rafmacalaba/datause-encoder-data huggingface dataset:
From a simple inspection of Figures 1a and 1b, it is evident that the sets of countries that are procyclical, countercyclical and acyclical according to the real or nominal exchange rate based indices overlap significantly, as one would indeed expect in (high frequency) quarterly data. To get a clearer image of the phenomenon that we are discussing here, we plot the cyclical components of GDP and NEER for Brazil, a country with procyclical currency, and Japan, a country with countercyclical currency, in Figure 2a and 2b below.

We find preponderance of procyclical currencies among emerging economies and of countercyclical currencies among advanced economies. While many of the advanced economies such as Switzerland, Germany/Eurozone, Japan and the US (in the more recent period) have countercyclical currencies, we find no emerging economy with independent countercyclical currencies. The only emerging economies with a countercyclical currency in our sample are Euro Zone periphery countries that “inherited” a countercyclical currency—the euro has indeed the same degree of cyclicality as the Deutsche Mark.

Despite the preponderance of emerging markets in the procyclical currencies group, the group is a bit more mixed. While virtually all the emerging market commodity exporters such as Chile, Argentina, Brazil and Russia are in the group of procyclical currencies, we also find that some of the advanced commodity exporting countries, such as New Zealand, Australia, Finland and Iceland, have procyclical currencies.<sup>9</sup>

> 9 An interesting counter example is Norway, which, despite being one of the larger commodity exporters in the world, relative to the size of its economy, maintains a countercyclical currency. This is seemingly because of the operations of the Norway oil fund; see, e.g., Velculescu (2008).

5
higher number of plots, though they are further away compared to the individual training group. We include all 15 covariates shown in Appendix Table 1 in our vector of covariates Xijs.

Second, we test whether farmers who completed the training differ from those in the overall sampling frame at the time of randomized assignment. Appendix Table 2 shows that the farmers who completed the training have 0.3 more household members on average, are 7 percentage points more likely to be married (rather than just cohabitating), own 0.5 fewer assets and have a 0.3 ha larger plot size on average. However, all these differences go in the same direction for _both_ farmers randomized into the individual training group and those randomized into the couples’ training group, indicating comparability between compilers when analyzing the action plan data comparing couples’ training attendees and individual training attendees.

Table 1 shows summary statistics for the final sample of farmers included in our study. On average, households have six members, evenly split between adults and children. 90 percent of the lead farmers are married (versus merely cohabitating with their partner), while 24 percent of households have a traditionally matrilineal ethnicity (in Côte d’Ivoire, matrilineal ethnicity mainly determines through which channel men inherit assets).

Table 1: Descriptive Statistics

|VARIABLES|Observations|Mean|SD|
|---|---|---|---|
|Age of household head|1,435|45.63|11.21|
|Household size|1,435|6.23|3.44|
|Number of adults|1,435|2.98|1.70|
|Number of children|1,435|3.07|2.35|
|Dependency ratio|1,435|1.18|0.88|
|Married planter|1,435|0.90|0.30|
|Polygamous relationship|1,435|0.12|0.36|
|Matrilineal ethnicity|1,435|0.24|0.42|
|Has off-farm income|1,435|0.66|0.48|
|Asset index|1,435|9.84|2.09|
|Credit constrained|1,435|0.13|0.34|
|Share of plots owned|1,435|0.94|0.21|
|Average distance to plots (minutes)|1,435|66.67|52.52|
|Number of plots|1,435|2.37|1.38|
|Average plot size|1,435|3.68|3.45|
|Planter age|1,435|45.13|11.15|
|Planter years of education|1,435|6.37|4.85|
|Planter weekly hours of agr. Work|1,435|26.17|15.01|
|Spouse age|1,254|37.53|10.42|
|Spouse years of education|1,254|3.05|3.59|
|Spouse weeklyhours of agr. Work|1,254|13.06|13.36|

