rafmacalaba/datause-encoder-data huggingface dataset:
and southern interior of India. We predict _average_ consumption declines as high as 13% for some of these districts, even after incorporating the moderating influence of cereal price

# **5.4 Climate change incidence curves**

We now turn our attention to the main objective of this study, understanding the distributional implications of climate change. We do so using a climate change incidence curve (CCIC), the smoothed relationship between per capita household expenditure percentiles and predicted changes in per capita expenditures induced by increases in temperature over the next three decades (%∆<sup>�</sup> _c_ ). Economic growth over this time-frame is presumed to be neutral with respect to wealth ranking; a rising tide that lifts all boats, proportionally speaking. Thus, in the absence of warming, our assumption is that a household’s–or, more precisely, a household _type’s_ –per-capita expenditure percentile would remain unchanged.

We draw CCICs, in Figure 10, that sequentially incorporate each of the three price effects, beginning with the land price effect alone, then adding the labor price or wage effect, and finally the cereal price effect. Our initial focus is, once again, on rural areas. Panels (a)-(c) show CCICs using different versions of the Ricardian parameter estimates, though the curves look practically identical across scenarios. Doubling the discount rate used to transform land values into annualized income has no detectable impact on the incidence of climate change losses, only exacerbating the predicted consumption declines a bit. While the Extended Ricardian scenario, as we have just noted, alters the geographical distribution of impacts relative to the Baseline, it makes no discernible difference for the distribution of impacts across per-capita expenditure percentiles.

Ignoring cereal price effects, climate change appears ”progressive” insofar as wealthier households suffer proportionally greater consumption losses. A household in the top percentile of the per capita expenditure distribution would experience nearly a two percentage point greater decline in consumption than a household in the bottom percentile. This progressivity is driven by the skewed land distribution and the fact that larger landowners are concentrated in the higher percentiles. By contrast, temperature-induced wage declines are relatively more costly to the poor than to the rich, mainly because the poor tend to engage in climate sensitive agricultural employment.

The picture changes dramatically after we incorporate the cereal price effect; the CCICs in panels (a)-(c) now slope upward! Indeed, climate change impacts become practically as regressive with cereal price effects as they were progressive without them. Underlying this incidence reversal is the fact that the net consumption ratio is increasing in wealth. Moving 23
A modern secured transactions law will need to include provisions related to the following enforcement principles: _Out of Court Enforcement Procedures_

**Seizure of the Collateral by the Secured Creditor** _._ The main aspect related to this principle is the possibility for the secured creditor to seize the secured asset in case of default. Ideally, the enforcement law should allow the secured party to the security agreement to perform enforcement without court assistance. Under this procedure the secured creditor takes the property from the debtor without the assistance of the execution office. The creditor should have access through the law to speedy seizure procedure. The timing for removing the property from the control of the defaulting person is critical, as the property can be relocated, hidden or damaged.  Delays in the process of enforcement can cause depreciation of the property value. Some jurisdictions have a pre-judgment procedure where, upon presentation of proof of the security agreement and an act or omission constituting default by the creditor, the court issues an order of seizure of the property without the possibility for appeal by the person in control of the property until after seizure is completed.  In other systems, the proof may be simply by sworn affidavit of the creditor.  This process is recommended, particularly for jurisdictions with lengthy court proceedings. According to the 2010 WB _Doing Business_ Report, more than 100 jurisdictions today accept the possibility of enforcing security interests in movable property out of court.

In relation to collateral execution, the primary ways in which the law may provide for a secured creditor to recover the secured asset without resort to judicial process are seizure by the secured creditor or by a specialized agent.<sup>28</sup> International practices on how the asset is repossessed in practice varies and a number of countries have found different solutions that work more or less well (see Box 3 for examples).

> 28 The simplest way is for the debtor to provide permission in the original contract for seizure upon default, to avoid court, if the company recognizes such a clause in an agreement.  When such a norm was introduced in Slovakia, the enforceability of mortgages greatly improved.  A more encompassing way is to create a legal right to non-judicial seizure/foreclosure.  Many countries find giving creditors such a right under the law a political or ideological difficulty. The actual repossession of the asset/s in order to sell it is a critical part of the enforcement process.

