rafmacalaba/datause-encoder-data huggingface dataset:
subsistence work to added market employed (compared to any of remaining in 3-month subsistence work, remaining in 3-month market work, or not working). We refer to this last outcome as “switch” for short.

Table 2 presents the results of our first model, testing H1, comparing reclassification for women to that of men. This essentially is a statistical test of the differences observed in Figure 8. The base probabilities for reclassification for men are less than a percentage point. The marginal effects for being female are consistent with H1; women’s employment/work rates increase more than men’s when adding enterprise-based detection methods to individual-level questions. Women’s employment rates increase by 0.9 percentage points more than men (base change 0.5 percentage points for men) for market employment in Tunisia and 1.6 percentage points more than men for market employment in Egypt (base change for men 0.6 percentage points). In Tunisia, women’s extended work rates increase by 1.9 percentage points more than men (men have a base probability of reclassification of 0.7 percentage points). Likewise, in Egypt, women’s extended work rates increase by 1.4 percentage points more than men (who have a base rate of 0.6 percentage points for reclassification). Being reclassified from subsistence work in the 3-month definition to market work in the definition adding enterprise questions is by far more common for women; only 0.2% of men in both Tunisia and Egypt are reclassified in this way, but women’s rate of reclassification is 0.6 percentage points higher in Tunisia and 1.3 percentage points higher in Egypt.

**Table 2. Marginal effects from logit models of changes in employment/work status by detection approach for individuals aged 15-64**

||**Tunisia -**<br>**Market**|**Tunisia -**<br>**Extended**|**Tunisia -**<br>**Switch**|**Egypt,**<br>**Arab**<br>**Rep. -**<br>**Market**|**Egypt,**<br>**Arab**<br>**Rep. -**<br>**Extended**|**Egypt,**<br>**Arab**<br>**Rep. -**<br>**Switch**|
|---|---|---|---|---|---|---|
|**Female (male**<br>**omit.)**|||||||
|Female|0.009***|0.019***|0.006***|0.016***|0.014***|0.013***|
||(0.002)|(0.003)|(0.001)|(0.002)|(0.002)|(0.001)|
|**Base (male)**<br>**probability**|0.005***|0.007***|0.002*|0.006***|0.006***|0.002**|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|**N (Obs.)**|9468|9334|9330|35404|35406|35404|

Source: Authors’ calculations based on TLMPS 2014, ELMPS 2018 Notes: *p<0.05; **p<0.01; ***p<0.001. Standard errors in parentheses. “Market” denotes an outcome of being reclassified from not 3-month market employed to added market employed (compared to either remaining non-employed or already being detected as 3-month market employed). “Extended” denotes an outcome of being reclassified from not 3-month extended work to added extended work (compared to either remaining non-working or already being detected as 3-month extended working). “Switch” refers to an outcome of being reclassified from 3-month subsistence work to added market employed (compared to any of remaining in 3-month subsistence work, remaining in 3-month market work, or not working).

18
the shortcomings also limit the operational effectiveness the risk-based system itself. In particular, the inability to assemble credible data on all the lab samples can make it difficult for the FVA to improve its sampling plans in the future.

We therefore offer some suggestions that we think are necessary to improve the ability of the FVA to monitor its own progress. As we understand it, the FVA is one of the more advanced technical agencies in FYR Macedonia (in terms of its capabilities to oversee import shipments). It is therefore likely that these suggestions apply to other agencies in FYR Macedonia. It is also likely that these suggestions are useful to technical agencies in other countries in the Western Balkans and elsewhere. We limit ourselves to suggestions for improved data management, although it is likely that there are operational suggestions that would improve the current system.<sup>57</sup>

1. FVA should itself retain information on all test outcomes in an electronic format, rather than relying on the laboratories to retain this information and to provide it when needed.  Negative test outcomes should be retained as well as positive outcomes.  The information should be stored in a way that allows precise matching of both positive and negative test results from each sample to the identification number in EXIM of the shipment from which the sample was taken.  Because more than one sample may be taken from a shipment, the system used for matching must allow matching on a many-to-one basis.  This recommendation can be implemented by the FVA without the cooperation of any external agencies (although additional funding and technical support may be necessary).  Improved data on test results is not only useful for evaluation, it is a resource for improved targeting of sampling in the future.  An electronic format would allow updating the sampling plan in real time.

