rafmacalaba/datause-encoder-data huggingface dataset:
9 THE INTENSIVE MARGIN IN TRADE _pct_ as _x_<sup>_pct_</sup> _ij_<sup>, we run the regressions:</sup> We define the IME for each percentile as IME<sup>_pct_</sup> _≡ α_ ˆ<sup>_pct_</sup> .

We plot the IME<sup>_pct_</sup> for each percentile (with confidence intervals) in Figure 3 along with the horizontal line at the overall IME of 0.4.<sup>13</sup> The IME is 0.5 for the highest percentile. But the positive overall IME is not coming exclusively from the export superstars: the IME<sup>_pct_</sup> rises steadily from 0.2 at the 50th percentile to 0.3 at the 80th percentile.

# **IME for multi-product firms**

We can dig deeper and study whether average exports per firm can be explained by the number of products exported per firm or by exports per product per firm. Let _mij_ be the average number of products exported from _i_ to _j_ by firms exporting from _i_ to _j_ , and let _x_<sup>_p_</sup> _ij_<sup>_≡xij/mij_bethe</sup> average exports per product per firm exporting from _i_ to _j_ . We define the IME _at the product level_ as IME<sup>_p_</sup> _≡ cov_ (ln ̃ _x_<sup>_p_</sup> _ij_<sup>_,_ln</sup><sup>_X_ ̃</sup><sup>_ij_)</sup><sup>_/var_(ln</sup><sup>_X_ ̃</sup><sup>_ij_).Since</sup><sup>_xij_=</sup><sup>_xp_</sup> _ij_<sup>_mij_,the IME is equal to the IME</sup><sup>_p_</sup> plus the extensive product margin elasticity, Table 5 reports the results for the IME<sup>_p_</sup> for the core sample.<sup>14</sup> Most of the IME is explained by the systematic variation in average exports per product per firm, rather than in the average number of products exported by firm.

# **Taking stock: the IME in the EDD**

Summarizing the results so far, we find the intensive margin elasticity to be positive and significant, both statistically and economically. This finding is robust to the inclusion of a variety of fixed effects, various samples, exclusion of small firms, and disaggregation by industry. The IME is positive and monotonically increasing across the whole distribution of exporter size. The systematic cross-country-pair variation of average exports per firm comes primarily from the behavior of average exports per product per firm.

> 13For exporter percentiles to be well-defined we focus on country pairs for which _Nij >_ 100.

> 14Bernard et al. (2009) present a similar decomposition for U.S. exports. We compare their results to ours below.
# **3 Data**

We use three sources of data. Baseline data come from the household survey administered to all households in March and April 2016. Outcome data come from records collected by our field team stationed at the NBS branch during the intervention and from NBS administrative data.

Panels A and B of Table 1 use data from the 2016 survey to compare the characteristics and savings-related behaviors of existing account holders who were assigned to the three different treatments in the transfer experiment of 2015. Appendix Table A3 contains the definition of variables. We report the p-value of the joint test of equal means across all three categories in column 5, and we do not expect (and do not observe) differences in time-invariant characteristics such as age and gender of the respondent, that could not have changed as a result of treatment. The p-value of the F-test that all characteristics in panel A are jointly zero is 0.480. Despite the differential attrition, we take comfort in the fact that account holders in the treatment and control group appear to be similar.

