rafmacalaba/datause-encoder-data huggingface dataset:
converted to USD). This may be due to a number of reasons, including methodological differences introduced by the 2011 ICP compared to the 2005 ICP.<sup>36</sup> When initially reviewing the country-level estimates of extreme poverty based on the 2011 PPP conversion factors, there were several countries with large �s, for which the new poverty estimates based on the 2011 PPPs were significantly lower than expected based on the previous estimates for 2011 (using the $1.25 line and the 2005 PPPs). A deviation from expectations is certainly not evidence that something is wrong with the 2011 PPP conversion factor. The change could be real, or there could be problems with the CPI data, or with the 2005 PPP conversion factor. However, the countries identified as problematic often had large increases in inflation as measured by the CPI, indicating a decline in purchasing power of the domestic currency, while the ratio of PPPs showed a decline in relative price levels, indicating an increase in purchasing power, relative to the USD.

Without an explanation for this divergence in price data, and after careful review with country poverty economists, we established a rule that if � exceeds two standard deviations from the mean, the poverty estimates would not be based on the 2011 PPP data, but would remain based on the 2005 PPP data.<sup>37</sup> This means that for poverty measurement in Iraq, Bangladesh, Yemen, Egypt, Jordan and Cabo Verde we continue to estimate poverty based on the $1.25 line and the 2005 PPPs. Countries where the ratio is more than one standard deviation from the mean were also examined on a case-by-case basis. For two of them (Cambodia and Lao PDR) the exploratory analysis indicated that the poverty estimates based on 2005 PPPs are more consistent with regional patterns than those suggested by the 2011 PPPs. Therefore, the 2012 poverty estimates for Cambodia, Lao PDR, Bangladesh, Cabo Verde, Egypt, Jordan and Yemen are based on the 2005 PPP data (and $1.25 line) and not the new 2011 PPP data.

> 36 See the discussion in section 2, drawing from Deaton and Aten (2014), Ravallion (2014), and Inklaar (2014).

> 37 That is if � is greater than 2.07. For no countries was � less than two standard deviations from the mean. An alternative for assessing outliers that was considered was the extent to which a country’s � deviates from the ratio of new and old poverty line (1.90/1.25=1.52). In this way the identification of outliers would be anchored in the change in the poverty line, rather than the overall change in the PPPsadjusted dollar value of incomes across all countries. This alternative identified the same countries as problematic.

21
**What are the causes of the growing trend of excess savings of the corporate sector in developed countries? An empirical analysis of three hypotheses**

_Leandro Brufman Lisana Martinez_<sup></sup> _Rodrigo Pérez Artica_<sup></sup>

# **Abstract**

This paper analyzes annual accounting data for a sample of 5,000 publicly traded manufacturing firms from Germany, France, Italy, Japan, and the United Kingdom. The analysis uses data from 1997 to 2011 and finds an increasing trend of excess savings (defined as the difference between gross saving and capital formation) and a gradual decline of gross capital formation. This trend is accompanied by a steady deleveraging process and a decrease in the share of operating assets in total assets. This process is more acute among the more credit constrained, the more volatile, and the less dynamic firms.

Keywords: capital formation, liquidity demand, financial leverage, financial constraints JEL Codes: G3, E2

>  _Universitat Rovira-i-Virgili_

>  _IIESS-UNS/Conicet, E-mail_ _<u>rodrigo.perezartica@</u>_ <u>uns.edu.ar</u> _This paper was written while Leandro Brufman was a Junior Professional Associate in the Treasury Department of the World Bank_
100,000 and imposed minimum trading lots of that amount. However, this excludes India’s large retail investor base from the SME market, reducing liquidity.

|Table 7. Investor Types<br>Exchange<br>Domesti|of SME Exchanges<br>c Investors|Foreign<br>|
|---|---|---|
|<br>Retail|Institutional|Investors|
|NSE India Emerge<br>90%|10%|0%|
|JSE AltX<br>—|—|—|
|GreTai<br>84%|10%|6%|
|Bovespa Mais<br>15%|55%|18%|
|WSE NewConnect<br>69%|24%|7%|
|BIST ECM<br>—|—|—|

_Source:_ WBG compilation of exchange information. _Note: — =_ not available.

