rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9:** Practices 2017

||(1)<br>Existing pra<br>Total practices<br>applied|(2)<br>ctices 2017<br>Standardized<br>Index|(3)<br>All new pra<br>Total practices<br>applied|(4)<br>ctices 2017<br>Standardized<br>Index|
|---|---|---|---|---|
|Extension (1=Yes)|0.10|0.11|0.24**|0.39**|
||(0.10)|(0.12)|(0.11)|(0.16)|
|Individual recommendation (1=Yes)|0.15|0.13|0.13|0.09|
||(0.11)|(0.12)|(0.13)|(0.19)|
|Grant (1=Yes)|-0.21**|-0.20*|0.09|0.14|
||(0.10)|(0.11)|(0.13)|(0.19)|
|Flexible (1=Yes)|0.05|0.05|0.45**|0.55**|
||(0.10)|(0.12)|(0.14)|(0.19)|
|Observations|678|678|678|678|
|R-squared|0.07|0.05|0.20|0.17|
|Mean dep. var. control|2.31|0.00|0.42|0.00|
|SD dep. var. control|0.89|1.00|0.79|1.00|
|T1: _βE_+_βI_+_βG_ =0|0.69|0.69|0.00|0.00|
|T2: _βE_+_βG_ =0|0.34|0.50|0.00|0.00|
|T3: _βE_ + _βG_ + _βF_ =0|0.59|0.76|0.00|0.00|

Note: this table reports results on the agricultural practices performed by the farmers in our study in the 2017 season. Using the full sample of 678 farmers, we run the following regression: _Yit_ = _β_ 0 + _βE Extensioni_ + _β I IndRecommi_ + _βG Granti_ + _βF Flexiblei_ + _αc_ + _εit_ , where _i_ corresponds to a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard deviation of the outcome for the control group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coefficients that map into the original study design. We use data from the Final survey conducted in May 2017. The dependent variable in column 1 is a sum of individual dummies. Each dummy takes value of 1 if the farmer performed one of the so-called existing agricultural practices. In column 2, the dependent variable is the standardized index of the outcome in column 1, computed by standardizing each dummy individually, adding them all and standardizing the sum. We use the mean and standard deviation of the control group as reference for the standardized index. In columns 3 and 4, the dependent variables are analogous to the outcomes in columns 1 and 2, computed for the so-called new practices. The existing practices are: (a) ploughing, (b) using inorganic fertilizer and (c) covering the fertilizer. The new practices are: (a) deep tilling (ripping), (b) using hybrid seeds, (c) fertilizing at sowing, (d) sowing with precision machinery, (e) using pre-emergent herbicide and (f) using high-quality fertilizers. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

39
supply. In the next section, we use a calibrated version of the model to quantify the effects of inelastic demand on both a government’s bond supply and its default policy.

# **5 Quantitative Analysis**

## **5.1 Calibration**

We calibrate the model at a quarterly frequency using data on Argentina, a benchmark case commonly studied in the quantitative sovereign debt literature. The calibration follows a two-step procedure. We first fix a subset of parameters to standard values in the literature or based on historical Argentine data. We then internally calibrate the remaining parameters to match relevant moments for Argentine spreads and other business cycle statistics.