In panel B, households that received the transfer did not expect more withdrawals in the next three months than those who did not receive the transfer (p-value is 0.227). Past and predicted usage for both samples is too limited to make the basic account worthwhile given the fee structure. According to their expected use in the three months after the baseline survey, existing account holders would save on average MK 1,067 (USD 5.80 according to the 2016 PPP exchange rate) even after accounting for the cost of the ATM card if they planned to keep it for four months. Panel B of Table 1 also reports account usage six months after the 2015 transfer, using administrative data. We find that individuals who received large transfers (either in cash or via direct deposit) made significantly more transactions (pvalue is 0.022). Although the overall mean is low, individuals who received the large transfer were also more likely to correctly report the fees for the basic account and to trust NBS more. All these variables were expected to be affected by treatment. While other variables in panel B could also have changed with the transfer, we find that they did not: excluding the percentage of individuals who correctly predicted the fees in the basic NBS account, the number of transactions in the six and 12 months after the large transfer, and the percentage who trust NBS, the p-value of the F-test that the remaining characteristics in panel B are jointly zero is 0.419. In particular, while households that received the transfer (particularly in cash) seem to have larger balances in 2016 than those without the transfer, these differences are not statistically significant (p-value is 0.375). The structural model described in the next section uses balance data to explain the decision of account ownership and for this reason it can only be estimated with individuals with available balance data. Although we requested balance data for the full sample of 747 individuals with accounts, the bank only provided 8
The use of disaster data from databases such as EM-DAT and NatCatSERVICE for estimating the macroeconomic consequences of natural disasters has been criticized for possible issues related to endogeneity and measurement.<sup>8</sup> An alternative is to use the physical strength of disaster events to measure their intensity. Albala-Bertrand (1993), in a seminal contribution, collected data on 28 events across 26 countries between 1960 and 1979 and argues that natural disasters have a positive effect on GDP. Strobl (2011) constructs a “Hurricane Disaster Index,” by combining estimates of monetary losses, wind speed, as well as local exposure, and finds that hurricane strikes significantly reduced the annual economic growth of coastal counties in the United States between 1970 and 2005. Hsiang and Jina (2014) show that tropical cyclones cause permanently lower levels of GDP, using a data set containing information on the exposure of all countries to all known cyclones between 1950 and 2008. Felbermayr and Gr ̈oschl (2014) construct a data set of physical intensities of earthquakes, volcanic eruptions, storms, floods, droughts, and extreme temperature events from primary geophysical and meteorological information. They find that, on an average, natural disasters have had a substantial, negative impact on the growth of per capita GDP in the years subsequent to the disasters, but that there is a recovery later.<sup>9</sup> Another approach consists of studying specific disasters in case studies. Disasters are treated as “natural experiments” for causal estimation of their impacts on different variables of interest. On the one hand, Coffman and Noy (2012) find that even 18 years after Hurricane Iniki hit Hawaii in 1992, Kauai’s economy had not fully recovered. On the other hand, Heger and Neumayer (2019) find that the 2004 Indian Ocean tsunami depressed economic growth in the Indonesian province of Aceh only for one year after the disaster. They argue that growth was considerably boosted subsequently by reconstruction efforts.

We contribute to this literature by causally estimating the impact of the Kerala floods on district-level economic activity, proxied by monthly nighttime lights and ATM transactions.

> 8See, for example, Felbermayr and Gr ̈oschl (2014).

> 9Both Felbermayr and Gr ̈oschl (2014) and Hsiang and Jina (2014) reject the notion of growth-enhancing creative destruction of natural disasters.

7
Additional controls used in regression (1) include a set of basic household characteristics such as household size, sex and education of the household head, denoted by the vector Xhmt, and a set of binary variable for the year of the survey, denoted by δt. The latter controls for aggregate effects, such as economic growth or growth in the real gross domestic product.<sup>12</sup> Overall, considering the exogeneity of the rainfall shock variable, and given that we control for municipality time-invariant or fixed effects, we are quite confident that our estimates reflect the causal effect of rainfall on household consumption. Controlling for additional characteristics could lead us to include household choices that are determined by weather.

_The geographic distribution of the impact of rainfall_ Understanding which categories of households are affected by weather shocks will help better target social safety nets to protect households from these shocks. Besides understanding the distribution of the impact of weather shocks, it is important to understand the extent to which households are able to smooth shocks.

We examine in which regions households are most affected by shocks. This is useful to understand the implications of climate change since, as described in the introduction, climate change is likely to differ across regions of the Philippines. We run the regression specified below: The variables are the same as in equation (1) and ZR is a categorical variable indicating the region of the household residence.

