rafmacalaba/datause-encoder-data huggingface dataset:
-65-

# Purchasing Power Parities in 17 Industries

Table 4.8 shows the PPPs for the 17 industries at both Brazilian and Mexican quantity weights and also their geometric average (Fisher index). These PPP estimates are derived from table 4.7, i.e. they are based on the "former national accounts concept" of value added. The average for the sample as a whole is an average of the industry PPPs weighted at the value added according to the former national accounts concept.

> In the last column of table 4.8 we show also the geometric average for the Mexico/Brazil price ratios, which were derived inferentially from the Mexico/USA and Brazil/USA comparison in chapter III (see table 3.13).

## ----------------------------------------------------------------

|Purchasing<br>Power Parities,<br> <br>-------------------------------------------------------------|Table<br>Mexico/Bra<br>|4.8<br>zil<br>(Peso<br>|s<br>to the<br>|__-----------<br>Cruzeiro).<br>1975<br>__--------------|
|---|---|---|---|---|
||PPP:<br>|Pesos/Cr<br>|uzeiro<br>|"Inferential"<br>PPP:<br>|
||Brazil<br>Quantity<br>Weights|Mexico<br>Quantity<br>Weights|Geometric<br>Average<br>|Pesos/Cruzeiro<br> <br>Geometric<br>Average|
|Grain Mill Products|1.8596<br>|1.9559|1.9070|1.7548|
|Sugar & Sugar Products|1.5087|1.4889|1.4988|1.5944|
|Malt and Malt Beverages|2.2821|2.1466|2.2133|2.2310|
|Tobacco and Tobacco Products<br>|1.6499<br>|1.6423<br>|1.6461<br>|1.8156<br>|
|Textiles<br> <br>|1.6540<br>|1.4990<br>|1.5746<br>|1.5795<br>|
|Footwear<br>and Leather Products<br>|2.2781<br>|2.2392<br>|2.2586<br>|2.3284<br>|
|Pulp and Paper<br>|1.8980<br>|2.5520<br>|2.2008<br>|2.2813<br>|
|Soap and Detergents<br>|1.8054<br>|1.3432<br>|2.4250<br>|1.6805|
|Paints|3.2343<br>|2.8333<br>|3.0272|3.5671|
|Agricultural<br>Fertilizers<br> <br> <br>|0.8029<br>|0.8108<br>|0.8068<br>|0.6726<br>|
|Petroleum<br>Refining<br>& Products<br>|0.7421<br>|0.7565<br>|0.7493<br>|0.7523<br>|
|Tires and Inner Tubes<br>|2.9480<br>|2.9369<br>|2.9424<br>|2.4987<br>|
|Cement<br>|1.2378<br>|1.2395<br>|1.2386<br>|1.2268|
|Bricks|2.5125<br>|4.4765<br>|3.3537|3.0200|
|Iron and Steel|1.5689<br>|1.3526<br>|1.4567|1.5612|
|Radio and TV Receivers|1.3962<br>|1.6273|1.5073|1.3123|
|Motor Vehicles|2.1647|2.1471|2.1559|2.1305|
|Weighted average PPP for sampl|e||||
|(value added weights, former<br>national<br>accounts<br>concept)|1.6818|1.5573|1.6184|---|
|Weighted average PPP for sampl|e||||
|(value added weights, US<br>|||||
|census concept)|---|---|---|1.7068|
|Exchange<br>Rates|1.5375|1.5375|1.5375|1.5375|