In terms of functional forms and stochastic processes, we assume that the government has CRRA preferences: _u_ ( _c_ ) =<sup>_<u>c</u>_</sup> 1<sup>1</sup> _−_<sup>_−_</sup> _γ_<sup>_γ_,where</sup><sup>_γ_denotestheriskaversion.Outputfollowsan</sup> AR(1) process given by log ( _y_<sup>_′_</sup> ) = _ρy_ log ( _y_ ) + _ε_<sup>_′_</sup> _y_<sup>,with</sup><sup>_ε′_</sup> _y_<sup>_∼N_(0</sup><sup>_, σy_).Ifthegovernment</sup> defaults, output costs are governed by a quadratic loss function _φ_ ( _y_ ) = max � _d_ 0 _y_ + _d_ 1 _y_<sup>2</sup> _,_ 0�. For _d_ 0 _<_ 0 and _d_ 1 _>_ 0, the output cost is zero whenever 0 _≤ y ≤−_<sup>_<u>d</u>_</sup> _d_<sup><u>0</u></sup> 1<sup>andrisesmore</sup> than proportionally with _y_ when _y > −_<sup>_<u>d</u>_</sup> _d_<sup><u>0</u></sup> 1<sup>.Thislossfunctionisidenticaltotheoneused</sup> in Chatterjee and Eyigungor (2012) and allows us to closely match the sovereign spreads observed in the data. As for the demand of passive investors, we assume that it is proportional to the (end-of-period) amount of bonds outstanding. Specifically, _T_<sup>_′_</sup> = _T_ ( _τ, B_<sup>_′_</sup> ) = _τ × B_<sup>_′_</sup> . We let _τ_ follow an AR(1) process given by log( _τ_<sup>_′_</sup> ) = (1 _− ρτ_ ) log( _τ_<sup>_⋆_</sup> ) + _ρτ_ log( _τ_ ) + _ε_<sup>_′_</sup> _τ_<sup>,where</sup> _ε_<sup>_′_</sup> _τ_<sup>_∼N_(0</sup><sup>_, στ_).</sup> Based on the analysis in Section 2, we consider the following functional form for the downward-slopping Ψ( _._ ) term: where _κ_ 0 _≥_ 0 captures the “slope” of the demand function and _A_<sup> ̄</sup> denotes the average holdings of active investors (as determined by the fixed component of their mandates, _ξj_<sup>_i_).</sup> For tractability, we assume time-invariant values for both _κ_ 0 and _A_<sup> ̄</sup> .<sup>26</sup> This specification introduces a wedge only for risky bonds (i.e., those with V _s′|s_ ( _R_<sup>_′_</sup> ( _._ )) _>_ 0). As we show next, it allows us to capture the two key features of our empirical analysis: a downward-sloping demand for active investors and a demand elasticity that increases (in magnitude) with

> 26As shown in Section 2, these terms could be, in principle, time-varying functions. We also use an exponential specification purely for computational reasons: to avoid having a negative price.

25
mortgages were subprime. The 45.2 percent is higher than 30 percent because many borrowers who purchased homes with prime loans later refinanced into subprime and then defaulted.

**Private and public mortgage insurance (MI) lost market share as a method of credit risk mitigation for lower income borrowers and for borrowers that make low down payments** (Chart 3) **.** MI plays several constructive roles in the mortgage market. First, it indemnifies lenders and investors against default by high LTV borrowers. In so doing, it contributes to access to finance for buyers that lack the savings for a 20 percent down payment. By indemnifying against loss and acting as a separate source of capital in severe economic downturns, MI contributes to financial system stability. To be eligible for MI, a lender has to adhere to the mortgage insurer’s standards for credit underwriting and management, and so MI providers are important in establishing such standards. MI providers play a part in maintaining credit standards, because they monitor the loans that they insure. During the subprime boom, loans backed by private MI fell from 17 percent of originations in 1995 to 9 percent in 2006, even as average LTVs rose. The market share for FHA and VA-insured loans fell much further, from 18 percent of originations in 1994 to 3 percent in 2006. As the boom ended, private MI market share has risen again, reaching 15 percent of originations at the end of March, 2008. The Federal government has turned to the FHA to refinance loans for troubled subprime borrowers, and its market share has risen slightly, to 8 percent at the end of March, 2008 (Inside Mortgage Finance, 2008).

# **Chart 3**

<!-- Start of picture text --> Mortgage Insurance Loses Market Share in the Boom,<br>Regains in the Bust<br>70%<br>GSE Purchases<br>60%<br>50%<br>40%<br>30%<br>Subprime MBS<br>Public MI Private MI ($2 billion 1Q08)<br>20%<br>10%<br>0%<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 1Q08<br>Source: Inside Mortgage Finance Year<br>Percent of Total Originations<br><!-- End of picture text --> 9
Policy ReseaRch WoRking PaPeR 4358

# **Abstract**

This paper shows how differences in aggregate human development outcomes over time and space can be additively decomposed into a pure economic-growth component, a component attributed to differences in the distribution of income, and components attributed to “non-income” factors and differences in the model linking outcomes to income or nonincome characteristics. The income effect at the micro level is modeled non-parametrically, so as to flexibly reflect distributional changes. The paper illustrates the decomposition using data for Morocco and Vietnam, and the results offer some surprising insights into the observed aggregate gains in schooling attainments. A user friendly STATA program is available to implement the method in other settings.

