rafmacalaba/datause-encoder-data huggingface dataset:
-66-

It appears from table 4.8 that 33 of the 51 PPPs for individual industries in the direct Mexico/Brazil comparison are above the exchange rate, and that the three average PPPs are also all slightly above the exchange rate.

The average weighted sample PPP for the direct Mexico/Brazil comparison turns out to be somewhat lower than the average "inferential" PPP for the sample derived from chapter III (see also below).

# Blowing-Up Our Sample to Get an Estimate for Total Manufacturing

Tables 4.9 and 4.10 show the results of our blowing-up procedure of the sample to arrive at quantitative relatives and PPPs for total manufacturing in the direct Mexico/Brazil comparison. Table 4.9 shows the reweighted PPPs by manufacturing branch, and table 4.10 shows the quantity ratios for Mexico/Brazil for manufacturing branches and for manufacturing as a whole (for procedure see chapter III).

TABLE 4.9

|**Purchasing**PowerParities<br> <br>|by Major Bran<br>|chofManufac<br> <br>|turin|
|---|---|---|---|
|Mexico/Brazil<br>(Peso<br>--------------------------------------------------------------|s to the Cruz<br>PPP:<br>|eiro).<br>1975<br>Pesos/Cruzeiro<br>|__-----------<br>|
||Brazil<br>Quantity<br>Weights|Mexico<br>Quantity<br>Weights|Geometric<br>Average|
|Food Products|1.6205<br>|1.6579<br>|1.6391<br>|
|Beverage<br>Products|2.2821<br>|2.1465<br>|2.2133<br>|
|Tobe.cco<br>Products|1.6499<br>|1.6425<br>|1.6462<br>|
|Textiles<br>and Wearing Apparel|1.6540<br>|1.4990<br>|1.5746<br>|
|Footwear<br>and Leather Products|2.2781<br>|2.2393<br>|2.2586<br>|
|Wood and Paper Products|1.8980<br>|2.5520<br>|2.2008<br>|
|Ci.emical<br>Products<br>|1.1029<br>|0.9344<br>|1.0152<br>|
|Rubber and Plastic Products|2.9479<br>|2.9368<br>|2.9423<br>|
|Stone, Clay and Glass Products|1.9365<br>|1.4809<br>|1.6934<br>|
|Metal products|1.5689<br>|1.3527<br>|1.4568<br>|
|Electrical<br>Machinery|1.3962|1.6272|1.5073|
|Machinery<br>and Transport||||
|Equipment|2.1648|2.1471|2.1559|

Sources and notes: PPPs from table 4.8. The PPP for food products is the weighted average for grain mill and sugar and sugar products. The PPP for chemical products is a weighted average for soap and detergents, paints, agricultural fertilizers, petroleum refining and products. The PPP for stone, clay and glass products is a weighted average for cement and bricks. In all cases value added figures (former national accounts concept) were used as weights. The peso/cruzeiro PPPs for "Other Manufacturing" and "Total Manufacturing" were derived from the sum of the values for all other branches from table 4.10.
# FIGURE 3: MONEY AND WATER TRANSACTIONS IN ONITSHA, NIGERIA (PER DAY)- Rainy Season

<!-- Start of picture text --> R CK3-200VENDORS - 9.900 t +7500 U<br>_~~~~~~~~~~~~~~~ _i<br>E ~~~~~~~~~~~~~~~~~~~H<br>E _ 0.11003541 _ *oo 0.075hCC _S<br>. 0* 2.40 0 V<br>0HLO EL L 02 I D<br>| STRE 0035 NI-T 0190-1_ 0 7 s- s L~~~<br>1~~~~~~~~~~0 19 5 C<br>PUBLIC SYSTEM 1 -<br>RAIN 2.3 0C1-0<br>RAIN *~~~~~- *0<br><!-- End of picture text -->