Thirteen percent of households report that they were credit constrained (i.e., were refused credit in the last year) and 66 percent of households had an income-generating activity. The average plot size used or held 9
The agriculture sector, on the other hand, does not rely as heavily on transportation. Therefore, it benefits from an increase in demand relative to other goods. This, in turn, increases the employment potential of rural unskilled labor compared to their urban counterparts. Fig 6.9 indicates that there is almost no effect on employment across scenarios in rural areas compared to the base case scenario.<sup>6</sup> _Figure 6.8: Changes in labor supply/demand for urban uneducated_ <!-- Start of picture text --> Change in labor supply/demand (as % change from base<br>case)- for (Uneducated)-Urban<br>2018 2022 2026 2030<br>(5.0 USD/ton) (13.3 USD/ton) (21.7 USD/ton) (30.0 USD/ton)<br>0.0%<br>-0.2%<br>-0.4%<br>-0.6%<br>-0.8%<br>-1.0%<br>-1.2%<br>-1.4%<br>-1.6%<br>DTAX STAX SAVING CORPORATE TRANS<br>0.0% 0.0% 0.0% 0.0% 0.0%<br>-0.3% -0.3%<br>-0.4% -0.4% -0.4%<br>-0.6%<br>-0.7%<br>-0.9%<br>% change from base<br>-1.0% -1.0% -1.0%<br>-1.0%<br>-1.4% -1.4% -1.4%<br><!-- End of picture text -->

> 6 Note that in the base case scenario, employment of low-skilled individuals in urban areas is assumed to be 4,186,322 in 2018 and 6,290,527 in 2030. Similarly, employment of low-skilled individuals in rural areas is assumed to be 38,516,966 in 2018 and 54,301,218 in 2030 **.**

20
8 Formal agricultural lending is not exogenously given or randomkly distributed. As discussed in Binswanger, Khandker and Rosenzweig (1988), both the farmers and financial institutions are influenced by agricultural opportunities implied in the agroclimatic endowments of a district. That means, the lending agencies will lend more in areas where agricultural opportunities are better, risk is lower, and hence loan recovery is higher (Binswanger and Rosenzweig, 1986). An unobserved variable problem thus arises for the econometric estimation which can be overcome by the use of district-level panel data.

The system o' equations to be estimated with the district-level time-series data are the followings

- (1) ICrit - ICr(Xjt. Zjt. pjt. _6j)_

-

- (2) Qjt Qjt(Xjt, ICrjt, pit, d;)

-

- (3) INpjt INpjt(Xjt. ICrjt, Pjt. _S)_

-

- (4) INvjt INvjt(Xjt. ICrjt. INvj(t-l), jSjt _6j)_

-

- (5) WAGEjt WA7Ejt(Xjt, ICrjt, sit, _Sj)_

where equation (1) is the district's prediction equation for institutional credit advanced to rural sector by the formal lenders; (2) is tbe output supply equation; (3) is the input dcmand equation; (4) is the investment equation and (5) is the wage equation. Here ICr stands for institutional credit advanced, X is a vector of exogenous explanatory variables (e.g., the output and input prices, government infrastructure, interaction terms between year and agroclimatic endowments, the rate of interest); Z is a vector of the number of formal lending agencies; Q is aggregate crop
# **S4 Supplemental Empirical Results on Formal Status**