This paper—a product of the Poverty and the Human Development and Public Services Teams, Development Research Group—is part of a larger effort in the department to understand the role played by economic growth and redistribution in human development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mravallion@worldbank.org and dvandewalle@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy Research Working Paper 9618

# **Abstract**

This paper describes new global evidence—derived from satellite data—for rates and patterns of urban spatial development since 1990 along three margins: horizontal spread (outward extension), infill development (inward additions in the gaps left between earlier structures), and vertical layering (upward construction). The end product of this growth is floor space, the amount and distribution of which are central to understanding how a city becomes livable and sustainable. Over the quarter century between 1990 and 2015, urban built-up area worldwide grew by 30 percent through horizontal spread and infill. While most cities grow through a combination of horizontal spread and infill, the paper provides the first estimates of the relative prominence of each type of expansion at different stages of economic development. In low-income and lower-middle-income countries, 90 percent of urban built-up area expansion occurs as horizontal spread. The study also finds that increasing incomes are a uniquely necessary condition for a rise in floor space per person through vertical layering: the reason is that building tall is capital intensive. The analysis highlights that if a city’s population doubles but incomes stay constant, the city’s floor space per person declines by 40 percent; by contrast, if per capita income doubles but population stays constant, the city’s total floor space per person increases by 29 percent.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at slall1@worldbank.org and mlebrand@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
lower (where R�� is 0.53 and 0.59 in those countries respectively).

**Figure 4.1:** **_LAYS_ based on share reaching a low benchmark instead of average score**

<!-- Start of picture text --> A: Rnc as measured by share reaching low B: LAYS based on share reaching a low<br>benchmark versus based on average score benchmark instead of average score<br>0.0 0.2 0.4 0.6 0.8 1.0 0 5 10 15<br>LAYS adjustment based on average score Average years of schooling<br>LAYS (average score)<br>LAYS (share reaching low benchmark)<br>Source: Authors’ analysis of TIMSS 2015 and Barro‐Lee data.<br>Notes: Numeraire country is Singapore. Illustration for case where learning starts at Grade 0. Correlation<br>coefficients between the two LAYS measures is 0.97. Spearman rank correlations between the two LAYS<br>measures is 0.97.<br>1.0 15<br>0.8<br>10<br>0.6<br>LAYS<br>0.4<br>5<br>0.2<br>LAYS adjustment based on share reaching low benchmark<br>0.0 0<br><!-- End of picture text --> Panel B shows graphically the magnitude of the adjustments to _LAYS_ . The general pattern of results is largely consistent with those based on average test scores. Both the correlation coefficient and the rank coefficient between the two measures are high, at 0.97. However, this alternative benchmark-based version does affect the point estimates for individual countries, most notably by further reducing the _LAYS_ values of countries that were already performing poorly.<sup>14</sup>

> 14 A similar exercise carried out using the “intermediate” benchmark exacerbates the loss due to the adjustment for learning, and the magnitude of this additional loss tends to be greater for those countries where the _LAYS_ adjustment

18
crime and violence in El Salvador, might endanger both children and instructors.

Instead, following Chandler et al. (2011), I estimate a predictive model of violence and crime from existing data using a Two-Sample Least Squares strategy. First, using an existing anonymized database of youths’ violence and crime from El Salvador (FUSADES, 2015),<sup>10</sup> I estimate the likelihood of having committed a violent act _Vf_ as a function of a wide range of covariates: where _Df_ is a vector of violence determinants of student _f_ in the FUSADES dataset. This vector includes variables that indicate individuals’ vulnerability to violence, such as student characteristics (e.g., age, gender, time spent alone at home, and education level); children’s household variables (e.g., residence area, mother’s education, and household composition); and school-level controls (e.g., school location and commuting time to school).<sup>11</sup> Descriptive statistics and comparison of means ( _p_ -values) between the FUSADES sample and the one from this study are in Appendix Table A1. Estimations indicate that both samples are similar in most of the determinants except for some variables such as student’s age and their report of being without adult supervision after school hours.