25
violent event on aggregate firm presence. The identifying assumption here is that killings are independent of economic factors after conditioning on _αd_ , _γt_ , _γd ∗ t_ and _μd ∗ t_<sup>2</sup> .

Table 2 displays our results. In column 1, we show the raw correlation without fixed effects, which is positive but statistically insignificant. This may be because terrorist killings often take place (and are reported) near urban centers with more economic activity. In column (2) we include district fixed effects to control for differences that are constant within geographical units, and find a significant, negative correlation. In column (3), we add month fixed effects to control for unobserved time trends across our sample like seasonal fighting or migration patterns. The magnitude of our estimated coefficient on violence implies that a major violent event is associated with a 6.4 percent reduction in the number of active firms in the following month. In column (4) we show that the estimated effect attenuates and falls just outside of conventional levels of statistical significance ( _p_ = _._ 103).

Next, we turn our attention to the persistence of these effects. In Figure 5, we show the coefficients from the following event study specification of major violent events on log active firms while including three leads and eight lags in addition to district and month fixed effects and district linear and quadratic time trends.

In column (13) of Table 3, we present the corresponding coefficients for the event study. We see no evidence of a significant pre-trend in the three months prior to a recorded killing. In the month of the event itself, we find a negative effect of 4.9 percent ( _p < ._ 1). And in the first month after a major event, we observe the effect grow in magnitude to 6 percent ( _p < ._ 05), which then persists for up to five months after the event.<sup>24</sup> Six months after the event, the estimates are no longer statistically distinguishable from zero.

In Table 4, we implement a difference in difference specification where we include a post-

> 24We find similar qualitative patterns in the separately estimated regressions in columns (1)-(12), though the effect sizes are smaller and note statistically significant.

21
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- [4] Bruhn, M. (2013). “A Tale of Two Species: Revisiting the Effect of Registration Reform on Informal business Owners in Mexico.” Journal of Development Economics, vol. 103(C), 275283.)

- [5] Calderon, G., J. M. Cunha and G. De Giorgi (2013). “Business Literacy and Development: Evidence From a Randomized Controlled Trial in Rural Mexico.” NBER Working Papers 19740.

- [6] De Mel, S., D. McKenzie and C. Woodruff (2010). “Who are the Microenterprise Owners? Evidence from Sri Lanka on Tokman versus De Soto,” NBER Chapters, in: International Differences in Entrepreneurship, National Bureau of Economic Research, 63-87.

- [7] De Mel, S., D. McKenzie and C. Woodruff (2012). “One-time Transfers of Cash or Capital have Long-lasting Effects on Microenterprises in Sri Lanka.” Science. vol. 335, no. 6071, 962966.

- [8] Fafchamps, M., and C. Woodruff (2014). “Identifying Gazelles: Expert Panels vs. Surveys as a Means to Identify Firms with Rapid Growth Potential” Competitive Advantage in the Global Economy (CAGE). mimeo.

- [9] Fafchamps, M., D. McKenzie, S. Quinn, and C. Woodruff (2014). “Microenterprise Growth and the Flypaper Effect: Evidence from a Randomized Experiment in Ghana. ” Journal of Development Economics, 106, 211-226.

- [10] Poschke, M. (2013).“Who Becomes an Entrepreneur? Labor Market Prospects and Occupational Choice.” Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), 693-710.

- [11] Staiger, D. and J. Stock (1997). “Instrumental Variables Regression with Weak Instruments.” Econometrica, Vol. 65, No.3, 557-586.

11
distress in all regions. These examples illustrate the consistency of individuals’ psychological evaluation of any event in the past. Unlike monthly consumption expenditure level, we find almost similar typologies of life-satisfaction based on events in the past. This leads us to examine attitude and values further, which we show in Table 3.2.

# **[Table 3.2 is about here]**

As noted by Kahneman and Krueger (2006), the validity of subjective measures of wellbeing can be assessed, in part, by considering the pattern of their correlations with other characteristics of individuals and their ability to predict future outcomes. Global life satisfaction questions have been found to correlate well with a variety of relevant measures. We find similar evidence from LiTS data. Among other characteristics, reported higher life satisfaction strongly correlated with “household lives better nowadays than around 1989” (0.56) and “satisfied with the present state of the economy” (0.52). Other attitudes and values like doing better than parents (0.38), high school classmates (0.45) or colleagues (0.44) are also moderately correlated with reported life satisfaction, which evaluates one’s relative position.