Table S4.1 Switching to the Formal Sector

|||Informality||Se|lf-employme|nt|
|---|---|---|---|---|---|---|
||Argentina<br>(1)|Brazil<br>(2)|Mexico<br>(3)|Argentina<br>(4)|Brazil<br>(5)|Mexico<br>(6)|
|Age 25-29|0.050***|0.006***|0.003***|-0.025***|-0.023***|-0.044***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Age 30-39|0.006***|-0.003***|-0.007***|-0.041***|-0.032***|-0.056***|
||(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Age 40-49|-0.016***<br>(0.000)|-0.038***<br>(0.001)|-0.017***<br>(0.001)|-0.069***<br>(0.001)|-0.066***<br>(0.001)|-0.055***<br>(0.001)|
|Age 50-64|-0.017***<br>(0.000)|-0.080***<br>(0.001)|-0.018***<br>(0.001)|-0.076***<br>(0.001)|-0.100***<br>(0.001)|-0.070***<br>(0.001)|
|Male|0.054***<br>(0.000)|0.075***<br>(0.001)|0.025***<br>(0.000)|0.025***<br>(0.000)|0.043***<br>(0.000)|0.008***<br>(0.000)|
|Primary education|-0.010***<br>(0.002)|0.035***<br>(0.002)|0.036***<br>(0.001)|0.013***<br>(0.001)|0.010***<br>(0.001)|0.011***<br>(0.000)|
|Secondary education|0.041***|0.107***|0.120***|0.015***|0.033***|0.036***|
||(0.002)|(0.002)|(0.001)|(0.001)|(0.001)|(0.000)|
|Terciary education|0.143***<br>(0.002)|0.172***<br>(0.002)|0.290***<br>(0.001)|0.059***<br>(0.001)|0.067***<br>(0.001)|0.099***<br>(0.001)|
|Survey-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Region FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|6,849,340|2,295,693|5,031,670|4,912,294|3,085,027|2,552,231|
|R-squared|0.037|0.044|0.059|0.023|0.029|0.027|

_Source:_ Authors’ calculations based on data from 12-months survey panels from Labor Force Surveys. _Note:_ Each column reports regression results from a linear probability model of the effect of the listed individual characteristics on the probability of switching to formal employment. Regressions include survey-year and region fixed effects. Columns 1-3 and 4-6 have different subsamples. The sample considered in columns 1-3 includes all panel individuals (interviewed in one survey and 12 months later) who worked in both periods, were 20-64 years old and were informal-employee at the initial period. The sample considered in columns 4-6, includes all panel individuals (interviewed in one survey and 12 months later) that worked in both periods, were ages 20-64 years, and were self-employee in the initial period. All totals from the household surveys are computed applying sampling weights-expansion factors provided by the institutes of statistics. Robust standard errors are in parentheses: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

68
<!-- Start of picture text --> ct = 0.18 * 2(vacr) + 0.19 * 2(cCer) + 0.19 * 2(gect) + 0.16 * 2(pSct) + 0.18 * 2(rdct) + 0.19 * z(rl¢t).<br><!-- End of picture text --> <!-- Start of picture text --> ct = 0.43 * z(Innov,,) + 0.44 * z(Edu,,) + 0.46 * z(Ef fice) + 0.47 * z(Infra,,) + 0.43 * z(Inst,,).<br><!-- End of picture text -->
**Plc,Planning, and Research World Development Report**

A review of flow of funds data for 17 developing countries reveals that most years in most countries: channeled from the household to the business sector much more by means of bank deposits and loans than through issue of equities.

- The household sector is a net lender, lending on average 7 percent of GNP - and more in countries that are more open and have higher income. The household sector typically saves more than twice what it needs to finance its accumulation of real assets, lending the rest to other sectors.

* The business sector is a net borrower, bonrowing on average 7 percent of GNP. About half of real capital formation by business is extemally financed.

* The government sector is sometimes a net lender but more often a net borrower.

Intersectoral flows in developing countries are achieved mostly through the banking system. The rest of the financial system is typically relatively underdeveloped. Thus savings are The foreign sectr.r cannot be relied upon as a passive or residual provider of funds to the domestic economy. A shortfall in foreign financing or an increase in govermment borrowing typically force the business sector to reduce its investment. Households and the government typically do little to absorb foreign financing shocks by adjusting their net lending.

The vulnerability of the business sector to the availability of foreign funds points up the need for the financial sector in developing countries to find more ways to attract domestic savings to finance worthwhile investment projects. This will require that savers are ensured a fair share of the retums on investment projects - which means realistic interest rates for savers and the development of financial instruments that allow savers to share in highrisk high-yield projects.

This is a background paper for the 1989 World Development Report. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Wilai Pitayatonakarn, room N9-005, extension 60353.

**'Pe** PPR Working Paper Series disseminates the fidings of work under way in the Bar's Policy. Plarming, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and concIlusions in these papers do not necessarily represent of ficial policy of the Bank.