All estimated coefficients _α_ ˆ1 have the expected sign according to the literature of violence determinants, as shown in Appendix Table A2. For instance, boys are more likely to be violent than girls, adolescents behave worse than children (Rodriguez-Planas, 2012), and lack of parental supervision increases the probability of committing a violent act (Gottfredson et al., 2004). Statis-

> 10This database was created using the _El Salvador Youth Survey_ ’s instrument. It consists of a sample of 8,640 students in sixth and ninth grades who are enrolled in public schools in El Salvador. This database includes a large number of variables that measure crime and violence and their determinants.

> 11Some relevant papers that find evidence that these variables are determinants of crime and violence are: for gender, Bertrand and Pan (2013); Rodriguez-Planas (2012); for age, Rodriguez-Planas (2012); for area of residence, Springer et al. (2006); for maternal education, Springer et al. (2006) and Gaviria and Raphael (2001); for time spent at home, Gottfredson et al. (2004) and Aizer (2004); for commuting time to school, Springer et al. (2006); Damm and Dustmann (2014); and for household composition, Gaviria and Raphael (2001).

11
respectively. Labor inputs to tradables sector 1 are measured from O1 and those to tradables sector 2 from O2 . For simplicity it is assumed that, at the given factor prices, the health sector has the same skill intensity as the economy, and that the incidence of illness, the provision of treatment and the responsiveness to that treatment are identical for all workers. Thus the north-east corner of the ‘health box’ and the south-west corner of the ‘waiting list box’ lie on the diagonal of the total endowment box, OHO W .

INSERT Figure 1 Here In the initial equilibrium the health sector employs SH and UH of skilled and unskilled labor respectively. These provide a health output that treats ill workers to the extent that numbers S and U of skilled and unskilled labor remain on the W W waiting list and hence are unable to work. Thus the numbers of skilled and unskilled labor available to work in the tradables sectors are ST = S-SH -SW and U = U-UT H -UW respectively, these being the dimensions of the factor box defined by O1 and O2 . The given relative factor prices determine the skill intensities in the two tradables sectors (sector 1 being the more skill-intensive), and the intersection of the rays O1X1 and O2X2 at point a determines the full employment outputs of sectors 1 and 2.

_Expanding the Health Sector Using Only Unskilled Domestic Workers_ Figure 2 illustrates the effects of an expansion of the health sector using only domestic unskilled workers (the endowment of health-specific skilled workers remaining at SH ). The changes (indicated by a _#_ ) result in a new tradables equilibrium at point b. The supply of skilled workers to the tradables sectors
Likewise, we can also examine the roles of the private and public sectors in Argentina's foreign debt build-up process. Table 4.5 shows that the economy's total external debt increased from 27.7% to 52.5% of GDP between 1993 and 2001. About half of this change reflects higher public indebtedness, while **fie** other half seemingly reflects aggressive private sector borrowing abroad. Indeed, private external debt increased from 5.6% of GDP to 17% during the period. One could view this as evidence that the private sector, as much as the public sector, was behind the accumulation of foreign liabilities.

However, Table 4.5 also shows that at the same time the public sector was borrowing massively fromn the private sector in the domestic capital market. Indeed, between 1993 and 2001 domestic public borrowing rose by over 20 percentage points of GDP. This effectively means that the private sector was borrowing abroad on behalf of the government.

In summary, Argentina's large external imbalances and its deteriorating net foreign asset position reflected the action of both the private and public sectors at different times. During the boom of 1997-98, the private sector was the one showing the larger imbalance. However, the public sector failed to put its finances on firm footing in those years (especially after part of the hidden liabilities in the pension system had become explicit as a consequence of pension reform). In turn, after 1998 the expanding saving-investment gap of the public sector was the main force at work behind the rising foreign liabilities of the Argentine economy.

