rafmacalaba/datause-encoder-data huggingface dataset:
on the working population to finance pensions for persons over age<sup>65. The level of</sup> contribution is set to be actuarially neutral for the cohort as a whole, providing a flat rate pension of 33 percent of average full-time male earnings (the<sup>required</sup> contribution rate in this case is 15% of earnings). In the LIFEMOD<sup>cross-section (as</sup> in any cross-sectional analysis of PAYG pensions) there is necessarily a<sup>flow of</sup> resources from young to old. However, over a lifetime in this steady<sup>state worli there</sup> are no flows between generations as the lifetime contributions and<sup>lifetime benefits</sup> of each birth cohort are identical. This does not however imply that<sup>there is no inter-</sup> personal redistribution of resources, but rather that when redistribution<sup>occurs, it</sup> does so between members of the same cohort. Twenty percent<sup>of the LIFEMOD</sup> cohort die before reaching retirement age (61% of these are men,<sup>39% women). By</sup> definition, these individuals are losers, i.e. tax > benefits, but<sup>their losses mean that,</sup> of those who survive to age 65, 60% are net beneficiaries from the PAYG<sup>pension</sup> system, and only 40% are net taxpayers. Those who survive to retirement age (65) on average receive benefits that total nearly twice the amount of contributions<sup>made.</sup>

**Table 1 Lifetime redistribution effects steming from flat-rate PAYG pension**

||Q1|Q2|Q3|Q4|Q5|
|---|---|---|---|---|---|
|ALL Ratio<br>(ben/contrib)|4.29|2.00|1.31|.94|.61|
|MEN Ratio<br>(ben/contrib)|3.82|1.52<br>I|1.16<br>I|.86<br>I<br>_I|.59|
|WOMEN Ratio<br>(ben/contrib)|4.31|2.12|1.50|1.13|.76<br>l|

Table 1 shows the ratio of benefits received to contributions paid to individuals<sup>who</sup> have survived to age 65, ranked (both for the whole population and<sup>for men and</sup> women separately) by quintile of lifetime earnings. Women experience<sup>a higher ratio</sup> of lifetime benefits than men in the same quintile. Unsurprisingly<sup>those persons with</sup> the lowest lifetime earnings receive the highest ratio of benefits to contributions.<sup>This</sup> holds within the sexes. Thus PAYG has the effect of redistributing<sup>resources from</sup> men to women and from rich to poor.

16
housing costs is another group of expenditures that are important to monitor when tracking the impacts of inflation on household welfare. In this case, however, they feature prominently in the expenditures of most households along the distribution, with budget shares ranging from 26% to 19% for the 1st and 10th deciles, respectively.

A further look into the year following the exchange rate crisis that started in the middle of 2018 and led to the highest inflation rate in the last decade offers additional insights about the inflation experience of the poor. As it can be seen in Figure 5, the overall 12-month moving average inflation for this period was 19.9%, with a slightly higher inflation for the 1st decile (20.6%) and somewhat lower for the top decile (19.6%). The fact that inflation was equally high for both income groups in the 2018-19 crisis is explained by the fast price acceleration of both food and nonalcoholic beverages (26.4%) and transportation goods and services (19.2%). The overall inflation subsided in 2020, decreasing to 12.3%, but it was again moderately higher for the bottom decile (12.6%) than for the top decile (12.2%) on account once again of faster food inflation.

Figure 5: Inflation by consumption deciles (June 2018 - June 2019) <!-- Start of picture text --> 20.8 Decile Overall<br>20.6<br>20.6<br>20.4<br>20.2 20.1<br>20.0 19.9 19.9 19.8<br>19.7 19.7 19.7 19.7<br>19.8 19.6<br>19.6<br>19.4<br>19.2<br>1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> Note: 12-month moving average for the period June/2018-June/2019. 95% confidence intervals shown in orange. _Source_ : World Bank calculations using data from TUIK.

