morality-ability correlation|||6.593***|7.118***|
||||(3.24)|(2.77)|
|Western Christian % 1970|13.744***|0.120***|||
||(3.96)|(2.67)|||
|Orthodox Christian % 1970|1.481|0.065|||
||(0.30)|(1.45)|||
|Other Christian % 1970|0.154|0.212*|||
||(0.01)|(1.82)|||
|Jewish % 1970|22.913***|0.095*|||
||(6.26)|(1.92)|||
|Muslim % 1970|9.659**|0.048|||
||(2.03)|(1.07)|||
|Hindu % 1970|2.259|0.121**|||
||(0.38)|(2.34)|||
|Other Eastern % 1970|19.179**|0.360***|||
||(2.07)|(2.66)|||
|Years of statehood (log)|-2.605|0.040**|||
||(-1.39)|(2.11)|||
|Constant|46.665|-0.184|5.306|4.823|
||(3.13)|(-1.07)|(4.70)|(3.50)|
|Centered R<sup>2</sup>|<br>.56|<br>.42|<br>.69|<br>.69|
|Mean dependent variable<br>|21.3|.14|9.14|9.14|
|Partial R<sup>2</sup>of excluded instruments|.22|.27|--|--|
|Shea partial R<sup>2</sup>|.21|.26|--|--|
|<br>Angrist-Pischke weak-ID p value|.0001|.024|--|--|
|Angrist-Pischke under-ID p value|.0001|.006|--|--|
|<br>Kleibergen-Paap Wald rk F|--|--|11.77|11.77|
|Over-identification test p value|--|--|0.77|0.79|

Test statistics (from robust standard errors) are in parentheses. A *,** and *** respectively indicate significance at .10, .05 and .01 levels for two-tailed tests. Number of observations is 74.

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is clearly what I found in the case of Equatorial Guinea<sup>14</sup> and Chad, and it is the case of other oil exporting countries in Africa, where the manufacturing sector is yet to be developed, to provide these countries with the much needed employment. Building on this simple observation, a convincing<sup>15</sup> case remains to be made if the Dutch Disease could possibly be the main factor explaining the poor development performance of SubSaharan Africa’s oil exporting countries.

Besides the simple observation above mentioned, it is not inevitable that existing natural resources prevent the emergence of a dynamic economy or that the discovery of such resource acts to dampen an already developed economy. Natural resources can be a blessing rather than a curse. Norway is a case in point. Norway is one of the leading oil exporters in the world, the country as yet to show clear symptoms of the Dutch disease. On the contrary, growth has thus far remained stubbornly high (Thorvaldur Gylfason 2001). Norway was already a developed country when oil was discovered in the 1970s.

Natural resource income may raise national savings and hence facilitate capital accumulation and growth; hence, there are benefits of owning large natural resources. Oil exporting economies have averaged twice the savings rate of non-oil exporters globally from 1960 to 1998 (Hausmann and Rigobon 2002). The position of a country before and during the influx of wealth is often a factor among the many different variables ranging from the political system in that economy to the status of other industries, if any, at the time of resource exportation. The applicability of Dutch Disease is therefore limited in context, particularly in the case of Africa, given the overall development level of these countries and the structure of their economy.

Indonesia, besides Norway, is another success story account and the country was similar to SSA’s oil exporting countries in the early 1970s. With a per capita income of only $200 in 1974, Indonesia was the poorest country in the world to receive a substantial

> 14 Achille Toto Same; Mineral-Rich Countries and Dutch Disease: Understanding the Macroeconomic Implication of Windfalls and the Development Prospects. The Case of Equatorial Guinea. - World Bank’s Policy Research Working Paper, 4595.

> 15 For more accuracy, Sachs and Warner (2001) empirically found that extremely resources-abundant countries such as Nigeria, or Mexico and Venezuela, have not experienced sustained rapid economic growth, in addition, empirical growth studies tend to confirm this casual evidence. But to which extent it is due to Dutch Disease in the case of Nigeria, remains an open question.