2. A more ambitious data management reform would seek to integrate information from laboratory test results with data recorded by other agencies, especially data from the customs agency. Risk analysis on a shipment-by-shipment basis is preferable to an annually updated sampling plan. It could be that the capabilities of the customs agency could be leveraged to improve the sampling and testing regime. Risk analysis that also incorporates shipment-level data drawn from the customs declaration would allow the improvement of targeting.

3. The FVA should update the sampling plan more frequently. New information - such as reports of outbreaks coming through RASSF or evidence of failed inspections in other agencies - should be incorporated in the sampling plan more frequently than on an annual basis. While emerging risks are currently communicated to border agents through other channels, it would be best to have the

> 57 We leave these to experts in the implementation of trade facilitation reforms.  It is our experience that trade facilitation reforms are context specific, with potentially many interlocking pieces (legislation, staff capabilities, IT capabilities, etc.) that make it difficult to paint with too broad a brush.

41
# **Figure 31 Patents Issued by the US to Residents of Foreign Countries (per million population)**

<!-- Start of picture text --> 1,4<br>Latvia<br>1,2<br>Denmark<br>Estonia<br>1,0<br>Finland<br>Hong Kong<br>0,8<br>Hungary<br>Ireland<br>0,6<br>Israel<br>Korea<br>0,4<br>Lithuania<br>Singapore<br>0,2<br>0,0<br>1996 1997 1998 1999 2000<br><!-- End of picture text --> Source: U.S. Patent Office, 2003, http://www.uspto.gov/ The following recommendations are designed to enhance the technology commercialization system in Latvia:

**RECOMMENDATION 5.  Prepare a clear government policy statement regarding the ownership of intellectual property (IP) funded in whole or in part with Latvian Government budget resources. Establish a fund to help defray the cost of patenting Latvian inventions in the EU, the US and Asia.  Some of the cost could be recouped by a small royalty payment to the patent fund when those foreign patents generate royalty or licensing income.**

The Latvian patent law should be revised to state unambiguously that IP funded in whole or in part with budget resources will belong to the institute, university or enterprise where it was created provided that the recipient of the IP makes a good faith effort to commercialize it. By making this change, GOL would ensure that its patent law is in line with prevailing patterns in the EU and the US and even now in Russia. Annex 2 contains a detailed discussion of the US approach to this issue.<sup>47</sup> Although Latvian patent law does not prohibit institute, enterprise or university ownership of this IP, it would be a good advertisement to make an explicit statement that the government will not attempt to claim ownership or put other impediments in the way of technology commercialization.

> 47  For a review of OECD policies related to patenting and licensing at public research organizations see, <u>Turning Science Into Business: Patenting and Licensing at Public Research Organizations, OECD,</u> 2003, especially Chapter 1.  In virtually every country, the Government has relinquished ownership of the intellectual property generated in public research organizations. Also see, <u>OECD Science, Technology and Industry Outlook, OECD, 2002, especially, Chapter 6.</u>

56
19 _Table 10. Determinants of Poverty Rates_

|Period|constant<br>term|<br>income<br>term|distrib.<br>term|<br>RSE<br>(F)||
|---|---|---|---|---|---|
|_Poland_||||||
|1978-87|391.53|-52.23|9.105|0.777|4.44|
||(0.00)|(0.00)|(0.04)|(51.5)||
|_Ytugoslavia_||||||
|1978-87|293.39|-42.98|46.405|0.890|4.05|
||(0.016)|(0.0)|(0.014)|(45.6)||

_Notes:_ Equations pool together data for different social groups for the period 1978-87. Equations are of the semi-log form: POOR = constant + B1 log (income) + B2 log (distribution), where POOR = percentage of the poor for a particular social group and year (workers, farmers and pensloners for Poland; non-agricultural and agricultural households for Yugoslavia), while Income and distribution terms are corresponding values of the average per capita Income and the Gini coefficient for that group and that year. Data in brackets below regression coefficients show the level of significance at wlhich the null hypothesis Is rejected. Number of observations Is 30 for Poland and 12 for Yugoslavia.

----------------------------------------

Elasticity of poverty rates with respect to Income Is about 0.52 for Poland, and 0.43 for Yugoslavia. This means that In order to rec wce poverty rate by one percentage point real per capita inco. needs to increase, on average and without any change In distribution, by about 2 percent in Poland and 2.5 percent In Yugoslavia. The Implication Is that more people In Poland than In Yugoslavia are "bunched" slightly below the poverty line.

