rafmacalaba/datause-encoder-data huggingface dataset:
only if the borrowing is used to create assets. The British government has adopted a version of the golden rule: it has said that “over the economic cycle” it will borrow only to invest and only so long as public‐sector net debt remains below 40 percent of GDP.<sup>26</sup> Accrual accounting suits the golden rule, since another way of stating the rule is to say that governments must not run an income‐statement deficit (roughly, must not reduce their net worth). Because the income‐statement surplus excludes expenditure on investment, the golden rule encourages investment. Because the income‐statement surplus includes depreciation, however, adopting the golden rule is not the same as simply exempting investment from the fiscal targets—it amounts to exempting _net_ investment from them. Observance of the golden rule over a long period of time would eventually result in a public debt stock no larger than the public capital stock, so that to a first approximation the outstanding debt would be fully backed by public assets.

However, the limitations of typical accounting mean that the golden rule doesn’t ensure solvency, or even expected solvency. The reason is that the assets may not yield an expected return high enough to cover the interest on the debt that financed their acquisition. Furthermore, by treating differently current and capital spending, the golden rule offers an incentive for opportunistic misclassification of the expenditures.

An alternative that, in principle, avoids these problems is the permanent‐balance rule. Roughly stated, it requires governments to set the tax rate at a constant fraction of output that over the long run pays for the government’s present and future expenditure.<sup>27</sup> Named by analogy with Milton Friedman’s permanent‐ income hypothesis, the rule allows governments to borrow when revenue is temporarily low or when present investment opportunities are greater than future investment opportunities. Long‐term fiscal projections suit the permanent‐ balance rule, since such projections are required to determine the required minimum tax rate. Implementing the permanent‐balance rule successfully would

> 26 H.M. Treasury (2004, page 4).

> 27 Buiter and Graafe (2004).

24
Waiver,” Geneva: ITC.

Echandi, R. and P. Sauvé. 2019. “Investment Facilitation and Mode 3 Trade in Services: Are Current Multilateral Disciplines Addressing the Key Issues?,” World Bank, mimeo.

- Gervais, D. and J. B. Jensen. 2019. “The Tradability of Services: Geographic Concentration and Trade Costs,” _Journal of International Economics_ 118(2): 331-350.

- Government of India, 2016a, “Concept Note for an Initiative on Trade facilitation in Services,” WTO Working Party on Domestic Regulation, S/WPDR/W/55.

- Government of India, 2016b, “Communication from India: Possible Elements of a Trade Facilitation in Services Agreement,” WTO Working Party on Domestic Regulation, S/WPDR/W/57.

- Hoekman, B. 2016. “The Bali Trade Facilitation Agreement and rulemaking in the WTO: milestone, mistake or mirage?” In J. Bhagwati, P. Krishna and A. Panagariya (eds.), _The World Trade System: Trends and Challenges_ . Cambridge, MA: MIT Press.

- Hoekman, B. and P. Mavroidis, 2015, “Embracing Diversity: Plurilateral Agreements and the Trading System,” _World Trade Review_ , 14(1): 101-16.

- Hoekman, B. and P. Mavroidis, 2016. “A Technical Barriers to Trade Agreement for Services?” in Pierre Sauvé and Martin Roy (eds.), _Research Handbook on Trade in Services_ . Edward Elgar.

- Hoekman, B. and P. Mavroidis, 2017. ““MFN Clubs and Scheduling Additional Commitments in the GATT: Learning from the GATS,” _European Journal of International Law,_ 28(2): 387-407.

- Hoekman, B. and B. Shepherd. 2019. “Services Trade Policies and Economic Integration: New Evidence for Developing Countries,” EUI Working Paper RSCAS 2019/57.

- Jafari, Y. and D. Tarr, 2017, “Estimates of Ad Valorem Equivalents of Barriers against Foreign Suppliers of Services in Eleven Services Sectors and 103 Countries,” _The World Economy,_ <mark>40(3): 544-73.</mark>

- Kox, H. and A. Lejour, 2005. "Regulatory heterogeneity as obstacle for international services trade," CPB Discussion Paper 49, CPB Netherlands Bureau for Economic Policy Analysis.

- Miroudot, S. and B. Shepherd. 2016. “Trade Costs and Global Value Chains in Services,” in M. Roy and P. Sauvé (eds.), _Research Handbook on Trade in Services._ Edward Elgar.