> Source: pesos/cruzeiro PPPs derived from table 4.7. Includes adjustments for indirect taxes and subsidies for malt and malt beverages, tobacco and tobacco products, and petroleum refining and products. Inferential peso/cruzeiro PPPs derived from table 3.13 (chapter III).
<!-- Start of picture text --> Number of Days with Rain Deviation in Number of Days with Rain<br>0 432 864 1,296 1,728 2,160 2592 3024 3456 3,888 4,320 0 432 864 1,296 1,728 2,160 2592 3,024 3456 3,888 4320<br>1 nHf1OTfiYTSffYT1fTY EOf1pei1i1iff1f<br>1' ' ' ' 1 ' i ' i} 1 ' 1 ' ' ' ' 1<br>1'1'1' ' ' 1'i'i'1'1|1|!'i|i' I}i)|'!'|<br>'' !' '! '' 'i '' '' !' '' 05<br>~ 55 ' 1 ' ' f ' 1 t 1 - "<br>& ' i ' ' 1 ' ' | ' ce ‘ = P 7<br>8 i i i i i i i | 1 g a 1 Lig Fe P= j 1 \ Nk<br>P= i i i i i i i j i = 0 , P<br>8 1 1 ! ] ' ! 1 1 ] g 7 J . - .<br>[o) 1 1 1 1 1 1 1 1 1 rs)<br>0 ~ ~ i - w ~ = - ~ ~ oe ~ oe _. ,<br>t ' t t { t f t t -054 5 { {<br>Ii 'i '{ 't 'i ' 1 'i 'I | I| I| 't 1'{ 1 t{<br>'' ' i ' 1 ' ! ' 1 ' ' ' ' I ' 1 i<br>5 1 ' i) ' ' 1 ' ' ' 1 | i | ' ' ' i) ' i<br>sf< < s e sss 3 3ES < ;&S s 3ES < 3gs S$ se gs §s& . & s s<br>RS Ss Ss < F & SS as as RS xs a ff oF ss ¥& as 4s xe<br>< ew ogF Ss e¢ ¢ PoS Ss<br>e e e we we &<br>Percentage of Days with Rain Deviation in Percentage of Days with Rain<br>0 432 864 1296 1,728 2,160 2,592 3,024 3456 3,888 4320 0 432 864 1296 1,728 2160 2592 3024 3456 3888 4,320<br>150-, iiii i i \ i i 2° ;i;i;ii;i;i7i7ii!<br>1! ' i 1 ! i I 1 ' ! ' ! ! 1 i i 1<br>100 1 ! i i 1 | | I 1 i | | 1 | ! ! 1 1<br>~ I' 1! 1! {i 1! 1! 1! !I f' 10 } 1 }<br>r=}2<50 |';!!'!}!!!|2<ie A ti : Li [re 1 i a o 7 i :<br>[o)gs 0 ( ( ( ( i f i 1 €[s)gs 0 r . i P , ~ ~<br>50+ {t1‘{'1i |'' i}Ii i]{ 1 {ii I1I'iII i }t -10 | t'' ]i{' ' ' |'i tt'i i'i iti{ ' '' t{11<br>-100 J x L' @ 41 Lez 11 G 11 z '1 4i r Li e '1 < i1 « -20 z 1‘ 3° 11 3 4' 4i = 4i SG i1 x i1 G 1i es 1\ «<br>rs eS eS 2 ~ SsKo £€of &c S SsKa < oP < cm A wwaS<br>Sf FF F F EF SF E€ 1 SF fF FF TF SF SF SF S§ LS EF<br>e e eg cS<br><!-- End of picture text -->
sign. Therefore, discussion of the TOT does not offer an explanation for coefficients with unanticipated direction of impact.

As designed and implemented by the government, the program is targeted to vulnerable households, who might participate at higher rates than randomly selected households. Across the full study, 57 percent of treated (i.e. randomly-selected) households in our study participated in MASAF (sum of coefficients in column 1 of Table 8). Since there are considerable within-village spillovers, using household treatment status as an instrument violates the Stable Unit Treatment Value Assumption (SUTVA) and is not a valid specification. Instead, we can instrument for PWP participation using village randomization, where the first stage equation is In this specification, the treatment effect incorporates direct and within-village spillover effects; the assumption is that there are no across-village spillovers within our sample and is justified by the distance between study villages. Participation is less than 100 % by design: _δ_ 1 = 0 _._ 145, and the first stage F-statistic 182.44. The national treatment-on-the-treated (TOT) parameters reported in Table 9 are larger in magnitude than the weighted average of the direct and indirect effects reported in Table 4, but imprecisely estimated and always in the direction of reducing food security.

Participation varies somewhat by region at 58 percent of treated households in the North, 42 percent in the Center, and 65 percent in the South. Regional TOT estimates are reported in Table 10, and indicate worse food security for PWP participants in all regions. Low take-up does not explain the lack of impact of PWP on food security.

# **6.2 Design of PWP**

## **6.2.1 Value of transfer**

A key design feature that may contribute to the lack of direct effect is low total value of PWP income. The wage rate for the program is set by the government at MK 300 ($0.92) per day, with total possible earnings of MK 14,400 ($44.16). The wage rate is low by international standards, but in a country with gross national income per capita of $320 ($730, adjusted for purchasing power parity), $44 is non trivial. Local political constraints made it infeasible to vary the wage rate for this study, so our experimental design does not allow us to speak to the effect of PWP with higher wages. Despite extensive expenditure data in our surveys, we are not able to detect increased spending on any category: food, agricultural inputs or business investments, non-food consumption, or durables. This limitation is shared with many studies on micro-finance, which similarly fail to detect the effect of increased cash on 20
- 4 -

spenders are spending less and that the average is down from peal' levels for the LDCs as a whole and in most regions. Consequently, this inial examination suggests neither a heavy "military burden" on the economy - outside the Middle East and North Africa - nor a growing military burden. There are of course regions (South Asia) and countdes (Honduras) which are excepdons to these general trends in military expenditure.'

