rafmacalaba/datause-encoder-data huggingface dataset:
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Rogge, K. S., & Reichardt, K. (2016). Policy mixes for sustainability transitions: An extended concept and framework for analysis. _Research Policy_ , _45_ (8), 1620–1635. Rufín, C. (2003). _The political economy of institutional change in the electricity supply industry: Shifting currents_ . Edward Elgar.

Schmidt, T. S., & Sewerin, S. (2019). Measuring the temporal dynamics of policy mixes – An empirical analysis of renewable energy policy mixes’ balance and design features in nine countries. _Research Policy_ , _48_ (10), 103557.

Sen, A., Nepal, R., & Jamasb, T. (2016). Reforming Electricity Reforms? Empirical Evidence from Asian Economies. In _The Oxford Institute for Energy Studies (OIES)_ .

Smets, L., Knack, S., & Molenaers, N. (2013). Political ideology, quality at entry and the success of economic reform programs. _The Review of International Organizations_ , _8_ (4), 447–476.

Steckel, J. C., Jakob, M., Flachsland, C., Kornek, U., Lessmann, K., & Edenhofer, O. (2017). From climate finance toward sustainable development finance. _Wiley Interdisciplinary Reviews: Climate Change_ , _8_ (1), e437.

Stock, J. H., & Watson, M. W. (2011). _Introduction to Econometrics, 3_<sup>_rd_</sup> _Edition_ . Pearson. Urpelainen, J., Yang, J., & Liu, D. (2018). Power Sector Reforms and Technical Performance: Good News from an Instrumental Variable Analysis. _Review of Policy Research_ , _35_ (1), 120–152. https://doi.org/10.1111/ropr.12275 Ward, W. H. (1967). _The Sailing Ship Effect_ . Institute of Physics and The Physical Society. Warner, R. (2007). Applied Statistics: From Bivariate Through Multivariate Techniques.

_Components_ . https://doi.org/10.1017/CBO9781107415324.004 Wooldridge, J. M. (2001). _Econometric Analysis of Cross Section and Panel Data_ . The MIT Press.

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**Table 3:Counter-notifications relating to new measures in the SPS Committee, 1995-2002*:**

|**Complaints against**<br>|**R**<br>|**egulatory G**<br>|**oal of Con**<br>|**tested Mea**<br><sup>******</sup>|**sure**<br>|
|---|---|---|---|---|---|
|**measures of**<br>**_Number o_**<br>|**Plant**<br>**Health**<br>**_f Complaints_**<br>|**Animal**<br>**Health**<br>**_by Develo_**<br>|**Human**<br>**Health**<br>**_ped Countri_**<br>|**Other**<br>**_es_**<br>|**Total**<br>|
|Developed Countries|16|7|44|3|70|
|Developing Countries|17|11|41|4|73|
|Multiple Countries|-|2|-|-|2|
|_Sub-total_|_33_|_20_|_85_|_7_|_145_|
|**_Number_**|**_of Complain_**|**_ts by Devel_**|**_oping Coun_**|**_tries_**||
|Developed Countries|12|12|34|2|60|
|Developing Countries|8|17|7|2|34|
|Multiple Countries|-|2|-|-|2|
|_Sub-total_|_20_|_31 _|_41 _|_4_|_96_|
|**Total**|**53**|**51**|**126**|**11**|**241**|

*Preliminary tabulation.

** Includes complaints about horizontal regulations (such as those regulating products of modern biotechnology) that reference human, animal, and plant health as objectives.

Source: Roberts (2004) Hence, the growing number of recorded complaints or counter-notifications by developing countries provides only a very crude indicator of the extent to which food safety and agricultural health measures impede their trade in high value agricultural and food products. These complaints probably represent the ‘tip of the iceberg’ as most concerns and disputes are raised bilaterally and the majority of negotiations are handled by technical organizations rather than country trade representatives. Some of the complaints have occurred in the context of expanding trade ties in which the previous non-effect (or at least nuanced effects) grows more serious with expanded levels of trade. There is little basis for associating the growing number of complaints with deliberate protectionism; the nature of many of the concerns seems to be more related to inadequate (scientific) information rather than discrimination. Further, the apparatus of formal complaints relates only to mandatory standards set by public agencies. As noted above, a growing array of standards are being set privately, either through consensus within particular industries or by the ‘gate keepers’ of the dominant supply chains. While many such standards are ostensibly voluntary, they are becoming the _de facto_ standards to gain or maintain access to particular buyers or market segments.