Not unexpectedly, In light of fairly egalitarian distribution of Income In Poland, change In inequality has less of an effect on poverty In Poland than in Yugoslavia: for poverty rates to go down by 1 point, Inequality of distribution (measured by the Gini coefficient) must decrease by 2 percent In Yugoslavia and by more than 10 percent in Poland.

The trade-off bet.ween growth and redistribution as the two alternative ways to reduce poverty, can be also considered in a slightly different way. The change In poverty rates between 1978 and 1987 can be decomposed Into its three components: changed overall level of income, changed pattern of distribution, and changed demographic composition of the population. The last
tobacco taxes directly affect household income because the share of household budgets allocated to tobacco purchases rises as the tax increases. Second, household medical expenses could decrease as a result of reduced tobacco consumption. Third, households could also experience a positive income change because of additional years of labor recovered through the extension of life expectancy. The aggregate effect of a tax policy is estimated as follows: Income effect = change in tobacco expenditure (A) + lower medical expenses (B) + higher income (C) (1) For further details on the methodology, see annex A or refer to Fuchs and Meneses (2017a).

# **4. Data and descriptive statistics**

## **Tobacco prices**

Data on the household consumption of and expenditure on tobacco products in Indonesia come from the 2015–16 Indonesia National Socioeconomic Surveys. The surveys ask households about daily expenditure and consumption for different tobacco products. These questions allow an estimation of the average price paid by households for tobacco products and of average price elasticities among expenditure groups. However, the survey is not based on a good representative sample of higher-income groups because nonresponse rates among these groups are high.

Table 1 shows significant price variation across deciles and minimal variation by cigarette type. For instance, the poorest decile in 2016 paid an average Rp 495 for clove cigarettes, whereas the richest decile spent Rp 909. For white cigarettes, the variation across deciles is within a similar range. Using Euromonitor data, one can compare the estimated prices from the expenditure survey.<sup>6</sup> The most expensive 20-pack of cigarettes in 2017 cost Rp 24,300, while the cheapest cost Rp 17,200 (figure 1). Moreover, while nominal cigarette prices increased from 2011 to 2017, the real prices remained relatively unchanged (figure 2).

**Table 1 – Mean and Median Price per Stick Paid by Households, by Cigarette Type and Decile, 2016**

|_Price_|_Type_|_1_|_2_|_3_|_4_|_De_<br>_5_|_cile_<br>_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Mean|Clove cigarettes<br>White cigarettes|495<br>523|530<br>559|557<br>571|587<br>615|630<br>633|691<br>714|741<br>746|781<br>792|831<br>812|909<br>884|
||Clove cigarettes|469|511|539|574|618|671|730|733|795|863|
|Median|White cigarettes|479|535|556|597|633|689|730|730|771|821|

_Source:_ Estimates based on Indonesia National Socioeconomic Survey 2016. _Note:_ Prices are in 2010 real rupiah. Deciles are constructed using per capita household expenditure.

> 6 See Euromonitor International (database), Euromonitor International, London, http://www.euromonitor.com/.

6
**Table 10: Granger Causality Test of Five Asian Countries**

||||Indonesia|Korea|Malaysia|Philippines|Thailand|Total|
|---|---|---|---|---|---|---|---|---|
|1|LIQ?|SOL|||||||
|2|LIQ?|BOP|**|||||1 (1)|
|3|LIQ?|CUR|||||||
|4|LIQ?|DEB|||||||
|5|LIQ?|GRO||*||||1 (0)|
|6|LIQ?|FIN|*|||||1 (0)|
|7|SOL?|LIQ||*|*|||2 (0)|
|8|SOL?|BOP|||||||
|9|SOL?|CUR|||||||
|10|SOL?|DEB|**|||||1 (1)|
|11|SOL?|GRO|||||||
|12|SOL?|FIN|||||||
|13|BOP?|LIQ|**|||*||2 (1)|
|14|BOP?|SOL|||||||
|15|BOP?|CUR|*||||*|2 (0)|
|16|BOP?|DEB|||||||
|17|BOP?|GRO|||**|||1 (1)|
|18|BOP?|FIN|||||||
|19|CUR?|LIQ|||||||
|20|CUR?|SOL||**||**||2 (2)|
|21|CUR?|BOP|**|*||||2 (1)|
|22|CUR?|DEB|*||**|||2 (1)|
|23|CUR?|GRO||||**|*|2 (1)|
|24|CUR?|FIN||**||||1 (1)|
|25|DEB?|LIQ|||||||
|26|DEB?|SOL||*||||1 (0)|
|27|DEB?|BOP|||||||
|28|DEB?|CUR|||||||
|29|DEB?|GRO|||||||
|30|DEB?|FIN|||||*|1 (0)|
|31|GRO?|LIQ|||||||
|32|GRO?|SOL|||||||
|33|GRO?|BOP|||||||
|34|GRO?|CUR|||||||
|35|GRO?|DEB|||||||
|36|GRO?|FIN|||||**|1 (1)|
|37|FIN?|LIQ|||||||
|38|FIN?|SOL|||||||
|39|FIN?|BOP|||||||
|40|FIN?|CUR||*||||1 (0)|
|41|FIN?|DEB|||||||
|42|FIN?|GRO|**|||||1 (1)|
||Total||8(5)|7(2)|3(2)|3(2)|4(1)|25(12)|

