rafmacalaba/datause-encoder-data huggingface dataset:
We examine more than 1,200 estimates of agglomeration elasticities from 70 studies covering 33 countries over the period 1973 to 2020. While raw agglomeration elasticities are positive and high in China, India and countries in Africa, countries such as Chile have negative elasticity estimates. Overall, raw estimates tend to suggest that developing countries reap huge benefits from urban density, nearly 5 points higher than developed countries. Further, these estimates are not driven by reverse causality.

These aggregate estimates, however, hide substantial heterogeneity. The differences in estimates can be attributed to estimation set-up (outcome and agglomeration measure), consideration of urban costs in estimating net benefits, and the variation in pay-off across sectors and skills. Studies using wages as an outcome variable have higher estimated elasticities compared to those using TFP measures, while studies using population size or density have larger estimates relative to market access measures. Service sector estimates are on average higher than manufacturing, while skilled workers disproportionately benefit from density relative to others. These findings are broadly consistent with the literature.

Our most important finding is that nominal wages, the canonical measure of agglomeration economies, seldom capture the costs of working and living in cities. These costs include higher housing costs, time lost in commuting as well as dis-amenities such as pollution or crime. Our metaanalysis shows that studies controlling for urban costs find elasticities to be 4.2 percentage points lower than studies that do not, implying a net agglomeration elasticity of 0.1 percent for developed countries, when using labor productivity as an outcome measure. Although our analysis confirms the relatively higher “gross” wage elasticities with respect to density; we find no evidence for “net” gains in light of high and increasing costs of working in developing country cities.

Further, rather than developing country agglomeration estimates being much higher than advanced countries as noted in country studies using wage data from China and India (e.g. Chauvin et al., 2017), there is no statistically significant difference between developing country and developed country estimates. Our novel estimates of urban dis-amenities suggest that although the elasticity of pollution and congestion in developing countries is comparable with developed countries, their levels are much higher. Higher costs in developing country cities are partly due to bad design and lack of capital investment, but also the fact that their growth is not driven by the process of structural transformation, which would create a critical mass of more productive firms that benefit from sharing, matching, and learning. Many developing country cities are not dense and productive—they are just crowded.

25
country groups, however. As shown in Figure 3, the proportional change was very similar for high-income and developing countries in the 1970s spike, albeit only half as large for wheat as for rice. In the more recent spike, the proportional change for high-income countries was somewhat smaller in the case of rice and very much smaller in the case of wheat than for developing countries.

Assuming that output was able to respond only to a limited degree in the first half of each spike, and that the national elasticities of demand (including stock demand) are similar across countries for each product, we set the γi’s to zero and use equation (2) to estimate the contribution to international price changes of priceinsulating behavior resulting from national price-insulating policy behavior is the (negative of the) consumption-weighted global average change in the national Ti’s. For rice the cumulative decline shown in the world row of Table 1 was 46 percent between 2005 and 2008, which is in the same order of magnitude as the decline between 1972 and 1974 of 58 percent. For wheat, the globally-weighted _T_<sup>ˆ</sup> was -28 percent over the 2005-08 period, compared with -30 percent in 1972-74.

According to World Bank price data, the world price of rice increased by 127 percent between 2005 and 2008, and the price of wheat increased by 114 percent. By taking the interactions between the proportional changes in trade policy and other factors into account, we can estimate the magnitude of the non-trade shocks. Comparing these with the estimated trade shocks suggest that in 2005-08 more than 45 percent of the explained change in the international price of rice is due to the changes in border restrictions that countries used in an attempt to insulate themselves from the initial increases in price. For wheat, the corresponding estimate was 29 percent. In 2008 alone, the change in protection on rice explains almost half of the 90 percent increase in rice prices observed for that year.

One important and encouraging difference between the 2008 price surge and the earlier one around 1974 is an apparent sharp reduction in the extent of price insulation in high-income countries. For rice, their NAC declined 45 percent between 1973 and 1974, while it fell only 8 percent between 2007 and 2008. In the case of wheat, the comparable numbers were 28 percent and 12 percent. While desirable, the reduction in insulating behavior by these countries has a very limited beneficial impact in the world market for rice as members not classifying themselves as 10
personal income between Rs. 8,000 and 20,000 are considered to have middle income (48% of the sample). Those with monthly personal income greater than Rs. 20,000 (8% of the sample) make up the high income group. Finally, about 67% of the sample is the primary wage earner for their household.

