rafmacalaba/datause-encoder-data huggingface dataset:
enters into the planner’s problem. Let   (0, 0.5], hence group 1 has less power than group 2. The unequal power distribution between groups indicates how polarized the government is. Similar to Alesina and Drazen (1991), I use  to measure the degree of government polarization. That is, the more unequal are the shares of resources that correspond to groups, the larger is the degree of polarization.<sup>20</sup> Revenue in period 2 ( ) is the only source of uncertainty in this economy. It is a random variable drawn from a uniform distribution with support [0, ].

# **3.1 Optimization Problem of the Unified Government**

This section solves the unified government case, which is the central planner solution. The objective of the planner is to maximize the sum of the two groups’ lifetime utility weighted by their relative power subject to the economy-wide resource constraint, where . In period one, the planner chooses aggregate spending, aggregate debt given the revenue shock and the schedule of interest rates.<sup>21</sup> In period two, the planner decides to repay or default the debt; when it chooses to default, there is a cost associated that is proportional to revenue. The objective function of the planner is given by where superscripts denote groups and subscripts denote periods. 0<  <1 denotes the discounting factor. The aggregate budget constraint in each period is given by Where denotes group _i_ ’s consumption in period one; and denote consumptions of group _i_ in period two in the event of default and repayment, respectively. denotes the level of debt engaged by the planner in period 1 to be repaid in period 2 in case that it chooses to. denotes period 1’s revenue, denotes period 2’s revenue, not known in period 1.  denotes the

> 20Alesina and Drazen (1991) use the fraction of tax burden borne by groups as measure of the degree of polarization.

> 21 We focus on the case of borrowing in period 1 to be optimal by assuming .

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<!-- Start of picture text --> Net Change between 2015 and 2050<br>Ceriops-Excoecaria-Heritiera |<br>Phoenix xylocarpus-Aegiceras Po<br>Ceriops SB<br>Avicennia-Bruguiera-Cereops Ez<br>Avicennia Ly<br>Sonneratia fL4<br>Excoecaria |<br>Excoecaria-Rhizophora-Ceriops ——=|<br>Excoecaria-Heritiera DIN<br>Ceriops-Excoecaria Ld<br>-20 -10 (e) 10 20<br><!-- End of picture text -->
The model described does, however, not take the panel feature of the data into account (see section 3). The model can be specified as a random effect model by extracting a time invariant individual specific effect from the error term: (18) _ui_ is the random disturbance characterizing the _i_ th observation and is constant through time. We make the following assumptions concerning the nature of this error term: 2 2 2 2 _E_ [ μ _it_ ] = _E_ [ _ui_ ] = 0, _E_ [ μ _it_ ] = σμ , _E_ [ _uit_ ] = σ _u_ , _E_ [ μ _itu j_ ] = 0 _for all i_ , _t_ , _and j_ , _E_ [ μ _it_ μ _js_ ] = 0 _if t_ ≠ _s or i_ ≠ _j E_ [ _u ui j_ ] = 0 _if i_ ≠ _j_ The model can easily be estimated by the use of the feasible generalized least squares method.

# **3. Data**

The data available for the evaluation of PAFPA are all collected by use of surveys. The data available for this study consist of 548 individuals where 227 have participated in the program and the remaining 321 individuals are included in the survey as a control group. Each individual was interviewed twice in 1999; one questionnaire about their economic conditions before the program participation and one about their situation after the program (both the beneficiaries and nonbeneficiaries).

In the first round the participating individuals were asked about basic individual information such as age, educational level, duration of work experience within the field of current work, etc. In the second round also nonparticipants, with similar characteristics, were included in order to be able to form a control group.

All individuals were then interviewed after PAFPA had taken place. In this survey the individuals were asked about their economic situation, how many employees they have, etc.,

> should be left out of the model when estimated, in order to avoid perfect collinearity problems. In this case NPAit is the left-out category and hence β 3 is normalized to zero).

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thermometers ranged from 65 percent in Bauchi to 93 percent in Lagos, and the share with malaria smears from 15 percent to 42 percent.

