rafmacalaba/datause-encoder-data huggingface dataset:
the project. In constructing this scenario, the authors have taken considerable care to distinguish those trade reforms that are actually being negotiated under the Doha Development Agenda from those that have already been agreed to previously. This distinction is complicated by the fact that virtually all of our policy data bases pre-date completion of the Uruguay Round Agreement. In fact, the starting point for all of the analysis in this project is the year 2001 – the most recent one for which comprehensive data are available for tariffs, domestic support and export interventions. Therefore, prior to constructing the Doha scenario, a “pre-experiment” is undertaken in order to account for the major developments in trade policy since 2001. These include: tariff reforms undertaken by newly acceding WTO members – most notably China, the phase-in of remaining Uruguay Round commitments by developing countries, EU enlargement to 25 countries, and the abolition of export quotas on textiles and apparel under the Agreement on Textiles and Clothing. Thus, even though the full impact of some of these reforms is yet to be felt, the analysis in this project looks beyond these reforms, envisioning a global economy in which they have been fully implemented, and focusing on the further impacts of trade liberalization undertaken in the context of the Doha negotiations.

The most important finding from Anderson and Martin (2005) is that, unless the Doha Agenda is considerably more ambitious than the Uruguay Round in terms of depth of cuts in bound tariffs and domestic support, it will achieve little development stimulus. The main problem on the market access side is binding overhang. For example, in agriculture – one of the key areas of the Doha Agenda with respect to trade and poverty – bound tariffs in developing countries average 48% while applied tariffs average are only 21%. In the case of the least developed countries, the respective figures are 78% and 9
technology is not available commercially. There are some experimental or pilot projects either operational or planned to use electric kilns for brick-firing, such as the Kortemark plant in Belgium (Wienerberger, 2023), Uttendorf plant in Austria (NEFI, 2023), a full understanding of technical and economic viability of an electric kiln might take years. (Geres et al., 2021) project that economically viable GHG-neutral brick production would be available in developed economies like Germany by 2050. Another pathway for carbon-neutral brick production is the use of green hydrogen. However, the technical feasibility and economic viability of this technology are yet to be tested.

Considering the availability of clean electricity and the series of environmental and fiscal problems caused by imported coal, Nepal should explore the opportunities to use electric kilns for brick production. Since the technology is not yet commonplace, pilot programs on such technology should be undertaken. During the rapid survey (Timilsina and Malla, 2023), brick producers showed a strong interest in electric kilns. Some producers are willing to host pilots if they can get financial and technical support from the government or development partners.

# 6.4 Alternate (not-fired) bricks

Policies to promote alternative bricks would be a solution to reduce CO2 emissions and other local pollutants from the brick industry in Nepal. At present, there are two alternative bricks in Nepal: unfired or uncooked bricks and cement blocks/bricks. The former is produced with a mix of clay, sand, stone dust, and some chemicals (for binding), whereas the latter is produced from cement. Since these bricks are not as strong as traditional bricks, they face marketing challenges. Many producers started to produce alternative bricks, mostly medium and small entrepreneurs, however, they face market barriers. Consumers are skeptical about the durability of such bricks. They argue that traditional bricks can survive thousands of years whereas alternative bricks may not survive for the lifetime of their buildings. While small producers are continuing production of alternative bricks, they are also uncertain about their future if the market does not develop (Timilsina and Malla, 2023). To develop the market for alternative bricks, the necessary policies needed are i) keeping the coal price at least from falling below the current level by collecting the difference as environmental levy if coal prices drop, ii) imposing an import duty to avoid the direct import of 32
# **Argentina**

Argentina has had a currency board since 1991. In this endogenous money system, the demand for base is important because increments in k imply an increment in the amount of Central Bank foreign assets. In this case the Central Bank's seignorage equals the interest on these international reserves. Equation (11) cannot be directly applied to a completely fixed exchange rate system like that of Argentina, since this equation has the explicit assumption that the rate of inflation is determined by the exogenous monetary base. The following charts present the evolution of Argentina's domestic and foreign debt, as well as total debt to GDP ratio.