# m. MILifARY SPENDING AND ECONOMIC GROWTH

There has been considerable research on the relationship between militay expenditure ("mllexc for short) and economic growth. Most of the work is empirical; attempts to resolve the issues theoretically have not been successful. The empirical studies work with a wise rray of specifications, country and time period samples, and esdmating methods. They also reach differing conclusions with regard to the impact of miiitary spending on growth.

&AlaA presents brief survey of the existing literature. As further explained therein, the existing literature on military expenditure and economic growth is beset by several problems: (I) there are significant omitted vt.iables in the analysis; (ii) regressors are not lagged; (iii) the studies often cover too short a time period; (iv) the regressors for military exnditure do not allow for non-linear relationships; (v) the regressors include factors influenced by the level of military expenditure; and (vi) military expenditure is treatd as a cost which could not possibly be directly beneficial by providing security. Given the problems with the existing literature, we developed our own approach, making use of the results of the author's previous work on the determna ts of economic growth (Landau 1986, 1990).

A. HIgeis Our hypothesis is that there are three types of effects of military expenditure on economic growth: (i) resource use; (ii) security; and (iii) policy efficiency. One, the resources used for military
Porter, G., Hampshire, K., Abane, A., Tanle, A., Esia-Donkoh, K., Obilie Amoako-Sakyi, R., ... & Asiedu Owusu, S. (2011). Mobility, education and livelihood trajectories for young people in rural Ghana: a gender perspective. _Children's Geographies_ , _9_ (3-4), 395-410.

- Premand, P., Brodmann, S., Almeida, R., Grun, R., & Barouni, M. (2016). Entrepreneurship Education and Entry into Self-Employment Among University Graduates. _World Development_ , _77_ , 311-327.

- Ruiz Abril, M.E. (2008). Liberia: Girls vulnerability assessment. _Government of Liberia, Nike Foundation, and World Bank._ Retrieved from https://docs.google.com/document/d/ 1FvEgQNMsZHVfMLYxfp62DUly8nY1850lh2vGmiYHk_0/ edit?hl=en_US&pli=1.

- Salon, D., & Gulyani, S. (2010). Mobility, poverty, and gender: travel ‘choices’ of slum residents in Nairobi, Kenya. _Transport Reviews_ , _30_ (5), 641-657.

- Sivasankaran, A. (2014). Essays on gender, intra-household allocation and development.

- Sneeringer, S.E. (2009). Fertility Transition in Sub-Saharan Africa: A Comparative Analysis of Cohort Trends in 30 Countries. _DHS Comparative Reports 23_ .

- Todd, P. (2012). Effectiveness of interventions aimed at improving women's employability and quality of work: A critical review. _World Bank Policy Research Working Paper_ , (6189).

- UN Habitat. (2013). State of women in cities 2012-2013: Gender and the prosperity of cities. United Nations Human Settlements Programme.

- UN Women. (2012). Informal cross border trade report. _UN Women._ Retrieved from http://www.unwomen.org/wp-content/uploads/2012/03/ICBTreport_Liberia.pdf

- Uteng, T. (2012). Gender and mobility in the developing world. _World Development Report_ .

- Vyas, S., Mbwambo, J., & Heise, L. (2015). Women's paid work and intimate partner violence: Insights from Tanzania. _Feminist Economics_ , _21_ (1), 35-58.

- World Bank. (2009). Unleashing the potential of Ethiopian women: Trends and options for economic empowerment. _World Bank Report No. 49366_ .

- World Bank. (2012). World development report: Gender equality and development. _World Bank Publications_ .

World Bank. (2015). World Bank Databank Gender Statistics. Retrieved from http://data.worldbank.org/data-catalog/gender-statistics.

World Bank. (2016). Enterprise Surveys. Retrieved from http://www.enterprisesurveys.org.