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3.2 to 4.4 percent under Scenario 1, and 5 percent under Scenario 2. Income inequality is also expected to increase as a result of the sustained crisis, with the Gini coefficient rising from 37.2 to 39.4 under Scenario 1 and to 41.4 under Scenario 2. Overall, we observe the economy to be hit hard due to the pandemic and the associated shutdown.<sup>12</sup>

**Table 2:  Projected impacts on Poverty and Inequality**

||Pre-COVID19|Post-COVID19<br>Scenario 1|Difference<br>Scenario 1|Post-COVID19<br>Scenario 2|Difference<br>Scenario 2|
|---|---|---|---|---|---|
|Extremepovertyrate|2.9%|7.4%|4.5|11.8%|8.9|
|Extremepoverty gap|0.5%|0.8%|0.2|0.9%|0.3|
|Povertyrate|13.7%|20.9%|7.3|25.6%|11.9|
|Poverty gap|3.2%|4.4%|1.2|5.0%|1.8|
|Gini coefficient|37.2|39.4|2.2|41.4|4.2|

Source: Author’s calculations based on EBCNV 2015 Not surprisingly and in line with global estimates,<sup>13</sup> the poor in Tunisia are more likely to be hit the hardest due to the COVID-19 crisis. To analyze the subgroups of the population along the welfare distribution that are most likely to be affected by economic slowdown, we plotted density and Growth Incidence Curves (GIC). Figure 8a shows the pre-crisis and post-crisis consumption distribution under Scenario 2, along with poverty line (red line). One can see that the poor will become poorer due to the economic shock induced by COVID-19. The GIC curve presents this information in a different way and plots the growth rate of consumption per capita across two time periods under Scenario 2, pre-crisis and post-crisis, for each percentile of the distribution. Not surprisingly, Figure 8b shows that for almost all subgroups of the population of Tunisia along the welfare distribution, there is expected to be a decline in consumption per capita (see that GIC is below 0). Households with per capita consumption in the poorest 20 percent of the distribution are simulated to be hit most, experiencing large declines.

> 12 We have also considered the growth of certain sectors in our simulations (such as agriculture).  In some African countries such as Uganda, the remittances have increased as per their Central Bank estimates. In the case of Tunisia, we do not know which way remittances would move. Intuition suggests that given the economic situation in Europe, remittances would probably decrease but there is no evidence of an increase or decrease in remittances. Hence, we do not consider this in our simulations.

> 13 According to World Bank (2020), the COVID-19 pandemic is estimated to push about 49 million people into extreme poverty in 2020 and will hit the poorest the hardest. <u>https://blogs.worldbank.org/voices/covid-19-will-hit-poor-hardest-heres-what-we-can-do-about-it.</u> According to the latest Poverty and Shared Prosperity Report (2020), The COVID-19 pandemic is estimated to push an additional 88 million to 115 million people into extreme poverty this year, with the total rising to as many as 150 million by 2021, depending on the severity of the economic contraction. Extreme poverty, defined as living on less than $1.90 a day, is likely to affect between 9.1% and 9.4% of the world’s population in 2020.

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# **4 Household Adjustment in Consumption and Production**

The discussion has followed the traditional first-order approach to assessing the distributional effects of commodity price changes. A major concern with this approach is that it does not allow households to respond to the changes in the economic environment. In fact, even when wage adjustments are incorporated, the standard approach takes the net-consumer/net-producer position of the household as “exogenous.” This is just a datum in the analysis that we can assess using survey data, as we did when we described the dependency of households in Latin America and Africa in section 2. The net-position of the household is, however, endogenous when households respond.

It is often argued that the first-order approximation can be accurate for small price changes. The “endogeneity” of a household choice is in these cases, a second order concern in the sense of being unimportant. This is true, and there are numerous examples where the framework works very well. The evaluation of the Doha Development Agenda is one instance, see Hertel and Winters (2006) or Hoekman and Olarreaga (2007). For larger price changes, as those that can be expected from a persistent food-price crisis (or a collapse of global commodity prices), the approximation error grows larger. This section thus explores how large this error can actually become and discusses how the approximation can be improved by measuring household responses in consumption and production.