# _Interaction of the rainfall shock with municipality-level infrastructure and activities_

We also analyze the extent to which access to policy related variables such as infrastructure leads to protecting household consumption from negative rainfall shocks. For this purpose, we use module 5 of the population census collecting information on _barangay_ infrastructure and other socio-economic characteristics, available for the years 2000 and 2007. Given that the census years do not match the years that FIES is available

> which has historically bad weather. If the historic rainfall conditions translate into high consumption levels, then one may find that a negative deviation is associated with a relatively higher level of consumption. 12 We do include controls for the month of interview because the survey is conducted in the same months across the 3 waves: July for the first visit and January for the second visit.

10
tionable things when they are just doing what an authority figure told them to do". It’s worth noting, however, that for all items a significant majority of all respondents do not agree with the statements. Overall, politicians score on average 0.32 standard deviation lower than bureaucrats on the aggregate Integrity Index.

Panels B and C present results on personality traits. Politicians do fare better than bureaucrats in other non-cognitive dimensions. They score 0.12 s.d. higher on average in the Big-Five Index, though their advantage is restricted to two domains in which we would indeed expect politicians to perform better: Extraversion and Agreeableness. When we consider other measures of non-cognitive ability, bureaucrats fare better on Grit, Decision Making and Hostility Bias, such that we find no distinction between the two groups when pooling all dimensions above in a Non-Cognitive Index. We find no significant difference between politicians and bureaucrats in the Public Sector Motivation Index: bureaucrats report higher Compassion but score lower on Civic Duty and Self-Sacrifice; in other dimensions we observe no gap.<sup>15</sup> .

Finally, Panel D presents results on cognitive traits and measured attitudes of altruism and risk preferences. Consistent with large differences in educational attainment, politicians fare significantly worse in both cognitive measures: on average they can recall 0.3 fewer numbers than bureaucrats, and are also 3 p.p. more likely to choose a dominated option on a simple risk game (choosing a UGX 1 million reward instead of a gamble of equal probability between UGX 1 million and UGX 2 million)<sup>16</sup> . Regarding altruism, politicians donate a slightly larger fraction of a hypothetical prize on average, and also make less risky choices in lottery games.<sup>17</sup>

> 15One advantage of using the same modules as other surveys in the literature is comparing the results obtained in Uganda with other settings. This paper uses the same modules for measurement of PSM and Big-Five as Dal Bo, Finan and Rossi (2013) in Mexico, so we can actually compare some of the descriptive statistics to their Table II. Our mean values for Big Five measures are across the board lower, indicating a "worse" pool of individuals in terms of these non-cognitive traits. For PSM, however, the scores are pretty similar.

> 16The share of individuals who make a mistake, for both politicians and bureaucrats, around 10 percent in the Uganda sample, stand in stark contrast to the results of Dal Bo, Finan and Rossi (2013) where almost 40 percent of individuals who were being considered for the position of social workers in Mexico made this error.

> 17While all figures discussed in these tables refer to differences in means, Figures 3 and 4 presents the CDF of traits for each group and Kolmogorov-Smirnov tests reject equality of distributions.

13
one would expect from renegotiating a withholding rate at a higher level. The results also suggest that after withholding rates with a country increase, some of the income flows to this country are redirected to other countries. The share of flows that are redirected and the share that is discontinued thus shape the overall sensitivity of the withholding tax base and the efficiency of tax policy changes. Finally, these results suggest that the base of royalty payments and interest flows react strongly to any changes, while dividend flows are less sensitive. Accuracy and magnitude of revenue estimates based on these elasticities are, however, subject to a number of assumptions and need to be interpreted with caution.<sup>27</sup> Tables 2a and 2b below translate our findings into an estimate of revenue losses linked to reduced dividend payments for the five most important recipient countries. (Cyprus, Netherlands, Russia, Austria, and Switzerland). The tables differentiate the mechanical and behavioral effects described above and report the estimated potential revenue impact of partial reforms of the withholding tax structure based on 2012 and 2014 data. In each row we examine what would happen if the current treaty based withholding tax rate with a specific country was reset to the domestic level of 15%. The mechanical effect is presented in the column on “Direct Effect” and the column named “Total Effect” combines the mechanical and behavioral effects. The table indicates, for instance, that parents in the Netherlands received from Ukraine more than US$ 400 million in dividends in 2014. If the withholding tax rate had been reset to 15%, the mechanical increase in revenues would have been around US$ 60 million. However, a share of the flows would likely have been redirected. Our estimates indicate that roughly 80% of the initial increase would remain.