46
|OUTCOME................................................................................................................................................................48|
|---|
|_Africa .................................................................................................................................................................48_|
|_Asia - Pacific......................................................................................................................................................49_|
|_Latin America.....................................................................................................................................................49_|
|_North America....................................................................................................................................................49_|
|_Europe................................................................................................................................................................49_|
|TALLY OF THEBASINS THAT WERE APPROACHED BYINBO ....................................................................................50|
|**ANNEX 2: STRUCTURE OF VARIABLES IN DATASET .................................................................................53**|
|LEGEND:..................................................................................................................................................................53|
|GENERALDATA.......................................................................................................................................................53|
|INSTITUTIONALSETUP.............................................................................................................................................54|
|FINANCING..............................................................................................................................................................64|
|PERFORMANCEINDICATORS....................................................................................................................................65|
|**ANNEX 3: VARIABLES UDED IN THE ANALYSIS ..........................................................................................68**|
|VARIABLES TAKEN DIRECTLY FROM THE QUESTIONNAIRE.......................................................................................68|
|VARIABLES THAT UNDERWENTPRINCIPALCOMPONENT(PC) ANALYSIS AND EXPECTATIONS REGARDING THEIR|
|IMPACT....................................................................................................................................................................70|
|INDICES CREATED....................................................................................................................................................72|
|**ANNEX 4: RIVER BASIN ORGANIZATION SURVEY .....................................................................................74**|

3
**Figure 2: Export restrictions and import facilitation in food and medical products**

<!-- Start of picture text --> Food:<br>Restriction<br>Medical:<br>16%<br>Facilitation<br>32%<br>Food:<br>Facilitation<br>19%<br>Medical:<br>Restriction<br>33%<br><!-- End of picture text -->

**Note** : Some 7% of the policy measures recorded by GTA involve both Food and Medical products. These measures have been counted in both sectors.

Table 1 reports the evolution of active measures over time. Apart from medical goods in March 2020, import facilitating measures outnumber export restrictions. Overall, import facilitation slightly dominates the trade policy responses in every month since February 2020, for both food and medical products. Export restricting measures peak in April/May 2020, but the overall number of measures does not vary significantly during the period.

**Table 1: Active measures over time**

||**Fo**|**od**|**Med**|**ical**|
|---|---|---|---|---|
||Import Facilitation|Export Restrictions|Import Facilitation|Export Restrictions|
|February|14|6|13|6|
|March|17|8|24|49|
|April|47|33|141|120|
|May|59|32|170|126|
|June|68|30|177|112|
|July|66|26|172|109|
|August|70|28|180|112|
|September|80|34|183|114|
|October|75|33|149|107|

**Notes:** The number of active trade policies is computed at the 15th of each month. Roughly 7% of total policies recorded by GTA involve both Food and Medical products. These measures have been counted in both sectors.

Similar patterns of relative importance across sectors and categories emerge when looking at the value of trade covered by recorded measures. To provide a rough estimate of such values we have used UN Comtrade data for 2019 (or latest year available) to estimate the amounts of trade which may be implicated by the interventions in our data set. For the inclusion into the GTA database, the products affected by each intervention are classified according to the Harmonized System (HS). For our analysis, we use HS codes at the 6-digit-level, the most granular product breakdown for which there is comparable international trade data available through UN Comtrade. To estimate the amount of trade that may be implicated by each intervention, we sum over the trade flow values in the affected products in the year prior to the intervention. The estimates account for geographic restrictions on the potentially affected trading partners such as FTA exemptions or targeted interventions. Furthermore, to remove de minimis trade flows, we apply a minimum threshold of USD 1 million per HS code – trading partner combination.

5
- NewConnect requires the issuer to have a contract with a market maker when they list.

- NSE India requires a market maker for an SME for three years after listing. All members of the exchange are eligible to act as a market maker for SMEs provided they meet exchange criteria and are registered.

Unlike the case on many main boards, short selling is typically not allowed on SME exchanges and would be difficult given the small size and often illiquidity of the float. Market makers are required to subscribe to an offering and hold shares to facilitate market making. This may lead to conflicts of interest because market makers have a financial stake in the SMEs.

|Table 14<br>|. Market Maker Models and Other Incentives of SME Exchanges<br>|
|---|---|
|Exchange|Market Maker Requirement|
|NSE India Emerge|Yes, for 3 years after listing|
|JSE AltX|No|
|GreTai|No for GreTai main board. Yes for the OTC GreTai Emerging Stock Board.|
|Bovespa Mais|Not required. The company hires a market maker, who is on the list of BM&F<br>BOVESPA accredited market makers, and the exchange validates the conditions<br>of the contract in terms of spread and minimum volume. Approved market<br>makers have a tradingfee exemption.|
|WSE NewConnect|Yes, market maker is mandatory (after 2 years of company debut WSE checks<br>liquidity parameters periodically and decides if the company could be listed<br>without market maker)|
|BIST ECM|Not mandatory|

_Source:_ WBG compilation of exchange information.