# **7. SOME ILLUSTRATIVE CASE STUDIES**

The available data on agricultural and food product rejections and disputes related to food safety and agricultural health measures provide certain insights yet an incomplete picture of how these are affecting the exports of developing countries. As a result, there is increased attention being given to case studies with analysis provided on the short or longer-term effects of specific standards or compliance problems on the trade of both particular countries and particular products. Earlier work of this nature has emphasized the potential disruptive impact which food safety and agricultural health measures have (or could have) on agricultural and food exports 25
# <u>Table 7 Panel A:  The Relationship between Trade Credit Demand and Trade Credit Supply</u>

The reported estimates are from logit in Columns (1), (3), (5), (7), OLS in Columns (2) and (6) and tobit regressions in Columns (4) and (8). AR_d is a dummy indicating the use of accounts receivable. AR_per is the percent of monthly sales sold on credit We transform AR_per in a continuous variable by using the inverse function of the logistic model. AR_discount is a dummy indicating if the firm offers a pre-payment discount on credit to its customers; and AR_days is the average number of days customers are allowed to use the credit before the firm imposes penalties. Columns (3-4) and (7-8) include only the subsample of firms that use accounts receivable. See Table 1 for variable definitions. All regressions include 12 sector dummies and 17 city dummies. Robust p-values are shown in parentheses, ***, **, * indicate significance at the 1%, 5%, and 10% level, respectively.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||_Unu_|_sed Line of _|_Credit (LC_Un_|_used)_||_Bank Finan_|_cing (D_Bank)_||
||_AR_d_|_AR_per _|_AR_discount_|_AR_days_|_AR_d_|_AR_per _|_AR_discount_|_AR_days_|
|L_Age|-0.13|-0.6859|-0.06|-2.09|-0.25|-0.67|0.14|-2.62|
||[0.11]|[0.00]***|[0.76]|[0.50]|[0.01]**|[0.02]**|[0.54]|[0.47]|
|L_emp|0.06|0.2743|-0.03|0.56|0.08|0.28|-0.11|1.82|
||[0.15]|[0.02]**|[0.76]|[0.73]|[0.11]|[0.05]**|[0.36]|[0.33]|
|D_Foreign|0.39|0.1974|0.03|-4.06|0.47|0.39|0.34|-3.69|
||[0.07]*|[0.77]|[0.93]|[0.54]|[0.05]*|[0.66]|[0.44]|[0.61]|
|D_State|-0.03|0.1048|-0.15|-5.78|-0.00|0.033|-0.17|-14.09|
||[0.85]|[0.78]|[0.63]|[0.28]|[0.98]|[0.94]|[0.69]|[0.03]**|
|D_Export|-0.33|0.2945|-0.87|9.87|-0.52|-0.42|-0.45|7.48|
||[0.10]*|[0.64]|[0.06]*|[0.18]|[0.02]**|[0.56]|[0.34]|[0.35]|
|LC_unused|0.16<br>[0.51]|0.68<br>[0.41]|-0.13<br>[0.77]|5.80<br>[0.50]|||||
|D_Bank|||||-0.08|-0.40|0.45|7.96|
||||||[0.55]|[0.32]|[0.11]|[0.09]*|
|AP_d|1.41<br>[0.00]***||||1.36<br>[0.00]***||||
|AP_per||0.09<br>[0.00]***||||0.08<br>[0.00]***|||
|AP_discount|||1.30<br>[0.00]***||||0.59<br>[0.17]||
|AP_days||||0.10<br>[0.02]**||||0.11<br>[0.03]**|
|Constant|-2.96<br>[0.00]***|-13.25<br>[0.00]***|-19.37<br>[0.00]***|64.48<br>[0.01]***|-3.16<br>[0.00]***|-13.18<br> [0.00]***|-0.41<br>[0.83]|73.42<br>[0.00]***|
|Observations|1,881|1,869|646|556|1299|1,291|477|409|
|PseudoR-squared|0.13|0.18|0.13|0.01|0.15|0.18|0.17|0.01|

43
different probabilities of falling into poverty with a specific level of income or consumption.<sup>13</sup> Therefore, this paper takes the approach proposed by Hertova, Lopez-Calva and Ortiz-Juarez (2010). The authors use cross-section data surveys to determine the vulnerable population; we adapt this methodology to find the amount of comparable income associated with a 10 percent risk of falling into poverty (as suggested in Lopez-Calva and Ortiz-Juarez (2014)).

