rafmacalaba/datause-encoder-data huggingface dataset:
and non-program villages was made using detailed contextual knowledge (since a wellunderstood weakness of PSM is that it only matches on ‘observable’ characteristics). This was critical to capture heterogeneity of the population and increase the validity of the results. This initial work contributed to the sampling of districts, research hypothesis formulation and design of adequate survey questions. Once the identification of a “counterfactual” was done using qualitative analysis and supported by quantitative methods, data was collected from a survey administered to a larger sample of households and used to assess the generality of the hypotheses emerging from the qualitative work. In addition to the quantitative analysis, the analysis of case studies of local conflict, interviews, surveys, key informant questionnaires and secondary data sources as newspaper evidence, provided a broad range of evidence to assess the validity of the hypotheses stating the conditions under which KDP could (and could not) contribute to solve local conflict.

Another common situation in which the usefulness of mixed methods can be seen is small N evaluations, such as the introduction of a business regulatory reform at the national or sub national level. Such reforms, by their very nature, make the construction of a counterfactual difficult or even impossible. In such circumstances, a process of elimination can be deployed to systematically identify and rule out alternative causal explanations of observed results. For example, firm performance could be attributed to the improvement of the business climate but this could be happening in ways unrelated to the actual business entry reform, such as via improvements in infrastructure or more information being available on business opportunities. A thorough qualitative analysis of the processes by which positive outcomes were attained could enable one to establish a detailed causal chain and define how the specific context interacts with the reform and outcomes. Quantitative approaches can be used in parallel for triangulation purposes, or can contribute by helping evaluators avoid some of the typical biases associated with qualitative analysis (such as selection bias), including selecting firms for in-depth analysis using randomization or purposive sampling techniques.

18
Appendix Table 17 Probability of Firms Using Public Programs: Dynamic Lockdowns, Including Spread

||(1)<br>Use Credit<br>Program|(2)<br>Use Employment<br>Program|
|---|---|---|
|_Panel A: All Firms in Municipality_|||
|Post _×_ Lockdown _×_ Spread|0.001|0.000|
||(0.001)|(0.001)|
|Post _×_ Lockdown|-0.010<br>|0.029<br>|
||(0.009)|(0.007)|
|Post _×_ Spread|-0.001|0.001|
||(0.001)|(0.000)|
|Lockdown _×_ Spread|-0.002|-0.003|
||(0.001)|(0.003)|
|Post|0.051|-0.031|
||(0.009)|(0.001)|
|Lockdown|0.023|0.041|
||(0.017)|(0.031)|
|Spread|0.000|-0.004|
||(0.000)|(0.004)|
|Number of Observations|28,161|32,409|
|Number of Firms<br>|3,111|3,581|
|R<sup>2</sup>|0.017|0.025|
|Region FE and Month FE|Yes|Yes|
|_Panel B: Firms along Municipality Border_|||
|Post _×_ Lockdown _×_ Spread|-0.000|-0.002|
||(0.003)|(0.005)|
|Post _×_ Lockdown|0.015|0.067|
||(0.049)|(0.058)|
|Post _×_ Spread|-0.003|0.002|
||(0.002)|(0.002)|
|Lockdown _×_ Spread|-0.000|0.013|
||(0.006)|(0.006)|
|Post|0.070|-0.019|
||(0.027)|(0.023)|
|Lockdown|0.193|-0.049|
||(0.072)|(0.066)|
|Spread|-0.002|-0.013|
||(0.002)|(0.004)|
|Number of Observations|3,888|3,195|
|Number of Firms|432|355|
|R<sup>2</sup>|0.040|0.052|
|Pair of Neighboring Municipalities FE and Month FE|Yes|Yes|