_Note: (*) and (**) indicate statistical significance at a level of 5 percent and 1 percent, LIQ-Liquidity type banking crises, SOL-Solvency type banking crises, BOP-Balance of payments crises, CUR-Currency crises, DEB-Debt crises, GRO-Growth rate crises, FIN-Financial crises Figures show the number of Granger causality with 5 percent confidence level, and those in brackets show significant Granger causality with 1 percent confidence level. Source: Author_

# **4. Conclusion**

This paper has two objectives. The first is to identify crisis periods and episodes consistently. The second is to analyze the relationships between crisis types. The motivation is that despite the volume of 22
Unlike GDP and poverty, the measures of labor market outcomes are expressed in relation to their pre-pandemic values. In this sense, the measures used for labor market outcomes may include some variation caused by pre-pandemic trends. Estimation results based on these measures may therefore have to be interpreted with caution.

We use several controls in estimating our empirical model. As we show in the previous section, the pre-pandemic country’s per capita GDP affects the size of the stimulus budget and the relative allocation of funds to job protection and income protection policy measures. Pre-pandemic GDP can also affect the speed of recovery and other outcomes of interest. We control for the share of services in pre-pandemic GDP, and the size of the informal sector as these sectors suffered disproportionately from the pandemic (OECD 2020, <u>World Bank 2020b). In addition, the degree</u> of informality may affect the effectiveness of job protection and income protection policies in mitigating the impact of the pandemic. The descriptive statistics of the variables used in our empirical analysis are shown in <u>Appendix Table A.2.</u> In our main estimations, we regress the four economic outcomes on variables representing expenditures on the two types of social protection policies and country-level controls: D 1I I I I I I I I 2 I I I I I 3 (1) 1 2 3I I I I I cc cc cc cc = αα+ ββ IIDDII + ββ JJIIJ IIDDII + ββ NNI NNIINNIIJJNNI JJI DDII where _DYcc_ is one of the four indicators described above for country + ππ LLIILLIIDDIINNIIcc + ππ NNI SSI IIJJIIDDIIcc + ππ IIcc _c._ + εε _IncomeProtectionGDPc_ cc _c_ is where _DYcc c. IncomeProtectionGDPc_ is the expenditure on income protection measures, _JobProtectionGDPc_ is expenditure on job protection policies, and _NonSPResponseGDPc_ is the stimulus budget spent on measures other than social protection in country _c_ , all expressed in percentage of 2019 GDP. _LogGDPpcc_ is the log of 2019 per capita GDP in PPP dollars. _ServicesGDPc_ represents the percentage of the services sector in country _c_ ’s GDP in 2019. _Informalityc_ is the share of informal output in GDP in the last available pre-pandemic year, expressed in percentage points.

# **6. Main results**

<u>Table 3</u> summarizes the main results of estimating equation (1). Expenditure on income protection measures appears to have no significant correlation with any of the four economic outcomes. Expenditure on job protection measures seems to be associated with a more robust short-term recovery, an increase in employment, a decrease in the inactivity rate, and a decrease in the poverty 13
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure A6: Informal Share of Industry Employment - 1991, 2000, 2010 <!-- Start of picture text --> 1<br>1991<br>0.9<br>2000<br>0.8  2010<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Informal  Share  of  Industry  Employment<br>Rubber  Apparel  Other  Manuf.  Pharma.,  Perfumes,  Detergents  PlasBcs  Auto,  Transport,  Vehicles  Nonmetallic  Mineral  Manuf  Electric,  Electronic  Equip.  Food  Processing  Machinery,  Equipment  Petroleum  Refining  TexBles  Chemicals  Wood,  Furniture,  Peat  Paper,  Publishing,  PrinBng  Metals  Footwear,  Leather  Mineral  Mining  Petroleum,  Gas,  Coal  Agriculture  Nontraded<br><!-- End of picture text --> Decennial Census data. Industries sorted from most negative to most positive tariff change over 1990-1995 (see Figure 1). Nontradable sector faces no tariff change, and is placed at the far right.