# **5.2** **_Implications on the estimation of poverty and inequality_**

Starting in 2016, TUIK stopped reporting absolute poverty and instead began to measure it in a relative sense, namely the share of people with incomes below 50% of the national median income. For that reason, Turkey does not use an absolute poverty line to monitor the evolution of 16
ernment Revenue Database of the International Monetary Fund (IMF).<sup>6</sup> . The most recent data available for most of the countries considered in this paper are from 2012. As a result, we compute the average CIT revenue % GDP between 2010 and 2012. In a few countries with no data on tax revenue during this period, we use the most recently available data as a proxy. Also, we use the average real GDP between 2010 and 2015, from the World Development Indicators, as the baseline GDP. Combining these data, we compute the real CIT revenues for each country as our baseline tax-revenue data. Further, we assume a baseline discount rate of 10 percent as used in Steinbuks and Foster (2010), and Trimble et al. (2016).

## **3.3 Instrument Construction**

As highlighted in the previous section, we compute transmission network density at the district level as an instrument for electricity access and reliability at both household and community levels. To compute the instrument, we rely on three sets of georeferenced data: the second administrative boundaries in Africa from GADM<sup>7</sup> ; electricity transmission network; and enumeration areas of the Afrobarometer survey.

First, we overlay the geocoordinates of the enumeration areas onto the administrative boundaries to match the survey areas to their respective districts based on their geolocation. The resulting shapefile together with the shapefile on the transmission network are used in computing the instrument. Using the Spatial Analyst Tool in ArcGIS, we compute the line density – defined as the total length of the grid lines divided by the area of the polygon – for each district. Figures 3 and 4 show, respectively, the geo-location of the survey communities and the distribution of network density across the study area.

# **4 Results**

The analysis of the paper is structured as follows: In the first part, we focus on the individual (household) data from Afrobarometer to estimate the effects of electricity access and reliability on attitude toward tax compliance. We also examine the mechanisms driving this relationship by estimating the impact of electricity access on individuals’ sense of national identity. Unfortunately, analyzing the relationship at the firm level is an empirical challenge since there are no available data set on firms’ attitudes toward taxes. Nevertheless, the results from the household sector apply to firms as well, particularly given the

> 6https://www.imf.org/en/News/Articles/2015/09/14/01/49/pr15374

> 7http://www.gadm.org/about

15
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WPS4124

# **TRADE EFFECTS OF REGIONAL STANDARDS LIBERALIZATION** A HETEROGENEOUS FIRMS APPROACH

## **_Silja Baller* World Bank_**

### **Abstract**

This study investigates trade effects of the regional liberalization of technical barriers to trade (TBTs) in the form of harmonization and mutual recognition agreements (MRAs) for testing procedures. The theoretical part of the paper is framed in terms of a heterogeneous firms approach. This paper adds to the existing literature by formalizing the effects of MRAs and harmonization initiatives on bilateral trade flows and by applying this new theoretical framework in the empirical part of the paper. The latter consists of a two-stage gravity estimation and investigates sectoral effects of regional TBT liberalization on parties to the agreement as well as excluded industrialized and developing countries. It finds that MRAs have a strong positive influence on both export probabilities and trade volumes for partner countries. Regarding harmonization, results seem to suggest that the impact on parties to the agreement is negligible, however that on excluded OECD countries is large and positive. Third party developing countries do not seem to benefit from the market integration effect brought about by harmonization in other regions. Overall, effects on the probability that a new firm will export are much more pronounced than effects on the trade volumes of incumbent exporters.

World Bank Policy Research Working Paper 4124, February 2007 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

### **Acknowledgements**

The author has benefited from the guidance of and helpful discussions with Richard Baldwin and Mona Haddad as well as valuable comments from Paul Brenton, John Cuddy, Barbara Fliess, Sudarshan Gooptu, Marion Jansen, Patrick Low, Marc Melitz, Roberta Piermartini, Benjamin Shepherd and John Wilson.