This table reports panel linear regressions of the probability of using a government program for a firm located in a municipality that is subject to a lockdown mandate for the selection and leverage models sample. The dependent variables are a dummy variable equal to one if the firm participates in the credit program (Column 1) and a dummy variable equal to one if the firm participates in the employment program (Column 2). Otherwise, the dummy variables are equal to zero. Post is a dummy variable equal to one after a lockdown mandate is implemented in the firm’s municipality and is zero otherwise. Lockdown is a dummy equal to one if the firm is located in a municipality subject to a lockdown and is zero otherwise. Spread is calculated as the average spread charged on bank credit obtained by each firm over the period 2012–2019. Panel A includes region and month fixed effects. The analysis in Panel B is restricted to firms located along the border of municipalities with and without lockdown mandates and includes month fixed effects and pair of neighboring municipalities fixed effects. The latter are equal to one for each pair of municipalities that are neighbors (share a border) and zero otherwise. All pairs of municipalities in Chile receive a value. Clustered standard errors at the region level and at pair of neighboring municipalities are shown in parentheses for Panels A and B, respectively.

35
_Source: World Bank’s World Development Indicators, and IEA’s Emissions data_ _<u>(2012). Data for 1960-2009 are</u> from the World Bank and data for 2010 are extrapolated using the International Energy Agency’s most recent data for emissions._ Focusing on global emissions is important because that is what matters for the atmosphere. Similarly focusing on global GDP is important to recognize the role played by energy demand in driving global emissions. As global GDP rises, demand for energy will rise in high-income countries less than proportionately. But there will be much greater increases in energy use from developing countries. These increases from very low current levels are not only unavoidable but arguably a moral imperative, since increased energy use will be crucial to lifting vast numbers of people out of extreme poverty.

How should this play out? One can ask what needs to happen by 2050 for the world to have a fighting chance of limiting temperature increases to below 2 degrees centigrade. Put more precisely, how much does the global emissions/GDP ratio need to fall to avert runaway climate change?

Suppose global GDP-per-capita growth were to average 3 percent over the next four decades, which is less than seen in recent years and assumes some under-performance by low-income countries; and global population growth to average about 0.8 percent. These assumptions, combined with the goal of limiting emissions in 2050 to about 15 gigatons of CO2 emissions, which climate scientists say is near the upper end of what might be safe, imply that the emissions/GDP ratio must fall from the current level of 0.8 to about 0.08—that is, a ten-fold decline.

In Figure 1, the barely visible bar at 2050 is the amount of emissions that the atmosphere just might be able to tolerate without rapidly overheating. For all practical purposes, we need to be in a zeroemissions world by 2050 to avert catastrophe. Note that the drop between now and 2050 will have to be dramatically steeper than anything we have witnessed in history. The basic assumptions can be altered but they will not change the basic fact of how much the emissions-GDP curve needs to be bent.

If, on the other hand, the emission-GDP curve heads anywhere close to the current trajectory, we are looking at emission levels of about 90-100 gigatons (2050 GDP is about $185 trillion, at 2000 prices), 5
|Decision 7|30|230|60|170|90|110|
|---|---|---|---|---|---|---|
|Decision 8|10|200|70|160|90|110|

_Trust game_ Trust attitudes towards the World Bank were assessed using a trust game. The basic structure of a trust game developed by Berg, Dickhaut, and McCabe (1995) involves Player A receiving an endowment of X and choosing how much of this endowment to send to Player B, Y є [0, X]. Player B receives 3Y – i.e., three times whatever A send him – and must decide how much of this endowment to send back to A, Z є [0, 3Y]. A receives a payout of X‐Y+Z and B receives a payout of 3Y‐Z. Y/X is used as a measure of trust. Z/3Y is used as a measure of trustworthiness. The table below summarizes payouts for the two players: _Table A4.2 Trust game payouts_

|**Player 1**|||**Player 2**|||
|---|---|---|---|---|---|
|Endowment|Sends|Payout|Endowment|Sends|Payout|
|X|Y|X�Y�Z|3Y|Z|3Y�Z|

