rafmacalaba/datause-encoder-data huggingface dataset:
income changes drastically.; Accordingly, it may be misleading to focus solely on key indicators of monetary policy for the purpose of detecting "a _credit crunch"._ Analyses of monetary and credit aggregates need to be complemented with a more detailed investigation of the channels through which firms, banks, and households are affected by changes in monetary policy. In fact, as Bernanke and Gertler (1995) argue, when the economy is hit by a negative shock, it is often impossible to distinguish whether the usual deceleration in bank lending stems from a shift in demand or supply. On the one hand, the corporate sector may be demanding less credit because fewer investments are undertaken; on the other hand, it could be that banks are less willing to lend and, therefore, charge higher interest rates or decline more credit applications.

In order to address this problem of identification, we will rely on literature that examining the transmission of monetary policy restrictions through the credit channel. In particular, we will focus on the evolution of the spread between bank lending rates and rates on risk-free assets (see Bernanke and Blinder, 1988). To Return to the example above, once we recognize that lending has shrunk while the spread has widened, it becomes clear that demand for loans could not have declined more than loan supply.

# **2.2 The Propagation of the Credit Crunch**

According to the Council of Economic Advisors (1991), credit crunch is _"a situation in which the supply of credit is restricted below the range usually identified with prevailing market interest rates and the profitability of investment projects"._ When a credit crunch occurs, it alters the relationship between credit availability and interest rates. A credit crunch mostly occurs in two forms: (i) a leftward shift of the credit supply curve at a given interest rate level (price mechanism), and (ii) rationing of the credit supply, irrespective of interest rates (non-price mechanism; Box 1 and Box 2 provide more details on the credit crunch concept).

Identifying a credit crunch in practice involves investigating the channels through which firms, banks, and economic activity are affected. For instance, both increases in 6
In addition, the annex supplies a description of many of the tools considered. It also includes information on the cost of implementation and appropriate software for some of the tools.

# **_2. Simulation Models of the Size Distribution of Income_**

The idea of simulating the distribution of income among individuals or households originated in the field of public finance because of the need to have reliable models for the detailed analysis of the incidence of a tax-benefit system. The basic idea is to model the distribution of household income and consumption _taking household behavior as exogenous_ , but fully accounting for applicable taxes and transfers (Davies 2004). To take full account of the diversity of the characteristics in a population, these simulation models require a nationally representative micro-data set. The fact that household behavior is exogenous means that these accounting models can predict only the first-round effects of a tax-benefit policy on inequality and poverty.

All the approaches reviewed in this section are various interpretations of the basic idea underlying tax-benefit simulation models. _POVCAL_ and _SimSIP Poverty_ illustrate the use of the Lorenz curve for poverty and distributional impact analysis. These two tools are particularly useful when only aggregate or grouped data are available. The second class of simulation tools considered under this heading relies on unit record data on the distribution of some money-metric measure of economic welfare at the individual or household level. Three approaches are examined. _PovStat_ uses per capita consumption as a measure of welfare. The second approach relies on _the envelope theorem_ and employs the _maximum value function_ to model the determinants of individual welfare (Chen and Ravallion 2004; Ravallion and Lokshin 2004). The last approach is based on a _reduced-form model of household income generation_ (Bourguignon and Ferreira 2005). Even though the envelope model and the income-generation models include some aspects of household behavior, these microsimulation models must be viewed as _statistical_ devices to the extent that they fail to fully account for market adjustment through endogenous prices or the adjustment of the behavior of agents from one equilibrium state to another (Ferreira and Leite 2003).

7
Waldner, F., et al. 2019. "Roadside Collection of Training Data for Cropland Mapping Is Viable When Environmental and Management Gradients Are Surveyed." _International Journal of Applied Earth Observation and Geoinformation_ 80: 82-93. Wilson, W. W., P. Lakkakula and D. W. Bullock. 2024. "Implications of the Russian Invasion on the Logistical Competition for Corn Shipments from the United States and Ukraine." _Journal of Agricultural & Resource Economics_ 49 (1): 162-184. Witmer, F. D. W. 2015. "Remote Sensing of Violent Conflict: Eyes from Above." _International Journal of Remote Sensing_ 36 (9): 2326-2352.

Zhou, W.-X., Y.-S. Dai, K. T. Duong and P.-F. Dai. 2024. "The Impact of the Russia-Ukraine Conflict on the Extreme Risk Spillovers between Agricultural Futures and Spots." _Journal of Economic Behavior and Organization_ 217: 91-111. Zhukov, Y. M. 2023. "Near-Real Time Analysis of War and Economic Activity During Russia’s Invasion of Ukraine." _Journal of Comparative Economics_ 51 (4): 1232-1243.