* Correspondence: <u>sballer@worldbank.org, East Asia and Pacific Region, Poverty Reduction and</u> Economic Management Sector
**Table 9.** MNL regressions- effects of gender role attitudes and personality traits on employment outcomes ( _women_ sample)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_Norms_|_Norms+_<br>_Controls_|_Norms+_<br>_Control+_<br>_Personality_|_Norms+_<br>_Controls+_<br>_Impulsive_|_Norms+_<br>_Controls+_<br>_Work_<br>_Central_|_Norms+_<br>_Controls+_<br>_Tenacity_|_Norms+_<br>_Controls_<br>_+_<br>_Polychro_<br>_nicity_|_Norms+_<br>_Controls+_<br>_Locus of_<br>_Control_|_Norms+_<br>_Control+_<br>_Achievemen_<br>_t_|_Norms+_<br>_Control+_<br>_Power_<br>_Motivation_|_Norms+_<br>_Controls+_<br>_Organized_|_Norms+_<br>_Controls+_<br>_Optimism_|
|**EMPLOYED**|||||||||||||
|Traditional|-0.028***|-0.011|0.002|0.020|0.040|0.022|0.006|-0.057*|0.046|0.036|-0.023|-0.116**|
||(-4.58)|(-1.57)|(0.22)|(0.84)|(1.10)|(0.71)|(0.21)|(-1.78)|(0.94)|(1.09)|(-0.72)|(-2.22)|
|Age||0.416***|0.418***|0.409***|0.410***|0.413***|0.418***|0.417***|0.420***|0.422***|0.416***|0.429***|
|||(9.75)|(9.63)|(9.51)|(9.57)|(9.67)|(9.74)|(9.70)|(9.76)|(9.81)|(9.76)|(10.08)|
|Age_sq||-0.005***|-0.005***|-0.005***|-0.005***|-0.005***|-<br>0.005***|-0.005***|-0.005***|-0.005***|-0.005***|-0.005***|
|||(-9.11)|(-9.03)|(-8.88)|(-8.95)|(-9.04)|(-9.12)|(-9.08)|(-9.11)|(-9.15)|(-9.12)|(-9.37)|
|Married||-0.031|0.004|-0.015|0.010|-0.014|-0.040|-0.035|0.018|-0.047|-0.037|-0.066|
|||(-0.15)|(0.02)|(-0.07)|(0.05)|(-0.07)|(-0.19)|(-0.17)|(0.08)|(-0.23)|(-0.18)|(-0.31)|
|At least secondarysch||0.295|0.255|0.256|0.294|0.261|0.294|0.286|0.260|0.305|0.291|0.291|
|||(1.55)|(1.29)|(1.35)|(1.51)|(1.35)|(1.56)|(1.49)|(1.36)|(1.56)|(1.51)|(1.50)|
|Gender of head(male=1)||-0.669|-0.497|-0.618|-0.807|-0.754|-0.674|-0.604|-0.816|-0.703|-0.684|-0.412|
|||(-0.94)|(-0.62)|(-0.87)|(-1.09)|(-1.05)|(-0.95)|(-0.87)|(-1.09)|(-0.98)|(-0.96)|(-0.55)|
|Muslim(Christian is base)||-0.109|-0.140|-0.124|-0.126|-0.144|-0.102|-0.102|-0.130|-0.084|-0.111|-0.063|
|||(-0.41)|(-0.50)|(-0.47)|(-0.48)|(-0.55)|(-0.38)|(-0.39)|(-0.50)|(-0.31)|(-0.42)|(-0.22)|
|Spousepresent||-0.049|-0.258|-0.119|0.077|-0.001|-0.045|-0.118|0.054|0.005|-0.034|-0.251|
|||(-0.07)|(-0.32)|(-0.17)|(0.10)|(-0.00)|(-0.06)|(-0.17)|(0.07)|(0.01)|(-0.05)|(-0.33)|
|Household size||0.032|0.033|0.040|0.035|0.036|0.034|0.030|0.033|0.028|0.033|0.025|
|||(1.11)|(1.12)|(1.33)|(1.16)|(1.22)|(1.20)|(1.03)|(1.14)|(0.98)|(1.14)|(0.90)|
|Poor||-0.807***|-0.823***|-0.780***|-0.785***|-0.812***|-<br>0.804***|-0.809***|-0.799***|-0.784***|-0.810***|-0.877***|
|||(-3.58)|(-3.45)|(-3.39)|(-3.48)|(-3.52)|(-3.55)|(-3.58)|(-3.48)|(-3.42)|(-3.57)|(-3.86)|
|Urban||0.445*|0.497*|0.443*|0.477*|0.456*|0.437*|0.434*|0.457*|0.449*|0.445*|0.457*|
|||(1.71)|(1.77)|(1.69)|(1.77)|(1.69)|(1.66)|(1.66)|(1.71)|(1.73)|(1.70)|(1.73)|
|Ashanti(base: Northern)||-0.071|-0.150|-0.095|-0.069|-0.062|-0.072|-0.056|0.017|-0.021|-0.070|-0.022|
|||(-0.25)|(-0.52)|(-0.34)|(-0.25)|(-0.22)|(-0.25)|(-0.20)|(0.06)|(-0.08)|(-0.25)|(-0.08)|
|Impulsive|||-0.333**|0.157|||||||||

