rafmacalaba/datause-encoder-data huggingface dataset:
Williamson’s (1965) classical account of the linkage between spatial disparities and the stage of economic development. In Williamson’s account, the level of withincountry spatial inequalities is fundamentally the result of the level of national economic development (proxied in this case by real GDP per capita and its growth). As countries prosper inequalities tend to diminish, making economic growth a primary driver of changes in spatial inequalities. Williamson’s theory is built-in into the WDR 2009. There it is stated that not only has “development [...] largely eliminated rural-urban disparities” (World Bank, 2009: 62), but also that “high urban shares and concentrated economic density go hand in hand with small differences in rural-urban well-being on a range of indicators” (ibid.: 62). As economic growth is also likely to be correlated with changes in trade (Sachs and Warner, 1995), a control for real GDP per capita and its interaction with the country’s development stage is included in the analysis.

# **4.3. The empirical model, data and method**

The above discussion leads to the transformation of equation (1) into the following empirical specification (4). Table A1 in the appendix presents the actual values of the structural conditions across the 28 countries.

ln Inequality<sup>*</sup> it<sup>= α + β1 [ln(GDPcap</sup> it<sup>) * Development</sup> i<sup>] + β2 [ln(Trade</sup> it<sup>) *</sup> ln(MarketAccessi) * ln(Coincidencei)] + β3 [ln(Tradeit) * ln(Sectorsi)] + β4 [ln(Tradeit) * ln(Governmenti)] + εit (4) where: _Inequalityit_ represents the level of within-country regional inequality in country i in year t, measured using the Gini index of regional GDP per capita.

_GDPcapit_ denotes real GDP per capita in PPP in constant US$ (2000) for country i in year t.

_Developmenti_ is a dummy variable which takes the value of 1 if country i is a developing or transition economy and 0 otherwise. The categories were assigned on the basis of historical World Bank classifications. Each country was assigned to its 15
**Table 13:** Job Flows and the Role of Labor Regulations (Difference-in-Difference Analysis) (continued)

|(1)<br>(2)|(3)<br>(4)<br>(5)<br>(6)|
|---|---|
||[0.0183]|
|Observations<br>940<br>940|940<br>940<br>940<br>940|
|Adjusted R-squared<br>0.55<br>0.56|0.69<br>0.69<br>0.69<br>0.69|
|Standard errors in brackets. <sup>_∗_</sup>signifcant at 10%, <sup>_∗∗_</sup>signifcant|at 5%, <sup>_∗∗∗_</sup>signifcant at 1%. Columns (1) and (2) include|
|region dummies. Columns (3)-(6) include country dummies.|USA SUM: industry/size job reallocation in the United|
|States. EU denotes the OECD European countries. Transiti|on denotes the countries in Central and Eastern Europe.|
|LAC denotes the countries in Latin America. EPL is the in|dex of stringency of hiring and fring regulations. EPL|
|(Adj) is the indicator of hiring and fring adjusted to take int<br>(see main text).|o account diferent degrees of enforcement of regulations|

Source: Own calculations based on harmonized firm-level database.
price increases to 14%, 3 years post-expansion, before stabilizing around 7%-8% thereafter. On the contrary, the impact on mobile BB price drops to around 11%, 2 years post-expansion, and continues dropping towards a non-significant impact 5 years post-expansion. Except for Europe and the Middle East regions, the impact of SMCs on broadband prices was negative and statistically significant, with the lowest impact estimated for the African region, followed by Asia and Latin America where broadband markets tend to experience their biggest drop in price with the arrival of SMCs.

Our findings suggest that cost savings are the main drivers of the price effects in the short term, with some indicative role of competition in the medium term. They also suggest that regulation can be a powerful tool to increase the pass-through rate of cost savings and limit further concentration of retail broadband markets. In particular, an independent regulator, with a mandate to enforce competition through (i) reduced barriers to entry for domestic and foreign operators, (ii) shared infrastructure, including radio spectrum, (iii) regulation of interconnection and market dominance, as well as (iv) universal service provisions, and (v) consumer protection can all contribute to boost the impact of SMCs on price and alleviate its effects on market concentration.