# **_C. Regulations, Fees, and Other Requirements_**

Given the small size of SME issues, a key challenge is to reduce the costs in time and money of issuance so it is economically viable and doable for the SME, but in ways that do not create prudential problems for the investors. Regulatory requirements cannot simply be shrunk with regard to the main board until they become attractive to the issuer.

Getting the right regulatory balance is at the heart of the challenge in developing an SME exchange. Workable structures are being employed in different exchanges that involve reducing fees and preparatory time without significantly reducing prudent elements such as the content and value of disclosure and transparency.

# 1. Workable Cost Structures

Time and money are key reasons why many SMEs hesitate to list on an exchange (see Table 8). Exchanges such as NewConnect and Bovespa Mais find that IPO costs of less than 5 percent are workable for most SMEs, but are still not feasible for very small companies. Bovespa MAIS and NewConnect estimate all-in IPO costs as 4.5 percent.

Many exchanges are lowering exchange and regulatory costs for issuers without significantly reducing the quality and quantity of information provided to investors, most notably: _i. Lowering or waiving fees for listing, custody, and market maker services:_

- The Borsa Istanbul lowered listing, post trade, and custody fees to one-tenth of the normal market fee and gave a 50 percent discount on market makers fees for BIST ECM.

21
- 23 -

exporters; no substitution effect of domestic production is considered, and any other export markets than the EU are ignored.<sup>35</sup> In theory, however, the EU market must reflect and be connected to the supply and demand conditions in the global markets through international arbitration based on the internal exchange rates.

For each country, two weather variables are introduced from the National Oceanic and Atmospheric Administration (NOAA) database, the Global Historical Climatology Network (GHCN) Version 2: average annual temperature and average annual precipitation. In order to take into consideration the possible persistence of past weather impacts, two lagged values are initially introduced in Equation (4). When a country has more than one observatory, they are averaged on the national level. If there is no available data for a particular country, its neighboring country’s data are used to maximize the sample size.<sup>36</sup>

> The exchange rate variable, _ER_ , is defined as annual changes in the bilateral exchange rate of

> the euro against the national currency; an increase in _ER_ means the exchange rate appreciation, which would have an adverse effect on local exporters through raising their export prices. The associated coefficient in the export price equation is expected to be positive.

The shadow price of labor is measured by pre capita GDP (in constant 2000 US$ terms), which is largely available for most country and thus allows us to use our trade data in hand to the maximum extent. This is potentially a rough proximation; however, not surprisingly, GDP per capita is highly correlated with monthly earnings for countries whose data are available (Figure 6). Data on the exchange rates and GDP per capita depend on _IFS_ and _World Development Indicators_ ( _WDI_ ).

> 35 For instance, large beef exporters in Latin America, such as Argentine and Brazil, experienced an economic boom in the sample period and boosted their exports to the United States. This may not be captured directly by our framework.

> 36 These treatments may create a risk of discarding much information in the original data, particularly for geographically large countries. Indeed, these weather variables have been found too noisy to explain livestock production in the sample.
are the budget shares of food and nonfood items (excluding rent) for the food price index and the budget share of rent for the rent index. The formula of the overall index is expressed below:<sup>14</sup>

- where : median price of food item observed from individual households in the urban Southeast : median price of food item paid by households in primary sampling unit

- pp<sup>0,ii</sup> mm : rental value paid by or implicit rent for household<sup>PP,ii</sup> h

- pp<sup>P</sup> mm FFP : median rental value paid by or implicit rent for individual households in the urban Southeast