A consumer will always lose from an increase in prices, but the losses can only be ameliorated by reducing purchases of the more expensive goods. A producer will instead always lose from a price decline. In this case, adjustments can mitigate the losses by shifting resources to more productive activities. Similarly, a farm that hires labor will lose from higher wages, even if labor is substituted with other factors of production. Finally, a worker will lose from a reduction in wages, even if he decreases labor supply to enjoy more leisure.

For a farm-household, or more generally in a model where the household participates in multiple markets, adjustments can play a bigger role. In fact, Porto (2008) shows that it is possible for a household to change sides of the market (that is, to become a net producer 19
digits level deflated by the consumer price index. The product real exchange rate is however not statistically significant. We also compute these relative prices as the growth rate of the average unit value at the HS 4-digits level deflated by the CPI. Also this variable is not statistically significant (column 6).

Finally we introduce dummies for the product differentiation defines using Rauch product differentiation classification (Rauch 1999), which classifies products into differentiated products, products with reference price and homogenous products traded on organized exchanges. The dummies are not significant, showing that export survival is not affected by the product’s degree of differentiation.

# **5. Conclusions and Policy Implications**

Since the turn of the century, export growth in Georgia has been high, suggesting that firms have taken advantage of available opportunities. However, most of the export growth experienced seems to be explained by more exports of the same products, to the same destinations. The contributions of diversification to export growth, either along the market or the product dimension, have been modest. What is more, the survival of exporters in the global marketplace has been low, both in absolute terms and comparatively, when looking at the performance of other firms in the region.

Low diversification increases the vulnerability of Georgia’s firms to product-specific or countryspecific shocks. On the other hand, low export survival depresses export growth, and may even be indicative of important welfare losses given the high fixed costs of entering export markets, and that are wasted when firms do not manage to survive in global markets.

Using detailed firm-level data matched with export transactions for Georgian firms in agriculture, industry and manufacturing over the period 2006-2012, this paper explored the survival of export flows, shedding light on the factors that affect the probability of surviving in export markets.

Georgian exporters are no exception in terms of being larger and more productive than domestic oriented firms. They grow faster than non-exporters in terms of employment and output, and more importantly, they are more productive. They also represent about 9-16% of the total number of firms in the economy (excluding services), which is in line with exporters’ proportions in other countries in the region.

Georgian exporters became more diversified, but new products or new destinations reached did not contribute substantially to export growth. Between 2006 and 2012, the number of export destinations per firm increased from 2.4 to 3.3, while the number of exported products increased from 2.2 to 3.9. Product diversification, however, has been mainly explained by the entry of new firms into the market. In fact, additions of new products by entrants explain three-quarters of the product diversification component in export growth, while diversification among continuing firms explained the rest. Destination diversification instead happens mostly within continuing firms (almost two-thirds) while new destinations introduced by new firms account for one-third of the extensive margin.

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counterparts (46.9% vs. 52.7%, and 31 vs. 42 years old). As mentioned above, Venezuelans are more educated than Peruvians: only 17.2% had less than secondary education and 38.8% had a university education compared to 47.8% and 13.4% only, respectively, among Peruvians. Despite the differences in human capital, immigrants had worst labor market outcomes: 13% were unemployed, 8% had a formal job and the average income was of S/ 941. On the other hand, Peruvians had a slightly higher unemployment rate (16.1%), but were almost three times as likely to have a formal employment (21.4%) and earned 50% more than immigrants (S/ 1,482). Despite being more educated, Venezuelans work in less skilled jobs than Peruvians, especially in sales and services and elementary occupations. The big exception is agriculture and fishing which is the occupation for 16.2% of Peruvians but almost no Venezuelans in Peru.

