rafmacalaba/datause-encoder-data huggingface dataset:
28 percent. According to the estimates, services exports would fall by 62 percent, which implies a 14 and 46 percentage points drops with respect to the intermediate and “Norway” scenarios. Intermediates value added exports would decrease by about a quarter and foreign value added in the UK’s exports would decrease by one‐third.

To better understand these magnitudes, we use the 2011 data from WIOD to obtain the dollar equivalent of the losses for the three different scenarios. It should be noted that these back‐of‐the‐envelope calculations account for the partial effects and do not have general equilibrium or welfare implications since they do not account for possible re‐direction of exports to non‐EU countries (i.e. trade diversion and deflection), price adjustments or changes in wages. Although the largest percentage changes are for service exports, in terms of dollar equivalents we find that export of goods would decrease the most in all three scenarios. The “Norway” case, in which the UK signs an agreement similar to the EEA, suggests that export of goods would decrease by $27.447 billion, while services and value added exports would drop by $19.906 billion and $16.445 billion respectively. To put these numbers in perspective, the impact on total (goods and services) trade is equal to 1.83 percent of the UK’s GDP and 0.63 percent for value added exports. In the worst case scenario, we find that goods exports would decrease by $114.363 billion, which is equivalent to 4.41 percent of the UK’s GDP.<sup>11</sup> For services and value added exports, the values are lower, around $78 billion each, which accounts for 3 percent of GDP.

_Table 6:_ Changes in UK’s bilateral trade with the EU under different scenarios

||"Norway"<br>scenario<br>(36)|"average PTA"<br>scenario<br>(14)|"no‐agreement"<br>scenario<br>(0)|
|---|---|---|---|
|Goods|‐12%|‐38%|‐50%|
|Services|‐16%|‐48%|‐62%|
|Domestic Value Added|‐6%|‐20%|‐28%|
|Intermediates Value Added|‐5%|‐18%|‐26%|
|Foreign Value Added|‐7%|‐25%|‐34%|

_Notes_ : Depth decreases from 44 to 36 in the “Norway” scenario, to 14 in the “average PTA”, and to 0 in the “no‐agreement” scenario. Calculations are based on estimates in Table 3, column 5 for goods and column 6 for services trade, while results for value added trade use estimates from column 2 in Table 5 We compare our results with estimates from earlier studies that either use a different approach or use different data. Baier et al. (2008) adopt a methodology similar to ours based on a gravity model to identify the effect of various agreements, among which are the EU and the EEA, using PTA specific dummy variables. Their estimates suggest that if the UK were to exit the EU and sign the EEA, its gross exports of goods would fall by 25 percent.<sup>12</sup> One possible explanation for the difference in effects is that in our database the EEA is one of the deepest agreements, as it covers 36 policy areas. Instead, Baier et al. (2008)

> 11 The data on GDP are from the World Bank.

> 12 Based on estimates in Table 3, column 1 of Baier et al. (2008).

19
32 WHO (2000), Global Water Supply and Sanitation Assessment, Geneva, available at <u>http://www.who.int/docstore/water_sanitation_health/Globassessment/GlobalTOC.htm</u>

# **Bibliography for PPI, the macro story**

Briceno, C., A. Estache and N. Shafik (2004), “Infrastructure Services In Developing Countries: Access, Quality, Costs and Policy Reform”, The World Bank, (forthcoming) Easterly, W. and L. Serven, editors (2003), **The Limits of Stabilization,** Stanford University Press, Stanford Estache, A. and M.E. Pinglo (2004), “Are returns to Public-Private Infrastructure Partnerships in Developing Countries Consistent with Risks since the Asian Crisis”, World Bank, PR Working Paper series, Number 3373 Guasch, J.L. (2004), **Granting and Renegotiating Infrastructure Concessions: Doing it Right** , WBI Development Studies Gomez-Ibanez, J.A. (2003), **Regulation of Private Infrastructure: Monopoly, contracts and discretion** **_,_** Harvard University Press Kessides, I. (2004), **Reforming Infrastructure: Privatization, Regulation, and Competition,** Oxford University Press Ramamurti, R. and J. Doh (2004), “Rethinking foreign infrastructure investment in developing countries” , _Journal of World Business_ , 39, pp151-167 U.K. Department for International Development, 2002, **Making Connections: Infrastructure for Poverty Reduction,** London

