rafmacalaba/datause-encoder-data huggingface dataset:
50 -

- Harris, J.R. and M.P. Todaro (1970). "Migration, Unemployment and Development: A Two Sector Analysis". _American Economic Review 60_ (March): 126-42.

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- Hong, Wontack (1979). _Trade, Distortions and Employment Growth in Korea._ Seoul, Korea: Korea Development Institute.

- Hong, Wontack and Lawrence B. Krause, eds. (1981). _Trade and Growth of the Advanced Developing Counties in the Pacific Basin._ Seoul, Korea: Korea Development Institute.

- Johnson, H.G. and M. Krauss (1970). "Border Taxes, Border Tax Adjustments, Comparative Advantage and the Balance of Payments". _Canadian Joumal of Economics_ 3 (November): 595-602.

- Kim, Kwang Suk (1988). "The Timing and Sequencing of a Trade Liberalization Policy: The Case of Korea". World Bank (mimeo).

- Korea, Ministry of Finance (1986). _Korean Taxation._ Seoul, Korea.

- Krueger, A. (1979). _Studies in the Modemization of the Republic of Korea, 1945-75: The Developmental Role of the Foreign Sector and Aid._ Cambridge, Mass.: Harvard University Press.

- Kwack, Sung Teung (1986). The Economic Development of the Republic of Korea: 1965-81". In Lawrence J. Lau (ed.), _Models of Development:A Comparative Study of Economic Growth in South Korea and Taiwan._ California: Institute for Contemporary Studies Press.

- Kwack, Taewon (1988). "Public Finance, Trade, and Economic Development: The Role of Fiscal Incentives in Korea's Export Led Economic Growth". Paper resented at the 44th Congress of the International Institute of Public Finance, Istanbul, August.

- Lewis, W.A. (1954). "Economic Development with Unlimited Supplies of Labor". _Manchester School of Economic and Social Studies_ 22 (May): 139-91.

- Mansur, A. and J. Whalley (1984). "Numerical Specification of Applied General Equilibrium Models: Estimation, Calibration and Data". In H. Scarf and J. Shoven (ed.), _Applied General Equilibrium Analysis._ Cambridge: Cambridge University Press.

- Nam, Chong Hyun (1981). "Trade and Industrial Policies, and the Structure of Protection in Korea". In Wontack Hong and Lawrence B. Krause _(ede.), Trade and Growth of the Advanced Developing Countries in the Pacific Basin_ Seoul, Korea: Korea Development Institute.
**THE ANATOMY OF A MULTIPLE** CRISIS:

**WHY** WAS **ARGENTINA SPECIAL AND WHAT CAN WE LEARN FROM IT***

Guillermo Perry Luis Serven

# The World Bank

JEL codes: E44, F31, F32, F41, G28, 054 This paper is the output of a collective effort led by the authors, and based on background work on (i) fiscal issues, by Norbert Fiess, Rodrigo de Jesus Suescun and Guillermo Perry; (ii) exchange rate issues, by Humberto Lopez and Luis Serven; (iii) financial sector issues, by Augusto de la Torre and Sergio Schmukler and (iv) capital flows and spreads, by Norbert Fiess and Luis Serven. We are grateful to these colleagues for their respective contributions. Though we borrow freely from them, the interpretations and conclusions offered in this paper are our exclusive responsibility. We also thank Christian Broda for his kind collaboration on exchange rate issues, Jose Luis Machinea for helpful discussions, and Myrna Alexander, Jerry Caprio, Daniel Lederman, Paul Levy, Emesto May, Pedro Pou, Andrew Powell, David Rosenblatt, Nick Stern, Adolfo Sturzenegger and Federico Sturzenegger for very useful comments. Ana Maria Menendez and Patricia Macchi provided valuable assistance.
|**Table 1: Descriptive statistics on study po**|**pulation**<br>(1)||(2)||(3)||
|---|---|---|---|---|---|---|
||**SELEC**<br>**Samp**|**TED**<br>**le**|**All infor**<br>**busine**|**mal**<br>**sses**|**Form**<br>**busines**|**al**<br>**ses**|
||Mean [SD]|<br>N|Mean [SD]|N|Mean [SD]|N|
|_Firm owner characteristics_|||||||
|Female owner|0.629|3,596|0.632|7,089|0.419|608|
||[0.483]||[0.482]||[0.494]||
|Age of the owner|39.5|3,557|39.4|6,955|43.6|589|
||[10.4]||[11.2]||[10.5]||
|Business owner has some formal education|0.712|3,591|0.708|7,081|0.884|606|
||[0.453]||[0.455]||[0.32]||
|Business owner has some secondary|0.409|3,596|0.38|7,090|0.74|608|
|education|[0.492]||[0.486]||[0.439]||
|_Firm characteristics_|||||||
|Trade|0.55|3,596|0.518|7,090|0.584|608|
||[0.498]||[0.5]||[0.493]||
|Services|0.262|3,596|0.277|7,090|0.26|608|
||[0.44]||[0.447]||[0.439]||
|Craft|0.16|3,596|0.17|7,090|0.09|608|
||[0.366]||[0.375]||[0.287]||
|Firm area in m2|18.7<br>|3,590|18.3<br>|7,078|52.5<br>|606|
||[43.5]||[50.8]||[106.5]||
|Business connected to electricity network|0.619|3,594|0.605|7,085|0.898|608|
||[0.486]||[0.489]||[0.303]||
|Number of employee|1.175|3,596|1.03|7,090|2.961|608|
||[1.687]||[1.603]||[4.59]||
|The firm does any form of accounting|0.179|3,594|0.156|7,089|0.642|604|
||[0.383]||[0.363]||[0.48]||
|Amount of sales in an average week|60,561<br>[56,508]|3,596|82,630<br>[298,695]|6,639|542,167<br>[4,434,990|528<br>]|
|Amount of profit in the last month|46,698<br>[46,578]|3,596|46,434<br>[141,423]|6,358|223,041<br>[726,068]|490|
|Firm owner owns a bank account|0.222|3,514|0.194|6,928|0.789|582|
||[0.416]||[0.395]||[0.409]||
|Firm pays taxes|0.547|3,560|0.466|7,005|0.836|597|
||[0.498]||[0.499]||[0.371]||
|Amount of taxes paid in the previous year|18,732<br>|3,482|16,649<br>|6,827|316,636<br>|533<br>|
||[27,265]||[30,727]||[2,591,065|]|
|Thinks that it's difficult to know in advance|0.744|2,665|0.764|4,921|0.725|520|
|how much taxes she will have to pay|[0.437]||[0.424]||[0.447]||
|Ratio tax/ annual profit for all businesses<br>|0.051|3,482|0.072|6,174|0.128|445|
||[0.089]||[0.174]||[0.221]||
|Ratio tax/ annual profit for businesses|0.094|1,870|0.165|2,859|0.169|372|
|Notes: sources: listing‐baseline survey March<br>paying taxes|[0.104]<br>2014||[0.286]||[0.313]||

33
_5_

# **_Five payment instruments_**

# **_Four types of transactions_**

- Cash (coin and currency)

- Point-of-sale (POS)

- - Bill payment

- Cards (debit and credit)

- Check (debit transfer)

-

- Disbursement (payroll, transfers) Check (debit transfer)

- - Financial - GIRO (credit transfer) - Wire transfer

Ranking the 4 types of transactions by their likely average value, POS would be the smallest, bill payments would be larger, disbursements would reflect an even higher average value, while financial transactions would clearly average the highest. The average value 2 per transaction for these five payment instruments for Europe (an average of 11 European countries), Japan, and the U.S. are shown below. As seen, cash transactions have the lowest average value, debit and credit cards are next highest, followed by check and giro payments which are markedly higher, with wire transfer payments in the millions of dollars per transaction.