This paper relates to the literature on digital development and the industrial organization of the telecom sector. Studies on digital development have investigated the economy wide effects of digital infrastructure, especially on GDP, jobs, productivity, innovation, entrepreneurship, and trade, but did not analyze the market outcomes of this infrastructure. A notable study is Hjort and Poulsen (2019) which exploits the gradual arrival of SMCs in Africa between 2000 and 2010 and found that changes in access to high-speed Internet post arrival of the cables led to job creation across all levels of education. A recent study conducted by Simione and Li (2021) exploited the arrival of SMCs in Africa after 2009 and found that a percentage point (pp) increase in Internet penetration induced a 0.37 pp increase in real per capita GDP growth, with increased labor productivity in the utilities, trade, and transportation sectors. These findings were supported by country case studies in Sub-Saharan Africa (SSA) (O’Connor and Anderson (2020)) which found a positive impact on GDP per capita, and an increased likelihood of being employed in fiber-connected areas. SMC connectivity can also affect trade in developing countries as suggested by findings from Imbruno et al. (2022) and Cariolle and da Piedade (2023).

Studies on the industrial organization of the telecom sector have looked, among others, at the drivers of price, especially the role of competition and regulation, but did not consider these effects in the context of infrastructure deployment. To our knowledge, the market effects of 4
shows the value of GVC exports for SACU and other regions like ASEAN, Eastern Europe, Mercosur and the East African Community. Even accounting for size, SACU lags: SACU countries export less than 3 percent of the value of GVC products that ASEAN exports, with a population that is roughly 10 percent of ASEAN and GDP that is 18 percent of ASEAN. Within SACU, South Africa accounts for about 92 percent of the region’s GVC exports.

**Table 7: Value of GVC Exports, 2012, by Region**

|**Region**|**Value(in US$000s)**|
|---|---|
|ASEAN|492,000,000|
|Eastern Europe|319,000,000|
|Mercosur|31,200,000|
|South Africa|11,200,000|
|SACU(excl. South Africa)|985,222|
|‐Lesotho|530,794|
|‐Swaziland|229,695|
|‐Botswana|137,418|
|‐Namibia|87,315|
|East African Community<sup>11</sup>|589,240|

Source: Authors’ calculations based on data from UN Comtrade SACU countries have accumulated different levels of GVC‐related capabilities (Table 8). In terms of human capital, as measured by the average years of schooling, Botswana and South Africa score high (at over eight years of schooling), while Namibia lags behind the other SACU countries (at six years). South Africa’s total natural capital valuation far outpaces other countries, with Botswana in a distant second. Botswana and South Africa have a substantial advantage on physical capital per capita. Having successfully managed revenues from resource extraction, Botswana’s institutions are rated ahead of the other countries, including South Africa, with Lesotho and Swaziland lagging behind the others in this area. All countries are in a similar geographic situation, relative to other markets. South Africa is a much more sophisticated logistical hub, however. South Africa, being a more industrialized country, has a higher minimum wage, in the context of higher productivity. In terms of market access and access to inputs, all countries are in a favorable position, with the exception of Swaziland on market access.

> 11 The East African Community (EAC) includes Burundi, Kenya, Rwanda, Uganda and Tanzania.

14
Investment returns fluctuated considerably from year to year, reflecting the large volatility of equity market returns in the first decade of the new millennium (Table 4.4). The fund achieved a substantial excess return over its benchmark in 2001 and 2002 mainly because of the delay in implementing its asset allocation strategy. This was partly caused by the long procedure imposed by EU regulations on public procurement, which delayed the selection of managers and the investment of cash during 2001. But it was also partly due to a deliberate decision to invest the funds slowly during 2002 because of the unsettled market conditions that prevailed at that time. Excess returns were highly negative in 2003 and 2004, but were close to zero in the last two years.