Chart 12 **Argentina: Domestic and Foreign Debt** Ratios to GDP <!-- Start of picture text --> FD 0.34 0.10 DD<br>0.32 foreign 0.08<br>0.30<br>0.28 \I / "domestic<br>0.26 / ,,0<br>0.24 / 0.04<br>0.22 , 0.02<br>0.20<br>0.18 _ 0.00<br>1990 1991 1992 1993 1994 1995<br>ARFDP--. ARDDP<br><!-- End of picture text --> Sources: Foreign Debt, World Bank debt tables Domestic Debt, Central Bank 30
# **Figure 2 Panel of 4 countries showing different stages of demographic transition**

Share of working age population, percent a) Nigeria b) India <!-- Start of picture text --> 80 80<br>75<br>70 70<br>65<br>60<br>60<br>55<br>50<br>50<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br><!-- End of picture text -->

# c) China

# d) Japan

<!-- Start of picture text --> 80 80<br>75<br>70 70<br>65<br>60 60<br>55<br>50 50<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br><!-- End of picture text -->

## Source: UN (2015)

Note: These countries are classified in different stages of demographic transition (World Bank, 2015a).

More generally, growth in the working-age share is associated with higher per capita income growth (Figure 3). Bloom and Williamson (1998) highlight that changes in the growth of labor force per capita and the savings rate are plausible channels through which a changing age structure might affect the rate of economic growth.<sup>10</sup> That paper’s results suggest that an increase of 1 percentage point in the growth of the working age population is associated with an increase of 1.4 to 2 percentage points in the growth rate. Bloom and Canning (2004) find that 1 percentage point growth in the share of working age population leads to an increase by 1.4 percentage points in growth in income per capita.<sup>11</sup> Kelley and Schmidt (2005) developed a framework to explain the

> 10 Using cross-sectional data for 78 countries, from 1965 to 1990, Bloom and Williamson (1998) find that demographic forces appear to have contributed 0.6 percentage point to the East Asian miracle via labor inputs per capita and 1 percentage point via capital accumulation per capita.

> 11 The OLS estimates of Bloom and Canning (2004) suggest that an increase of 1 percentage point in the share of working age population leads to 1.0 percentage point increase in income per capita.

7
especially, **by** the steady declining foreign wealth **/** domestic wealth ratio, itself driven **by** rapid capital accumulation. This raises Chile's demand for foreign assets, bringing their equilibrium position close to zero.

The case of Mexico, portrayed in Figure _4.5,_ does not display such clear cycles. Both risk and return remain relatively steady, with the latter experiencing a sharp decline in **1977-81** that accounts for the marked rise in the equilibrium ratio. In the late 1980s, a rise in the risk index and a decline in the return index both tend to bring up the equilibrium series. Unfortunately, asset data for Mexico stop in 1994, when our estimates show a widening gap between the actual and equilibrium nfa **/** wealth ratios.

In Korea (Figure 4.6), large gaps between the actual and equilibritun series do not arise (with the exception of the year **1981).** The initial part of the sample period is characterized **by** high values of the return index, which declines steadily since the early 1980s. Risk declines too, but in a less pronounced manner, and rapid capital accumulation brings down the foreign domestic wealth ratio and raises the demand for foreign assets, bringing the equilibrium asset position close to zero **by** the end of the sample period.

The distinguishing feature of Singapore (Figure 4.7) is that it displays a very large and persistent gap between the equilibrium and actual values of the foreign asset **/** wealth ratio. Our parameter estimates yield a very slow speed of adjustment, which accounts for the persistence of the divergence Figures 4.8-4.11 portray industrial countries. In general, the gaps between actual and equilibrium nfa **/** wealth ratios are of a smaller order of magnitude than in developing countries. In Germany (Figures 4.8-4.9) the results suggest that at the end of the sample period foreign assets relative to wealth exceed their equilibrium value **by** a small amount, while the opposite happens in the **U.K.** In both cases, however, the equilibrium ratio is positive but small in magnitude, mostly reflecting the large wealth of these countries.

**By** contrast, in Canada (Figure **4.10)** both the actual and equilibrium position remain negative throughout the sample period, and their divergences are mostly small. Both series display a rising pattern.