# **FIGURE 4. AVAILABILITY OF MEDICAL EQUIPMENT IN FOUR NIGERIAN STATES**

<!-- Start of picture text --> Medical equipment<br>Malaria smear<br>Disposable syringes and<br>needles<br>Bandages<br>Thermometer<br>Microscope<br>Refrigerator<br>0 10 20 30 40 50 60 70 80 90 100<br>Facilities with availability (%)<br>Source: World Bank (2008). Bauchi Cross River Kaduna Lagos<br><!-- End of picture text --> Leakages of drugs pose a challenge to all countries and vigilance is crucial, especially for drugs with high “street prices”. In Ethiopia, users and providers in focus groups reported the stealing of public sector drugs, their resale in the private market, and dealings in contraband medicines. They acknowledged the lack of drugs in the public sector and the ready availability of those drugs in private pharmacies and clinics (Lewis 2006). In Costa Rica, 32 percent of users indicated that they had prior knowledge of theft in government pharmacies (Cohen 2002). The average leakage rate for drugs in Uganda was estimated at 73 percent, ranging from 40 to 94 percent across 10 public health facilities. High demand drugs, such as those to treat malaria, were the least available to patients because health workers and the Health Unit Management Committee members, the entities meant to provide local oversight, expropriated them (McPake et al. 1999). However, a later facility exit survey in Uganda showed much higher drug availability and improved distribution (Lindelöw, Reinikka, and Svensson 2003).<sup>11</sup> Public officials typically have significant discretion over the decisions made

> 11 More insidious and even harder to address is drug mishandling from the importation of substandard medications to the repackaging of drugs, substituting lower cost/quality medications, to the pilfering of drug supplies at points of service. The health consequences of tampering can be serious but rarely traced to the source of the problem due to lack of regulation, information, and enforcement (Lewis 2006).

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In the case of the HIES 2016/17, ���<sup>�</sup> � is the 10 percent RSE desired for the mean total household expenditure estimated at the district level; _DEFF_ is the average design effect of the target variable across all districts; ��������� is the coefficient of variation of total household expenditure estimated at the national level; and ��/� is set at 1.96, which corresponds to a – significance level of 5 percent.<sup>15</sup> We use a national estimate of the CV necessary to estimate – the sample size needed to achieve the desired accuracy because we cannot always estimate district level CVs with enough precision based on the HIES 2010 data. Therefore, the above calculations assume that all districts have the same DEFFs and CVs for the target variable of interest. It is possible, that the final HIES 2016/17 sample might estimate district level welfare estimates with RSEs slightly larger than 10 percent, depending on the how close the CVs and DEFFs of the target district variables are to their actual values.

It is also important to note that we focus on district-level subsamples to estimate sample size, because, as mentioned earlier, we expect that achieving accuracy statistics at the district level will be more difficult than at the higher division and quarterly levels of aggregation. By substituting all values in equation (1), we find that the sample size needed is 715 households for each district in order to obtain RSEs of mean total household expenditure below 10 percent. This brings the preliminary total sample size to 45,760 households (715*64 districts) for the HIES 2016/17. However, we allocate 720 households to each district to fit pre-established field protocols used by BBS in the HIES 2010, and to facilitate field work and survey implementation management. More specifically, 720 households is divisible by 36 (i.e. the number of PSUs ultimately allocated to each district), which allows easy allocation of 20 households per PSUs. Also, two enumeration teams can easily cover the 36 PSUs in each district over 12 months without having to move to a different district (each team of two enumerators will visit 18 PSUs each over one year). Lastly, the number PSUs, 36, allocated to each district is a multiple of 4, which allows dividing into quarters.

> 15 To estimate _DEFFh_ , for each district, we use the Stata `‘svy: mean, over(district)’` command combined with the ‘ `estat effects, srssubpop` ’ and ‘ `estat sd, srssubpop` ’ routine. An important note to mention here is that Stata uses a linear approximation to estimate survey design effects, which opens the possibility that for some district-stratum pairs we might observe _DEFF_ values that are lower than 1. When this happens, the strategy adopted was to replace the estimated _DEFF_ values with _DEFF_ =1.