|Table4.4: <br>|Investment <br>|Returns b<br>|yAsset C<br>|lass,200<br>|1-2006<br>||
|---|---|---|---|---|---|---|
|percent|2001|2002|2003|2004|2005|2006|
|Equities||-29.6|18.1|10.4|26.9|14.0|
|Bonds||10.7|4.2|11.3|8.9|-2.1|
|Property||||||27.0|
|Private Equity||||||4.9|
|Commodities|||||25.9|-23.9|
|Cash|3.3|3.4|2.4|2.1|2.1|3.0|
|All Assets|3.3|-16.1|12.8|9.3|19.6|12.4|
|Benchmark|-3.5|-21.6|16.6|11.2|20.1|12.2|
|Excess Returns|6.8|5.4|-3.8|-2.9|-0.5|0.2|
|||Source: NP|RF||||

The annualized average rate of return over the whole period 2001-2006 amounted to 6.5 percent. This exceeded inflation by a comfortable margin. The real rate of return was 4.2 percent. The overall average excess return amounted to 1.4 percent (140 basis points), though as just noted, this was the result of the fortunate delay in implementing the investment strategy.

Equity returns averaged 5.8 percent, which was slightly below the 6.5 percent earned by the bond portfolio. However, in the long run, equities are likely to earn significantly more than bonds. Alternative asset classes, especially private equity and real estate, are expected to make a large relative contribution to investment income in the longer run. However, these asset classes incur high upfront expenses and thus it will be some time before their full results are realized.

Operating expenses, including the expenses of NTMA, increased over time and amounted to 21 basis points in 2006 (Table 4.5). The combined Commission and NTMA expenses ranged between 6 and 7 basis points and are comparable to management expenses of other large pension funds. However, external management fees look high at over 15 basis points. Investment management fees charged indirectly through participation in investment funds are not included in reported fees but are deducted from reported investment returns.

53
**Table 4:  Gender Wage Gap for Regular Salaried Employee by Industry:  2007-08**

|**Industry**|**Rural (%)**|**Urban (%)**|
|---|---|---|
|Agriculture (01-05)|69.09|61.89|
|Mining and Quarrying (10-14)|56.96|56.43|
|Manufacturing (15-22)|51.69|58.73|
|Manufacturing (23-37)|41.7|83.32|
|Electricity, Gas and Water (40-41)|90.04|69.49|
|Construction (45)|95.71|74.69|
|Trade (50-55)|77.78|80.71|
|Transport and Storage etc. (60-64)|106.01|129.9|
|Services (65-74)|53.57|86.71|
|Services (75-93)|51.32|68.62|
|Private hhs with emp. Persons (95)|44.47|39.93|
|All|61.69|77.11|

Source: Adapted from Chakravarty and Gandhi (2016). Note: Codes in brackets represent National Industrial Classification (NIC), 2004 industry codes.

The micro-level data from the employment/unemployment survey of the Indian labor market conducted by NSSO depicts that the largest gaps in wage (wage gap is calculated by female average wage (in Rs.) as a percentage of male average wage/ salary received per day) exists in private households with employed persons in both rural and urban areas where females get nearly 40 percent of the wage given to that of males. The manufacturing sector has higher concentration of women workers and huge gap in wages exits in rural areas was compared in urban areas. The only sector which provides higher wages to female workers as compared to men is transport and storage industry. Other sectors like construction; electricity, gas and water; and trade depict relatively lower wage gap for both rural and urban areas, however, these are the sectors with low concentration of women workers as well. Thus, it could be interpreted that the industries with high concentration of women also depict high gender wage gap and vice versa.<sup>15</sup> As seen in Table 4, the gender wage gap is often observable in almost all the industries and sectors in India, it is expected to vary also across areas of residence, i.e. urban or rural. The main reason behind this gap is the skewed sex ratio and female labor force participation rate across the organized and unorganized sectors of the economy. The social norms and stereotypical perceptions about women’s work lead to scarce employment opportunities and wage discrimination against women in India. Due to marginal increase in the sex ratio in India for rural and urban areas and overall sex ratio in the last two decades, the changes in female workforce participation gap have not been much visible yet. Though several 15 Chakravarty, Kavita and Tanya Gandhi 2016. “Extent of Gender Wage Gap in India”, paper presented 25th IAFFE Annual Conference, Galway, Ireland, June.

16
_13_ of the system with its associated lower capital requirement may be necessary and should be recognized from the outset.