**.** Finally, the **U.S.** (Figure **4.11)** is a special case in that, as noted earlier, its estimated dynamics are unstable. As a result, the actual and equilibrium series show an increasingly large divergence. The equilibrium nfa **/** wealth ratio is positive and shows a modest rising trend

**_25_**
of _A_<sup> ̃</sup> ) under the _A_<sup> ̃</sup> decomposition of Ωand the _m ×_ 1 dimensional column vector _ri_ ( _k_ ) as the vector response [ _r_ 1 _i_ ( _k_ ) _, ..., rmi_ ( _k_ )]’, the _m ×_ 1-dimensional impulse response _ra_ ( _k_ )<sup>11</sup> at horizon _k_ to the impulse vector _a_ is given by: where _qi_ is the _i_<sup>_th_</sup> entry of _q_ . Identification of the structural shock _s_ , that is the impulse vector _a_ = _a_<sup>(</sup><sup>_s_)</sup> with sign restriction consists of selecting the right impulse vector _a_ = _a_<sup>(</sup><sup>_s_)</sup> that satisfies sign restrictions imposed on the IRFs. The procedure is explained in detail in the Appendix.

# **2.3 Data description**

The VAR-system is composed of two blocks _y_ 1 _t_ and _y_ 2 _t_ of quarterly data from the first quarter of 1970 to fourth quarter of 2010. The first block _y_ 1 _t_ contains Canadian variables: (a) GDP, (b) private consumption, (c) private non-residential investment, (d) government spending, (e) net tax revenue, (f) GDP deflator, (g) REER, (h) net-exports-to-GDP ratio and (i) the 3-month T-bill rate. Variables (a) to (e) are presented in real per-capita terms by dividing each real variable by the labor force.

Except for the net-exports-to-GDP ratio and the 3-month T-bill rate, all the variables are expressed in log form. Private consumption is comprised of non-durable goods and services. Government spending is government purchases (i.e. the sum of current consumption and public investment). Net tax revenue is the sum of personal and corporate income tax revenues, social security contributions and taxes on production and imports, net of transfers to households and private corporations. Real net tax revenue is obtained by dividing the nominal tax revenue by the GDP deflator (2002 = 100). The definition of these variables is in line with previous studies, in order to facilitate consistent comparisons.

Except for the REER, all the variables in the Canadian block are from Statistics Canada’s CANSIM database. The REER, based on relative unit labor costs in the manufacturing sector, is from the Federal Reserve Bank of St. Louis (FRED) database. A rise in the index as shown in Figure A1 (the net-exports-to-GDP ratio declines with an increase of the REER) in the Appendix represents a deterioration in the country’s competitiveness. The second block, _y_ 2 _t_ , includes the logarithm of U.S. real GDP (2005=100) per capita and real oil prices. U.S. GDP is from the U.S. National Income and Product Accounts database. Oil prices are expressed in real terms by dividing the West Texas Intermediate Oil Price (in U.S. current dollars per barrel) taken from the IMF-WEO database by the U.S. GDP deflator(2005 = 100). I chose the GDP deflator instead of the consumer price

> 11The _jth_ element _rja_ ( _k_ ) of _ra_ ( _k_ ), _j_ = 1 _, .., m_ is the impulse response at horizon _k_ of the _jth_ variable to the impulse vector _a_ .

7
Occupations (ISCO) classification (ILO 2021)<sup>16</sup> and use five dummies corresponding to different firm sizes (Bryson, Erhel, and Salibekyan 2021). We also include 29 country dummies to control for country-specific differences in the labor market and 26 year-dummies to capture the time trend.

To identify the time effects in equation (2), we assume that the coefficients of our control variables are constant over time. Burgess and Rees (1998) find little evidence of significant changes in the effects of the main control variables over time. We derive the marginal effects of the year dummies estimated at the sample means. These marginal effects could be interpreted as conditional sampleyear average proportions of each tenure duration.<sup>17</sup> In order to investigate the presence of a secular trend in the probability of having each tenure duration, we then proceed to the second-stage estimation, which consists of linear regression with the marginal effects of each year dummy as a dependent variable and a linear time trend as the single independent variable (equation 3). We bootstrap the standard errors of the second-stage estimations to account for the small sample error heteroskedasticity:<sup>18</sup> Estimation of equation (2) on a pooled sample of 29 countries and 26 years demonstrates that on average, workers who are younger, male, have higher levels of education, and work on temporary contracts or part-time were more likely to have short tenure (less than one year). Short job tenure is inversely related to firm size. Workers who are older, male, hold full-time jobs on permanent contracts, and work for larger firms were more likely to hold their jobs for more than 5 years or more than 10 years. Better-educated workers were less likely to have longer tenure.<sup>19</sup> <u>Table 3 shows the results of the second-stage regressions for three categories of tenure as dependent</u> variables (columns) and different subsamples (rows).<sup>20</sup> For example, the coefficients for four