*'CO: MIlIl0N1 Hi CAGlONS O1WAIMI PIE **JAY** * Water Input is not equal to water output because a small amount of water is consumed by small retail vendors themselves.
<!-- Start of picture text --> 2<br>LK<br>1<br>0<br>1970 1980 1990 2000<br>== Dynamic RTA Spain TM noRTA RTA Spain TM World avergae RTA<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>-10<br>-20<br>-30 SN]<br>-40<br>-50<br>1970 1980 1990 2000<br>== Dynamic RTA Spain TM NoRTA RTA Spain TM World avergae RT/<br><!-- End of picture text -->
22 The first source of bias may not be a serious problem in this specific case, given the ARDL methodology produces consistent estimates of the long run coefficients (Pesaran an Shin, 1997). We tested for the correlation between both private and public capital and the residual of the regression, and were unable to reject the exogeneity of these variables. The second source of potential bias may be a problem, because based on the ARDL approach and the proposed method to test for long run relationships (Pesaran, Shin and Smith, 1999) the computed F-statistic between the upper and lower bounds that do not allow firm rejection or non-rejection of the null hypothesis of no long-run relationship.

|**_Error-correction Representati_**<br>**_Dependent v_**|**_Table 10_**<br>**_on for the Sel_**<br>**_ariable: d GD_**<br>|**_ected ARDL_**<br>**_P per capita_**<br>|**_models 1952-2_**<br>|**_002_**<br>|
|---|---|---|---|---|
||AIC|RBSC|SBC|HQC|
|Error-correction term (-1)|-0.52*<br>(0.08)|-0.57*<br>(0.09)|-0.52*<br>(0.08)|-0.52*<br>(0.08)|
|d(Private Capital Stock per capita)|1.66*<br>(0.23)|1.87*<br>(0.27)|1.66*<br>(0.23)|1.66*<br>(0.23)|
|d(Private Capital Stock per capita)-1|0.55***<br>(0.28)|0.63**<br>(0.31)|0.55***<br>(0.28)|0.55***<br>(0.28)|
|d(Public Capital Stock per capita)|0.37*<br>(0.05)|0.15<br>(0.23)|0.37*<br>(0.05)|0.37*<br>(0.05)|
|d(Gov. Expenditures: subsidies per capita)|0.004<br>(0.008)|0.004<br>(0.008)|0.004<br>(0.008)|0.004<br>(0.008)|
|d(Gov. Expenditures: consumption per<br>capita)|0.06<br>(0.04)|0.06<br>(0.04)|0.06<br>(0.04)|0.06<br>(0.04)|
|d(Gov. Expenditures: social security and<br>assistance transfers)|0.002<br>(0.032)|0.02<br>(0.03)|0.002<br>(0.032)|0.002<br>(0.032)|
|d(Tax Revenue to GDP Ratio)|-0.53*<br>(0.17)|-0.46**<br>(0.18)|-0.53*<br>(0.17)|-0.53*<br>(0.17)|
|d(Total Debt to GDP Ratio)|-0.17**<br>(0.06)|-0.16**<br>(0.06)|-0.17**<br>(0.06)|-0.17**<br>(0.06)|
|d(Total Debt to GDP Ratio)-1|0.06<br>(0.07)|0.04<br>(0.09)|0.06<br>(0.07)|0.06<br>(0.07)|
|d(Total Debt to GDP Ratio)-2|0.24*<br>(0.06)|0.26*<br>(0.06)|0.24*<br>(0.06)|0.24*<br>(0.06)|
|d(Constant)|-0.15<br>(0.52)|0.01<br>(0.64)|-0.15<br>(0.52)|-0.15<br>(0.52)|
|d(Trend )|-0.001<br>(0.002)|-0.001<br>(0.002)|-0.001<br>(0.002)|-0.001<br>(0.002)|
|R<sup>2</sup>|0.88|0.89|0.88|0.88|
|D.W.|1.99|1.92|1.99|1.99|
|*Significant at the .01 level.**Significant|at the .05level.|***Significan|t at the .10leve|l|

To examine further this potential problem, we adopted a multiple equation cointegrating VAR approach. This approach will also allow examination of relationships between variables that the single-equation ARDL approach did not allow. With the same set of variables, we were unable to reject the hypothesis of up to four cointegrating vectors. To reduce the dimensionality of the problem (and based on the variance decomposition) we excluded the debt variable and were able to reduce the number of cointegrating vectors to two.<sup>12</sup>