**Figure** 2: **Regulating supply and demand for payment services**

<!-- Start of picture text --> Demand CaDacitv<br>Forecast Planning<br>(Demand Management) (Supuly Management)<br>Prices Mareginal<br>! Prices r ~~~~Costs<br><!-- End of picture text --> Part II of the document deals with the more practical question of moving from costs to prices, providing a discussion of the informational requirements, implementation problems, and some experiences and examples in pricing payment services.

# **PART II**

# **7. Information needed to price payments and problems in implementation**

**_Information needed._** Cost and payment volume information are needed to properly price payment services. Importantly, prices should generally be set to recover all costs since this results in proper use of payment services by consumers, businesses, and banks. One of the best pricing methodologies is **_two-part pricing_** as this pricing structure will (a) reflect the likely scale economies in payment processing and (b) pass the benefits of high volume operation on to those high volume users who generate the economies of scale.

The implementation of two-part pricing is illustrated in Table A3, which follows procedures on how the U.S. central bank develops its payment pricing structure. The first step is to obtain estimates or projections of total variable cost (TVC) and total fixed cost (TFG) by each of the i payment service categories to be offered. TVCi reflects the direct costs (wages, fringe benefits, supplies, transportation, etc.) incurred in producing the ith payment service. TFCi represents (1) the annual cost of leased/rented facilities or equipment plus (2) the depreciated value of any wholly-
TABLE 40: TENDER PREPARATION COST SAVINGS BY ORGANIZATION

|Organization|Savings per<br>tender(taka)|Savings per tender<br>(US dollar)|Number of<br>tenders|Total Savings<br>(taka)|Total Savings<br>(US dollar)|
|---|---|---|---|---|---|
|BWDB|7,280|86|6,793|5.0 crore|0.6 million|
|LGED|7,279|86|54,124|39.4 crore|4.6 million|
|RHD|10,964|129|8,323|9.1 crore|1.1 million|

c. Security Cost Savings: Due to widespread coercive practices employed by bidders preventing other bidders from physically submitting bids that frequently led to violent incidents, procuring entity officials often needed to request the help of security forces, primarily the police, to deal with such situations. Soliciting the help of the police implied certain costs that were often informal in nature. Procuring Entity Officials were asked for what percentage of tenders did they need to engage security personnel and what was the amount the organization paid or spent for it. The product of the two was calculated and averaged for the organizations. As for tender preparation costs, the security cost savings have not been adjusted for inflation. The total savings for not requiring security was estimated to be Tk. 11.5 crore (US$ 1.4 million).

TABLE 41: SUMMARY OF SECURITY COST SAVINGS BY ORGANISATION

|Organization|Savings per<br>tender (taka)|Savings per tender<br>(US dollar)|Number of<br>tenders|Total Savings<br>(taka)|Total Savings<br>(US dollar)|
|---|---|---|---|---|---|
|BWDB|1271|15|6,793|0.9 crore|0.1 million|
|LGED|1777|21|54,124|9.6 crore|1.1 million|
|RHD|1276|15|8,323|1.1 crore|0.1 million|

- d. Staff-time savings: We observe that eGP reduced the times for the different constituent processes of procurement. However, these reduced in times do not translate to staff-time savings for the Procuring Entity Official. PE officials were asked about the time they spent on each constituent activity of the procurement process using the manual and the electronic systems. These times were averaged by the method of procurement used and the difference of the times between the manual and electronic systems was multiplied by the remuneration per unit time. The table given below shows the time saved per activity.

TABLE 42: TIME TAKEN FOR EACH ADMINISTRATIVE ACTIVITY

|Activity|Description|Time<br>taken -<br>Manual|OTM<br>Time<br>taken -<br>eGP|Time<br>Saved|Time<br>taken -<br>Manual|LTM<br>Time<br>taken -<br>eGP|Time<br>Saved|
|---|---|---|---|---|---|---|---|
|Tender<br>Preparation|Creating the tender in the e-<br>procurement system, defining<br>the technical specifications.|4 hours<br>15 mins|1 hour|3 hours<br>15 mins|2 hours<br>30 mins|30 mins|2 hours|

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as regions or urban and rural areas. If relative income is modeled in terms of predicted income and predicted income is estimated using a set of personal and/or household characteristics, these characteristics should normally not be used also in the utility model, although this is not always the case in empirical papers.