35
|Equity*Big0.1||-0.039||||0.165|||
|---|---|---|---|---|---|---|---|---|
|||(0.052)||||(0.468)|||
|Liquidity*Big0.1|||-0.050||||0.552||
||||(0.055)||||(0.397)||
|Return on assets*Big0.1||||-0.226**||||0.105|
|Panel C: Big0.25 included||||(0.090)||||(0.969)|
|Big0.25|-0.002|-0.000|0.005|-0.001|-0.052**|-0.015|-0.063|-0.025|
||(0.002)|(0.004)|(0.004)|(0.003)|(0.026)|(0.057)|(0.046)|(0.032)|
|Equity *Big0.25||-0.033||||-0.684|||
|||(0.048)||||(1.256)|||
|Liquidity *Big0.25|||-0.092*||||0.606||
||||(0.054)||||(0.629)||
|Return on assets|||||||||
|*Big0.25||||-0.153||||-2.593|
|Panel D: Big0.5 included||||(0.142)||||(4.289)|
|Big0.5|-0.006|-0.003|0.003|-0.005|-0.059**|-0.113**|-0.093|-0.069**|
||(0.004)|(0.008)|(0.008)|(0.005)|(0.024)|(0.047)|(0.059)|(0.032)|
|Equity *Big0.5||-0.057||||1.088|||
|||(0.107)||||(0.717)|||
|Liquidity *Big0.5|||-0.116||||0.625||
||||(0.112)||||(0.965)||

34
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Using the Climate Policy Assessment Tool (CPAT), we estimate the required level of the carbon tax for each economy to reach its NDC in 2030.<sup>8</sup> Under the policies implicit in the latest NDCs, most South Asian economies will easily reach their Paris targets (i.e., the required level of the carbon tax is essentially zero or quite low). However, this could also reflect relative lack of ambition in these economies’ mitigation pledges.<sup>9</sup> As of end-2022, no South Asian economy had adopted pure market-based carbon pricing schemes, including Emissions Trading Systems (ETSs) and feebates. India’s special tax on coal is the closest to a direct carbon price in the region. Pakistan’s government is considering some form of monitoring. A few emerging markets (Argentina, Colombia, Chile, Mexico, and South Africa) have experimented with moderate carbon taxes (less than US$10/ton CO2e as of 2019; World Bank 2022c). In our analysis we will assume the carbon tax is introduced domestically, covering all sectors/fuels (i.e., power, industry, transport, residential/buildings – see also Section 3 below). Refer to Pryor et al. (2023) for a more detailed discussion of these issues.

# **3. Data and methods**

## **3.1. Simulating carbon tax implementation**

The most efficient way to reduce GHG emissions is through a comprehensive adoption of carbon taxes (Nordhaus, 2015). A carbon tax is a per-unit charge on the carbon content (emissions per unit of use) of fossil fuels. Carbon taxes, unlike regulations, incentivize businesses and individuals to switch to greener fuel consumption options. This is also true for ETSs. However, ETSs are generally limited to the power sector and large industry<sup>10</sup> and require significant administrative capacity to monitor compliance (Table 2).

> 8 This calculation implicitly assumes that the only new policy applied is an economy-wide carbon tax. In this sense, the calculation is illustrative and aims to gauge the amount of mitigation ‘effort’ that South Asian economies’ NDCs would require. The NDCs could, however, be met via a combination of measures including green investments and the provision of research and development (R&D) subsidies for the renewables sector, etc. This calculation only focuses on fossil-fuel CO2 emissions (e.g., excluding methane and LULUCF emissions).

> 9 Taken together, economy-specific Paris pledges are at most consistent with warming of 2.5-3 degrees Celsius (which is not in line with the overarching goal of the Paris Agreement to limit warming to below 2-1.5 degrees Celsius (see also discussion in Parry, Mylonas, and Vernon, 2021).