41
of the survey villages have rain station data, but there are rain stations in and around Nahouri province, we use an interpolation procedure that enables us to use these nearby rain stations to estimate rainfall in each village for each year. We purchased rain station data from the Burkina Faso Direction de la Météorologie and from the Ghana Meteorological Agency for the 50 rain stations within 100 kilometers from any of our survey villages (26 in Burkina Faso and 24 in Ghana). On average across the survey villages, the nearest rain station is 9.6 kilometers away.

With the data from the 50 rain stations, we use ArcGIS (geographic information system) to implement an inverse distance weighted (IDW) interpolation procedure, which uses the measured rain data from the stations surrounding a given survey village to predict rainfall in the village. Those rain stations that are closest to the village will have more influence on the predicted value by being weighted more heavily, while those further away will have less influence.<sup>11</sup> The intuition behind the IDW procedure is that locations closer together should have more similar rainfall patterns than those further apart, and therefore in estimating the village‟s rainfall, additional weight is put on those closer rain stations.<sup>12</sup> For several reasons, we believe the IDW interpolation procedure provides a more accurate estimated rainfall measure than alternatives such as using the nearest rain station. First, in much of West Africa, there is a strong north-south rain gradient with locations in the north generally receiving less rain than those areas further south. If we were to use only the nearest station and that station were further south than the village of interest, then we would potentially overestimate rainfall in the village. On the other hand, if the closest station were north, then we might underestimate actual rainfall. By using the rain stations in all directions surrounding a given village, this interpolation procedure allows us

> 11 Weights are proportional to the inverse distance squared, which is the power that minimizes the root mean squared predicted error and therefore determines the smoothness of the interpolation results. Intuitively, as the distance from the village to the rain station increases, the weight in the interpolation procedure decreases extremely rapidly. 12 The topography around Nahouri province and the surrounding rain stations consists of gently rolling hills without any significant topographical features such as mountains that might create areas with a discontinuity in rainfall.

10
# **References**

- **Abramitzky, Ran, and Leah Boustan.** 2017. “Immigration in American economic history.” _Journal of economic literature_ , 55(4): 1311–45.

- **Albarosa, Emanuele, and Ben Elsner.** 2021. “Forced Migration, Social Cohesion and Conflict: The 2015 Refugee Inflow in Germany.” Unpublished Working paper. Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group.

- **Alesina, Alberto, and Eliana La Ferrara.** 2000. “Participation in heterogeneous communities.” _The quarterly journal of economics_ , 115(3): 847–904.

- **Alesina, Alberto, and Eliana La Ferrara.** 2002. “Who trusts others?” _Journal of public economics_ , 85(2): 207–234.

- **Alesina, Alberto, and Eliana La Ferrara.** 2005. “Ethnic diversity and economic performance.” _Journal of economic literature_ , 43(3): 762–800.

- **Alesina, Alberto, Reza Baqir, and William Easterly.** 1999. “Public goods and ethnic divisions.” _The Quarterly Journal of Economics_ , 114(4): 1243–1284.

- **Algan, Yann, Camille H ́emet, and David D Laitin.** 2016. “The social effects of ethnic diversity at the local level: A natural experiment with exogenous residential allocation.” _Journal of Political Economy_ , 124(3): 696–733.

- **Allport’s, Katz I Gordon.** 1954. “The nature of prejudice.” _Cambr., Mass_ .

- **Baldwin-Edwards, Martin.** 2004. “Albanian emigration and the Greek labour market: Economic symbiosis and social ambiguity.” _SEER-South-East Europe Review for Labour and Social Affairs_ , , (01): 51–65.

- **Bauer, Michal, Alessandra Cassar, Julie Chytilov ́a, and Joseph Henrich.** 2014. “War’s enduring effects on the development of egalitarian motivations and in-group biases.” _Psychological science_ , 25(1): 47–57.

**Bauer, Michal, Christopher Blattman, Julie Chytilov ́a, Joseph Henrich, Edward Miguel, and Tamar Mitts.** 2016. “Can war foster cooperation?” _Journal of Economic Perspectives_ , 30(3): 249– 74.

- **Bauer, Thomas K., Sebastian Braun, and Michael Kvasnicka.** 2013. “The Economic Integration of Forced Migrants: Evidence for Post-War Germany.” _The Economic Journal_ , 123(571): 998–1024.