In our study, participants were asked to play several rounds of a trust game. In the first game, Player B was framed as the World Bank to extract a measure of trust toward the World Bank or official institutions in general. Participants may hold the World Bank responsible for the (non‐) payment of the business start‐up grants. This framing of Player B as the World Bank allows for a direct measure of how willing participants are to partake in an interaction with the World Bank that could have financial consequences. Hence, it can act as a measure of how not receiving the promised grant had influenced their level of trust and their willingness to interact with the World Bank. The reciprocal behavior of Player B was modeled to mirror the probability of non‐disbursement of the cash grant. In 34 percent of the cases documented by the phone survey, participants received the grant. This information was used to define the reciprocal behavior of Player B. Player B played fairly 34 percent of the time – that is, returns back exactly half of what they obtain from the study participant. Player B 66 percent of the time acted unfairly and kept all that is sent to them, regardless of what the respondent sent. In the end, the participant was paid out the budget of Player A.

To obtain a more general measure of the respondents’ trust levels, and to accompany the first measure, a second game was played which pit the participants against each other. The survey respondents were equally and randomly selected as players A and B, stratified by treatment groups and treatment strands. Regarding the implementation of the games and pairing of the players, a lab‐ in‐the‐field experimental setup was impossible to organize because respondents had to be interviewed 49
**Figure 1:  Economic** **<u>Status Based on Parent’s Education and Occupation</u>**

||**Parent’s Low Education**|**Parent’s High Education**|
|---|---|---|
|**Farmer Parents**|**2494**<br>(children-parents sample)<br>**11,571**<br>(parents-grandparents sample)|**2403**<br>(children-parents sample)<br>**2204**<br>(parents-grandparents sample)|
|**Parents in off-**<br>**farm**|**621**<br>(children-parents sample)<br>**690**<br>(parents-grandparents sample)|**1746**<br>(children-parents sample)<br>**312**<br>(parents-grandparents sample)|
specifications. The results from the simple difference estimators (A3, panels A and B, column 4) provide reassurance in particular for the validity of the interest rate results, since baseline rates are imbalanced at baseline in the opposite direction as the estimated treatment effect.

# **5. Discussion**

Chronic indebtedness and exploitative moneylenders are well-established tropes in the fictional and cinematic accounts of poverty in rural India. Several studies, including an important recent survey by the National Sample Survey (NSS, 2014) and technical papers by the Reserve Bank of India (RBI 2007, RBI 2011) have documented the widespread reliance of rural households on high-cost debt from the informal sector at rates of up to 150 percent per annum. Over the past two decades, micro-finance institutions have rapidly expanded into markets previously served almost exclusively by traditional moneylenders. One would expect this influx of competition to affect existing credit markets, but the nature of these impacts is not obvious _ex ante_ .

The limited empirical literature on this question consists of observational studies and finds zero or positive impacts on lending rates charged by informal sector lenders. Despite the existence of a large literature on the impact of micro-credit programs, no randomized impact evaluation to date has documented a significant effect on interest rates. This is the first study to do so. The randomized roll-out evaluated in this study, conducted in seven of the 38 districts of Bihar, increased self-help group (SHG) membership by 50 percentage points. Those who joined a Jeevika SHG shifted almost 30% of their household debt from high-interest informal sources to Jeevika, on average. The massive influx of relatively low-cost credit extended through Jeevika allows us to detect, for the first time, the impact of a micro-finance intervention on informal credit markets through a randomized evaluation.

At baseline, annual informal interest rates paid by landless households were an average of 6.1 percentage points per year higher than those paid by the landed. SHG entry caused a shift in the informal lending rate offered to these households that reduced this gap by approximately 40%, while the point estimate of the impact on rates paid by landowning households is close to zero and not statistically significant. The fact that the impact on lending rates is limited to the economically marginalized is indicative of the segmented nature of credit markets in this context.
Overall, the results presented in this paper indicate that mental health among adolescent girls can substantially improve when they experience positive income shocks. However, if these income shocks are administered as part of a cash transfer intervention such as the one examined here, these mental health benefits can also be quickly eroded if sufficiently large payments are made to the parents conditional on the actions of their adolescent daughters. Policy-makers would be wise to take into consideration the spillover effects as well as the heterogeneous program effects on mental health with regards to the conditionality, the transfer size, and the recipient of the transfers within the household while designing cash transfer programs.