> 16 We use two-digit occupation classification because the LFS transitioned from the ISCO 88 classification that was used before 2010 to the ISCO 08 classification after 2010. A correspondence between both classifications is possible only at the two-digit level.

> 17 For the second-stage regression we could also use the probit estimated coefficients (πt). However, using the marginal effect simplifies the interpretation of our results.

> 18 We estimate the standard errors of the second-stage regression using 500 bootstrapped sample draws (Imbens and <u>Kolesar 2016).</u>

> 19 The results of these estimations are available from the authors on request.

> 20 <u>Figure A1</u> in the Appendix shows the scatter plot of 26 marginal effects against the survey year and the OLS fitted line corresponding to the estimation of the probability of having tenure less than 1 year for the total sample of observations (the first cell in <u>Table 3).</u>

17
Survey data. Specifically, we utilize the GHS-FUA spatial package developed by Moreno-Monroy et al. (2020), which offers a global delineation of metropolitan or functional urban areas (FUAs) combining a morphological and functional approach. Metropolitan areas consist of urban centers with at least 50,000 inhabitants and their surrounding commuting zones. Since detailed GPS or place name data are not readily available for every Enterprise Survey, particularly for the earlier years, and not all relevant establishment characteristics – most notably sales and employee education – are recorded for every establishment, our initial _full sample_ consists of 51,702 establishments in 649 metropolitan areas of 98 economies.

Most surveys have been conducted in the main urban centers although in recent years it has become more common to conduct surveys in smaller urban centers and more rural areas. In our data set, we link the location of the establishments to FUAs (also called metropolitan areas) with at least 50,000 inhabitants. Hence, our data set excludes rural areas and towns with fewer than 50,000 inhabitants. Of the establishments located in metropolitan areas, 68% are located in metropolitan areas with more than 1 million inhabitants, 29% are located in medium sized FUAs (between 100,000 and 1 million) and the remaining 3% are found in small FUAs between (50,000 and 100,000). Thus, our data set covers predominantly the largest metropolitan areas in developing economies. Second, our _common_ sample, which we use to test out hypotheses on urban mobility, consists of 38,526 establishments in 356 metropolitan areas in 80 developing economies because only for these metropolitan areas we have information on urban mobility and congestion from the work of Akbar et al. (2021). Appendix A1 includes the list of economies included in the _common_ sample.

# _3.2. Description of variables_

# _Dependent variable: Labor productivity_

Labor productivity is measured as the natural logarithm of sales per worker, computed as the revenue deflated and represented in 2009 USD during the last fiscal year divided by the number of employees (see also Amin & Okou, 2020). We use labor productivity instead of the TFP measure (Francis et al., 2020) because information for the latter is only available for less than 40% of the observations and is not available for firms in the non-tradable sectors.

# _Independent variable: Urban density_

We use urban density instead of urban population size to measure agglomeration since recent research argues that in the developing world agglomeration externalities predominantly arise from density rather than absolute market size (Henderson et al., 2019). Urban density is obtained from 9
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Gregory, CA and JC Altman, 1989, _Observing the Economy_ , Routledge, London Gudeman, Stephen, 2001, _The Anthropology of Economy_ , Blackwell Publishers, Malden and Oxford Himamshu, Peter F Lanjouw and Nicholas Stern, 2018, _How Lives Change: Palanpur, India and Development Economics_ , Oxford University Press, Oxford and New York <mark>Humphreys, Macartan and Alan M. Jacobs, 2015, “Mixing Methods: A Bayesian Approach.”</mark> _The American Political Science Revie_ _<mark>w</mark>_ <mark>109, no. 4 : 653–73.</mark> Jayachandran, Seema, Monica Biradavolu and Jan Cooper, 2021 “Using Machine Learning and Qualitative Interviews to Design a Five-Question Women’s Agency Index,” Northwestern Global Poverty Research Lab Working Paper no. 21-104, March Kanbur, Ravi and Paul Shaffer, “Epistemology, Normative Theory and Poverty Analysis: Implications for Q-Squared in Practice,” _World Development_ , Volume 35, Issue 2, 2007, Pp: 183-196 Lareau, Annette, 2021, _Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up_ , University of Chicago Press, Chicago 21
7 Table 2. A survey of the literature on efficiency measures in the ports sector