- <mark>Moïsé, E. and S</mark> . Sorescu. 2013. "Trade Facilitation Indicators: The Potential Impact of Trade Facilitation on Developing Countries' Trade," Trade Policy Papers 144, OECD Publishing, Paris.

- Mustilli, F. and J. Pelkmans, 2013, _Access Barriers to Services Markets Mapping, tracing, understanding and measuring_ , Brussels: CEPS Special Report 77.

- Nordås, H. 2016, “Services Trade Restrictiveness Index (STRI): The Trade Effect of Regulatory Differences”, Trade Policy Papers 189, OECD Publishing, Paris.

- OECD, 2017, _International Regulatory Cooperation and Trade: Understanding the Trade Costs of Regulatory Divergence and the Remedies_ . Paris: OECD.

- Weissinger, R. 2019. “Methods to Determine Needs for Service Standards,” University of Geneva, mimeo.

- WTO. 2019. _World Trade Report 2019: The future of services trade_ . Geneva: WTO.

24
# **Table 1: Impacts of mental health on labor outcomes**

|Dependent variable:|Employ<br>|ment status<br>|Weekly l<br>($1,0<br>|abor income<br>00 AUD)<br>|
|---|---|---|---|---|
||Logit-FE|IV-Logit-FE|FE|IV-FE|
||(1)|(2)|(3)|(4)|
|Standardized mental health<br>scores|-0.046***|-0.064***|-0.005***|-0.192***|
||(0.009)|(0.009)|(0.001)|(0.055)|
|_First stage of 2SLS (dependen_|_t variable is stan_|_dardized mental healt_|_h scores)_||
|Trauma experience*Year<br>after arrival||-0.039***<br>(0.009)||-0.058***<br>(0.016)|
|Kleibergen-Paap F stat.||17.692||19.635|
|AR 95-CIs||[-0.111, -0.031]||[-0.400, -0.124]|
|Adjusted_p_-value|0.000|0.000|0.001|0.011|
|Dep. Mean|0.455|0.455|0.716|0.716|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|3,687|3,687|3,687|3,687|
|Number of individuals|1,609|1,609|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results in Columns (1) and (2) are presented as marginal effects. Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6- item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F- test from Stock and Yogo (2005) is 16.38. Full estimation results are reported in Table A1 (Appendix A). The adjusted _p_ -value describes a conservative (upper bound) probability that rejecting the null hypothesis will result in one or more Type I errors, as implemented in Newson (2010).

27
Unlike the preceding measures, _TD_ directly reflects the program’s coverage of the target group. In fact it can be readily shown that _TD_ = ( _CR_ − _P_ ) /(1 − _H_ ) where _P_ ≡ _N_ ( _D_ = /)1 _N_ is the * overall participation rate. (The corresponding formula for _TD_<sup>*</sup> is _TD_ = ( _CGH_ − )1 _P_ /(1 − _H_ ) .) On implementing these measures on the UHSS data, we find that 7.7% of the population had a net income (observed income _minus_ DB receipts) below the relevant DB line (Table 1). So the program’s total participation is equivalent to about half of the eligible population, defined as those with income below the DB line for the relevant municipality. However, there is some leakage to ineligible households, as can be seen in Table 1.<sup>19</sup> About 40% of DB recipients are ineligible according to these data (0.43=1.69/3.91), giving _TR_ =0.57. Almost three-quarters of those who are eligible are not being covered by the program (0.71=5.48/7.71), i.e., _CR_ =0.29.

We find that _SHARE_ =64%, _CI_ =0.78 and _CGH_ =8.3. This is excellent targeting performance by international standards. For example, Coady et al., (2004a,b) provide estimates of _CGH_ for 122 programs across 48 developing countries. Argentina’s _Trabajar_ program has a _CGH_ = 4.0, making it the best performer by this measure amongst all programs surveyed by Coady et al.<sup>20</sup> The median _CGH_ across the 122 programs is 1.25. By this measure, DB is a clear outlier in targeting performance internationally.