## **Average value per transaction for five payment instruments** (1993, in US$)

|||**_C_**|**_ard_**|||**_Wire_**|
|---|---|---|---|---|---|---|
|**_Area_**|**_Cash'_**|**_Debit_**|**_Credit_**|**_Check_**|**_Giro_**|**_transfer_**<br>**_2_**<br>|
|||||||_(In millions)_|
|Europe|6- 14|52|91|3,405|14,423|4.9|
|Japan|25|165|163|79,754|3,820|93.1|
|U.S.|5|44|45|1,147|4,602|4.2|

_1 Boeschoten (1992), table 1-1, p.200._ _2 Europe averages CHAPS (U.K.), SAGITTAIRE and TBF (France), EAF (Germany), and ME (Italy); Japan is BOJNET; the U.S. averages CHIPS and Fedwire._ The match between transaction type and the payment instruments used for these transactions is related to the average value of the transaction and is illustrated below. Both check and giro payment methods are seen to be quite flexible in that they are often used for a wide range of payment values and transaction types. In contrast, cash and cards are typically tied to smaller value point-of-sale transactions while wire transfers are almost exclusively used for large value financial transactions.

_A ranking is used because only scattered data are available on the actual average value of each of the 4 types of transactions._ _The 11 countries are: Belgium, Denmark, Finland, France, Germany, Italy, Netherlands, Norway, Sweden, Switzerland, and the U.K._

**_3_** _Japan's average value per wire transfer is high because many of its transfers represent settlement payments for sums of transactions rather than individual transfers. Japan's average check value is high because it is almost exclusively used for a small number of large value business/financial transactions._
Table 12: Average Job Quality between 2007-2019, by formality

||Employees|Entrepreneurs a|nd Self-employed|
|---|---|---|---|
||(1)<br>(2)<br>Formal<br>Informal|(3)<br>Formal|(4)<br>Informal|
|2007|0.81<br>0.32|0.59|0.38|
|2008|0.77<br>0.31|0.53|0.38|
|2009|0.80<br>0.35|0.56|0.39|
|2011|0.81<br>0.32|0.57|0.43|
|2012|0.83<br>0.35|0.56|0.40|
|2013|0.84<br>0.34|0.60|0.41|
|2014|0.82<br>0.30|0.55|0.40|
|2015|0.83<br>0.34|0.56|0.40|
|2016|0.83<br>0.33|0.53|0.41|
|2017|0.82<br>0.32|0.56|0.40|
|2018|0.84<br>0.30|0.58|0.40|
|2019|0.85<br>0.29|0.56|0.41|

_Notes:_ Based on data from EH 2007-2019. We define formality for employees with some written contract and employers and self-employed with a Tax Identification Number (TIN). The 2020 and 2021 values are not included as the National Household Survey for those years did not capture information on the Tax Identification Number.

# **5.2 Drivers of job quality**

As Figure 3 illustrates, job quality has consistently been higher for workers in larger firms and employers compared to those in micro and small firms and the self-employed. However, as above-mentioned, the latter group represents approximately 70 percent of the employed population in Bolivia. The differences in job quality across these groups can be attributed to the varying shares of good jobs within each component. Table 4 (presented in section 3) provides the distribution of good jobs for each component in Bolivia from 2007 to 2021.

Examining gender disparities, Table 13 highlights the differences between men and women in the percentage of workers who satisfy each component of job quality. Unsurprisingly, men exhibit higher satisfaction rates than women in almost every component except overqualification. It is worth noting, however, that the gap in the share of good jobs is more pronounced in components related to income rather than access to the pension system (as depicted in Figure 4). These findings, in line with Brummund et al. (2018), suggest that the income gap between genders poses a greater challenge for equity in the labor market than access to formal jobs, which is approximated by access to social security.

27
13 during the 1980s. In 1981 the question did not appear in the core questionnaire and in 1985 less than 5% of the sample responded to the question. In the last two or three years of the 1980s the response rate to the race question grew, and it became almost universal following a successful information campaign implemented in the run-up to the 1991 Census<sup>22</sup> . Hence race is only used here for the analysis of 1993 and 2004. Following the standard practice in studies of Brazil, mixed race heads of households are grouped together with black and indigenous heads.