The center panel of table 2 reports information about commuting and vehicle ownership. The average commute takes 36 minutes and costs Rs. 490 a month. These figures, however, mask the high variance in commuting times and costs across modes: Approximately one-quarter of respondents walk to work, one-quarter take the bus and one-quarter drive a two-wheeler. Only 7 percent drive a car to work. About 30% of respondents travel while on the job.

Vehicle ownership in our sample is higher than reported in the 2001 Indian census, as would be expected given our education and employment criteria: About 44% of the respondents live in households that own a two-wheeler, and 15% in households that own a car.<sup>9</sup> Over half of our respondents drive a two-wheeler, which bodes well for the salience of our two-wheeler helmet scenario.

Experience with traffic accidents may influence the rate at which people are prepared to trade income for risk reductions, so we examine our respondents’ accident history, safety behavior and opinions about safety in the bottom panel of table 2. Twenty-three percent of our respondents report having been in an accident, with 17% actually suffering an injury. In addition, almost 14% have a family member or a friend who has had a road traffic accident.

> statistically significant effect on mean WTP.

> 9 The 2001 Census reports that 28% of households in Delhi own a two-wheeler and 13% own a car.

- 11 -
We believe our findings are fully consistent with the view that growth in developing countries is largely a matter of technological catch-up, as first proposed by Gerschenkron, i.e. based on learning from and adapting technologies developed elsewhere rather than on advancing the technological frontier.<sup>3</sup> This probably requires eytensive investments in education and training, including in engineering, but not extensive R&D activity.

The paper is organized as follows. Section II describes our data. Section III sets out cross country analysis of the growth-R&i) ink, and Section IV sets out our analysis of R&D differentials across countries. Section V concludes.

# (I. Data

The first international attempt to standardize the definition of R&D and the measurement of R&D inputs was made by OECD member countries at a conference in Frascati, Italy, in 1963. The resulting "Frascati Manual" has provided the basis for OECD's collecting and publishing R&D data on a regular basis. Since 1969, UNESCO has published standardized R&D statistics for some developing countries, based on the Frascati concepts. We use the published UNESCO sources, plus a supplementary UNESCO tape, for our country R&D data.<sup>4</sup> 3Page and Pack (1993) distinguish between technological catch-up and increases in "technical efficiency" and show that the most successful economies of East Asia have grown primarily on the basis of the former. Their finding is consistent with the view of Romer (1989) that growth is affected by the transfer of ideas, and the empirical finding of Kim and Lau (1993) that there has been little or no increase in TFP growth in East Asia, when TFP growth is defined narrowly as increases in technical efficiency.

'We are grateful to Mr. Morton M. Brown of the statistical office in the UNESCO, Paris for providing a supplementarv tape.

2
# **_Byzantium 1000_**

|Social group|Percentage of<br>population|Per capita<br>income (in<br>nomisma per<br>annum)|Income in<br>terms of per<br>capita mean|
|---|---|---|---|
|Tenants|37|3.5|0.56|
|Farmers|52|3.8|0.61|
|Large landowners|1|25|4.02|
|_Rural_|90|3.91|0.63|
|Urban ‘marginals’|2|3.5|0.56|
|Workers|3|6|0.97|
|Traders, skilled craftsmen|3.5|18|2.90|
|Army|1|6.5|1.05|
|_Urban excluding nobility_|9.5|9.9|1.60|
|Nobility|0.5|350|56.31|
|_Total_|_100_|_6.22_|_1_|

Notes: Nobility includes civil and military nobility. The average household size estimated at 4.3 (see Lefort, 2002).

**Income distribution data.** Taken directly from Milanovic (2006: Table 5, p. 465). Rural incomes are based mostly on Lefort (2002) who quantifies population shares and  incomes of several classes; rural population is divided into tenants ( _pariokoi_ ); farmers that include both landowning peasants and (not very numerous) hired farm workers and slaves working on large estates; and large landowners. Urban population is, following Morrisson and Cheney (2002), divided into four classes plus nobility (both civilian and military). Additional explanations given in Milanovic (2006: pp. 461-8).