Note that, in contrast with the regressions in Section 3, the set of control variables or observable characteristics is not limited to time invariant characteristics. **Table 4** shows the estimated vulnerability lines for four alternative specifications:

- Specification 1: only includes variables associated with time invariable characteristics of the head of the household (i.e., education level, year of birth and gender).

- Specification 2: adds a set of variables associated with labor market outcomes (i.e., employment status, sector of the economy, type of employment).

- Specification 3: adds characteristics of the household such as household size and access to basic services.

- Specification 4: adds to specification 3, additional controls for exposure to shocks such as losing a job.

The results suggest that the vulnerability line is sensitive to the model specification. For instance, models controlling for a larger set of variables tend to produce lower income estimates associated with a 10 percent risk of falling into poverty.<sup>14</sup> A plausible explanation is that models including more control variables do a better job capturing the elements associated with the probability of falling back into poverty, thus given that the model already controls for such elements the level of income associated with a particular risk of transition into poverty is lower.

# **4.3. Identifying the vulnerability line**

Even though there is no objective method to identify the vulnerability line, the final choice of the vulnerability line seeks to be informed by the results from previous exercises. From the sensitivity analysis, a convergence pattern of the vulnerability index seems to happen within the 30-32 percent across time. This

> 13 Recent advances with synthetic panel techniques such as that of Bourguignon and Moreno (2015) may be applied to address this issue. Other alternative approaches have been proposed that aim to construct some measure of income mobility based on averaging the error terms of the household consumption model in some way (see, e.g., Stampini et al. (2016) and Lucchetti (2017)), but we would like to caution against such approaches since these studies do not offer an underlying theory that supports doing so.

> 14 We find the same patterns as Lopez-Calva and Ortiz-Juarez (2014), each time the authors add control variables the vulnerability line become smaller. For example, for Chile the vulnerability line when they control just for education, sex and age of heads is $9.6 PPP 2005, and when they include locational effects, marital status and measures of changes the vulnerability line is $8.5 PPP 2005.

14
Note: n = 1024 female employees; the total number of female employees across enterprises is 698 in manufacturing and 326 in services. Includes firms with female owners, who are also counted as workers.

**Figure 7. Share of women employees: by enterprise size**

<!-- Start of picture text --> By size (in %)<br>100%<br>12.1 8.9 10.2<br>90% 20.6<br>80%<br>70%<br>60%<br>50%<br>87.9 91.1 89.8<br>40% 79.4<br>30%<br>20%<br>10%<br>0%<br>Micro Small Medium Large<br>Men Women<br><!-- End of picture text --> Note: n = 1024 female employees; includes firms with female owners, who are also counted as workers In terms of industry type, there were larger shares of women in industries which traditionally employ women (e.g. manufacturing of food products, apparel) or in services that involve a client interface (e.g. tutoring centers, beauty salons, tailoring units and photo studios) (table 1). Women were largely absent in firms that manufactured furniture; motor vehicles and trailers; radio, television and communication equipment and apparatus; and real estate activities, indicating clear occupational sex segregation. This is in line with global findings on the sectors where women usually work. The World Bank’s annual World Development Report for 2019 finds that women have a relatively higher presence in both clerical support and services and sales occupations (44 percent). Their lowest presence is recorded as plant and machine operators and assemblers, where women fill just 16 percent of such positions (World Bank 2019).