15
using the Heckman two-step procedure and the probabilities estimated in stage one of the specification were introduced as correction for biases from firm heterogeneity.

Results suggest that MRAs have no effect on the volume of bilateral exports in the telecoms sector, though they significantly increase trade in medical devices, implying that conformity assessments impose significant variable costs on producers of medical devices. The effect of harmonization on partner countries is negative and significant for both industries. Industry research (EC 2005, Medical Device Sector competitiveness study) shows an increase in intra-EU trade flows of medical devices around 1994, one year after the second harmonization initiative. However, extra-EU, US and Japanese medical device exports and imports pick up at the same time, suggesting an overall increase in demand, rather than a direct reaction to harmonization. The increase in trade volumes may therefore be picked up in the time fixed effects. The seemingly weak reaction to the 1990 and 1993 directives could be explained by the fact that harmonization completed product market integration: since firms lose their ability to price discriminate as soon as markets are no longer segmented, TBT liberalization must not necessarily lead to more trade, but could instead even cause a drop in trade volumes (Brander and Krugman, 1983). Ultimately, it would be interesting to see whether prices of partner countries converged after harmonization.

It should also be noted that the sample includes only two harmonizing regions, the EU and ASEAN, whereby ASEAN’s harmonization efforts are very recent such that effects may not fully show until after the end of the sample period. Once more years will become available for ASEAN it will also be interesting to compare the effects on third countries of EU harmonization to regional standards with ASEAN harmonization to international standards.

Effects from harmonization on excluded countries are either positive or negligible. Harmonization seems to have no important effect on the intensive margin of telecoms equipment or medical devices traded for competitive producers. For developing country exports, the coefficient on third-party harmonization is significantly positive for telecoms while those for medical devices also turn out to be insignificant.

# **SECTION VII**

# **Policy Implications**

Overall, the picture emerges that TBT liberalization initiatives have been very effective. MRAs and harmonization are shown to affect firms’ decisions to enter new markets more strongly than the export volumes of those already exporting. The recent export promotion literature increasingly recommends focusing measures on encouraging more firms to export rather than encouraging current exporters to export more. The results obtained in this paper suggest that MRAs could be a supportive policy instrument in this process.

25
of transport equipment (ISIC 384) with, respectively, 62, 26 and 8 percent of total expenditure. Thus, these three industries account for 96 percent of total Latin America’s lobbying expenditure. Concentration is also very high when one looks to the share of different countries in some of these industries. In agriculture and livestock production, Colombia accounted for 98 percent of the total; in crude petroleum and natural gas production, Venezuela accounted for 99 percent of the total, and, in the case of transport equipment, Brazil was responsible for 98 percent of the total.

We can also split the countries in three groups according to geographic localization. In this case each country can belong to South America or Caribe and Central America or NAFTA (used for Mexico). South American countries are the largest lobbyists with 95 percent of total lobbying expenditures. The sector with the largest expenditure in South America is agriculture and livestock production (ISIC 111), which accounts for 64 percent of total South American lobbying expenditures.

# 2.2 Preferences

Tariff preferences are de fi ned as the share of the Most-Favoured-Nation tariff that is waived to Latin American exporters under different preferential schemes. The data source is the excellent customs data set provided by the United States International Trade Commission (USITC).<sup>10</sup> It provides information on the value of imports and duties paid under different import regimes from each country at the tariff line level (Harmonized System (HS) 8-digit). This allows us to calculate actual tariff preferences (i.e., those ac-

> 10See dataweb.usitc.org.

6
15 Participation”, and then dividing this difference by overall GVC participation. This measure, when applied to individual country-pair-industries and GVC-trade, can be written as follows: where the vector **P** _sr_ measures the “forwardness” of country _s_ exports to country _r_ , for each sector _n_ , and each of its elements is bounded between -1 and 1. Forwardness at the country level can be obtained simply by summing across sectors and bilateral partners both the numerator and the denominator.<sup>14</sup> In the same way, a natural measure of the type of participation in output of countries and sectors in GVC can be straightforwardly obtained as At the global level, _P_ is equal to zero both for trade and output, given pure backward and pure forward participation are equal at that level of aggregation,<sup>�</sup><sup>_G_</sup> _s_<sup>_Ps_= 0.Thus,any</sup> index different from zero at any level of aggregation might be interpreted as a deviation from the world aggregate average.