Produced at the PPR Dissemination Center
- 34 -

Not many tax administrations can fulfill these requirements. Those countries that have tried to implement systems similar to the forfait without adequate resources have ended up with highly flawed systems. One of the principal obstacles has been the lack of resources to draw up detail d sectoral profitability studies. Some countrles have managed to study a half dozen sectors, and no more. In such circumstances, discussions between officials and taxpayers about assessments are based mainly on subjective impressions. This has led to unrealistic assessments and to corruption among tax officials.

In their search for more objective systems for estimating incomes, a number of countries have tried to adapt the system developed originally in Israel under the name of tachshiv. This system emphasizes the use of objective factors and indices to estimate the income of taxpayers who do not keep adequate books and records. Physical Inputs and factors such as the number of employees are highly important for the determination of each enterprise's income. If, for example, the enterprise to be assessed is a barbershop, the assessment guidelines (tachshiv) for this activity will contain Instructions for estimating the enterprise's income according to the different kinds of services provided, the equipment of the establishment, its location, work schedules, and the visits to a representative sample of businesses. The average profitability of the sector and its relationship to specific factor and indices is discussed with representatives of the sector before the tachshiv is issued. More than 80 tachshiv have been developed.
**MONGOLIA'S INFORMAL SECTOR -**

**Appendix B: Methodologies and Descriptive** Statistics

**39**

# **Taxicab Survey Instrument**

# Name of Researcher:

|a. Distric|t surveye|d<br>___<br>b. Gender<br>M F**[2]**|c. Numbers<br>(D<br>[31<br>private<br>organization|
|---|---|---|---|
|d. Day s|urveyed|(I<br>3<br>()i)@@)[4]<br>e. Age<br>**[51**|f. Numbers<br>Q<br>'<br>[61<br>Ulaanbaatar Other|
|g. Hour s|urveyed|<br>[7 h. Married?<br>Y N**[81**|i.Education: (X ))<br>3<br>(<br>**_[9]_**<br>|
||||primary secondary vocational higher|
|1.|Are yo<br>drive i|u driving this car as a taxi on your own behalf ( (D ),<br>t as a taxi ( 0 )?|or are you paid by someone else to<br>**()**<br>a<br>**[10]**|
|2.|In an a|verage week, how many hours do you operate this ca|r as a taxi?<br>**[1]**|
|3.|In an a|verage month, what percentage of kilometers are dri|ven for personal use, not as a taxi?<br>%[12]|
|4.|Is this|car registered as a taxicab?|Y<br>N<br>**[131**|
|_5._|Are y|ou the only person who drives this car as a taxi?|Y<br>N<br>[14]|
|||If no, in an average week, how many hours is this<br>anyone dciving?|car operated as a taxi, with<br>[151|
|6.|Do yo|u have another job or source of income in addition to|driving this car as a taxi?<br>Y<br>N<br>**[16]**|
||(I)|If yes, is the other job in<br>state|**[1]**|
||<br>(3)<br>(3)|state enterprise, private company, cooperative, pa<br>another form of self- or family-employment|rtnership, or public organization|
|||If yes, what is your average monthly income from<br>If yes, how many hours per week do you work at|your other job? (th.tog.)<br>[18]<br>your other job?<br>**[19]**|
|7.|When|the duureg administration asks if you are employed|, how do you answer?<br>Y<br>N<br>[20]|
||1-|If you respond that you are not employed, what d|o you give as the reason?<br>3 )<br>(X) 6<br>**[211**|
|t) il|lness, or|watching a<br>(a) because of | ( not finding||) fired, or the organization | ) other|
|chil|d, sick or|old person<br>seasonal work<br>professional work|doesn't work any more<br>l|
|||If you respond that you are not employed, have y<br>in the past month?|ou actively looked for a job<br>Y<br>N<br>[22]|
||tI)|If you respond that you are not employed, which<br>I am currently registered at the employment regul|of the following is correct?<br>d )<br>@<br>**[23]**<br>ation office.|
||(Z)|I was registered in the past, but I am not registere|d now.|
||(Z|<br>I've never been registered at the employment reg|<br>ulation office.|
Turning to dynamics, there is very little evidence on the effects of foreign acquisitions on bank performance. One study finds a negative selection effect in the U.S. – that foreign banks tend to acquire domestic banks with performance problems – and that the dynamic effect of these acquisitions is modestly positive but not enough to raise the acquired banks’ performance up to the levels of their domestic peers (Peek, Rosengren, and Kasirye 1999).