**Table 1. Share of women employees: by industry type**

|**Industry**|**% of total**<br>**women in**<br>**employment**|
|---|---|
|Office, accounting and computing machinery|2.0|
|Health and social work|2.8|
|Retail trade; except of motor vehicles and motorcycles; repair of personal and<br>household goods|3.8|
|Manufacture of chemicals and chemical products|4.4|
|Textiles|4.7|
|Wearing apparel: dressing and dyeing of fur|6.7|
|Other business activities|6.9|
|Rubber and plastic products|7.7|
|Wholesale trade and commission trade, except of motor vehicles and motorcycles|8.0|

11
different countries. The overall cost-of-living comparison is based on a comprehensive set of consumption items. Relative to Washington D.C., the cost of living is estimated to be 38.1% cheaper in Tonga, 10.6% cheaper in New Zealand, 4.8% cheaper in Ghana, 14.6% more expensive in Micronesia, and 27.7% more expensive in Papua New Guinea. The higher cost in Papua New Guinea and Micronesia reflects much more expensive communications, recreation, clothing, alcohol and tobacco, and transport costs in these countries.

The second column of Table 3 shows the gross gain using this adjustment. The biggest change is seen for New Zealanders, whose adjusted gain is $25,329 compared to $46,155 without cost-of-living adjustments. This reflects the fact that New Zealand is relatively cheaper to live in than the United States, while Australia and the United Kingdom, the other two main destinations for this group, are more expensive. The adjustments are smaller for the four developing countries, and in some cases high costs-of-living at home are offset by higher costsof-living in the main destination countries.

These calculations make no adjustment for the relative quality of living conditions in different locations. Xpatulator also provides a second comparison, which incorporates both costof-living differences and the hardship experienced by lower quality living conditions in different locations. We asked them to calculate the amount in local currency that would give the same quality of living as someone living on $US60,000 in Washington D.C. and use this to obtain a second measure of the gross gain. Column 3 gives these estimates, which are similar to those using only the cost-of-living adjustment.

Grogger and Hanson (2010) find that post-tax earnings are a stronger correlate of migration than pre-tax earnings. It is therefore instructive to also examine the size of the net income gains, despite the fact that we are unable to measure the government benefits that might accrue to migrants in different locations from paying different tax rates. Column 4 provides the net income increase at market exchange rates, and Column 5 adjusted for cost-of-living. The net income gains are less than the gross gains in absolute terms, but are still considerable. Even after adjusting for cost-of-living differences and taxes, we still estimate the annual gain in income to be $30,000-$45,000 for the four developing countries, and $21,000 for the New Zealanders. There is thus a large economic benefit to the high-skilled individuals from migrating, and the 12
of the RER is lower than that after appreciations.<sup>13</sup> We also conduct a more agnostic two-tailed test to check robustness.

We then test robustness of this result by using product-destination-year level data for Pakistan’s exports over the period 2003-2017. Product-destination-year level allows us to exploit additional sources of heterogeneity in exports to test the three hypotheses regarding the underlying causes of the asymmetric export response to changes in the RER. Also, because Pakistan’s export levels by product are substantially small in global markets, it is reasonable to assume exogeneity of world income and BRER (particularly considering the nominal exchange rate of the PKR to the dollar has been managed during the period).

Our baseline model using product-destination level data is specified as follows: l 3 3l l l 3<sup>l</sup> 3<sup>l</sup> ∗ (3) jjiitt iitt<sup>+ φφ</sup> iitt<sup>× dd</sup> iitt<sup>+ γγ</sup> iitt tt jj iitt ll = αα + ββ<sup>llll</sup> + θθ + μμ + εε where is export volumes of product to partner in year ; l l jjiitt iitt<sup>is the bilateral real</sup> exchange rate expressed (with ll l l jj ii)⁄ , which implies that a rise tt l l LL ii iitt<sup>= (NNl</sup> iitt<sup>× PP</sup> tt tt iitt means a decrease of Pakistani price-competitiveness)<sup>14</sup> ; PP is a dummy that takes value 1 if the iitt ∗ BRER movements correspond to a real depreciation and 0 otherwise; dd is the GDP of the trading iitt partner _i_ at time _t_ ; are time fixed-effects for ; are product fixed-effects at HS6 ll tt jj level; and is the error in the model. θθ tt= (1, ... , TT) μμ jjiitt εε

> 13 This is in turn based on the international empirical evidence and on Pakistan’s conventional wisdom.