> 12 See Appendix for the cointegration tests. One of the vectors, however, showed no persistence in the deviations from the equilibrium relationship to system-wide shocks.  The other vector, on the contrary,
We stratified the universe of establishments by firm size, sector of activity,and geographic regions.Our sample is representative across these dimensions.<sup>54</sup> In the firm size stratification, we have three strata: small firms (5-19 employees), medium firms (20-99 employees), and large firms (100 or more employees). Regarding sector, for all countries, we stratified at least for agriculture (ISIC 01), food processing (ISIC 10), Wearing apparel (ISIC 14), Retail and Wholesale (ISIC 45 and 56), other manufacturing (Group C, excluding food processing and apparel), and other Services (including all other firms, excluding retail). We use this sector structure of the data for most of the analysis in this paper. Additional sector stratification that were specific for each country included: Motor vehicles (ISIC 29), for Brazil; Leather (ISIC 15), Pharmaceutical (ISIC 21), and Motor vehicles (ISIC 29), for Vietnam; and Land transport (ISIC 49), Finance (ISIC 64), and Health (ISIC 86), for Senegal.<sup>55</sup> In the geographic stratification, we use sub-national regions. In Brazil, we cover only Cear ́a. In Vietnam, we make 8 geographic strata: Region 1 (Bc Ninh; Hi Ph`ong; Ninh B`ınh), Region 2 (Th ́ai Nguyˆen; Bc Giang), Region 3 (Thanh Ho ́a; H`a T ̃ınh; B`ınh nh), Region 4 (Kon Tum; Lˆam ng), Region 5 (B`ınh Dng; ng Nai), Region 6 (Long An; V ̃ınh Long), Region 7 (H`a Ni), and Region 8 (H Ch ́ı Minh). In Senegal, we have 7 regional strata including Dakar, Diourbel, Kaolack, Kolda, St. Louis, Thies, and Ziguinchor. For Senegal, we additionally stratify by formality. To calculate the optimal distribution of the sample, we followed a similar methodology as described by the World Bank (2009).

For the state of Cear ́a, our universe includes 24,288 establishments. We collected data for 711 establishments randomly selected from RAIS. Table A.2 and Table A.3 provide the information on the distribution of firms in the population and the sample for Cear ́a, by size group and sectors.

Table A.2: Population Distribution, Brazil

|||Agri-|Food|Wearing|Other|Wholesale|Other|Total|
|---|---|---|---|---|---|---|---|---|
|Region|Size|culture|Processing|Apparel|Manuf.|& Retail|Services|Region|
|Cear ́a|Small|240|523|788|2487|9255|5362|24488|
||Medium|111|220|295|937|1764|1643||
||Large|47|51|54|202|243|266||
|Total||398|794|1137|3626|11262|7271|24488|

> 54An additional dimension was included for Senegal, splitting firms by formality status based on the ANSD definition.

> 55These specific stratifications were taken into consideration when determining sampling weights.

50
underemployment<sup>3</sup> . In all age groups, only a minority of jobseekers report preferences for part-time jobs (Appendix Table 7). The probability of having a part-time job as the primary job is decreasing in education. Thus it appears that part-time employment in Latvia is largely a result of a shortage of fulltime jobs.

**Figure 7.  Labor Reallocation Between Main Sectors, 1990-2002**

(Estonia, upper panel; Latvia, middle; Lithuania, bottom) <!-- Start of picture text --> 100<br>90<br>80<br>70<br>services<br>60<br>construction<br>50<br>industry<br>40<br>30 agriculture<br>20<br>10<br>0<br>100<br>90<br>80<br>70 services<br>60<br>construction<br>50<br>40 industry<br>30 agriculture<br>20<br>10<br>0<br>100<br>90<br>80<br>70 services<br>60<br>construction<br>50<br>40 industry<br>30 agriculture<br>20<br>10<br>0<br>1990199119921993199419951996199719981999200020012002<br>1990199119921993199419951996199719981999200020012002<br>1990199119921993199419951996199719981999200020012002<br>percent<br>percent<br>percent<br><!-- End of picture text --> _Source:_ National statist ical offices of Estonia, Latvia, and Lithuania

> 3  See Trapeznikova et al. (2003) for in-depth study of part-time employment and underemployment in Latvia.