83
<!-- Start of picture text --> Figure 8. Impulse Responses of GDP to Various Shocks: Countries with no fiscal rules<br>reer tby rrate_us<br>.0015 .002 .002<br>.001 .001 0<br>.0005 0<br>−.002<br>0 −.001<br>−.0005 −.002 −.004<br>0 5 10<br>gdp govexp<br>.015 .008<br>.006<br>.01<br>.004<br>.005<br>.002<br>0 0<br>rrate gdp_us<br>.002 .004<br>.001 .002<br>0 0<br>−.001 −.002<br>0 5 10 0 5 10<br>step<br>95% CI Orthogonalized IRF<br><!-- End of picture text --> We confirm our findings from the impulse responses using the forecast error variance decomposition computed from the panel VARs for the two groups. Table **6** reports the results. It shows the share of GDP variance in the two groups of countries accounted for by various shocks. We see that shocks to GDP and government expenditures are responsible for a larger share of GDP volatility in fiscal rule(s) non-adopters. On the other hand, shocks to the world demand conditions (proxied by the US GDP) and trade balance play a greater role for GDP volatility in fiscal adopters. These results are consistent with the findings from individual country VARs reported in Table **5** .

**Table 6. Forecast error variance decomposition from panel VARs by fiscal rule(s) presence**

||**rrate_us**|**(log) gdp_us**|**(log) govexp**|**tby**|**rrate**|**(log) gdp**|**(log) reer**|
|---|---|---|---|---|---|---|---|
|**fiscal adopters**|0.017|0.054|0.126|0.054|0.022|0.718|0.010|
|**fiscal non-adopters**|0.042|0.013|0.196|0.007|0.002|0.736|0.002|

# **4.6. Robustness: Adding terms of trade**

Terms of trade shocks have been highlighted as an important source of business cycle fluctuations, especially in developing countries. The existing literature, however, lacks consensus on exactly how important these shocks are for GDP volatility. In this section we revisit the evidence by extending our benchmark SVAR specification to include terms of trade. As discussed in Section 3, our modified specification for the SVAR now includes a modified external block as � � � � � ����, �� , �� �′, where ���� is the terms of trade, �� and �� are world real interest rate and external 24
# **Industrial Districts**

# **International Hubs**

|--in diverse places|--in the world's major cities|
|---|---|
|--small firms|--vertically integrated companies|
|--production networks|--market ties|
|--spatial co-location|--spatial separation|
|--localization economies|--urbanization economies|
|--spatial proximity to production|--time proximity to demand|
|--manufacturing|--services|

Cluster theory finds innovative firms aggregating in a diverse range of places: from small towns, like Modena in the Third Italy, [<sup>4</sup> ] to clusters in major city-regions, like Silicon Valley in the San Francisco Bay Area. International hub theory emphasizes the world's major cities, like London and New York. Cluster theory roots innovation in small firms networked in local industrial districts. International hub theory stresses large, vertically integrated corporations interacting with clients and customers via international markets. Cluster theory stresses the flexibility and transaction cost advantages of spatial co-location among firms performing specialized functions in a production system. International hub theory stresses the advantage of time proximity to world demand enabled by the spatial separation of functions in a transnational division of labor. Cluster theorists emphasize localization economies. International hub theorists stress urbanization economies. Cluster theorists focus primarily on innovative manufacturing firms. International hub theorists emphasize traded services.

Just as there is no one generally accepted model of urban innovation, empirical research suggests there is no one set of factors distinguishing highly innovative cities in the OECD countries. Each of the two models stresses a subset of factors found to be stimulants to innovation in particular places, but neither one covers the entire spectrum. The five city European study, by James Simmie and his colleagues (2001), is perhaps the most comprehensive recent attempt to specify place based sources of innovation. Part of an ESRC Research Program investigating urban and regional efforts to cultivate high technology industries, the authors compare and contrast five highly innovative European cities—Stuttgart, Milan, Amsterdam, 4