# **2.3. State ownership and privatization**

Studies of the effects of state ownership typically compare performance with domestically-owned banks. The main static questions regarding state ownership generally involve credit availability and portfolio allocation, as well as efficiency. This emphasis stems from the different objectives of stateowned banks, which often engage in directed lending or pursue objectives such as developing specific industries or regions, export expansion, and so forth. Portfolios may also be allocated for political advantage (e.g., Sapienza forthcoming). Even more so than for foreign-owned banks, caveats are in order when examining standard performance measures for state-owned institutions because the portfolio allocations are not designed to maximize profits, and they generally operate with government subsidies.

Most of the static research literature on state ownership focuses on developing nations and nearly always finds unfavorable effects. Individual state-owned institutions have relatively low efficiency and high nonperforming loans, and large market shares for state-owned banks are associated with reduced access to credit, diminished financial system development, and slow economic growth (e.g., La Porta, Lopez-de-Silanes, and Shleifer 2002, Barth, Caprio, and Levine 2004, Beck, Demirgüç-Kunt, and Maksimovic 2004, Berger, Hasan, and Klapper 2004). Research on Argentina also suggests unfavorable effects of state ownership (e.g., Clarke and Cull 2002, Delfino 2003).

Several studies examine the dynamic effects of bank privatization in developed nations.<sup>1</sup> A cross-country study of privatization in OECD countries finds moderate performance improvements in terms of profitability ratios, fee income and capital adequacy (Verbrugge, Megginson, and Owens 2000). Another study finds post-privatization improvement in efficiency for the Commonwealth Bank of Australia (Otchere and Chan 2003). Results for Portugal also suggest that efficiency increases with privatization (Barros 2003). Finally, Italian bank privatizations are associated with cost reductions and improved profitability (Farabullini and Hester 2003).

> 1 See Megginson and Netter (2001) for an extensive review of empirical studies on the general (i.e., nonbanking) impacts of privatization.

6
TABLE 11 DEMAND ESTIMATES FOR INTEREST EARNING DEPOSITS LOG(D/P) = C + A1.LOG(D/P)(-1) + A2.INT + A3.INF D= M2-M1 (Interest earning deposits from FIEL databank) P= Consumer Price Index INT= Nominal Interest on Deposits, monthly basis (IFS Line 60L)

||R(a)|R(b)|R(c)|
|---|---|---|---|
|C|0.981|0.758|0.991|
||(4.6)|(2.1)|(4.0)|
|Al|0.929|0.946|0.928|
||(60)|(35)|(51)|
|A2|0.629|0.524|1.10|
||(6.0)|(3.92)|(3.59)|
|A3|-0.765|-0.659|1.09|
||(-9.6)|(-6.2)|(-4.52)|
|R2 ADJ.:|0.977|0.964|0.973|
|D-W|1.72|1.87|1.83|
|F|1876|530|1601|
|N:|132|60|132|
|PERIOD:|1978.01/|1984.01/|1978.01/|
||1988.12|1988.12|1988.12|
|METHOD:|OLS|OLS|TSLS|

Instruments for Regression (c): Log(DP-1),INF(-1),INF(-2),INT(-1) INT(-2) 46
# Figure 4.  Country correlations for commodity exporters versus non-commodity exporters, 1980-2006