GEORGE WASHINGTON UNIVERSITY TINBERGEN INSTITUTE AND VU UNIVERSITY AMSTERDAM THE WORLD BANK 27
Table 7 gives the shares of electricity generation (by kWh) projected in IEA (2014) under their two scenarios – “Current Policies” and “New Policies” for two selected years. We first compute the change in shares for each year of the projection period between the two policies, e.g. under NPS the coal share in 2030 falls by 8.8 percentage points, and the wind share rises by 2.2 points. Although our base case shares not identical with the IEA’s Current Policies projections in each year, they are set to be very close. We set the annual renewable targets in our policy scenarios as our base case shares plus the differences between the IEA scenarios, as illustrated in Table 7 for 2020 and 2030. In the RRsub policy we set subsidies separately for nuclear, hydro, wind, solar and other to hit the higher shares under the New Policies Scenario.

The results of using the carbon tax to subsidize renewables and other low-carbon energy sources are given in Table 8, together with those from R1cut and R2cut for comparison. In RRsub, the subsidies for renewables start at low rates but become quite substantial in the later years as given in the last column of Table 7. The share for wind has to rise from 5.7% in the base case to 7.9%, which requires a subsidy of 48%, while the share of hydro has to rise from 12.8% to 14.7%. As noted in Section 2, these renewables require natural resource inputs that have upward sloping supply curves and thus require higher returns to meet higher resource demands.

Table 7. Renewables target from IEA (2014) “New Policies Scenario” and subsidies in the model.

|||2020|<br><br>|<br>||2030<br>||
|---|---|---|---|---|---|---|---|
||Current<br>Policies<br>|New<br>Policies|Differ-<br>ence<br>|<sup>Current</sup><br>Policies|<br>New<br>Policies|Differ-<br>ence|RRsub<br>subsidy (%)|
||%|contribut|ion|%|contribut|ion||
|Coal|65.4|61.9|-3.6|<br>64.1|<br>55.3|-8.8||
|Nuclear|5.8|6.3|0.5|<br>7.6|<br>9.2|1.6|<br>18.9|
|Hydro|15.6|17.1|1.4|<br>12.8|<br>14.7|1.9|<br>13.2|
|Wind|4.8|5.6|0.8|<br>5.7|<br>7.9|2.2|<br>48.3|
|Solar|1.4|1.8|0.4|<br>1.5|<br>2.5|1.0|<br>46.7|
|Gas, other|7.0|7.4|0.5|<br>8.3|<br>10.4|2.1||
|Total|100|100|0|100|100|0||

The value of output of the electricity sector was ¥3240 billion in 2010, with a value added of 810 billion equaling 2.1% of GDP. These high subsidies thus require a sizable carbon tax to finance them. By 2030 the CO2 tax needed is ¥58/ton, which is in between the ¥26 carbon price ‐24‐
**_53_**

74. Argentina offers some interesting observations which account for the relatively large percent deviations of its MWXR from the _official_ rate. **A** closer look at the quarterly official exchange rate (Table 14 below) reveals significant volatility in the quarterly rates in the 1980s.