Turning to our fourth measure of targeting performance we find that while 29% of the poor receive DB, this is only true of about 2% of the non-poor; thus we find that _TD_ =0.27. The mean DB payment across all those with _Y_ < _Z_ is 87.61 Yuan per person per year, while the

> 19 These are sample means.  If one weights by city population then the proportion receiving DB rises to 4.69% of which 2.64% had incomes below the DB line.  The estimated proportion below the DB line falls slightly, to 7.64%, of which 5.00% were not receiving DB.

> 20 _Trabajar_ is a combination of a workfare program and social fund, whereby participants are offered low-wage work to do things of value to poor communities; see Jalan and Ravallion (2003). Coady et al., calculate _CGH_ from _Trabajar_ from Jalan and Ravallion (2003) who estimate that 80% of _Trabajar_ participants come form the poorest 20% of the Argentine population ranked by income net of _Trabajar_ receipts (which are roughly constant across recipients).  The corresponding _CGH_ for the poorest decile is much higher, at about 6.0, though still less that for DB.

18
# **Table 6: Heterogeneity analysis by level of initial assets**

|**Panel A: Household**|**Income, Assets, **<br>|**and Consumption**<br>|||
|---|---|---|---|---|
||Round 1 x<br>RCBP|Round 1 x RCBP x<br>High initialassets|Round 2 x<br>RCBP|Round 2 x RCBP x<br>High initialassets|
|Food consumption from market purchases (birr)|-1.61|12.5|-18.5|8.58|
||(9.70)|(10.8)|(13.0)|(15.9)|
|Total value of assets (birr)|-56.9|382*|13.1|692*|
||(108)|(202)|(188)|(350)|
|Non-food consumption (birr)|3.74|307|-328|1,233**|
||(255)|(373)|(364)|(596)|
|Nb of household members who contribute to income|.121|-.268*|.238|.243|
||(.151)|(.153)|(.183)|(.226)|
|Nb of household members who work on household farm|-.171|-.431**|.099|.015|
||(.113)|(.193)|(.105)|(.141)|
|Total plot area (ha)|.164|-.604|.366**|.056|
||(.138)|(.524)|(.147)|(.223)|
|Livestock ownership (=1)|-.019|.007|-.027|.013|
||(.027)|(.029)|(.029)|(.036)|
|Total value of livestock owned (birr)|553|449|6,277**|-4,106|
||(1,417)|(1,354)|(2,565)|(8,327)|
|Total agricultural sales (birr)|295|1,129|670|637|
||(425)|(896)|(629)|(807)|
|**Panel B: Agricultura**|**l outcomes and h**<br>|**igh-value farming**<br>|||
||Round 1 x|Round 1 x RCBP x|Round 2 x|Round 2 x RCBP x|
||RCBP|High initialassets|RCBP|High initialassets|
|Productivity (harvest value/ha)|-253|-204|378|-740|
||(459)|(611)|(777)|(1,133)|
|Tried new agricultural activities in the past year (=1, head)|.084|-.051|-.006|-.002|
||(.056)|(.068)|(.055)|(.065)|
|Intercropping (=1)|-.129***|.085|-.074*|.030|
||(.045)|(.052)|(.041)|(.052)|
|Irrigation (=1)|<br>-.017|<br>.095**|<br>-.010|<br>.112***|
||(.037)|(.047)|(.022)|(.041)|
|Crops with improved seeds (number of crops)|-.106|.361**|-.026|-.070|
||(.092)|(.174)|(.101)|(.153)|

22
thus appears that mechanization did not contribute to any major increase in output or technological progress in agriculture. Resources that were directed towards mechanization may have been better spent in maintaining the resource base. Labor was displaced by mechanization, without a rise in the productivity of any other inputs. Pakistan's cheap tractors policy therefore cannot be judged a success. The continuation of past policies in this area will supply exceberate these problems.

# Ititutions

From a sustainability perspective, the most important institutions are those which focus on the knowledge of farmers research and extension. Crop management research emphasizes increases in productivity through research on such issues as timing and method of application of input rather than type of input, and land preparation and harvesting. Resource management research focuses on preservation of the natural resource base. For most crops in Pakistan, it is difficult to find good examples of Crop and Research Management Research which have been translated to farmers' fields. Much evidence indicates that key inputs such as water and fertilizer are inefficiently used.