<!-- Start of picture text --> Commodity exp Non-Commodity exp<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Commodity Average = 0.35***<br>-0.2<br>Non-Commodity Average = 0.21***<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>RomaniaArgentinaTanzaniaHungaryLatviaMyanmarBelarusMoldovaGeorgiaBosnia and HerzegovinaRwandaUkraineUruguaySierra LeoneAzerbaijan<br>St. Kitts and NevisMauritiusEcuadorGreeceGhanaSurinameParaguayNicaraguaEthiopiaEritreaEgyptMoroccoKenyaMicronesiaMadagascarMozambiqueEstoniaAlbaniaTajikistanCameroonSri LankaPakistanBurkina FasoLao P.D.R.Congo, Dem. Rep.PeruIndiaBurundiIcelandJordanGabonMongoliaHaitiBoliviaBangladeshSudanDjiboutiSeychellesSouth AfricaLiberiaMalawiTrinidad and TobagoChina<br>LithuaniaNigerMontenegroGuineaSenegalSamoaEquatorial GuineaOmanThe GambiaAntigua and BarbudaDominicaCabo VerdeSt. Vincent and the GrenadinesQatarVanuatu<br>TurkmenistanGuatemalaMaldivesKoreaPapua New GuineaPortugalMexicoThe BahamasLebanonIran, Rep. of IndonesiaMaltaSyriaBelizeHondurasBrazilAustraliaTunisiaColombiaCzech RepublicYemenComorosGrenadaCentral African Republic<br>ChadCyprusTimor-LesteRussian FederationBahrainNigeriaLesothoGuyanaSolomon IslandsCroatiaCongo, Rep.GermanyAngolaNepalBelgiumSt. LuciaKiribatiTurkeyPanamaJapanEl SalvadorTogoMaliNew ZealandUnited KingdomTaiwan, ChinaCôte d'IvoireBhutanMarshall IslandsSlovak RepublicSloveniaSaudi ArabiaGuinea-BissauLibyaAlgeriaEswatiniBotswana<br>Fiji PolandBeninPalauJamaica Tonga<br>Philippines Kyrgyz Republic<br>Italy KuwaitVietnamCambodia Brunei Darussalam<br>Israel NamibiaDenmark<br>Uzbekistan SpainNorwayAfghanistan Costa Rica<br>ThailandSwedenNetherlands<br>Malaysia<br>Austria CanadaBulgariaSerbiaFranceSingapore Dominican Republic United Arab Emirates<br>Chile<br>Ireland Barbados<br>Switzerland United States<br>FinlandLuxembourg Hong Kong SAR, China<br>Uganda<br>Correlation between cyclical components of real Kazakhstan North Macedonia<br>government expenditure and real GDP 1980-2006<br>Kosovo Zambia São Tomé and Príncipe<br><!-- End of picture text --> Note: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for commodity exporters versus non-commodity exporters. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on WEO (IMF) data. Commodity classification from World Bank..
|Max|413.000|284.000|32.788||43.065||4.242|3.797|3.904|925.024|606.593|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Min|0.000|0.000|0.000||0.000||-1.694|0.000|0.000|0.000|0.000|lysis||
|Std.Dev.|84.398|55.928|2.960||3.311||0.999|0.506|0.514|70.729|50.173|pirical ana||
|Mean|64.994|60.906|1.277||2.119||-0.002|0.757|0.770|45.557|43.644|for the em||
|Observations|19305|19305|19229||19228||18850|18850|18850|18850|distance in productivity)<br>18850|atistics of the main variables employed||
||e Spillovers in quality|e Spillovers in variety|Spillovers in quality|verse of distance between countries)|Spillovers in variety|verse of distance between countries)|ty|etween Index of Productivity and|y of the Innovators in quality<br>etween Index of Productivity and|y of the Innovators in variety<br>e Spillovers in quality|ce in productivity)<br>e Spillovers in variety (weighted by|: This table presents summary st|m product innovations.|
||owledg|owledg|wledge|the in|wledge|the in|ductivi|tance b|ductivit<br>tance b|ductivit<br>owledg|distan<br>owledg|Note|of fr|
||Kn|Kn|no|by|no|by|ro|dis|ro<br>dis|ro<br>Kn|by<br>Kn|||
|Variable|Domestic|Domestic|Foreign K|(weighted|Foreign K|(weighted|Index of P|Absolute|Average P<br>Absolute|Average P<br>Domestic|(weighted <br>Domestic|||

28