# **[Table 3.3 is about here]**

As we noted earlier, financial stability matters a lot for life-satisfaction. But how significant are psychological factors like temperament, mood or conduct during the interview? According to some, these factors account for much more of the variance in life satisfaction than life circumstances (see review in Kahneman and Krueger (2006)). In table 3.3 we show the correlation of reported values and attitudes with answers to “Did you feel that the respondent was reluctant to answer any questions?” and “How often did the respondent have difficulties answering questions?” Both of these variables are recoded on a scale (1 = never had any difficulty, 2 = almost never had / now and then had some difficulty and 3 = usually had difficulties). A negative correlation with subjective well being would mean life satisfaction is lower for someone showing reluctance or 15
_Table 6 Intention‐to‐treat effects of the original intervention on main psychological and behavioral outcomes_

||(1)<br>ITT|(2)<br>ITT|
|---|---|---|
||(no controls)|(controls)|
|_Main outcomes – Ps_|_ychological and beha_<br>|_vioral_<br>|
||‐0.009|0.002|
|Psychological wellbeing index|(0.845)|(0.965)|
||[0.845]|[0.965]|
|Risk index|‐0.043|‐0.052|
||(0.501)|(0.383)|
||[0.692]|[0.537]|
|Trust index|‐0.035|‐0.055|
||(0.482)|(0.274)|
||[0.692]|[0.480]|
|Crime and violence index|‐0.080|‐0.089*|
||(0.119)|(0.090)|
||[0.343]|[0.315]|
|Migration index|‐0.026|‐0.015|
||(0.593)|(0.767)|
||[0.692]|[0.896]|
|List experiment cattle index|0.172*|0.169**|
||(0.075)|(0.050)|
||[0.343]|[0.315]|
|List experiment argument index|‐0.135|‐0.132|
||(0.147)|(0.149)|
||[0.343]|[0.322]|
|Observations|1,523|1,495|

_Note: P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level. Adjusted Benjamini‐Hochberg p‐values are reported in square brackets. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, number of buildings at baseline, and exposure to conflict between baseline and endline._ 29
# **Figure 11a. Differences between the Change in Average Log Wages for Stayers and the Change in Average Log Wages for All Workers in Tradable Industries Grouped by the Magnitude of the Direct Export Shock, 2007-2009**

<!-- Start of picture text --> Differential Change in Log Nominal Wage (Stayers Minus All)<br>(industries grouped by size of shock to exports)<br>time<br>Diff log wage change (stayers minus all, small shocks)<br>Diff log wage change (stayers minus all, medium shocks)<br>Diff log wage change (stayers minus all, large shocks)<br>Note: groups defined by % drop in ln(exports) from Oct - Nov 2008<br>Magnitude of the Related Export Shock, q12007-q42009<br>Differential Change in Log Nominal Wage (Stayers Minus All)<br>(industries grouped by size of shock to exports in related industries)<br>time<br>Diff log wage change (stayers minus all, small related shocks)<br>Diff log wage change (stayers minus all, medium related shocks)<br>Diff log wage change (stayers minus all, large related shocks)<br>Note: groups defined by % drop in ln(exports) in related inds from Oct - Nov 2008<br>.06<br>.04<br>.02<br>0<br>-.02<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br>.06<br>.04<br>.02<br>0<br>-.02<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br><!-- End of picture text -->

**Figure 11b. Differences between the Change in Average Log Wages for Stayers and the Change in Average Log Wages for All Workers in Tradable Industries Grouped by the Magnitude of the Related Export Shock, q12007-q42009**