� _Household type._ Five types of households are identified: (i) “single adult” households comprised of only one adult; (ii) “couple, no kids” households comprised of only adults, i.e. all aged over 14 or over; (iii) “couples with kids” households with more than 1 adult plus children; (iv) “single parent” households with a single adult plus children; and (v) elderly households whose head is aged 65 or over, with or without children. This is a simplification of the categories used by Tanner (1987) for Northeast Brazil.

- _Region._ There are five official, standard geographical regions in Brazil: North, Northeast,

- Southeast, South and Centre-West.

- _Urban/Rural location of household._ Urban and rural areas are those so defined by IBGE

- and used in the PNAD.

The point of the static decompositions is to separate total inequality in the distribution into a component of inequality _between_ the above groups in each partition ( _IB_ ) - the explained component - and the remaining _within_ -group inequality ( _IW_ ) - the unexplained component. Unfortunately, many widely used inequality measures are not decomposable, in the sense that overall inequality can not be related consistently to the constituent parts of the distribution. In particular, we are interested in measures where _IB_ + _IW_ = _I_ . This is not generally true, for instance, of the Gini coefficient, but it is true of all members of the Generalised Entropy class of measures (see Cowell, 1995).

where _fj_ is the population share and _vj_ the income share of each subgroup _j, j=1,2,....k._ Between-

> 22 This was the “Não Deixe a Sua Cor Passar em Branco” - or “Do not let your color go blank” – campaign. The Portuguese words for blank and white are the same.
91

# Budget DOficit(-)/GDP Highly Indebted Countrles

<!-- Start of picture text --> Deo. DfI*t /0<br>O<br>I1 /..- .<br>-11~~~~~~~~~~~~~~~~~~~~~~~-<br>.20 -2~~~~~~~~~~~~~4<br>0 07 Ss St0 00 0I 02<br>Year<br>Prlgflon +-tuals<br><!-- End of picture text -->

> Gross Domestic InvestmenV,IGDP Hlghly Indebted Countrles

<!-- Start of picture text --> @01 -iI--<br>25 4<br>2'X ' I<br>16<br>00 07 00 6~09 0 of 92<br>Your<br>-PrPeotione +IAetus<br><!-- End of picture text -->

# Real Export of GNFS Growth Rate Highly Indebted Countrles

<!-- Start of picture text --> Expet ONF8O* wg<br>4 | S ? 90 9 92 t<br>Year<br>-Pmoo-n +4otule<br><!-- End of picture text -->

Current Account/GDP Hlghly Indebted Countries <!-- Start of picture text --> umA.u/O<br>:1 .2 I<br>.30<br>* *r 66 IS oS t 02<br>Yew<br>~Prl +t ft..l *WMUW 0 .TOTa4uu.d<br>TOT u Terms d Trade<br><!-- End of picture text --> Domestl Savings/GDP Highly Indebted Countries <!-- Start of picture text --> .-<br>-<br>~~~~~~~~~~~2-<br>00 0 00 9 90 of 92<br>Ywar<br>-Projtone +Atuals<br><!-- End of picture text --> Real GDP Growth Rates Hlghly Indebted Countries <!-- Start of picture text --> W wg Rub.<br>SI Ss Da<br>Yew<br>-Pe t<br><!-- End of picture text -->
# **Figure 5: Incidence and distribution of ninis by income decile**