> 14 Where<sup>is the bilateral nominal exchange rate between Pakistan and trading partner ii (expressed as foreign</sup> currency price of one unit of Pakistani rupee); is the Consumer Price Index of Pakistan, and is the Consumer iitt Price Indices of Pakistan’s trading partner NNl . LL ii tt tt PP PP

> ii 18

18
Finally, we endogenized the behavior of the governments in the two regions. When the Southern government cares only about long-run growth, its optimal strategy will always be to set research subsidies that ensure the Southern researchers will be working in the same sector as the Northern researchers. If the Northern government values both long-run growth and the quality of the environment, the only possible subgame perfect equilibrium in this setup is the one with subsidies ensuring that both regions work only on the growth of the clean sector. Importantly, this result rests on the assumption that both governments place less emphasis on the economic costs of the policy during the transition period. It also rests on the assumption that the North region can commit to its strategy of supporting clean technologies and it will not alter it under any circumstances.

The results obtained in this paper raise additional questions. In the model we assumed that there are only two regions and that one of them always invests in clean technologies. However, the strategic interaction between regions could be more complex. In reality the number of regions is large and their exposure to the effects of climate change is heterogeneous. One could explore under what conditions some of these regions could join a coalition that commits to R&D in clean technologies.

For the case when the R&D sector was larger in South than North, our analysis was focused on the asymptotic steady states with corner solutions regarding the allocation of scientists ( _s_ = 1 or _s_<sup>_f_</sup> = 0). Contrasting these two steady states allowed us to picture and discuss the main forces shaping the incentives of researchers and governments to choose clean R&D. Further research would be necessary to explore whether there are multiple steady states, perhaps some with the interior solutions for _s_ = 1 or _s_<sup>_f_</sup> = 0. The presence of the business-stealing effect (that disincentivizes research in crowded sectors) suggests that an interior solution could exist for some initial conditions, at least in the short run. Exploring such cases would shed more light on the role of lock-in in South and the transition dynamics of the system after North introduces a subsidy for clean R&D. Consideration of interior solutions is also necessary to confront the predictions of the model with the pattern observed in data on research expenditure in clean and dirty industries (Fried 2018).

Future research could also explore optimal policy mixes. The only instrument of climate policy we considered in the paper was a clean R&D subsidy. In the future one could consider 40
We begin by discussing the impacts of the intervention on project quality as measured through the project assessment. This analysis is done on data collected before the scorecard intervention, and so we do not consider the _β2Si_ and _β3Ti*Si_ terms in Equation (1).<sup>14</sup> We then present the results of the household survey conducted six months later, including the scorecard effects. To explore the potential mechanisms, we discuss impacts on monitoring of projects and reporting to government officials as well as other community activities as reported by respondents. We end by looking at important heterogeneities in treatment and local spillovers.

Note that we do not use multiple hypothesis correction for outcomes. This is because we have only two main outcomes: project score (measured during the assessment) and household assets (measured at the household endline). Both of these are indices of family of outcomes. We present analysis on the individual family components and test for mechanisms without correction as we consider these to be exploratory analyses. Note that the endline survey was powered to identify a minimum detectible effect size of 8% on our main household outcome and mechanisms. Due to the endline survey being very well powered and the high level of significance of estimations, most of the results from that survey would pass conservative correction procedures.

# **5.1 Initial impacts on project performance**

The impacts for the main outcomes from the project assessment survey are presented in Table 6. These include the overall score for each of the NUSAF2 projects in the sample (columns 1 and 2), which is created by multiplying the project quality score (columns 3 and 4) and quantity score (columns 5 and 6) together. We also look at whether the project could not be located (columns 7 and 8). Each of these indicators is from the first project assessment and is estimated at the community level. The indicators are standardized, as discussed previously.

Odd-numbered columns report results for all project types in the sample. We find a small positive and statistically significant impact on the overall score of the project of 0.135 standard deviation. This effect appears to be coming mostly from the quantity indicator (column 5) and is not driven by whether the project could not be located. The results suggest that the training led to

> 14 However, we estimate equation (1) for some outcomes measured before the scorecard intervention to provide additional balance tests.

20