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Conservative Elasticity Scenario <!-- Start of picture text --> Figure.<br>Ukraine: Official and Unofficial GDP<br>Overall GDP Index=100 in 1989<br>Index<br>Official GDP Index=88 in 1989<br>100.0<br>- 100.0<br>90.0 ]J.o l lcotnomny . . -. .<br>71.6<br>40 0<br>~~~~~~~~~~~~~38.9<br>10.0<br>1989 1990 1991 1992 1993 1994<br>Year<br><!-- End of picture text -->
# **Table A.19. Predicted poverty rates (and SEs) based on LASSO, Elastic net and Random Forest from NPS 2020/21 to TA 2 using 2020/21 Poverty Line, Tanzania (percentage)**

||||**TA**|**2**|||
|---|---|---|---|---|---|---|
|**Method**|**Model M1**|**Model M2**|**Model**<br>**M3.1**|**Model**<br>**M3.2**|**Model M8**|**Model M9**|
|1 Mltil itti PMM|**18.0**|**18.1**|**25.3**|**17.7**|**17.9**|**19.0**<sup>**a**</sup>|
|) upe mpuaon ()|(2.0)|(1.9)|(1.9)|(1.7)|(1.9)|(2.0)|
||**17.8**|**17.6**|**24.5**|**17.6**|**18.0**|**18.2**|
|2) Multiple imputation (linear)|(2.0)|(1.9)|(1.9)|(1.7)|(2.0)|(2.0)|
||8.4|10.8|26.2|**18.1**|14.0|13.7|
|3) Lasso|(3.2)|(3.0)|(2.7)|(2.0)|(3.6)|(3.7)|
||<br>8.7|<br>10.8|<br>26.2|<br>**18.1**|<br>14.3|<br>13.7|
|4) Elastic Net|(3.2)|(3.0)|(2.7)|(2.0)|(3.6)|(3.7)|
||11.4|8.5|**24.5**|**17.7**|9.3|10.8|
|5) Random Forest|(1.9)|(1.5)|(2.1)|(1.7)|(1.6)|(1.7)|
|_Control variables_|||||||
|Food expenditures|||Y|Y|||
|Utilities: water, kerosene, lighting|||||Y|Y|
|<br>Household assets & house characteristics||Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|
|N1(base survey)|4,644|4,644|4,644|4,644|4,644|4,644|
|N2 (target survey)|701|701|701|701|701|701|
|**True poverty rate in TA 1**<br> <br>Note: Estimates shown in boldface or with a “<sup>**a**</sup>” respective<br>net linear models and Random Forest models are trained in<br>(validation). For multiple imputation, we use five nearest n<br>is the estimate directly obtained from the survey data.|ly fall within the 95<br>the first round when<br>eighbors with the pre|% confidence int<br>the data is split<br>dictive mean mat|21<br>(2.<br>erval or one stan<br>into two subsets<br>ching method. 5|.0<br>2)<br>dard error of th<br>: 50% used for<br>0 simulations a|e true poverty ra<br>training and 50%<br>re implemented.|te. Lasso, Elastic<br>used for testing<br>True poverty rate|

Note: Estimates shown in boldface or with a “<sup>**a**</sup> ” respectively fall within the 95% confidence interval or one standard error of the true poverty rate. Lasso, Elastic net linear models and Random Forest models are trained in the first round when the data is split into two subsets: 50% used for training and 50% used for testing (validation). For multiple imputation, we use five nearest neighbors with the predictive mean matching method. 50 simulations are implemented. True poverty rate is the estimate directly obtained from the survey data.

63
how losing access to groundwater influences village-level outcomes. Wells deeper than 8m cannot use atmospheric pressure based pumps to extract water and therefore require more expensive mechanical pumps. This creates a clear threshold around water accessibility. Using this threshold, she shows that villages just below this threshold had poverty rates 9-10% higher than those just above and were 25% more likely to experience conflict over water. This builds on earlier work showing that a 1 meter increase in the depth to groundwater reduces food grain production by 9% and cash crop production by 5% (Sekhri et al., 2013).

# **3 Data**

## **3.1 Water depth data**

We collect data on groundwater levels from the Central Ground Water Board (CGWB) observation wells.<sup>6</sup> The CGWB monitors groundwater levels at nearly 23,000 monitoring stations across the country. Water depth data is collected in August (monsoon), November (kharif), January (rabi), and March/April/May (pre-monsoon), where the pre-monsoon collection times vary by state depending on the normal beginning of the monsoon (CGWB, 2021). On average, we have nearly 25 wells per seasons per district year. CGWB monitoring wells include a mix of open dug-wells and tube wells. Typically tube wells have a much deeper monitoring depth compared to dug wells.