- cc<sup>h</sup> : share of household h ’s food budget devoted to food item cc<sup>0</sup> h : share of household h ’s total budget devoted to food and nonfood, nonrent expenditures

- h,ii

- ww mm : share of household h ’s total budget devoted to rent

- FF,NNFF

- ww rr<sup>h</sup>

- ww

Each household’s welfare aggregate was then divided by the household-specific overall price index. Table 3 presents the average indices by region. Compared to the Southeast-Urban reference, prices are lower in all other regions. Differences are smallest for South-Urban price indices and larger for price indices in the Northeast and North. These results are in line with differences in economic performance across the regions. Moreover, within major geographic region, urban-rural differences are as expected, with urban price indices being higher. Differences in food prices are relatively smaller. This result is in line with earlier work on Brazil and Brazil’s level of economic development (World Bank, 2007; Deaton and Dupriez, 2011). The largest differences in terms of urban-rural as well as regional differences in prices are observed in the cost of rent.

Finally, using the spatial and temporal deflated welfare aggregate, we use the population in the bottom 40 percent as the reference group for the estimation of the food poverty line. This group is selected based on the expectation that it includes the poor and vulnerable sectors of the Brazilian population. By using this group and its consumption patterns, our approach aims to reflect how much necessities consumed by the less well-off cost. Incidentally, it is worth noting that the calorie mapping described above yields a projected average daily caloric intake of 2,368kcal per capita. The estimated average for the bottom 40 percent is 1,243kcal per capita per day. This further reflects the level of destitution of the reference group chosen.<sup>15</sup>

> 14 To calculate the rent price Paasche index, we used the same approximation as in the calculation of the food price index (equation 4).

> 15 This back of the envelope calculation is done as follows. Daily caloric intake is calculated based on the caloric intake assignment to quantities of food items purchased, using our main calorie mapping specification. We complement this with an extrapolation of the calorie intake to account for unassigned expenditures (coming from a small share of food consumed at home and away from home).

13
# **3 Scenarios**

## **3.1 Base-Data and Base-Year Adjustments**

The starting point for the model simulations is the GTAP dataset, Version 6.0, which is aggregated to 27 regions and 25 sectors (see Appendix A). The aggregated dataset is then adjusted to incorporate additional information on protection. In particular, an output subsidy of 36 percent is imposed on U.S. cotton (the plant-based fiber [pfb] sector),<sup>11</sup> and bilateral tariffs are lined up with the scenario file prepared by CEPII.<sup>12</sup> A process based on the GTAP _Altertax_<sup>13</sup> procedure is used to impose these changes while minimizing the distortions to the original GTAP dataset.

## **3.2 Baseline Scenario**

The starting point for the baseline scenario is the adjusted 2001 GTAP database. The model is solved in recursive dynamic fashion through to 2015, lining up with the World Bank’s most recent medium- and long-term forecast for the global economy.<sup>14</sup> The baseline scenario includes two changes to the base-year level of protection. First, the export tax equivalents on textiles and clothing are removed, emulating the removal of quotas. Second, tariffs are adjusted to match existing commitments—completion of Uruguay Round commitments, Chinese WTO accession, EU expansion, and so forth. Both of these changes are phased in between 2002 and 2004. From 2005 forward, there are no changes in the levels of protection.

> 11. This is an approximation of the effect of the various programs supporting cotton production in the U.S., see Sumner (2005).

> 12. The CEPII scenario file contains 10 Doha scenarios, the base tariffs, and the preshock tariffs. The base tariffs are consistent with the GTAP tariffs at the 87-region, 57-sector level, that is, they correspond exactly to the GTAP Version 6.0 tariffs. However, when aggregated to the level of aggregation used in the model, they could differ from the CEPII aggregation if the CEPII trade weights are used because the aggregate tariffs can be quite sensitive to the trade weights. In all aggregations, the GTAP CIF-based trade weights are used to aggregate the tariffs. In the adjustment process, the GTAP weighted CEPII base tariffs are imposed over the GTAP tariffs. 13. See Malcolm (1998).