Other incomes and wages (for comparison and illustrative purposes):

||Amount in<br>nomisma|Amounts in terms<br>of the estimated<br>average annual<br>income|Source|
|---|---|---|---|
|Heads of_themes_(administative<br>units) annual wage (around year<br>900)|360 to 720|58 to 115|Ostrogorsky (1969,<br>p. 246)|
|Heads of the three most<br>important_themes_(around year<br>900)|2880|~460|Ostrogorsky (1969,<br>p. 246)|
|Military commanders|144|23|Morrisson and<br>Cheynet (2002, p.<br>861)|

**Population and area** . For population, see Milanovic (2006, p. 461). It is a  compromise estimate (15 million) based on Treadgold (2001), Andreades (1924) and Harl (1996). Area: Treadgold (2001, p. 5).

45
- The size of the regulatory agency. A larger body would limit the probability of capture by different interest groups (as well as the government). Whether or not the prospect of being able to capture the agency would make a smaller agency more attractive to private investors is an empirical question. A larger size would allow for a range of professional expertise and diversity of opinion (see, e.g., Smith 1997a, b, c), both critical to the competence of the agency. On the other hand, commentators have argued that a smaller agency could be more efficient in decision making and more predictable and that

individual regulators might be more accountable than those in large commissions - which would all make a smaller agency more attractive from the investor point of view. Thus, the direction of the relationship between the size of the regulatory body and private investment would depend on the net effect of these factors.

- The degree of risk borne by the investor as measured by whether the tariff regime is rate of return or price-cap. Rate- of-return tariff regulation limits the risk taken by the investor vis a vis a price-cap regime and might hence be positively related to private investment in infrastructure. Also, Alexander and Irwin (1996) present evidence that price-cap regulation, by subjecting firms to greater risk, increases the cost of capital.

# ffV. Data

Data sources are described in Table 1. Macroeconomic data are taken from the World Bank's World Development Indicators database and the IMF's International Financial Statistics. Investment data by sector were obtained from official publications and directly from the governments of the countries studied. (Further details are provided in Calderon and Easterly (2001)). Data on regulatory variables were obtained from Guasch (2001).

10
was made in exchange for the voucher indicating the assigned amount. Later, each redeemed voucher was matched with the baseline data. Our measure of clinic attendance is based on these administrative data.<sup>39</sup>

# **Descriptive Statistics and Balancing Tests**

The analysis is based on 2,482 women aged 15 to 35 years old for whom information of basic baseline characteristics and GPS coordinates are available. The proportions of respondents who were offered each of the three amounts of cash incentives are very similar across Clinic condition, Vaccine condition, and Vaccine condition & Fear; the proportions of these three treatment types are also very similar for each amount of cash incentives (Figure 1).

On average, respondents are 25 years old. About half of the sample are Muslim, almost half (48.3 percent) have never received any education, 15.3 percent have never gotten married, 18.0 percent are pregnant, and 76.5 percent have at least one child. Many respondents (43.5 percent) engage in paid work, including selling agricultural produce, and the average amount of household earnings per capita in the past month is about 5,000 naira (approximately 33.3 US dollars). On average, the distance to the assigned health clinic measured by GPS coordinates is 1.7 kilometers, while the one-way transportation costs to the clinic are around 125 naira and the opportunity costs of clinic visit are about 4 naira.<sup>40</sup> Overall, the majority of respondents (72.2 percent) had visited the assigned health clinic before, and 39.8 percent had received tetanus toxoid vaccination at least once. These characteristics and health behaviors of women in the sample are comparable to those of the nationally representative sample (DHS, 2008).<sup>41</sup> In the whole sample, more than one third of respondents (37.8 percent) thought that they were likely to contract tetanus; on average, respondents thought that 30 people out of 100 would die of tetanus; and substantial proportions of respondents (35 to 50 percent) felt worried about tetanus, thought that tetanus is bad, and felt that it is important to get protected from tetanus. On average, respondents thought that 22 people out of 100 could be saved from tetanus with 13
(2016). One problem with using eBay flows is that the platform originally started as a peer-to-peer online market where second-hand products were auctioned, even if it rapidly became a platform mainly used by professional sellers on fixed price transactions. Because we want our proxy to reflect the access of firms to foreign consumers via an online platform rather than the general expansion of e-commerce, we decided to disregard transactions that were undertaken under the auction system. We are still left with measurement error due to the fact that cross border eBay flows are only a fraction of total online transactions between firms and foreign consumers. We will address this measurement error using an instrumental variable strategy described below.