> 10 However, some ETSs (e.g., Germany) apply midstream to, for example, buildings/transport fuels, with the EU ETS being expected to cover these sectors as of year 2027. See: <u>https://www.europarl.europa.eu/news/it/press-room/20221212IPR64527/climate-change-deal-on-a-more-ambitiousemissions-trading-system-ets.</u>

8
- 2 -

Slade (1982), examining rural growth linkages in **the** Nuda River Region of Malaysia, compute an agriculturally induced rural income multiplier of 0.8. Employment multipliers are also substantial. Evidence from the Philippines (Gibb, 1974) and India (Krishna, 1976; Mellor and Mudahar, 1974) places agriculture-to-nonfarm employment elasticities between 1.0 and 1.3. Although decomposition of the growth multipliers in rare, Bell, Hazoll, and Slade (1985), in their Muda. study, do provide a breakdown, estimating the contribution of the consumer demand generated by agricultural growth to be double the production linkages.

As attention turns increasingly to Sub-Saharan Africa, government leaders and donors alike view small farmer agriculture as r- necessary centerpiece of development efforts. Equity, nutrition ant *ty considerations argue persuasively for such a focus. In adCLa .' .n, many believe that a small farmer strategy will generate maximu growth rates, Asia-style, through linkage multipliers with the rural economy.

But no one has yet marshalled the empirical evidence necessary for assessing the power of agricultural growth linkages in Africa. It may now be possible to do so given the accumulation, over the past 25 years, of a body of detailed survey work examining the structure of Africa's rural nonfarm economy. 2/ This paper examines that evidence. First, it systematically reviews empirical evidence on the nature and magnitude of the African rural nonfarm economy. It then explores differences across locality size, across countries and over time, in an effort to assess likely patterns of growth. A subsequent review of key production and consumption parameters allows an estimate of the magnitude of the agricultural growth multipliers in Africa. The paper concludes with a brief discussion of policies and programs that will be necessary if farmnonfarm growth linkages are to achieve their full potential.
# **Figure 2: Food gap in number of months**

<!-- Start of picture text --> a) Food insecure households (%)<br>59.5 60.7 57.6<br>60 55.2<br>51.5<br>49.1<br>40<br>20<br>0<br>All Non-PSNP PSNP<br>b) Food gap (number of months)<br>2<br>1.6 1.7<br>1.5<br>1.5 1.3 1.4<br>1.2<br>1<br>.5<br>0<br>All Non-PSNP PSNP<br>c) Food gap (number of months) by region<br>2.5<br>2.5<br>2 1.8 1.8 1.8<br>1.5 1.2<br>0.9 0.9<br>1 0.6<br>.5<br>0<br>Tigray Amhara Oromia SNNP<br>2019 2020<br><!-- End of picture text --> _Notes_ : Sample weights applied.

30
_the relevance and exogeneity criteria for instrumental variables for yi1 in (*). The simple correlation coefficient_  _yi_ 1<sup>_y_</sup> _i_ 2 _can then be approximated with the synthetic panel cohort-level simple correlation coefficient_  _yc_ 1<sup>_y_</sup> _c_ 2 _, where c indexes the cohorts constructed from the household survey data._

# **Proof**

See Appendix 1.

In the absence of true panel data, we do not observe yi1 for the same household with household consumption in period 2, but we can predict it by projecting household consumption in period 1 on the cohort dummy variables; this process is equivalent to an instrumental variables (IV) estimation where the instrumental variables are the cohort dummy variables, and practically results in taking period-by-period sample averages within the head of household i’s cohort (Verbeek, 2008). Thus a consistent estimate for  _yi_ 1<sup>_y_</sup> _i_ 2 (and  ) requires that these instruments are relevant (i.e., statistically significant in the regression of household consumption on themselves) and exogenous (i.e., being uncorrelated with the error term<sup></sup> _i_ 2<sup>).</sup> While the former assumption can be easily checked given the available data, the latter needs to be assumed and depends on our assumptions or on prior knowledge about the specific data under consideration. For example, while we expect the latter assumption holds in most contexts, it would be violated if there are cohort effects in the random error term<sup></sup> _i_ 2<sup>(see, for</sup> example, Moffitt (1993) or McKenzie (2004)).<sup>9</sup> Furthermore, in addition to the relevance and exogeneity conditions, good instruments also need to be strong for unbiased estimates (Stock and Yogo (2005)), which effectively requires cohort dummy variables to be strongly

> 9 On a related note, surveys that focus on a particular age group of the population (e.g., youth surveys) or with particular designs (e.g., oversampling certain age cohorts) are unlikely to provide consistent estimates for the whole population.

10