**Table 4.5. Debt Stocks (Percentage of GDP)**

||**Total**<br>**External**|**Consoli**<br>**Total**|**datedGovernm**<br>**ofw **|**entDebt**<br>**hich:**|**Nonfinancial**<br>**Privateand**|**PrivateExternal**<br>**Debt NetofGov.**|
|---|---|---|---|---|---|---|
||**Debt**||_Extemal_|_Domestic_|**Financial**<br>**ExternalDebt**|**BondHoldings**|
|**1993**|27.7|**213.7**|22.1|6.6|5.6|-1.0|
|**1994**|29.6|30.9|23.5|7.4|6.2|-1.3|
|**1995**|39.0|34.8|26.8|7.9|12.2|4.2|
|**1996**|41.8|363.6|27.4|9.3|14.4|5.1|
|**1997**|44.8|38.1|28.2|9.9|16.6|6.7|
|**1998**|48.6|40.9|30.2|10.6|18.3|7.7|
|**1999**|52.6|47.6|33.4|14.3|19.2|**5.0**|
|**2000**|52.7|50.9|33.8|17.1|18.9|**1.8**|
|**2001**|52.5|62.2|35.4|26.8|17.0|-9.7|

**Source:** World Developmenit Indicators World Bank.

41
Table 1: Pre-COVID Engagement in and Earnings from Income Sources

||**I**|**ncome (USD)**|
|---|---|---|
|**Share Engaged**|**Mean**|**Standard Deviation**|
|_Panel A: Ethiopia_|||
|Crop Income<br>0.522|279|498|
|Livestock Sales<br>0.318|249|240|
|Livestock Product Income<br>0.489|533|1,202|
|Wages<br>0.215|1,490|3,740|
|Casual Employment Wages<br>0.088|179|387|
|Temporary Employment Wages<br>0.087|96|97|
|Non-Farm Enterprises<br>0.224|1,271|2,933|
|In-Kind Transfers/Gifts<br>0.024|57|96|
|Cash Transfers/Gifts<br>0.099|304|504|
|Food Transfers/Gifts<br>0.048|61|79|
|In-kind Transfers from Govt and NGOs<br>0.009|39|30|
|Cash Transfers from Govt and NGOs<br>0.032|64|62|
|Free Food<br>0.049|36|47|
|Pension<br>0.013|279|242|
|Rental Income<br>0.084|337|584|
|Asset Sales<br>0.087|262|244|
|Savings, Interest, Investment<br>0.002|145|355|
|Other<br>0.008|354|424|
|Observations|3,247||
|_Panel B: Malawi_|||
|Crop Income<br>0.776|131|206|
|Tree Crop Sales<br>0.062|28|32|
|Livestock Sales<br>0.263|56|76|
|Livestock Product Income<br>0.298|42|89|
|Wages<br>0.261|1,617|3,109|
|Casual Employment Wages<br>0.609|314|453|
|Non-Farm Enterprises<br>0.438|1,894|5,154|
|Cash Transfers/Gifts<br>0.263|68|131|
|Food Transfers/Gifts<br>0.272|13|11|
|In-Kind Transfers/Gifts<br>0.114|30|50|
|Cash from Children<br>0.197|65|87|
|In-Kind Transfers from children<br>0.131|45|48|
|Free Food<br>0.182|20|14|
|Cash Transfers from Govt and NGOs<br>0.064|57|36|
|Cash or Inputs for Work<br>0.019|56|28|
|MASAF Public Works Program<br>0.043|32|15|
|Pension<br>0.011|1,355|1,320|
|Rental Income<br>0.080|321|392|
|Asset Sales<br>0.076|87|102|
|Savings, Interest, Investment<br>0.068|64|77|
|Other<br>0.043|43|108|
|Observations|1,726||
|_Panel C: Nigeria_|||
|Crop Income<br>0.644|401|449|
|Tree Crop Sales<br>0.064|233|369|
|Livestock Sales<br>0.216|165|214|

Continued on Next Page...

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1, -1 on the β vector is insignificant. Hence, we conclude that _e_ + _wfp_ − _pc_ is stationary.

Figure 6: Log of cereal prices, _pc_ , exchange rate, _e_ , and world food prices _wfp_ (indexes 2006:12=1) <!-- Start of picture text --> Note: The exchange rate and world food price series have been mean and variance adjusted to<br>highlight long-run relationships.<br><!-- End of picture text -->

> Figure 7: Log of relative price indexes for food, _e_ + _wfp_ − _pc_ and non-food _e_ + _wp_ − _pnf_

22