TabLe 14: **ARGENTINA** --Fluctuating Quarterly Exchange Rates and Trade Weights

|||**IFS**Quar|terlyRate|s|**%**|Change||
|---|---|---|---|---|---|---|---|
||**01**|<br>02|<br>**03**|04|02|**03**|04|
|**1980**|**0.00017**|**0.00018**|**0.00019**|0.00020|**7.0**|**5.2**|**3.5**|
|**1981**|0.00022|**0.00038**|**0.00051**|**0.00065**|**71.3**|**36.5**|**27.0**|
|**1982**|**0.00103**|**0.00137**|**0.00377**|0.00420|**33.2**|**176.0**|11.4|
|**1983**|**0.00575**|**0.00782**|**0.01073**|**0.01781**|**35.9**|**37.3**|**66.0**|
|1984|**0.02784**|0.04118|0.06934|0.13224|47.9|68.4|**90.7**|
|**1985**|0.24952|**0.55672**|**0.80050**|**0.80050**|**123.1**|43.8|**0.0**|
|**1986**|**0.80050**|**0.84896**|**0.97216**|**1.15051**|**6.1**|14.5|**18.3**|
|**1987**|1.40493|**1.61000**|2.14627|**3.41599**|14.6|**33.3**|**59.2**|
|**1988**|**4.37877**|**6.79771**|**11.18150**|12.65240|**55.2**|64.5|**13.2**|
|||Trade|Weights||Z|Change||
||**01**|<br>**02**|<br>**03**|**04**|02|**03**|04|
|**1980**|**0.2172**|<br>0.2240|<br>**0.2662**|<br>**0.2926**|**3.1**|**18.8**|**10.0**|
|**1981**|**0.1263**|0.2403|<br>**0.3309**|<br>0.3024|**90.2**|**37.7**|**-8.6**|
|**1982**|0.1439|<br>**0.1970**|<br>0.2455|<br>0.4136|**36.9**|24.6|**68.5**|
|**1983**|**0.1308**|<br>0.2002|<br>0.2643|<br>0.4048|**53.1**|**32.0**|**53.2**|
|1984|**0.1071**|**0.1836**|<br>**0.2831**|<br>0.4262|**71.5**|54.2|**50.5**|
|**1985**|**0.0953**|<br>0.2654|<br>**0.3336**|<br>**0.3057**|**178.3**|**25.7**|-8.4|
|**1986**|**0.1781**|0.2427|<br>**0.2876**|<br>**0.2916**|**36.3**|**18.5**|1.4|
|**1987**|0.1408|<br>**0.1903**|<br>**0.2613**|<br>0.4076|**35.2**|**37.3**|**56.0**|
|**1988**|**0.0897**|<br>**0.1838**|<br>**0.3500**|<br>**0.3765**|104.9|**90.5**|**7.6**|

**%** Deviations **of** taR from IFS RF **1980 0.7 198513.5 1981** 10.2 **1986** 2.2 **1982 18.9 198714.6 1983** 17.4 **198817.9** 1984 **27.9**

Source: **IFS**
Table 9: Structural Estimates - Cities

|**Parameter**|||||**Estimate**||||
|---|---|---|---|---|---|---|---|---|
|Parameter|Name|All|Dla|Yde|Bda|Krb|Baf|Gar|
|Avg wage|_w_|104.2|117.3|108.3|80.97|94.96|74.25|77.55|
|||(15.9)|(12.1)|(11.2)|(9.27)|(9.35)|(7.12)|(7.36)|
|Ability - educ|_δ_1|0.51|0.48|0.49|0.52|0.56|0.41|0.63|
|||(0.04)|(0.02)|(0.03)|(0.01)|(0.01)|(0.13)|(0.03)|
|Capital share|_α_|0.23|0.28|0.27|0.20|0.21|0.18|0.17|
|||(0.05)|(0.02)|(0.07)|(0.01)|(0.03)|(0.08)|(0.06)|
|Labor share|_β_|0.499|0.523|0.531|0.471|0.601|0.432|0.425|
|||(0.06)|(0.03)|(0.07)|(0.02)|(0.01)|(0.08)|(0.06)|
|Borrow. constr.|_λ_|2.27|2.37|2.51|2.83|2.79|1.14|2.60|
|||(0.31)|(0.51)|(0.45)|(0.79)|(1.33)|(0.32)|(0.98)|
|Elast - agglo|_κ_|0.163|0.065|0.091|0.257|0.275|0.211|0.195|
|||(0.031)|(0.055)|(0.044)|(0.069)|(0.032)|(0.027)|(0.05)|
|Elast - infrast|_ρ_|0.266|0.201|0.214|0.292|0.197|0.258|0.299|
|||(0.107)|(0.093)|(0.094)|(0.096)|(0.085)|(0.102)|(0.121)|
|Number of Obs.||10709|4334|3647|653|87|850|1138|