12
D74 - Dum=y variable, 1 if 1974, 0 if not

- DIV - Minimum feed grain diversion acreage (computed from COMPFGA, DRFG, and BACGS)

- DPC - Diversion payment for corn (paid diversion) in dollars per acre _(Feed:_ **_Outlook_** _& Situation Report,_ text of various issues)

- DRFG - Diversion requirement of feed grains in percent of base acreage _(Feed: Outlook & Situation Report,_ text of various issues)

- EYLDCGS - Expected yield for corn and grain sorghum (computed from lagged values of YLDCGS)

- EYLDS - Expected yield for soybeans (computed from lagged values of YLDS) ICCCA - Interest charged on CCC non-recourse loans in percent (Wheat c Feed: _Outlook & Situation Report,_ text of various issues)

- LCOMPFGA - Linearized logistic representation of the feed grain program participation rate (computed from COMPFGA)

- MAXRATE - Interest rate subsidy on stocks under CCC loan (computed from RS and ICCCA)

- MAXYLDFG - Expected corn/grain sorghum yield eligible for price support (computed from EYLDCGS and YLDFGP)

- MSPC - Effective farm price for corn/grain sorghum including price support payments (computed from SPRCA and PAFC1)

- MTPC - Effective farm price for corn/grain sorghum including deficiency payments (computed from TPC and PAFCl)

- NON - Program nonparticipation rate for corn and grain sorghum (computed from COMPFGA)

- NONLAGA - Hypothetical lagged acreage of corn and grain sorghum that would occur with no program (computed from ACGS, DIV, and COMPFGA)

- NONPROFC **-** Real quasirent per acre for corn and grain sorghum without participation adjusted to by percent of noncompliance (computed from PROFFGN and COMPFGA)

- NONPROFS - Real quasirent per acre for soybeans adjusted by percent of noncompliance (computed from PROFS and COMPFGA)

- NOPROG - Dummy variable, 1 if a program is in effect, 0 if not

- PAFC1 - Corn, price at farm, U.S. average in dollars per bushel, januaryMarch (Feed: _Outlook & Situation Report)_

- PAFS1 - Soybeans, price at farm, U.S. average in aollars per bushel January-March _(Oil Crops: Outlook & Situation Report)_

- PROFFG - Average real quasirent per acre among all farms for feed grains (computed from COMPFGA, PROFFGN, and PROFFGC)

- PROFFGC - Average real quasirent per acre on farms participating in the feed grain program (computed from DRFG, VDFG, RETFGP, RVOL, DPC, and GNPD) PROFFGN = Real quasirent per acre for corn/grain sorghum without participation (computed from PAFC1, EYLDCGS, COSTCGS, and

- GNPD)

- PROFS - Real quasirent per acre for soybeans (computed from PAFS1, EYLDS, COSTS, and GNPD)

- RATIO - Ratio of quasirent per acre on feed grain to soybeans (computed from PROFFG and PROFS)

- RCOSTCGS - Real variable costs per acre for corn and grain sorghum (computed frcm COSTCGS and GNPD)

- RCOSTS - Real variable costs per acre for soybeans (computed from COSTS and GNPD)

- RETFGP - Quasirent per acre for corn/grain sorghum acreage in production under participation (computed from MTPC, YLDFGP, MSPC, MAXYLDFG, MAXRATE, SPRCA, EYLDCGS, and COSTCGS)

11
the short-term (Ravallion, 1990; Barrett and Dorosh, 1996; Ravallion, 2006, Mghenyi et al., 2011; Headey, 2013). The winners will include large land owners who are net producers while the losers will arguably include small land owners who are net consumers. However, in Lao PDR as many as 27% of households sold some rice produce in 2010/11 according to the 2012 Lao Agricultural Census.<sup>15</sup> In fact, only about 34% of the Lao households are net rice buyers, and close to a third of them spend less than 10% of their food budget on market rice purchases. It is estimated that only 26% of the poor are net-buyers compared to about 36% of the non-poor (World Bank, 2015).

In the medium to long term, many of the poor will ultimately also be better off as the benefits from the expansion of the agricultural sector will eventually trickle down. These benefits will likely be in the form of higher rural wages and an increase in demand for farm workers, see for example the study by Jacoby (2013) that establishes a significant rural wage response in India. A recent World Bank (2013) report on Cambodia also finds empirical evidence in support of this indirect effect, pointing to a significant reduction in poverty following an increase in the price of rice (which is argued to have led to an increase in rural wages and rural employment opportunities). Ivanic and Martin (2014b) obtain a similar result when simulating the impact of a rice price increase in Cambodia and Vietnam.