Existing research has failed to increase input efficiency. Outcomes from well-controlled experiments need to be better tailored to farmer conditions. Issuing technical packages to large, heterogeneous groups of farmers is not enough and they have to be given the means to adapt these packages to their own circumstances. Crop and resource management research lacks a systems perspective. Specialized research tends to ignore interactions between different crops and different agronomic issues. Key long-term issues such as necessary natural resource investments are generally ignored.

Extension: Most studies indicate that the variables that consistently explain farmer inefficiency are farmers' knowledge and skills. As emphasis switches from input intensification to input efficiency, improvements in information and skills play a bigger role in increasing productivity. The information burden on farmers will only increase as agriculture becomes more commercialized and sustainability issues come to the fore. Extension thus has a central role in improving productivity growth, and also in making the distribution of benefits from knowledge more equitable. Small farmers may find it more expensive to acquire knowledge, and so public extension has a role in equalizing access to new methods.

Extension programs take most of the current agricultural budget of the government but their impact is negligible. An elaborate extension system is already in place for crops, but many farmers question its usefulness. There are now over 5,000 village extension workers (EWs) but they often lack adequate training, which makes their task of improving the management skills of farmers very difficult. Extension workers with higher education and communication skills are often likely to be assigned administrative responsibilities. The system is characterized by a oneway transfer of technology to a few inadequately selected farmers. Women and small farmers 25
of physical capital was excluded from the analytical model for simplicity but it is a crucial determinant of growth. As a proxy for that, I use log of aggregate investment per employee.

Finally, I control for the share of foreign ownership in the firm. This variable has been analyzed in many studies such as Criscuolo, Haskel, and Slaughter (2005) and Almeida and Fernandes (2008). Foreign ownership can facilitate the transfer of better technology to the firm which reduces the cost of R&D and promotes growth. For the ownership structure, I set a dummy variable equal to one for firms with more than 10% of foreign ownership. This level is used by statistical agencies in many countries (e.g., the U.S. Bureau of Economic Analysis) and it is the amount defined in IMF’s Balance of Payment Manual (1993). Correlation matrix for all these variables is given in Table 9.

Table 10 shows the main estimation results. The first three columns show the growth rates of employment, sales, and productivity. The fourth column shows the log productivity and the fifth to ninth columns show the measures of technological innovation: product innovation, process innovation, use of foreign licenses, and use of quality certificate in respective order. The coefficients in the probit regressions show the marginal effects at the mean values and all standard errors are clustered at industry, country, and year level.

The estimation results show that firms with some level of foreign exposure perform better than non-traders. They grow faster; they are more productive and more innovative. The results are in accordance with the results from the descriptive regression analysis. Two-way traders are the best performers. They are followed by only exporters who are followed by only importers in almost all measures of interest. The coefficients identifying foreign exposure are highly significant despite of the large set of variables that control firm characteristics. For the employment growth, two-way traders grow 4% faster than the non-traders and only exporters grow 2% faster. The growth premium is slightly less for the only importers (1.5%). The significance of the results and the persistent ranking according to trade orientation in all growth rates and indicators of technology adoption leads to two conclusions. There exists a positive and significant relation between trade orientation of firms and their evolution and there is complementarity between importing and exporting in generating this heterogeneity in evolution.

Results in Table 10 only show that traders grow significantly faster than non-traders and they innovate more. In Table 11, I look at whether traders significantly differ among themselves in evolution. For each regression performed in Table 10, I test whether firms that trade significantly among themselves. The values in Table 11show the p-values for these tests. Test results show that two-way traders perform significantly better than only exporters in employment growth and product innovation and they perform better than only importers in all measures except in the use of 19
The evolution of the retirement and employment numbers in the 1989-94 (table 8) is this far not so different than those of 1980-85 (table 9). Fortunately, we can hope that the current recovery in GDP will be stronger and last longer than the one that followed the deep depression of 1980-82.