The forwardness index offers a synthetic means of evaluating the primary mode through which a country or a sector engages in GVCs. Previously, a separate strand of the literature has developed specific indicators, such as “upstreamness”, “downstreamness” and “positioning”, to characterize more accurately the participation mode (Antr`as and Chor, 2019 and Wang et al., 2017; see Mancini et al., 2024 for a comprehensive database of such measures based on the most popular Inter-Country Input-Output tables). It is reassuring to see that the forwardness index proposed above shows a strong positive correlation with those measures of positioning.<sup>15</sup> Incorporating both the dimensions of the extent of GVC participation and the primary participation mode within a unified framework provides a coherent and easily comprehensible method for a thorough evaluation of a country’s or a sector’s engagement in GVCs. Additionally, the fact that the forwardness index is bounded between -1 and 1 and neutral at the world level streamlines assessments when moving from a global perspective to specific country-sector interactions. Collectively, the set of indicators we present constitutes a straightforward and comprehensive toolbox of GVC descriptive statistics, readily applicable for policy analysis.

# **4 Results: Assessing GVC Participation Through the New Measures**

We used the GVC participation measures from Section 3 on four popular Inter-Country InputOutput (ICIO) datasets: EORA, the Asian Development Bank MRIOT, OECD TiVA, and WIOD. This helped us create a detailed and up-to-date set of indicators for 189 countries and 26 to 56 industries (depending on the dataset) from 1990. The industry classification and time coverage vary based on the raw data available in each dataset. These measures are available in the World Bank’s WITS Platform, which also provides details on industry classification and time coverage. Using these measures, we can answer two main questions: First, how much

> 14See Section S2 in the supplementary online appendix for more details.

> 15See Section S3.4 in the supplementary online appendix.
- SCHOTT, P. (2004): “Across-Product versus Within-Product Specialization in International Trade,” _Quarterly Journal of Economics_ , 119(2), pp. 647-78.

- SHAKED, A., AND J. SUTTON (1987): “Product Differentiation and Industrial Structure,” _Journal of Industrial Economics_ , 36, pp. 131-146.

- STOCKEY, N. L. (1991): “The Volume and Composition of Trade Between Rich and Poor Countries,” _Review of Economic Studies_ , 58, January, pp. 63-80.

- SUTTON, J. (2007): “Quality, Trade and the Moving Windows: Competitiveness and the Globalization Process”, February, mimeo, LSE.

- WORLD BANK (2007): _Starting a Business_ , Doing Business, The World Bank, Washington, D.C., http://www.doingbusiness.org/ExploreTopics/StartingBusiness/, accessed December 15.

# **6. Data Appendix**

Exporting countries included in the sample:

- Austria (AT)

- Belgium (BE)

- Denmark (DK)

- Finland (FI)

- France (FR)

- Germany (DE)

- Greece (GR)

- Ireland (IE)

- Italy (IT)

- Luxemburg (LU)

- Netherlands (NL)

- Portugal (PT)

- Spain (ES)

- Sweden (SE)

- United Kingdom (GB)

Definition and data source of each variable used in the estimations: _pijk_ denotes the logarithm of the unit value (value per quantity) of the trade flow from country _i_ to country _j_ of subheading _k_ (eight digit Combined Nomenclature). The unit value can only be constructed for those products for which quantities are reported. The value is reported in current euros and is the FOB price. The unit value can therefore be interpreted as the average 40
To obtain the demand function for infrastructure, the model includes some additional regressors: (i) agricultural share of value added, (ii) manufacturing share of value added, (iii) urbanization rate, and (iv) population density. For each country, we created scenarios for these variables by obtaining their growth rate for the 1990–2012 period, and then applying that growth rate to obtain a forecast for the 2014–20 period. Projections of GDP composition are not available (share in agriculture and in manufacturing) so we keep them at 2010 values. For urbanization and population, we use the 2014–20 projections provided by the United Nations.