# 3. Encouraging Research

Very little research analyst coverage is available for listed SMEs, because analysts typically do not find it commercially viable to cover small companies, because of both size and the difficulty of getting information. Lack of research and knowledge has a negative impact on investors and means that small stocks are not exposed to institutional investors by research houses once they are listed. This reduces liquidity.

As a result, some SME exchanges take steps to encourage research, including _paying for research for a period of time, including paying analyst fees_ (see Table 15). NSE pays for research on all SMEs for a two-year period, and analysts are paid to do multiple companies at one time, with a cost of around $1,000 per company. Bovespa Mais subsidizes research for its companies for two years, and the cost is $15,000 for two reports a year. JSE AltX pays for two or three reports per year. To help both the exchange and companies, a broker or analyst association often assigns an analyst to the company.

33
# **V.  Redistribution through trade protection**

To analyze the impact of existing trade protection on international inequality, we used the World Bank Linkage Model.<sup>19</sup> This model calculates the long-term steady-state gains from complete liberalization of trade in goods around the world simulated to take full effect by 2010. These gains are expressed as the difference in welfare in 2015 relative to the baseline scenario of no additional trade liberalization. In our exercise, we are discounting these future gains to 2002, our baseline year. Thus, when we refer to “lost potential income from existing trade protection” or the “benefit from future trade liberalization,” we imply the steady-state gains calculated by the model.

The model works with 27 “country units,” some of which are individual countries and others are regional groupings of countries. (The details of the country groupings are presented in Appendix 3.) To remain consistent with our initial framework, it has been assumed that all countries within a country group were affected in the same way by trade liberalization. In order to capture the impact of rich countries’ policies on the developing countries, we used a scenario modeled by van der Mensbrugghe (2005) whereby only highincome countries remove all tariffs on merchandise trade and all domestic agricultural support.<sup>20</sup> By estimating the difference with the benchmark situation, these simulations inform us on the impact of existing protection imposed on developing countries by highincome countries. The model provides results for the static changes in GDP evaluated in PPP terms and changes in real household income; here, we concentrate on changes in real household income as a more appropriate welfare measure. Economies of scale, productivity

> 19 Our gratitude to Dominique van der Mensbrugghe for providing the results and advising us on this exercise. Technical details on the model are to be found in van der Mensbrugghe (2005). 20 See World Bank (2004a) for details.

15
versus consumption measures, as suggested by Deaton (2005) and Ravallion (2003). Rather, the average gaps are closely aligned with the level of economic development, with gaps being largest for middle-income countries, both in terms of levels and growth rates. The gaps, and corresponding implications for poverty and inequality measures, are largest for middle-income countries which have experienced periods of rapid growth. With the large majority of the extreme global poor currently living in middle-income countries, the implications of these gaps for measuring and understanding the evolution of global poverty and inequality aggregates are large.

It is increasingly documented that the HHS-NAS gap at least partly originates from the inability of surveys to capture the full level of consumption and income for all households. This paper illustrates the potential implications for common poverty and inequality measures from adjusting survey data based on differing assumptions of the source of the gap. Under a distribution-neutral gap scenario, which would justify substituting survey means with national accounts means, global poverty would be much lower using the $1.90 line, and the Sustainable Development Goal of ‘ending’ extreme poverty would already be close to being met, or at least be easily within reach. This is in line with findings of Sala-i-Martin and Pinkovskiy (2016) that uses this method to measure global poverty. However, we argue that when using national accounts for measuring poverty, the $1.90 line should be adjusted. Using an international poverty line adjusted for systematic differences between surveys and national accounts would result in global poverty measures that are more in line with survey-only measures, but still cause relatively large changes to country level measures. Regardless of the poverty line used with national accounts, the rate of poverty reduction is greater than that measured in surveys because national accounts growth rates are typically higher. This last observation illustrates the perils of using growth rates from national accounts to extrapolate global poverty, which is the current practice. The current approach likely exaggerates the decline in poverty estimates when no survey data is available.

The scenario which assumes that the HHS-NAS gap is due to surveys not fully capturing consumption or incomes of the richest households in societies, and therefore makes adjustments to the top segment of survey distributions, has small implications for poverty measures, but drastic implications for typical inequality measures. Adjusting survey data for missing top 28