The second challenge we face is endogeneity. Indeed, other unobserved factors such as technological progress may simultaneously determine online trade and the wage gap, thus introducing a bias in our estimates. To correct for this (and measurement error) we use an instrumental variable estimator. We use three instruments: i) the time varying geography determinants of bilateral online trade flows following Feyrer (2009); ii) the value of domestic online sales in each of the partner countries; and iii) the average import tariff in the importing countries. These three variables are likely to be valid instruments. Indeed, geography related variables and variables capturing demand shocks in importing countries are unlikely to be correlated with _other_ determinants of the wage gap in the _exporting_ country.

The third challenge is to provide estimates of the wage gap between skilled and unskilled workers that are consistent across countries. We do this using the methodology and data provided by Cruz and Milet (2017). The raw data are based on labor and household surveys in different countries and at different points in time harmonized by the World Bank in the International Income Distribution Database (I2D2). This makes feasible the use of the same methodology and variables to estimate the wage gap across countries and time.

We are not the first to look at the determinants of the wage gap between skilled and unskilled workers. Labor economists have emphasized the role played by changes in supply and demand to explain changes in the wage gap and ultimately in wage inequality. The workhorse framework linking supply and demand to factor prices is developed in Katz and Murphy (1992), later refined by several authors (see Card and Lemieux, 2001, for instance). The most recent version of this model features skill-biased technologies, i.e. technologies that shift outward the relative demand for skilled workers. This skill-biased technology change (SBTC hereafter) induces a permanent shift in the demand for the labor service of skilled workers.<sup>2</sup> However, technological change need not be

> 2The fact that wage inequalities increased at the same time when many industries were investing in computers

2
- [17] Galasso, Emanuela and Martin Ravallion, “Social Protection in a Crisis: Argentina’s Plan Jefes y Jefas”. World Bank Policy Research Working Paper No. 3165. November 2003.

- [18] Galasso, Emanuela and Martin Ravallion, “Decentralized Targeting of an Anti-Poverty Program”. Journal of Public Economics. Forthcoming 2004

- [19] Holmstrém, Bengt R. and Paul Milgrom, “Multi-Task Principal-Agent Problems: Incentive Contracts, Asset Ownership and Job Design”. Journal of Law, Economics and Organization, 7. 1991.

- [20] Jacoby, Hanan G., “Self-Selection and the Redistributive Impact of In-Kind Transfers.” The Journal of Human Resources 32(2), 233-249. Spring 1997.

- [21] Jacoby, Hanan G., “Is there an Intrahousehold ‘Flypaper Effect’? Evidence from a School Feeding Programme.” The Economic Journal 112, 196-221. January 2002.

- [22] Khandker, Shahidur R., Mark M. Pitt, and Nobuhiko Fuwa, “Subsidy to Promote Girls’ Secondary Education: The Female Stipend Program in Bangladesh”. Mimeo. March 2003.

- [23] Kochar, Anjini, “Institutional Deterrents to the Spread of Schooling”. Mimeo Stanford University. July 2000.

- [24] Kochar, Anjini, “Migration and Schooling Rates of Return”. Mimeo Stanford University. August 2000.

- [25] Lundberg, Shelly J., Robert A. Pollack, and Terence J. Wales, “Do Husbands and Wives Pool Their Resources. Evidence from the United Kingdom Child Benefit”. The Journal of Human Resources 32(3), 463-480. Summer 1997.

- [26] Mahal, Ajay, Abdo S. Yazbeck, David H. Peeters, and G.N.V. Ramana, “The Poor and Health Service Use in India”. Mimeo. March 2001.