> 14. See World Bank (2005).

8
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# Conclusions

The retail food price data by city indicate that significant progress toward market integration occurred in the seven months following the price liberalization of January 1992. A decline in cities' price deviations from the all-Russia meaa price occurred for 17 of the 19 commodities analyzed.

SuLi, iarge differences between cities persist for which there is no explanation in terms of available economic variables. It is likely that this lack of apparent market integration is in part attributable to food pricing policies in some remote cities, or cities where dominant industries subsidize food. Beyond this, there must be significant barriers to the movement of products from low-price to high-price cities. To what extent these barriers are local policies preventing shipments out of low-price areas, lack of entrepreneurship among buyers or sellers within the distribution system, or other constraints or costs is not ascertainable from the data we have. Much of the remaining distortion of prices between and among localities can probably be attributed to the lag in initiating reforms at the enterprise level and in the transport system. Some may be attributable to economic agents' inability to keep up with changes in relative prices associated with the credit-induced general inflation.

It seems clear that large economic gains could be achieved by further market integration. The best evidence of this is from the price relationships between cities in the same region. For example, Ulianovsk on the Volga region had the largest volume of sugar sales (in monitored stores) in the Volga region, and the lowest price, at 25 rubles per kilo as of August 25. At the same time, the two nearest cities in our sample, Syzran' and Samara, 150 and 286 kilometers away, and both also on the Volga River, had prices of 62.5 rubles. Transportation costs in this situation could not have been more than a few rubles per kilo. The profit potential appears enormous, as do the potential welfare gains.

- To obtain a crude approximation of potential welfare gains, Ulianovsk consumes 3000 kilos of sugar weeldy at 25 rub./kg., Samara and Syzran' consume 1000 kilos at _62.5_ rub./kg. Suppose sugar can be moved from Ulianovsk to these cities for 5 rub./kg. (20 percent of its price), and that all three cities have an elasticity of demand for sugar of -.5. Shipping about 300 kilos would equilibrate price at about 37 rub./kg. in Ulianovsk, and 32 rub./kg. in Samara and Syzran'. The gain from the shipment would average about 24 rub./kg., for a 19 rub./kg.

29
finds a correlation between lower economic growth rates with conflict deaths and institutional fragility (World Bank 2020), where countries affected by conflict and fragility often have the highest poverty rates in the world.

Moreover, the impending consequences of climate change are expected to disproportionately affect these countries, potentially exacerbating their struggles in the coming decades, and displacing millions of people seeking a better life. For instance, recent literature suggests that in the absence of climate mitigation policies, by 2100, increases in global temperature could reduce global GDP growth around 1 to 3 percent (Newell et al 2021) or even reduce the world’s GDP per capita by more than 7 percent (Kahn et al 2017). This climate-driven economic contraction also directly impacts poverty. According to recent estimates, climate change could push an additional 132 million people into extreme poverty by 2030 (Jafino et al 2020).

What can be done about the widening gap? And what are the consequences for the world if these trends continue?

# Catching up or falling behind?

At the time of many countries gaining their independence from former imperial nations, around the 1960s, the gap between the richer world of Europe and North America, and the rest of the world was significant. Per capita GDP in the US was around $19,000 in 1960 and by 2021 had reached almost $62,000. Countries like China and Botswana were far behind in 1960, but have made rapid progress, from $240 and $400 to about $11,000 and $6,000 in 2021, respectively. Rapid industrialization has seen many emerging market economies transformed. Catch-up, when it happens, can take place in the space of just four or five decades.