The benefits of the indirect effect, which constitutes a primary channel through which the poor net-consumers of rice will be compensated for the higher cost of living, may

> 15 As a percentage of farm households it is estimated that 71% sold some rice produce in 2010/11, up from 35% in 1998/99, which suggests that we are seeing a shift toward market-oriented agriculture (Lao Census, 2012).

28
4 of the section is an overview of the challenges of measuring the productivity of spending and its impact on capital formation based on aggregate macro data.

# **A. Expenditure volatility impact on capital formation and welfare**

When public spending grows beyond the long-run sustainable funding level provided by taxation, it is subject to the volatility of financial markets. Though volatility may be cushioned by public debt management in the short run, in the long run disequilibria between taxation and expenditure lead to policy instability, as revenue or spending have to be adjusted. This mismatch produces stop-go cycles of public spending, generating fiscal policy volatility that compounds economic uncertainty arising from other sources. Not surprisingly, Table 1 shows how public spending is more volatile in poor and heavily indebted countries that rely on external saving to close financial gaps. Another interesting fact depicted in Table 1 is that volatility decreases as the income level rises, reaching a minimum in the subset of the European Monetary Union countries within the high income group.

Not only is public spending volatility higher in absolute terms in less developed nations, but it is also larger relative to that of household consumption or GDP, as Table 1 shows. This fact by itself would not be problematic if it reflected active use of a policy instrument with the target variable (output or consumption) being stabilized. In these circumstances, fiscal policy, and in particular public spending, could play a positive role within the tools disposable to the policymaker to stabilize output. But the procyclical nature of fiscal policy in most developing countries, explored elsewhere (Alesina and Tabellini,2005; Talvi and Vegh, 2005), suggests that fiscal policy may be destabilizing. More importantly, and in the same direction, ther is a positive association between public spending volatility and output and consumption volatility as shown in Figures 1 and 2.<sup>2</sup> 2 Volatility is defined as the standard deviation of the per-capita GDP and consumption per capita growth rates during 1961-2005.
**Table 3 Evolution of key economic indicators for SPA couni 1967-88** c (average annual **percentage change, unle otherwie indcated)**

||1967-73|1974-80|1981-84|1985-87|1988|
|---|---|---|---|---|---|
|GDP|3.8|1.4|0.4|3.3|4.0|
|Export volume|4.7|1.1|_-5.5_|6.0|0.1|
|Import volume|3.6|1.9|-2.3|4.0|5.0|
|GDI (percent of GNP) a/|15.2|19.1|17.2|17.7|17.7|
|GNS (percent of GNP) a/|12.0|10.6|8.5|7.9|6.8|
|Private consumption|2.2|0.1|_-0.5_|2.7|2.2|
|Gross ODA (percent of GNP) b/|4.7|7.5|10.!|12.5|15.0|
|Terms of trade index (1980=100) a/|124.0|129.9|_109.5_|107.2|108.5|
|GNY||||||
|Total|3.6|0.8|0.9|2.7|5.8|
|Per capita|0.8|-2.0|-2.2|-0.6|2.4|

- -- - -- -- - -- - -- - -- -- - -- - -- - -- -- - -- - -- - -- - -... **-** - -- - -- - -- -- - -- - -- - -- -- - -- - -- -

Note: a/ Refers to 1966-73 b/ Refers to 1970-73 c/ Eligible countries at the end of 1989 included: Benin, Burundi, Central African Republic, Chad, The Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Madagascar, Malawi, Mali, Mauritania, Mozambique, Niger, Sao Tome and Principe, Senegal, Somalia, Tanzania, Togo, Uganda and Zaire.