Panel A: Incidence of ninis by income per capita decile (ninis as % of youth aged 15-24) <!-- Start of picture text --> 40.0%<br>36.6%<br>34.7%<br>35.0%<br>30.3% 30.5%<br>30.0% 27.9% 28.2%<br>25.9% 25.9%<br>24.2%<br>25.0% 23.2%<br>20.2% 21.2%<br>20.0% 17.3% 18.1%<br>14.8%<br>15.0% 13.1% 12.0%<br>9.8% 9.4%<br>10.0% 6.6%<br>5.0%<br>0.0%<br>1992 2010<br>D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10<br><!-- End of picture text --> Panel B: Distribution of ninis by income decile (ninis in given income decile as % of ninis aged 15-24) <!-- Start of picture text --> D1<br>D2<br>D3<br>D4<br>1992 12.5% 11.5% 12.8% 11.7% 11.3% 11.4% 9.9% 8.5% 6.5% 4.0% D5<br>D6<br>D7<br>D8<br>D9<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% D10<br>D1<br>D2<br>D3<br>D4<br>2010 16.5% 14.7% 13.8% 13.0% 11.3% 9.8% 7.4%6.3%4.5% 2.8% D5<br>D6<br>D7<br>D8<br>D9<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% D10<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015) 10
# **Conceptual Framework**

We derive the yield function and the profitability of fertilizer from the farm profit component of the agricultural household model of Sing, Squire and Strauss (1986). Smallholder farmers are considered to be firms whose production set is made up of food and cash crops. Maize is the most widely cultivated crop in Malawi – it is cultivated by about 90% of farmers on 70% of their farm plots – and the most important crop in terms of fertilizer application (NSO, 2013). The study therefore focuses on farmers’ decision to produce maize using inorganic fertilizer and other inputs, with the objective of maximizing farm profit, . The profit function is given by: ππ where YY and are quantity and price of maize respectively; II is a vector of inorganic fertilizer ππ= PP YY(II) −PP II (1) YY and other inputs used in the production of maize; and YY PP is a vector of the prices of the inputs. The II II term _Y(I)_ represents the agronomic production function where a vector of inputs is turned into PP maize output _Y_ . Guan et al. (2006) classify the inputs into _growth inputs_ and _facilitating inputs_ based on the agronomic ideology that different inputs affect yield differently. The _growth inputs_ include such inputs as soil nutrients, seed and water that are directly involved in the biological process of plant growth and development, while the _facilitating inputs_ involve such inputs as labor and pesticides that are not directly involved in the growth and development process of plants but influence the response rate of plants to the _growth inputs_ (Guan et al, 2006). Previous literature extends the _facilitating inputs_ to include household characteristics such as wealth, education, household size and dependency ratio (Xu et al, 2009; Sheahan et al. 2012).

The present study categorizes the _growth_ and _facilitating_ inputs into plot-level and household-level variables. The plot-level variables include soil nutrients, seed application rates and labor, as well as variables such as soil quality, slope of the plot, whether or not the plot is swampy, soil type and the extent of soil erosion that capture the conditions of the plot. The factors 7
**Figure 9. Average Psychosocial Stimulation Scores Among Children Aged 0-72 Months in Pakistan by Parent Gender and Educational Attainment**

<!-- Start of picture text --> 8.50<br>8.00 7.88<br>7.70<br>7.65<br>7.50<br>7.09<br>7.00<br>6.62 6.61<br>6.50<br>6.00<br>Less than Primary Education Primary or Middle Education Secondary or Higher Education<br>Mothers/Female Caregivers Fathers/Male Caregivers<br>Note: All estimates are weighted. Psychosocial stimulation as measured by children’s access to learning materials and<br>participation in stimulating activities with adult caregivers in the past 24 hours on a scale of 0-14. Sample includes all children<br>(N=2,896))<br>Psychosocial Stimulation Sum-Score<br><!-- End of picture text -->

# **4. Discussion**

ECD has been an understudied area in Pakistan, particularly for very young children, and this study contributes to our understanding of how young Pakistani children are faring, through a holistic lens. Other national-level surveys tend to measure nutritional and physical growth metrics among children under age 3, academic performance among older children, or provide province-level but not national-level data, as in the case of the MICS surveys. This study, which provides national estimates of ECD, fills key gaps in the literature on children’s development under age 3 and school readiness for children aged 3-6 years. We quantify the compounding and pernicious impact of multiple risk factors on children’s development for both children under 3 and aged 3-6, with clear declines in development and school readiness as risk factors increase.