<mark>Since 1990, global GDP has exploded. From $36 trillion, it more than doubled to $87 trillion by 2021, measured in constant 2015 US$. This more than doubling has not been evenly distributed though. Average GDP per capita among the already-rich OECD countries rose from just below $30,000 to almost $45,000, a 50% increase. Emerging markets on the other hand experienced an average increase in GDP per capita from below $5,000 to approaching $15,000 by 2021. Indexed to 1990 values, this is an almost 300% increase.</mark> Meanwhile, a significant minority of countries in the post-independence era have seen incomes per capita barely shift from their starting point. As the rest of the world has continued its economic ascent, the gap has grown much larger than existed in the 1960s.

# The left behind

Nearly two decades ago, a group of struggling nations were identified as the "Bottom Billion". This collective term refers to a set of approximately 58 countries across Africa, Asia, and Latin America, which by 2021 had a combined population of about 1.4 billion people (Collier 2009). These nations garnered particular attention due to their shared characteristics, which included being caught in cycles of persistent poverty and low growth, and their inability to replicate the remarkable achievements of their peers and neighboring countries. Factors contributing to 5
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 3. Massive inefficiencies are related mainly to underpricing and undercollection of bills**

||Power billings|System<br>losses|Implicit collection<br>ratio|Cost-recovery<br>benchmark<br>Average<br>effective tariff|Total hidden costs|
|---|---|---|---|---|---|
||(GWh/year)|(%)|(%)|($/kWh)<br>($/kWh)|($ millions/year)|
|2001|10,152|16.2|<br>49|0.10<br>0.05|855|
|2002|11,327|13.6|<br>49|0.10<br>0.07|771|
|2003|10,411|13.1|<br>49|0.10<br>0.02|953|
|2004|10,405|11.5|<br>49|0.10<br>0.01|964|
|2005|10,755|10.8|<br>62|0.10<br>0.05|772|
|2006|10,451|10.5|<br>62|0.10<br>0.06|643|
|2007|9,239|11.0|<br>65|0.10<br>0.01|874|
|2008|8,756|11.0|<br>62|0.10<br>0.01|560|
|2009|8,912|11.0|<br>62|0.10<br>0.065|485|
|_Source:_Calc<br>Bank 2008al<br>_Note:_The co<br>power from n<br>GWh = giga<br>**Figure 5. B**|<br>ulations based on the South<br>ow-income countrieand Wor<br>st-recovery benchmark in Zi<br>eighboring country utilities a<br>watt-hour; kWh = kilowatt-ho<br>**enchmarking Zimbabw**<br>Mozambique|ern African Po<br>ld Bank staff es<br>mbabwe takes<br>t $0.02–$0.03<br>ur.<br>**e’s hidden co**|wer Pool (SAPP) annual<br>timates.<br>into account the unit co<br>per kilowatt, and the co<br>**sts against souther**|reports 2005–09, World Bank 2011, Wo<br>st of power generation within Zimbabwe,<br>st of transmission and distribution of pow<br>**n African peers, 2008–09**|rld Bank 2009b , World<br>the cost of buying<br>er.|
||Zambia|||||
||Botswana|||||
||Zimbabwe (2009)|||||
||Tanzania|||||
||Malawi|||||
||Zimbabwe (2008)|||||
||DRC Congo|||||
|||0<br>10|0<br>200<br>30|0<br>400<br>500<br>600||
|||Losses|Underpricing<br>Co|llection Inefficiencies||

_Source:_ AICD calculations. _Note:_ DRC = Democratic Republic of Congo.

The reliability of Zimbabwe‘s power supply is much worse than that of its peers, with serious effects on the productivity of businesses. Zimbabwe has faced greater power outages than what low- or middleincome countries in Africa typically encounter. Households with electricity connections lack power for 15 days a year due to power outages (table 2). Erratic power supply has resulted in loss of productivity across various sectors. For example, a sample of 50 wheat-producing farmers indicated that problems of load shedding resulted in low agricultural yields. Load shedding ranged on average from 8 to 12 hours a day, halving expected yields (from 6 to 8 metric tonnes to 3 tonnes per hectare). Around 70 percent of farmers indicated that frequent load shedding damaged their farm equipment. The economic loss due to 18