|**Author**|**Data (1)**|**Model (2)**|**Functional Form**|**Variables (3)**|**Estimation**<br>**Method (4)**|**Efficiency measure (5)**||
|---|---|---|---|---|---|---|---|
|Liu<br>(1995)|Panel<br>28 UK ports<br>1983-1990|SPF|Translog<br>Neutral and non neutral<br>Technological change|Yl (X1, X2, Zj, Z2, Z3, Z4,T)|Model 1:<br>OLS, ML<br>Model 2:<br>Within, GLS,<br>ML|Technical efficiency<br>Mod. I (ML):<br>Mod 2 (GLS):<br>Mod 2 (ML):<br>Mod 2 (ML, with T):|1983-1990<br>78.0<br>76.9<br>68.3<br>69.7|
|Coto, Batlos and<br>RodrHguez|Panel<br>27 Spanish ports|SCF|Translog<br>Non neutral|CTo (YO, WI, W2, W3, T)|Within|Economic efficiency<br>Maximum:|1985-1989<br>100|
|(2000)|1985-1989||Technological change|||Minimum<br>Average|11<br>33|
|Bafios, Coto and<br>Rodriguez<br>(1999)|Panel<br>27 Spanish ports<br>1985-1997|SCF<br>DF|Translog<br>Neutral<br>Technological change|CVO (YO, WI, W3, W4, W5, T)<br>D (YO, XI, X3,X4, X5, T)|Instrumental<br>Variables|Technical efficiency<br>Distance function<br>Maximum:<br>Minimum<br>Average|1985-1997<br>100<br>15<br>41|
|Roll and Hayuth<br>(1993)|Cross Section<br>1993|DEA|Not applicable|Yo, Yl, Y2, Y3, XI, X2, X6|Not relevant|Average Medium<br>Efficiency<br>Total ports:<br>Ports region I and 2|1993o<br>78.2<br>.93.4<br>86.1|
|Martinez, Diaz, Navarro<br>and Ravelo<br>(1999)|Panel<br>26 Spanish ports<br>1993-1997|DEA|Not applicable|Yo, YI, XI, X2, X3|Not relevant|Average global<br>efficiency<br>Group I:, II and III|1993-1997<br>88.7<br>80.1<br>85.7|
|Tongzon (2001)|Panel<br>16 ports<br>1996|DEA|Not applicable<br>Tests both CRS and<br>VRS|Yo XI, X2, X3, X4, X5,ZI<br>Delay Time|Not relevant|Average efficiency if:<br>Constant Retum to Scale<br>Variable return to Scale|1996<br>59.5<br>93.1|

(1): To indicate sample size (2): SPF: Stochastic production frontier; SCF: Stochastic cost frontier; DF: Distance function and D Distance; DEA: Data Envelopment Analysis; (3): CT: total cost

# CV: variable cost

Yo: Production (measured in tons of merchandise or TEUs handled) YI: Production (measured by billing for services) Y2: Service level (number of containers moved/hour), Y3: User satisfactiooY4: N° of ships arrivals Xl: Labor Input; X2: Capital Input;X3 : Intermediate Inputs;X4: Variable capital; X5: Quasi fixed Capital InputX6: Uniformity of merchandise WI: labor price; W2 : capital price; W3: intermediate input price; W4: variable capital price; W5 : quasi fixed capital price T: time trend; Zl: port size/terminal area; Z2: port location73: port ownership; Z4: capital intensity (4) OLS: ordinary least squares, ML: maximum likelihood; GLS: generalized least squares _(5):_ Measured in %; and in some cases calculated by the authors based on published results.

(6): Specific Efficiency levels assessed