Table 8: Population (31 December, millions)

||1989|1990|1991|1992|1993|1994|
|---|---|---|---|---|---|---|
|l.a. Labor force|17.4|17.3|17.6|17.5|17.6|17.7|
|I.b. Employed and others|12.6|11.2|10.2|9.6|9.3|-|
|I.c. Self-employed|5.0|5.3|5.7|5.9|-|-|
|**ld.**Unemployed|0.0|1.1|2.2|2.5|2.9|2.8|
|2.a. Pension earners|6.8|7.1|7.9|8.5|8.7|8.9|
|2.b. of which: invalids<br>2.c.<br>farmers|(2.2)<br>(1.4)|(2.2)<br>(1.5)|2.3<br>1.8|2.4<br>2.0|2.5<br>2.0|2.6<br>2.0|
|3. Contributors to ZUS|1 +.7|14.1|13.6|13.3|12.7|12.6|
|4. Farmers (self-empl.)|4.0|-|-|-|3.4|-|
|_5._Benef.-earn. unempl.|0.0|0.9|1.7|1.3||l<br>-|
|Total population|38.0|38.2|38.3|38.4|38.5|38.6|
|Young < 18|11.4|11.3|11.3|11.2|11.0|10.9|
|Working age<br>|22.8<br>|23.0<br>|23.0<br>|23.2<br>|23.4<br>|23.5<br>|
|Working age<br>Old>65|3.8|3.9|4.0<br>1|4.0|4.1|4.2|

# not available

- annual average (not 31 December)

- ** aged between 18 and 64, there is another definition next page of Rocznik

Note: New definition of all labor force data: Individual farmers were overestimated by GUS from 1989 to 1993 (GUS Bulletin 1994, 7, p. 11 et 40, GUS Rocznik 1994, p.<sup>1</sup> 15, note 3). GUS data on subgroups of the labor force add up to less than the total: the residual is in lb (employed and others).

- Sources: GUS Rocznik 1992, p.42, 203 et 1993, p. 47, 109, 111, 122, 215. 1994, p.49, 117, 227

- GUS Bulletin 1994,6: p.37, 40, 49; 1995,2: p.<sup>4 2</sup> , 51

The current recovery is expected to originate in sounder economic and market bases than the previous one. Nevertheless, at the next downturn it will be harder for the economy to "grow out" of a new 30
safer to offer our results as establishing stylized facts, rather than providing causal explanations. We need to leave this difficult task to future research.

The rest of the paper is organized as follows. In Section 2, we describe our data and methodology and present the currency cyclicality index that we calculated. In Section 3, we relate the degree of currency cyclicality with the countries’ resource dependence and the nature of capital flows, as well as the conduct of monetary policy. In Section 4 we look at how these relationships have evolved over time and the implications for monetary policy. Section 5 concludes.

# **2. Data and Measurement of Currency Procyclicality**

Our data set consists of an unbalanced panel of 63 countries from 1975q1 to 2013q1. The criterion for including countries in the sample is the availability of quarterly real GDP data, required to calculate the short term cyclical co-movements of GDP and the exchange rate. The data availability varies across countries, as reported in Appendix A.

For our analysis we compute the currency cyclicality index (CCI), as the coefficient of correlation between the cyclical component of real GDP and the cyclical component of the nominal effective exchange rate (NEER), where the cyclical components are calculated as deviations from the trend computed using an HP filter on log values of the respective series.<sup>6</sup> A positive value of the CCI implies that higher GDP growth is associated with an appreciation of the exchange rate, and lower GDP growth is associated with a depreciation of the exchange rate; a negative value indicates the opposite relationship.<sup>7</sup> We call a currency procyclical if the CCI is positive and significantly different from zero at the 10 percent level; countercyclical if it is negative and significantly different from zero at the 10 percent level; acyclical if it belongs to the residual category of currencies with insignificant CCI. To check the robustness of the significance threshold, we use a 15 percent confidence interval, as well as a one tailed test, and find the classifications according to the different significance criteria to be very similar (Appendix Table C.1). We also calculate the CCI using the real effective exchange rate and find a large overlap with the one calculated using the nominal effective exchange rate.<sup>8</sup> The CCI calculated using NEER is presented in Figure 1a, and the one using REER is in Figure 1b.

> 6 We run the HP filter (with lambda equal to 1600) after seasonally adjusting the series using Census X11.

> 7 Since nominal exchange rates are relative prices we have a _numéraire_ problem and we lose a degree of freedom, which in principle could affect the interpretation of the results. However the problem is not very severe in our analysis, which is based on nominal effective exchange rate with weights that differ across countries. We would like to thank Andrew Powell for raising this important point.

> 8 The Spearman rank correlation between NEER and REER procyclicality measures is 0.86.

4