# **Bibliography for the Fiscal space debate**

Ballasone, F. and D. Franco (2000), “Public Investment, the Stability Pact and the Golden Rule, Fiscal Policy, vol. 21, 2, pp207-229 Blanchard, O.J. and F. Giavazzi (2003), “Improving the SGP through a Proper Accounting of Public Investment”, European Economic Perspective newsletter, CEPR, No1. February Buiter, W. and C. Grafe (2004), “Patching up the pact: some suggestions for enhancing fiscal sustainability and macroeconomic stability in an enlarged European Union”, _Economics of Transition_ v12, n1 (2004): 67-102 Calderon, C. and A. Chong, (2004), “Volume and Quality of Infrastructure and the Distribution of Income: An Empirical Investigation”, _Review of Income and Wealth_ , Vol. 50, No. 1, pp. 87-106, March Easterly, W. and L. Serven, editors (2003), **The Limits of Stabilization** , Stanford University Press, Stanford Kalaitzidakis, P. ; S. Kalyvitis (2004), On the Macroeconomic Implications of Maintenance in Public Capital : _Journal of Public Economics_ v88, n3-4, 695-712 McCrae, M. and M. Aiken (2000), “Accounting for infrastructure service delivery by governement: generational issues”, _Financial Accountability and Management_ , 16(3), pp265-287
as well. We include a dichotomous variable as to whether the school received CONAFE compensatory program funding in 2001 and a continuous variable indicating the portion of students in the school in 2001 that received an Oportunidades scholarship.

Most training evaluations include pre-program earnings and pre-program employment trends. Heckman et al. (1999) emphasize that the variables to include as controls are those that determine participation and are not determined by participation into the program. Some emphasize that the similarity of pre-program outcomes between comparison and treatment groups can be a litmus test for the acceptability of comparison groups in evaluating training programs (Heckman and Hotz 1989, Angrist and Krueger 1999). By definition, PEC participation cannot affect pre-program outcomes or trends, but these variables may influence a school’s decision to participate in PEC. Following such work, we include one-year pre-intervention one-year trends in matching and regression controls. Longer-term pre-intervention trends might better capture factors which affect the trend of education outcomes in the school. Since our main estimating equations use differences-in-differences, which eliminate time-invariant school and community factors, we have more concern in capturing short-term changes in schools – changes in principals or teachers, an influx of migrants, new facilities, or others – which would affect outcomes during the PEC intervention.

_Changes in intermediate outcomes_ Before describing method and results of our estimate of PEC’s impact on outcomes, we discuss a reflexive comparison of changes in intermediate outcomes in PEC schools using data on changes between June 2002 and June 2004 from surveys of students in 505 PEC schools. This information serves two purposes. First, by showing trends, these results show if PEC schools are moving towards the desirable goal of having schools function more effectively. Second, by providing detailed information on the state and trends of parent9
across sectors: among those 4-digit sectors where the estimated coefficients are statistically significant, the productivity growth dividends of a 10 percent decrease in markup vary from 7.01 percentage points to 0.16 percentage points, respectively, for NACE sector 1041– Manufacture of oil and fats and sector 4649–Wholesale of other household goods (figure 15).<sup>35</sup> Productivity dividends from a potential markup reduction are also positive in sectors where the top decile firms in the markup distribution are overly represented; for instance, in sector 4711– Retail sale in non-specialized stores with food, beverages or tobacco predominating, a 10 percent decrease of average markup would be associated with an increase in TFPR growth of 0.22 percentage points per year. For sector 4690–Non-specialized wholesale trade and sector 4673–Wholesale of wood, construction materials and sanitary equipment, the productivity dividends would be 0.18 and 0.21 percentage points, respectively.

**Figure 15. Productivity (TFPR) growth dividends from a 10 percent reduction of average markup: Results by NACE 4-digit sector**

<!-- Start of picture text --> 8.0<br>7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>Percentage points per year<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on SBS data set.

> 35 There are, however, some sectors where markup reduction brings detrimental impacts to productivity expansion. See complete set of results in table A1.8in annex 1, for instance, in NACE sector 8010–Private security activities and sector 2432–Precious metals production, where a reduction of average markup in the 2008–17 period would imply a decrease of TFPR growth of 0.30 percentage points and 6.63 percentage points, respectively **.** Following Aghion, Braun, and Fedderke (2008), the Schumpeterian effect of product market competition would be the underlying driver behind this result; in these sectors, competition reduces innovation incentives and therefore productivity growth by reducing the rents from innovations of firms below the technological frontier.