Since infrastructure stocks tend to change reasonably slowly over time and have a long life span, we included a lagged value of the dependent variable in the regression in order to increase the explanatory power. We take Fay and Yepes (2003) estimate equation below to estimate the infrastructure demand: where all variables are in natural logs to linearize the model, I<sup>j</sup> i,t is demand for infrastructure stock of type j in country i at time t; Iji,t-1 is the lagged value of the infrastructure stock; y is income per capita; A is share of agriculture value added in GDP; M is the share of manufacturing value added in GDP; Di is a country fixed effect; Dt is a time dummy; and  is the error term.<sup>12</sup> In order to calculate maintenance cost, the report provides estimates based on a topdown approach of using data on infrastructure and parameters for maintenance costs. The maintenance cost is calculated by applying a depreciation rate to the predicted total value of the capital stock.

The unit costs used to value infrastructure were obtained by applying GDP deflator inflation rate to previously estimated unit cost values presenting prices at 2011 level. Unit costs are assumed constant over time. Since more recent data were not available, these numbers were validated by World Bank experts.

For transport, energy and water sectors, a GDP deflator inflation rate is applied to the lasted available price in the literature and they were later validated by World Bank experts. In terms of telecoms sector, the costs have come down significantly in the last decade, mainly due to competition, technological change, market entry by Chinese companies and market volume. However, according to experts in the field, the projected price level is expected to remain constant in the coming decade.<sup>13</sup>

> 12 Manufacturing rather than industry was used here because industry includes mining, which has very different implications on the demand for infrastructure.

> 13 This is due to: (i) decline in shipments, due to market saturation, (ii) technological change is now pushing in favor of higher performance networks (eg DSL, fibre, 4G) which will tend to swap out performance for price, (iii) the shake up to the market

8
middle-income trap.<sup>3</sup> The contribution of this paper is to provide evidence that MENA is subject to much lower levels of growth along the income ladder compared to EAP.

**Figure 1: MENA Growth performance has been subpar**

<!-- Start of picture text --> 2000-2021<br>Lower<br>0.04 Upper Middle Middle<br>0.03<br>0.02 High Income<br>0.01<br>0.00<br>-0.01<br>-0.02<br>Bahrain Kuwait Oman Qatar Saudi Arabia United Arab Emirates Algeria Iran Iraq Lebanon Libya Djibouti Egypt Jordan Morocco Tunisia West Bank & Gaza Yemen, Rep.<br><!-- End of picture text --> Note: The blue diamonds are country average growth in GDP per capita. The red lines capture the median growth in GDP per capita in non-MENA countries in the same income group. Source: World Economic Outlook and World Bank’s Macro and Poverty Forecasts This paper is also related to the literature on the link between innovation and economic growth. In Schumpeterian growth theory, faster growth is associated with higher rates of firm creation and destruction driven by R&D and innovation (Aghion and Howitt, 1992). In this environment, incumbent firms’ innovation and productivity growth would be stimulated by competition and entry, particularly in firms near the technology frontier (Aghion et al, 2014).<sup>4</sup> There is strong empirical evidence that competition and productivity growth display an inverted-U shaped relationship: starting at an initially low level of competition, higher competition stimulates innovation and growth; however, starting from a higher initial level of competition, higher competition may hurt innovation and productivity growth.<sup>5</sup> This paper documents MENA’s slow pace of adoption in GPT which can help explain the pervasively low economic growth and TFP. This paper also provides evidence that technology adoption is slower when concentration is higher in key (upstream) sectors of the economy.

> 3 Bulman et al. (2017) find that the fraction of countries “trapped” at the middle-income level is not larger than the fraction of countries “trapped” at the low-income level. Similarly, Han and Wei (2017) find that the probability of escaping from the middle-income level is not smaller than the probability of escaping from the low-income level.

> 4 See Aghion et al. (2014) for a recent review.

> 5 See for instance Aghion et al. (2005).

3