_Table 2a: Estimated revenue loss from reduced withholding on dividends (2014)_

|**Country**|**Flow in 2014**<br>**_Total non‐_**<br>**_portfolio_**<br>**_dividend flow_**<sup>**28**</sup><br>**_(Million USD)_**|**Differential**<br>**_of qualifying_**<br>**_rate with_**<br>**_standard_**<br>**_withholding_**|**Direct Effect**<br>**_Tax revenue_**<br>**_foregone_**<br>**_Million USD)_**|**Total Effect**<br>**_Tax revenue_**<br>**_foregone_**<br>**_Million USD)_**|**Efficiency**|
|---|---|---|---|---|---|
|**Cyprus**|175.95|0.10|17.59|14.49|0.82|
|**Netherlands**|416.40|0.10|41.64|34,14|0.82|
|**Russia**|71.44|0.10|7.14|6.01|0.84|
|**Austria**|77.03|0.10|7.70|6.46|0.84|
|**Switzerland**|73.79|0.10|7.38|6.20|0.84|
|**Mean**|162.92|0.10|16,29|13,46|0.83|

> 27 Most importantly, we impose a specific balance of own‐ and cross tax elasticities to ensure that our results are economically meaningful (we require that the aggregate tax base does not increase in response to an increase in any withholding tax rate).

> 28Assumed to be 80 % of total flow.

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ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The country‘s current irrigated area could be increased substantially with good economic returns. Simulations suggest that with a threshold internal rate of return (IRR) of 6 percent, it would already be economically viable to develop a further 374,335 hectares of land for irrigation, of which 99 percent would be developed through large-scale projects. If the threshold of the IRR is raised to 12 percent the economically viable area for irrigation shrinks to 143,588 hectares, mainly for large-scale projects. The required investment for attaining this expansion is $286 million (table 6). This area is concentrated in the southeast and central parts of the country (figure 6b), which has the highest agricultural potential given the significant clay content of the soils.

**Table 6. Zimbabwe’s irrigation potential**

||L|arge-scale||S|mall-scale|||Total||
|---|---|---|---|---|---|---|---|---|---|
||Investment|IRR|Area<br>increase|Investment|IRR|Area<br>increase|Investment|IRR|Area<br>increase|
|Cutoff (%)|$ million|%|ha|$ million|%|ha|$ million|%|ha|
|0|1,132|8.2|579,876|40|3.0|7,692|1,171|8.1|587,568|
|6|723|10.8|370,481|20|0.0|3,854|743|10.7|374,335|
|12|277|14.7|141,846|9|0.0|1,742|286|14.6|143,588|
|24|0|0.0|0|3|0.0|561|3|0.0|561|

Source: Derived from You and others (2009).

_Note:_ Water for irrigation can be collected in two ways: through large, dam-based schemes, or through small projects based on the collection of runoff from rainfall. The investment costs of large-scale irrigation development reflect only irrigation-specific infrastructure, such as distribution canals and on-farm system development. The potential for small-scale irrigation is assessed not only on the basis of agroecological conditions, but also in terms of market access, since irrigation is typically viable only if the increased yields can be readily marketed. The unit cost for large-scale projects is set at $3,000 per hectare and for small-scale projects at $2,000 per hectare. IRR = internal rate of return.

Whereas Zimbabwe‘s irrigation potential for small-scale projects is one of the lowest among southern African countries (figure 7a), the nation stands as the country with the highest potential area increase for large-scale projects when the cutoff for the IRR is set at 12 percent (figure 7b).

Water for irrigation can be collected in two ways: through large, dam-based schemes, or through small projects based on the collection of run-off from rainfall. The investment costs of large-scale irrigation development reflect only irrigation-specific infrastructure, such as distribution canals and onfarm system development. The potential for small-scale irrigation is assessed not only on the basis of agroecological conditions, but also in terms of market access, since irrigation is typically viable only if the increased yields can be readily marketed.