29
# **Table 21: Impacts on Number of Firms from EU FTA (% change from benchmark)**

**No initial rent capture case**

||Tanzania|European<br>Union|Africa|Rest of the<br>World|
|---|---|---|---|---|
|**Business Services**|||||
|Telecommunication|-0.7|22.1|-1.4|-4.5|
|Insurance|1.1|71.9|0.9||
|Banking|0.4|41.5|-0.1|-0.3|
|Professional business services|-0.2|36.5|-1.1|-3.5|
|Air transport|0.9|8.4|0.9|3.6|
|Road transport|2.9|9.6|2.9|6.0|
|Railway transport|0.7|||3.9|
|Water transport|-10.5|116.4|-12.9|-35.0|
|**Dixit-Stiglitz Goods**|||||
|Mining & quarrying|0.0|13.4|0.8|1.7|
|Grain milling|-0.2|25.3|-0.3|-0.5|
|Processed food|0.5|23.7|-0.8|-2.2|
|Beverages & tobacco products|-1.8|63.5|-2.2|-3.5|
|Textile & leather products|-0.2|41.8|0.1|0.3|
|Manufacture of basic & industrial chemicals|-0.1|9.5|-0.9|-2.7|
|Manufacture of fertilizers & pesticides|0.6|1.8|0.7|2.0|
|Petroleum refineries|0.2|1.4|0.1|0.2|
|Glass & cement|-0.1|17.4|-0.3|-0.6|
|Iron steel & metalproducts<br>Source: Authors' estimates.|-0.6|19.1|-1.0|-2.6|

89
**Figure  2.   Demand for Representative CRTS good g in Regional Market r**

<!-- Start of picture text --> Armington Aggregate Demand<br>for CRTS good g in RM r<br>CES<br>σ = sigmadm a /<br>Russia goods<br>Rest of World Imports<br>CES<br>σ = 1.5 * sigmadm<br>Own Regional Market Other Russian Imports<br>CES<br>σ = 2 * sigmadm<br>Imports from Imports from<br>Imports from<br>Regional Market 1 Regional Market q<br>Regional Market m<br><!-- End of picture text -->

> **<u>a/</u> sigmadm = 3 in CRTS sectors, except in OTH (other goods producing sectors). For OTH we rely on estimates from Ivanova (2005).**

65
disbursement related to a previously signed investment agreement.<sup>6</sup> All IFC investment data are sourced from the iDesk internal database. Investment data for each country are denominated in US dollars and reported on a quarterly basis. This study examines the 20-year period from the first quarter of 2000 through the fourth quarter of 2019. The raw investment data have been reformatted from the fiscal year to the calendar year and deflated using the US Federal Reserve’s quarterly Consumer Price Index Deflator<sup>7</sup> to eliminate any potential price effects.

# **3.2 Countries Output Series**

Real quarterly GDP is used to model each recipient country’s economic output. The data series are sourced from the IMF’s International Financial Statistics (IFS) database and have been seasonally adjusted using the simple moving averages method.<sup>8</sup> A brief discussion of the theoretical basis for removing seasonality from a time series is presented in the Appendix, along with an empirical example from Brazil.

# **3.3 Extracting Cycles for Correlation Analysis**

This study uses two time series, GDP data and IFC investment data, to determine whether the latter is procyclical, countercyclical, or acyclical. To extract business cycles within the time series, the de-trending method is used to separate the trend from the original series. While many econometric methods can decompose a series into its trend and cycles, this study uses the Hodrick-Prescott (HP) filter.<sup>9</sup> Additional details on de-trending are presented in the Appendix. As a final step, the cyclical component (CC<sup>10</sup> ) of IFC investment variable and country-level de-seasonalized real GDP are analyzed using the Pearson’s correlation coefficient ( _r_ ).<sup>11</sup> For interpretation purposes, a positive _r_ value implies procyclicality, a negative value implies countercyclicality, and a value of zero implies acyclicality.

> 6 The IFC also has a “hold clause” that can allow it to delay disbursements. However, this clause is rarely invoked, and in practice disbursement delays are essentially a demand-side phenomenon.

> 7 Source: FRED, Index: 2015=100.