Finally, the **distinction between cross-section and longitudinal models** leads us to three types of studies, those that rely on one cross-section survey focusing therefore on individuals or households within a single country, cross-country studies, and longitudinal studies. The empirical specification of relative income models is essentially the same with the only difference being the change in subscripts but the interpretation of the coefficients is evidently different. For example, the great debate on the Easterlin paradox revolved around the paradox of countries showing little increase in happiness as they grew richer but this is very different from considering individual happiness and income within these same countries. The measurement of collective utility over time and the measurement of individual utility over a given society in a given point in time are two very different lines of investigation even if the concept of relative income is central in both types of studies.

In essence, the standard econometric model to test the relative income hypothesis can be described as where β1 and β2 are the parameters of interest.

Provided that cross-section and longitudinal data are available, one can of course try to reconcile the two types of relative income hypotheses. Di Tella et al. (2003) offer, for example, one of the most comprehensive models by considering individuals, countries and time at once trying to test the relative income hypothesis using countries and time as reference: These complex relations are nicely depicted in the following figure from Clark et al. (2008) where the cross-section within countries relation between happiness and income is superimposed over the longitudinal cross-country relation. The figure shows how difficult is to capture these relations in one equation, especially if the empirical model is linear. It also shows that we can think of two forms of relative income hypotheses. One is relative income across individuals within countries while the second is relative income across time.

13
In addition to the concentration curves, we calculate concentration coefficients for each type of spending. Concentration coefficients are calculated in the same manner as is the Gini; for cases in which the concentration coefficient is above the diagonal, the difference between the triangle of perfect equality and the area under the curve is negative and spending is progressive in absolute terms (that is, the size of the transfer per capita falls with per capita income). Spending is absolutely progressive if it is less than -0.1. As shown in figure 18, this is true for pre-school education, grant-in-aid, old age pension, primary education, the disability, foster care and child support grants, and most notably for the care dependency grant. What is also very clear is that when free basic services are targeted as cash transfers, they are extremely progressive. When they take the form of an indirect subsidy benefiting all households, they are progressive only in relative terms when compared to the Gini of market income.

|Figure 18. South Africa. Concentration coefficients for Spending<br><br>|
|---|
|(0.39)<br>Free basic services (as transfers)|
|(0.37)<br>Care dependency|
|(0.34)<br>Child support grant|
|(0.32)<br>Foster Care grant|
|(0.25)<br>Disability grant|
|(0.19)<br>Primary education|
|(0.19)<br>Pre and Primary education|
|(0.17)<br>Old age pension|
|(0.15)<br>Grant-in-aid|
|(0.12)<br>Secondary School Education|
|(0.11)<br>Pre-school education|
|0.00<br>Health Spending|
|0.01<br>Other grants|
|0.16<br>Free basic services (as indirect subsidies)|
|0.50<br>Tertiary Education Spending|
|0.77<br>Market income Gini|
|(0.60) (0.40) (0.20)<br>-<br>0.20<br>0.40<br>0.60<br>0.80<br>1.00|
|Source: Own estimates for South Africa based on IES 2010/11.|

**V. The Net Fiscal System and the Distribution of Income**

Section IV makes clear that taxes and social spending (for the items that we have examined) are each globally progressive. These findings are in line with previous research for South Africa. However, the question that really interests us is the impact on equity of the net fiscal system. That is, how large is the impact of the fiscal instruments discussed above on inequality and poverty? In this section we aim to address this issue by combining the two elements that are at play: the level of taxes and transfers, and their progressivity. The distinction is important, since a tax or expenditure instrument could be very progressive, but so small that the impact on poverty and inequality is negligible. As we show below, both elements are important in South Africa: both the size of taxes and transfers and their progressivity is large, making the overall impact on poverty and inequality greater than in comparator countries. In addition, a fiscal system could generate a lot of re-ranking (i.e., individuals moving places in the income scale) but without really making any significant changes on the distribution of income.<sup>57</sup>

> 57 Furthermore, one cannot really assess the redistributive effect by analyzing the effect of an individual tax or transfer in isolation as in the redistributive process both interact.

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