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2 to the operations of firms. Therefore, it could be that work experience contributes significantly to human capital in a way that deserves more attention.

Few studies have consistently estimated returns to experience and explored how they vary across a sample of developing and developed countries (Lagakos et al., 2018b,a). Even less is known about the patterns of returns to experience for a global sample, and how they are affected by policy. Human capital accumulation at work could have important implications for long-run growth and the divergence in income between rich and poor countries. Since output per worker is the main contributor of output per capita, if workers see their earnings increase due to higher returns to experience in their economy, then tracking the evolution of these returns over time could provide valuable information regarding the ability of developing economies to eventually catch up to the income level of developed economies. In this paper, we provide additional evidence that work experience is a key factor of economic development.

First, we study wage-experience profiles, and obtain returns using data from 1,084 household surveys and census samples across 145 countries (1990-2016). We find average population-weighted returns to experience of 2% for the world as a whole. Returns to education are 4 times higher, at 8%. However, returns to education do not vary much across income levels, whereas returns to experience are strongly correlated with economic development. We find returns of 1.7% for developing economies and 3.2% for developed economies, which suggests that workers in developed economies accumulate almost twice as much human capital at work than workers in developing economies. In our preferred specifications, returns are then higher in middleincome countries than in low-income countries, but only significantly so for a few specifications. Significantly higher returns to experience in all specifications are only observed when an economy achieves high-income status. These patterns could support our conjecture that the evolution of a country’s returns to experience could proxy for this country’s ability to “catch up” with the most developed economies.

Second, we explore how returns to experience vary across several economic and policy-relevant dimensions. Exploiting the fact that we are able to estimate returns to experience for more than 100 countries and multiple years of data during the period 1990-2016, we employ panel regressions to investigate how changes in the returns to experience over time correlate with several important time-varying factors. We find
# **Assessment of Net Mitigation in the Context of International Greenhouse Gas Emissions Control Mechanisms**

## **Jon Strand**

## **Development Research Group, Environment and Energy Team**

## **The World Bank**

**<u>Jstrand1@worldbank.org</u>**

### **JEL Classification** : Q54; Q56.

**Key words** : Greenhouse gas mitigation; climate policy; net mitigation; net atmospheric benefit; own contribution.
Manufacturing is a perfectly competitive sector as well. A homogenous manufacturing output is produced using labor and intermediate services ( _Ys_<sup>_m_):</sup> Equation (6) is the only non-standard assumption in the model. In order to motivate this assumption empirically, we present in Figure (5) the importance of the services covered by the STRI index as inputs for manufacturing production in several countries. We use data from the OECD STAN Input-Output tables for the mid-2000 for the OECD countries and some developing countries.<sup>11</sup> We report in Figure (6) the importance of manufacturing as intermediate input in the production of the services covered in the STRI index. Looking at Figures (5) and (6) two main features emerge. First, the average input share of STRI services into manufacturing production (32%) is more than two times higher than the importance of manufacturing in the production of services (14%).<sup>12</sup> Second, there is a lot of heterogeneity in the Input-Output structure of different countries.<sup>13</sup> From equation (6) it also follows that _Pm_ = _w_<sup>_γ_</sup> _Ps_<sup>1</sup><sup>_−γ_</sup> .

Services, finally, is a sector characterized by product differentiation and monopolistically competitive firms. The output of services is a C.E.S. aggregator of individual service varieties <u>1</u> with elasticity of substitution _σ_ = 1 _−ρ_<sup>_>_1:</sup>

> 11Unfortunately, only about a third of the countries for which the STRI index is built are represented in the OECD-STAN database.

> 12Obviously, manufacturing itself is a more important input into the production of manufacturing than services, and services are a more important input into the production of services than manufacturing. We are not explicitly incorporating these features into the model, and choosing instead to write ”net-output” production functions for each sector.

> 13This also calls for some caution about the practice, common in the literature, of using the input-output structure of the U.S. as a proxy for the input-output structures of other countries.

12