> 8 Additional information on seasonal decomposition using moving averages and code is available at: <u>https://www.statsmodels.org/stable/_modules/statsmodels/tsa/seasonal.html#seasonal_decompose.</u> 9 Additional information on the HP filter optimization problem and code is available at: <u>https://www.statsmodels.org/stable/_modules/statsmodels/tsa/filters/hp_filter.html.</u> 10 C CC = ~~.~~ 11 iillii Further information on the _r_ test statistic and code is available at: TTi iiee <u>https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html.</u>

7
> _(3a)_ **_NZ=_** _Znw_ **_-_** _Zold_

<!-- Start of picture text -->
- 1  d  t=T  1  100<br>(3b)  Zo=  .W  +  Bold<br>(3c)<br>Z  =b.  "  - 1-  1  = - 1-  p  1  r=T  1  100<br>Znew  ={  11)  *w 1   _  18)  E  1   1  I  ~X(-i}<br>""  =0  (1  - )  (1  )bt=b  t 1,a8=ctW<br><!-- End of picture text -->

where **NZ** is the lifetime wealth net change. **Zold** and **Znew** are the flows of income under the old and new system. They are expressed in percentage of worker's wage. Contribution rate is assumed to be changed at the period **b,** and r and **_U_** are the contribution rates before and after this period. _B,old_ and **_B,"'_** are worker's old-age benefits before and after the reform. The starting period, **0,** is the time the reform is implemented.

**3.6** In the calculation presented below, a discount rate fi _of_ **_5_** percent is applied to the flow of future income. This rate should reflect households' discount rate. Unfortunately, there is no available estimation of this parameter for any country in the **ECA** region. The discount rate used in the World Bank economic evaluation of investment operations in the **ECA** region is currently set at about **10** to 12% in the case of industrial projects. Our estimate seems quite conservative if we consider that, evaluated as the opportunity cost for financial resources, the discount rate should be higher than the real interest rate because households often face borrowing constraints, especially at lower income levels.

**3.7.** _Profiles of women._ Neither men nor women are homogeneous groups. We can, however, generalize about certain labor market characteristics, which tend to affect pension outcomes. First of all, women tend to have interrupted labor force participation. They take time out of the labor force to bear and raise children. They also have more periods of unemployment. Second, women tend to have lower incomes than men for various reasons,

**13**
and Maloney, 2000, EBRD, 2011, Arrondel et al, 2010, Thomas et al, 1999, Corbacho et al, 2003). Given this definition, 452 families are classified as ‘crisis-affected’.<sup>6</sup> We focus on three indicators that are available in the data and provide insights into nutritional and health seeking behavior in the Russian Federation: (i) total food consumption; (ii) total outof-pocket health expenditure; (iii) number of doctor’s visits.<sup>7</sup>

# 4. <u>Descriptive statistics</u>

Based on the definition above, we divide households into two main groups – those ‘crisisaffected’ and ‘not affected’ by income shocks. As mentioned above, there are 452 families in the sample that were affected during the 2009 crisis in Russia. This section presents summary statistics of our main variables of interest for both crisis-affected and not-affected households.

There are a few important conclusions that stem from the descriptive statistics presented in Table 1. First, the average monthly expenditure of the entire sample fell by about 2,000 rubles (US$66) (in real terms) during the crisis in 2009 relative to average monthly expenditures in 2008. While expenditures in households that did not suffer income shocks grew steadily, real expenditures of crisis-affected households fell dramatically in 2009. Second, food expenditures of the non-affected households grew in 2009 relative to 2008, while food expenditures of crisisaffected households fell sharply in 2009. Third, out-of-pocket health expenditures declined during the crisis in 2009 among households that suffered an income shock but grew among households that were not affected. Finally, there appears to be no uniform pattern between households affected and non-affected by an income shocks on doctor visits. In fact, and surprisingly, the total number of doctors’ visits fell in 2008, the year prior to the crisis, while it increased in 2009 during the crisis year.