Asymptotic standard errors in parenthesis.

# **5 Policy Simulations**

The informal sector can generate negative externalities (Loayza, 2007). For example, by using and congesting public infrastructure without paying a contribution, and by taking formal business market shares. The latter externality is however theoretically ambiguous and there is little empirical evidence that informal firms unfairly take market share from law-abiding formal firms. However, while informality helps firms avoid certain costs, it may also preclude access to certain opportunities, including greater access to cheaper credit, increased opportunities to engage with larger firms and government contracts, reduced harassment by police, and access to broader training and support programmes. In this section, we perform a set of counterfactual simulations to evaluate the impact of public policies on entrepreneurship and informality across cities and industries in Cameroon. The estimated model represents the current state and the purpose is to evaluate how this equilibrium would change as a result of possible changes in some quantities in the model. For each policy change, an equilibrium wage rate is computed assuming the same distribution of wealth and the same exoge32
# **Table O3: Net Effects of the Illness of a Worker on Household‐Level Labor Outcomes, using Winsorized Income measures**

|Sample of households|all|2 + earners<br>at baseline|Househol<br>0 male<br>earners at<br>baseline|ds with...<br>1+ male<br>earner at<br>baseline|2+ male<br>earners at<br>baseline|HH in<br>which<br>woman is<br>highest<br>earner at<br>baseline|
|---|---|---|---|---|---|---|
|_Panel A: Winsorized at 1st/99th percen_|_tile_||||||
|Worker sick|‐88.410***|‐103.989***|‐4.617|‐95.371***|‐137.590***|‐3.546|
||[24.418]|[39.034]|[20.507]|[26.954]|[47.327]|[18.661]|
|Worker sick X Female sick|28.856|61.79|‐93.565**|68.664**|118.720**|‐79.762**|
||[30.776]|[40.455]|[44.968]|[30.359]|[55.901]|[32.739]|
|Observations|2,345|1,268|889|1,456|353|940|
|R‐squared|0.62|0.631|0.639|0.601|0.564|0.59|
|Mean Dependent Variable|202.60|259.80|126.9|248.80|307.80|183.90|
|_Panel A: Winsorized at 5th/95th perce_|_ntile_||||||
|Worker sick|‐85.421***|‐100.121***|8.56|‐90.377***|‐130.919***|3.496|
||[23.260]|[36.993]|[12.730]|[24.520]|[42.545]|[17.802]|
|Worker sick X Female sick|41.037|65.525*|‐94.353***|78.114***|137.657***|‐68.488**|
||[27.202]|[38.809]|[28.243]|[27.485]|[47.645]|[27.735]|
|Overall effect of female sickness|‐11.9609|18.8973|‐30.5124|‐11.8007|18.2577|‐23.1955|
|P‐value|0.5319|0.0976|0.0431|0.5164|0.0847|0.0206|
|Observations|2,345|1,268|889|1,456|353|940|
|R‐squared|0.652|0.644|0.670|0.626|0.588|0.613|
|Mean Dependent Variable|202.60|259.80|126.9|248.80|307.80|183.90|

_Notes: Worker sick = 1 if a household member reported missing work for an entire week in which s/he was planning to work that week and the reason for missing work was injury/illness. All specifications include household fixed effects, city X week‐of‐interview dummies, and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
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