## **3.2 Winter cropped area**

We collect data on area planted in winter crops from NASA’s Earth Observing System Data and Information System provided by the SEDAC center at Columbia University. This data provides a raster layer that indicates whether a pixel is planted in crops based on the peak Enhanced Vegetation Index value detected by the MODIS platform over the period October to March (Jain et al., 2017a,b). We aggregate the underlying 1-km pixel-level data to district level to determine the area of each district planted in winter crops in each year in the sample.

## **3.3 Meteorological data**

We collect weather re-analysis data from ERA5. ERA5 is a weather re-analysis product produced by the European Commission’s Copernicus Climate Change Service.<sup>7</sup> We use data from the ERA5 Land hourly product. This provides data at an hourly level on a grid of 0.1<sup>_◦_</sup> _×_ 0.1<sup>_◦_</sup> , which translates to a 9km resolution. We use data on temperature and precipitation over the full sample from 2003 to 2014. We aggregate these weather variables to the district level by averaging over all grid points that fall within a district boundary.

> 6Data accessed from: http://cgwb.gov.in/GW-data-access.html

> 7Data available here: https://cds.climate.copernicus.eu/cdsapp!/dataset/reanalysis-era5-land?tab=overview

6
business activity and banks’ revenue composition.

12. **Declining intermediation is partly due to crowding out by the public sector** . Between 1998-2003, the exposure of Colombian banks to the public sector through bonds and loans increased by 166%, compared with a mere 6% growth of loans to the private sector, and a total asset growth of 17%. As of December 2003, banks invested 20% of their assets in public bonds and provided an additional 5% of total assets in loans to the public sector (see Figure 1.3). Public bonds account for over 70% of banks’ total securities portfolio. With a share of 30% of banks’ operating income<sup>12</sup> at the end of 2003, interest income from government securities is almost as important a revenue source as interest income from loans (40% of banks’ operating income)<sup>13</sup> . Given attractive interest rates<sup>14</sup> and the zero risk weighting of government bonds for solvency requirements, Colombian banks currently lack incentives to increase lending to the private sector.

**Figure 1.3  Public sector bonds and loans as percentage of financial sector assets**

<!-- Start of picture text --> Breakdown of total system assets, COP trillion System assets by type, % to total assets<br>120 100%<br>90%<br>100 31% 32% 30% 29% 28% 27%<br>80%<br>80 25 26 24 24 25 27 70%60% 5% 7% 7% 6% 15% 19% 20% 22%<br>6040 5 4 5 6 125 165 185 214 50%40% 7% 6% 6% 4%<br>30% 58% 54% 48% 46% 46% 47%<br>20 46 43 39 39 41 46 20%<br>10%<br>- 0%<br>1998 1999 2000 2001 2002 2003 1998 1999 2000 2001 2002 2003<br>Loans to private sector Loans to public sector Loans to private sector Loans to public sector<br>Public sector bonds Other securities & assets<br>Public sector bonds Other securities & assets<br>COP trillion % of total assets<br><!-- End of picture text --> _Source: World Bank calculations based on Superintendencia Bancaria data_ 13. **Private banks are the most important financial intermediary in Colombia.** Private domestic banks account for 59% of financial system assets and 65% of gross loans and deposits. Following private domestic banks, foreign banks’ activity accounts for 16% of financial sector assets and a 17% and 15% of gross loans and deposits, respectively. Private domestic and foreign banks have an intermediation ratio of 85% and 91% respectively.

14. **Public banks are also important financial actors.** The share of public banks in the financial system has been relatively stable over the past

> 12 Defined as net interest income plus other net non-interest income, before operating expenses 13 Non-interest income from fees and commission, trading and leasing activities accounts for the remaining 30% of operating income.

> 14 For example, the 10 year government bond due April 2012 is yielding 13.54% and the two year government bond due September 2005 is yielding 8.42% ( _Banco de la República_ ).

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