The study also explores which household- and caregiver-oriented variables have the strongest associations with child outcomes. For children under age 3, we found that higher levels of psychosocial stimulation at home and lower levels of caregiver distress are most significantly associated with higher scores on child development. For children ages 3-6, higher levels of 28
_Ingo Borchert and Mattia Di Ubaldo_ 2. Services value-added from country _i_ in total exports of country _j_ From these statistics, we can construct our two main variables of interest by taking the following ratios: Ratio 3.1 denotes the share of services VA from country _i_ in services exports of country _j_ , out of total services VA in services exports of country _j_ . Note that the denominator in 3.1 does not correspond to the total of services exported by country _j_ , as it does not include services that are exported by non-services industries, such as manufacturing. This ratio allows us to inspect changes in services value-chains or, more precisely, whether a PTA between _i_ and _j_ affects the amount of services VA that _j_ sources from _i_ (and that enter _j_ ’s services exports), at the expense of services from other countries or services produced domestically in country _j_ .

Ratio 3.2 denotes the share of services value-added from country _i_ in total exports of country _j_ , out of the total services VA in total exports of country _j_ . Compared to ratio 3.1, ratio 3.2 captures also services VA exported by other industries, such as manufacturing. Since the export of services value-added as embodied inputs into manufacturing products is known as so-called “Mode 5” services trade, the comparison of the two ratios will allow us to say something about PTA effects on Mode 5 as well.

It is worth noting that both ratios allow us to assess whether entering a PTA has an impact on the _composition_ of services VA in exports, from non-PTA and domestic VA, to VA from PTA partners. To do so, we exploit ratios 3.1 and 3.2 in a structural gravity 19
definitions of corruption. Index values for the three Baltic countries are based on three distinct combinations of indicators. Values for Bulgaria, Romania, and Croatia - which like the Baltic nations are often compared to each other – are also based on three different combinations of indicators. The same is true for the three Caucasus countries, Armenia, Azerbaijan and Georgia. This comparability problem is even more severe for the 2004 WBI index on Control of Corruption. It uses 23 different combinations of sources for the 27 ECA countries. No one combination of sources is used to construct index values for even three countries. There are only four pairs of countries whose values are based on a common set of sources: Russia and Poland are based on the same 14 sources, Estonia and Romania on the same 13 sources, Bulgaria and Lithuania on the same 12, and Croatia and Latvia on the same 11.<sup>17</sup> In principle, more strictly comparable comparisons could be performed simply by computing a composite index that deletes any source not common to the two countries in question. Alternatively, one could simply compare two countries source by source, not bothering to construct a composite index at all. Either of these options requires access to the underlying data, however, which neither TI nor WBI provide.

The definitional inconsistency across countries entailed by using a different mix of sources is the price of maximizing the number of countries covered by the index. Corruption ratings are generated for more countries by TI and WBI when more sources (even those with spotty coverage) are aggregated, but for any pair of countries the index values are very likely to reflect differing implicit definitions of corruption.

_Tracking Changes Over Time_

> 17 This information was provided by Jim Anderson, who closely examined data sources in the TI and WBI indexes.

19
**Figure 12: Average monthly electricity consumption by quintile**

<!-- Start of picture text --> 250<br>200<br>Mexico<br>150<br>Guatemala<br>Bogota<br>100<br>Uruguay<br>50<br>0<br>1 2 3 4 5<br>Income quintiles<br>Average monthly consumption (kWh)<br><!-- End of picture text --> Source: Own elaboration from LSMS household survey data

**Figure 13: Index of relative monthly electricity consumption by quintile**

<!-- Start of picture text --> 300<br>280<br>260<br>240<br>220<br>Mexico<br>200 Guatemala<br>180 Bogota<br>160 Uruguay<br>140<br>120<br>100<br>80<br>1 2 3 4 5<br>Income quintiles<br> first quintile consumption<br>Electricity consumption as percentage<br><!-- End of picture text --> Source: Own elaboration from LSMS household survey data 32