> 6 In addition to the above method of identifying affected households, we also use an alternative measure, namely unemployment by any member of the household to determine which households were affected by the crisis. Households are crisis affected if they suffered a significant drop in: (i) the total number of household members that were employed; (ii) the percentage of household members employed in household, while also controlling for changes in the size of the household (due to births, deaths, migration, etc.). Whilst this is a narrower definition of a crisis affected household, we find a significant overlap between the ‘affected’ families derived by the both methods. 7 The RLMS also contains household’s self-assessment of their health status and some anthropometric indicators. We omitted the self-assessment questions because those questions bring up a series of caveats might are exacerbated during pessimistic periods such as crises.  We omitted the anthropometric measures because of the small sample size across the longitudinal survey.

8
29

# **Figure 3**

### **Emerging countries: compensation of employees (credit)**

_(millions of US dollars)_ <!-- Start of picture text --> 20000 75<br>compensation of employees (credit)<br>16000 number of emerging countries included (right scale) 60<br>12000 45<br>8000 30<br>4000 15<br>0 0<br>1976197719781979198019811982198319841985198619871988198919901991199219931994199519961997199819992000200120022003<br><!-- End of picture text -->

**Figure 4**

## **Emerging countries: migrants' transfer (credit)**

_(millions of US dollars)_ <!-- Start of picture text --> 5000 30<br>migrants' transfer (credit)<br>4000 number of emerging countries included (right scale) 24<br>3000 18<br>2000 12<br>1000 6<br>0 0<br>1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text -->
|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
|Type.8.Diabetes.Me|llitus.(T8DM).outcomes||||0.83, 95% CI 0.71, 0.99), and<br>vegetable(OR 0.83,95% CI 0.70,0.99)|
|B. Li et al. (2023)|Meta-analysis 72 articles of<br>prospective cohort studies,<br>11 in Asia, 11 in Europe. 46<br>in the United States|Adults|SSB,ASB, and fruit juice<br>consumption|T2DM, hypertension|T2DM risk was associated with SSB<br>(RR 1.27; 95% CI 1.17, 1.38), ASBs,<br>(RR 1.32; 1.11, 1.56) and fruit juices<br>(RR 0.98; 0.93, 1.03). Intakes of SSBs<br>and ASBs were signifcantly associated<br>with risk of hypertension.|
|Neelakantan et al.<br>(2021)|SR of 17 studies (9<br>prospective, 7 cross-<br>sectional, 1 clinical), Hong<br>Kong SAR, China; Republic<br>of Korea; Thailand;<br>Singapore;Japan|Adults, Asian<br>population<br>(n=114 208)|SSBconsumption|T2DM|High SSB consumption was associated<br>with greater T2DM risk (RR 1.38; 95%CI<br>1.09-1.73)|
|Mishali et al.<br>(2019)|SRM of 16 prospective<br>cohort studies, 2 in China,<br>rest in HICs|Men and women<br>> 18 years<br>(n=545 677)|Dairy consumption|T2DM (16 studies)|T2D is inversely associated with dairy<br>intake. Subgroup analysis for sex<br>showed that the association between<br>dairy intake and T2D is signifcant in<br>women but not in men.|
|Khoramdad et al.<br>(2017)|SRM of 14 prospective<br>cohort studies, 1 from<br>China, rest from HICs|n=458 082|Dairy consumption|T2DM|Total dairy consumption associated<br>with decreased risk of T2DM (RR 0.88,<br>95% CI 0.80, 0.96), even lower risk<br>with consuming low-fat dairy (RR 0.81;<br>0.68,0.96)|
|D. Gao et al.<br>(2013)|SRM of 16 cohort studies<br>(15 prospective cohort, 1<br>case-cohort), 1 from China,<br>rest from HICs|n=526 998|Dairy consumption, high<br>vs low total dairy intake<br>(13 studies, n=457,893)|T2DM|Found a nonlinear association of total<br>dairy intake and T2DM risk (RR 0.89;<br>95% CI 0.81, 0.98)|
|Aune et al. (2013)|SRM of 16 cohort studies, 1<br>from China, rest from HICs|Adults<br>n=258 078|Whole grain(10 studies,<br>n=385,868) andrefned<br>grain(6 studies,<br>n=258,078)<br>consumption, high vs low<br>intake|T2DM (10 studies)|Non linear association was observed<br>between whole grains and T2DM (RR<br>0.74; 95% CI 0.71, 0.78) but not<br>refned grains (RR 0.94; 0.82, 1.09).<br>Size of the association between whole<br>grains and T2DM was stronger when<br>the analyses were not adjusted for BMI<br>compared with adjustment for BMI (RR<br>0.53 vs. 0.69)|

40