- [27] Miguel, Edward, and Michael Kremer, “Social Networks and Learning about Health in Kenya”. Mimeo. July 2003.

- [28] Miguel, Edward, and Michael Kremer, “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities”. Forthcoming Econometrica. 2003.

- Identifying Impacts on

- [29] Nahlen, L. Bernhard, John Paul Clark, and David Alnwick, “InsecticideTreated Bed-Nets”. Supplement to The American Journal of Tropical Hygiene and Medicine, 68(4). 2003.

- [30] Ravallion, Martin, “Targeted Transfers in Poor Countries. Revisiting the Tradeoffs and Policy Options.” World Bank Policy Research Working Paper No. 3048. May 2003.

<!-- Start of picture text --> 22<br><!-- End of picture text -->
by OPEC as a supply event. In our 28 events, the reactions to the WTI oil price all indicate that the events are supply driven.

The third concern is that some of the geopolitical events (such as ISIS making advances in Iraq) could generate uncertainty, which is a demand factor. We argue that demand factors, if any, are weaker than the supply factors, by observing the price action. Take the example of ISIS making advances in Iraq: uncertainty would cause oil prices to go down, while the negative supply shock associated with the ISIS disruptions would cause oil prices to go up. In the equilibrium, we observe an increase in oil prices. Following the same logic about the shifts in demand and supply and the price elasticity of demand and supply for oils in the short run, we argue that oil supply shocks dominate demand shocks in these types of events.

For the heteroskedastic-based strategy to work, the changes in oil price on event days have to be larger than the changes on non-event days.<sup>8</sup> Table 2.2 shows the results of several test statistics to confirm that the variance of the log change in the WTI oil price for the event days is larger than that for the non-event days.

Table 2.2: Tests of differences in variance of oil price changes<sup>**9**</sup>

|Test|F-statistics|p-value|
|---|---|---|
|Levene|13.6400|0.0002|
|Brown-Forsythe trimmed mean|12.8885|0.0003|
|Brown-Forsythe median|13.5831|0.0002|

> 8 In a traditional Instrumental Variable method, it is the result of the first stage.

> 9 Notes: “Test” describe the F-statistic being computed. The Levene test for unequal variances is described in Levene (1960). The Brown-Forsythe tests are described in Brown and Forsythe (1974). These tests all formally test the hypothesis that the variances of the changes in oil prices are equal on event days and non-event days.

12
72 presentational purposes, the figures with our export results (Figure IV and Appendix Figure A8) only show coefficients for the four quarters prior and twelve quarters subsequent to the ban, even though the sample period used in the estimation is longer.

# **C Additional Empirical Findings**

## **C.A Robustness of Stylized Facts**

Figure A4: Dynamic Ban Effects on Imports: Fertilizer Only <!-- Start of picture text --> −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12<br>Month relative to period when treatment first changes (t=0) Month relative to period when treatment first changes (t=0)<br>(a) Extensive Margin, fertilizer only (b) Intensive Margin, fertilizer only<br>.6<br>.5 5<br>.4<br>.3<br>.2<br>0<br>.1<br>0<br>−.1<br>−.2<br>−5<br>Effect on Pr(imports>0) −.3 Effect on log(imports)<br>−.4<br>−.5<br>−.6<br>−10<br><!-- End of picture text --> _Notes_ : The figures show De Chaisemartin and d’Haultfoeuille (2022) estimates of the effects of import bans on the extensive and intensive margins of imports, relative to the last month before the ban takes effect, using an HS8 product-month level panel. The sample covers the January 2019September 2022 period (but only coefficients in January 2021-May 2022 are reported) and includes only fertilizer HS8 products. The treatment variable is a set of dummies _ban_<sup>_τ_</sup> _ct_<sup>indicating whether product</sup><sup>_c_was first banned</sup><sup>_τ_months before month</sup><sup>_t_, and the not-yet-treated products serve</sup> as the control group. The y-axis shows the effects of fertilizer import bans on the probability of nonzero imports (panel A4a) or on the log value of imports (panel A4b), where negative values correspond to a decrease in probabilities/values. The p-values of the joint significance tests for the placebo estimators are 0.846 (panel A4a) and 0.925 (panel A4b).