# **4.4 Empirical Model and Identification**

We first examine the impact of local labor market conditions on immigrants’ reports of experiencing discrimination. More specifically, we estimate the following regression model: where _yij_ equals one if individual _i_ in centro poblado _j_ reports having experienced discrimination in the 2018 in the ENPOVE (in general, or in a particular location) and zero otherwise. _lnEmpj_ is the log informal employment rate in the same centro poblado measured in the 2017 census. We control for a variety of individual ( _X_ ) and centro poblado ( _Z_ ) level controls.<sup>7</sup> We also include origin municipality (in Venezuela) fixed effects ( _αo_ ) to control for any origin-specific factors that could affect perceptions of discrimination (e.g. skin color, accent). _εij_ is an error term clustered at the centro poblado level as this is the level of aggregation of our main explanatory variable.

Among our centro poblado ( _Z_ ) level controls, we include the (log) number of Venezuelans who lived in centro poblado _j_ in 2007 (as identified in the census). Clearly, the number of immigrants in a certain location is an important determinant of discrimination, but including the current number of immigrants in the regression would introduce additional endogeneity

> 7Individual level controls in the regression include gender, age, education, marital status, months living in Peru, household socioeconomic strata, household size and number of people sharing one’s bedroom. Employment and occupation controls include total income, whether in formal employment, and occupation including not working. Centro poblado level controls include log population in 2017, the log number of Venezuelans in 2007, log mean household expenditure in 2013, log agricultural rate in 2007, log manufacturing rate in 2007 and log travel distance to Lima.

12
- Hastings, Justine and Lydia Tejeda-Ashton 2008. Financial literacy, information, and demand elasticity: Survey and experimental evidence from Mexico. _NBER working paper no. 14538._

- Hugo, Graeme 2007. “Indonesia’s Labor looks abroad”, _Migration Information Source Country Profile_ <u>http://www.migrationinformation.org/Profiles/display.cfm?ID=594</u> [accessed 9 May, 2012].

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<u>http://idbdocs.iadb.org/wsdocs/getdocument.aspx?docnum=547263</u>

- International Organization for Migration (IOM) _Labour Migration from Indonesia: An overview of Indonesian migration to selected destinations in Asia and the Middle East._ IOM: Jakarta. <u>http://www.iom.int/jahia/webdav/shared/shared/mainsite/published_docs/FinalLM-Report-English.pdf [accessed 20 April 2012].</u>

- Lusardi, Anna Maria 2008. “Household Saving Behavior: The Role of Financial Literacy, Information, and Financial Education Programs”, NBER Working Paper no. 13824.

- McKenzie, David 2011. “Beyond baseline and follow-up: The case for more T in experiments”, _Journal of Development Economics_ , forthcoming

- McKenzie, David 2012. “Learning about migration through experiments”, _CReAM Working Paper_ no. 07/12.

- Mohapatra, Sanket , Dilip Ratha and Ani Silwal 2011. “Outlook for Remittance Flows 2012-14”, _Migration and Development Brief No. 17_ , World Bank: Washington D.C.

- Seshan, Ganesh and Dean Yang 2012. “Transnational household finance: A field experiment on the cross-border impacts of financial education for migrant workers”, Mimeo. University of Michigan.

- Woodruff, Christopher, and Rene M. Zenteno 2007. Migration networks and microenterprises in Mexico, _Journal of Development Economics_ , 82(2): 509-28.

- World Bank 2006. _Global Economic Prospects 2006: Economic Implications of Remittances and Migration._ World Bank: Washington, D.C.

- World Bank 2010a. _Enhancing Access to Finance for Migrant Workers in Indonesia: Evidence from a survey of three provinces._ World Bank, Jakarta.

- World Bank 2010b. _Improving Access to Financial Services in Indonesia._ World Bank, Jakarta.

- Yang, Dean. 2008. “International Migration, Remittances, and Household Investment: Evidence from Philippine Migrants’ Exchange Rate Shocks.” _The Economic Journal,_ 118: 591630.

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