Source: World Bank data files GDP growth has averaged about 3.4 percent annually since reforms started, close to the average before the first oil shock, thus reversing a decade of declining per capita growth. Imports and exports have performed very strongly, reversing a decline. Because of the high GDP and export growth and favorable terms of trade, gross national income increased at an average of more than 4 percent a year during 1985-88, and per capita income has increased in 1988 for the first time since the first oil shoclk Private consumption in the SPA countries has posted increases after the reforms started, but not sufficiently high to offset population growth. However, the share of gross domestic investment in GNP has improved only modestly from its 1981-84 level, and the share of national saving in GNP has continued to deteriorate. To compensate for the low national savings (7 percent 12 l
MULTILINGUAL CHILD ASSESSMENT

|27|Copies a vertical line ( as drawn by<br>the examiner) with charcoal/chalk<br>within 30 degrees|29|Picks heaviest box|
|---|---|---|---|
|28|Picks longest stick 3 of 3|18/19|Scribbles in any way|
|29|Picks heaviest box 3 of 3 – is the<br>child able to tell you which box is<br>the heaviest?||**IF >=36 MONTHS AND FAILS:<br>SKIP TO FOLDS **|
|30|Can make a bridge with bricks:||**END IF FAILS SMALL BRIDGE,<br>HEAVIEST BOX AND<br>SCRIBBLES**|
|31|Makes a doll or complicated car out<br>of clay|27|Copies a vertical line within about 30<br>degrees|
|32|Copies a circle (needs to be<br>complete) with chalk or in the sand<br>with a stick|34|Copies a circle|
|33|Copies a cross with chalk|33|Copies a cross|
|34|Can draw a square||**END IF FAILS LINE, CIRCLE,<br>CROSS**|
|35||NEW|Can color within lines|
|36||34|Copies a square|
|37||NEW|Can copy letters**E C A M J H**|
|38||NEW|Can fold paper into quarters|
|39|||**END IF FAILS 3 OF ABOVE<br>(COLORING, SQUARE, WRITING<br>4 LETTERS AND FOLDING)**|
|40||NEW|Can copy a pattern of 4 bottle tops<br>**END IF FAILS 4 BOTTLE<br>TOPS**|
|41||NEW|Can copy a pattern of 6 bottle tops<br>**END IF FAILS 6 BOTTLE<br>TOPS**|
|42||NEW|Beans and beads|
|43||43||

<u>Table 2: Comparison of original MDAT Language items with Western Kenya MDAT</u>

|Item|Language/Hearing|Item|Western Kenya MDAT|
|---|---|---|---|
||Startles or jumps/responds to<br>sounds|||
|**1**|Happy vocalising or making<br>sounds–not crying|||
|2|Laughs/chuckles|||
|3|Turns to voice – if you are out of<br>sight, does she/he look in the<br>directionofyourvoice orsound|||
accounts for a quarter of the total SME lending volume. However, 90 percent of this amount is in China where the SME definition is broad and SMEs can employ up to 1,500 people. Excluding China, the total SME lending volume in East Asia and Pacific is comparable to Eastern Europe and Central Asia or about 3 percent of the total. Sub-Saharan Africa, Middle East and North Africa, and South Asia together account for only 1.7 percent of the total SME volume.

# **5. Conclusion**

Expanding access to financial services by SMEs is a critical objective of the financial inclusion agenda. The G-20 has started focusing on financial inclusion in the global policy agenda since 2009 and has identified financial inclusion as a key driver of economic growth, reduced economic vulnerability for individual households, poverty alleviation, and improved quality of life for people around the world. SME access to finance is a fundamental component of this agenda. As part of the global efforts to collect data on SME financing, the _Financial Access_ survey by CGAP/WBG initiated data collection on SME access to finance as a first attempt to identify the feasibility of collecting consistent cross-country data on SMEs from financial regulators.

This paper presents the data set resulting from this initiative, as well as a first analysis of the data. The data set is a first attempt to form a consistent cross-country database on SME access to finance. The main message emerging from the new data is that what constitutes an SME is quite different in different parts of the world. Nevertheless, these differences in SME definitions do not drive the variation in SME lending around the world. Income per capita, private credit to GDP, the legal and business environment, and the efficiency of the banking system are among the factors that influence SME lending.

Based on this data set, the total volume of SME loans around the world is predicted as roughly $10 trillion in 2009. The bulk of SME lending volume—70 percent—is concentrated in highincome countries. The median ratio of SME loans to GDP in high-income countries is 13 percent, compared with only 3 percent in developing countries.

13