21
**Table 8. Emission Standards in selected Latin American countries**

|**Country**|**Vehicle type **|**Effective**<br>**Date**|**CO**<br>**(g/km)**|**HC**<br>**(g/Km)**|**NOx**<br>**(g/km)**|**PM(g/km)**<sup>(1)</sup>|
|---|---|---|---|---|---|---|
|Argentina|New Vehicles|1/1/1995<br>|12|1.2|1.4|0.373|
||All imports|1/1/1997<sup>(2)</sup>|2|0.3|0.6|0.124<br>|
||All new regular|1/1/1998|6.2|0.5|1.43|0.16<sup>(3)</sup>|
|Brazil|Cars|1/1/1992|24|2.1|2|-|
||Light Duty|01/01/1995|||||
|Chile|Passenger cars<br>Light & Medium Duty|1/1/1995|2.11|0.25|0.62|0.125|
||(gvw < 3860 kg)|1/2/1995|6.2|0.5|1..43|0.16|
|Costa Rica|Gasoline passenger cars<br>and light duty vehicles||||||
||< 1800 kg|1/1/1995|5.7|0.25|0.63|-|
||1800-2800 kg|1/1/1995|6.2|0.5|1.1|-|
||2800-6400 kg|1/1/1995|19.2|1.2|10.6|-|
||>6400 kg|1/1/1995|49.8|2.3|10.6|-|
|Mexico|Cars<br>light duty vehicles|1/1/1993|3.4|0.41|1|-|
||gvw < 6012 lb<br>light duty vehicles|1/1/1994|14|1|2.3|-|
||gvw 6013-6614 lb|1/1/1994|14|1|2.3|-|

(1) Diesel Vehicles only (2) 01/01/99 for all new registrations (3) PM 0.31 g/km for vehicles < 1700 kg Source: CONCAWE (2006) The introduction of emission standards for both new and old cars, along with travel demand management programs, and regulatory measures such as vehicle inspection and maintenance programs (I/M), fuel specification, etc., have reduced vehicular emissions in Latin American countries. For example in Mexico City, the total daily CO and NOx emissions from light and medium gasoline vehicle in 2000, were 48 percent and 26 percent lower, respectively, from 1998 levels (Schifter et. al., 2005).

# **4.5 Vehicle Emissions Standards in Asia**

Emission standards have been widely implemented in Asia. Some Asian countries (e.g., Singapore, Hong Kong) have introduced and strictly enforced stringent emission 25
# **Table 5: Estimation Results**

||20|08-2012|2008-|2020<br>Std error|
|---|---|---|---|---|
||Estimate|Std. error|Estimate|.|
|||Symmetric 3-paramet<br>|er model<br>||
|m|575.414<sup>a</sup>|7.002|799.071<sup>a</sup>|14.280|
|b0|-3.638<sup>a</sup>|0.089|-3.852<sup>a</sup>|0.090|
|b1|0.003<sup>a</sup>|0.0001|0.003<sup>a</sup>|0.0001|
|||Asymmetric 4-parame<br>|ter model<br>||
|m|694.776<sup>a</sup>|21.338|1,078.38<sup>a</sup>|48.620|
|b0|-20.705<sup>b</sup>|9.178|-29.608<sup>b</sup>|12.334|
|b1|0.002<sup>a</sup>|0.00007|0.001<sup>a</sup>|0.00007|
|b2|17,104.28<sup>a</sup>|5.430|17,330.63|11.856|

Superscripts<sup>a</sup> and<sup>b</sup> indicate significance at the one and five percent level, respectively. The standard errors were adjusted to take into account monthly variations in the number of projects entering the CDM baseline.

18
# **1. Introduction**

In recent years a growing number of impact evaluations have been conducted in the area of private sector development. Of these, the most commonly explored topics relate to small and medium size enterprises (SME) such as the effectiveness of training programs to improve performance and management, and the impact of microfinance and insurance on the expansion of agribusiness. The focus of this paper, however, is more recent work exploring the role of investment climate reforms in promoting the growth of the private sector, and the specific ways — in which qualitative research in combination with quantitative methods (i.e., mixed methods approaches) — can contribute to evaluations of such reforms. This paper provides a (partial) literature review on the use of mixed methods in international development evaluations and a framework that can be used to identify those types of investment climate interventions wherein the integration of qualitative methods can add significant (and distinctive) value.

Examples of investment climate reforms<sup>2</sup> , which may be implemented at the national/subnational level and to all or specific sectors, include: Business regulation: Reforms designed either to remove unnecessary or antiquated regulations, or to introduce new ones aiming to make it easier for businesses to get started and to operate securely. These reforms include activities such as business registration, business licensing (including sector licenses), construction permits, business inspections, regulatory governance, information and communication technology solutions for regulatory reform, and environmental regulation.

Competition policy: Reforms aimed at removing sector-specific constraints that affect market competition and support effective antitrust and competition rules in key sectors.

Investment policy: Reforms aiming to attract and retain investment, such as investment promotion activities, investment incentives, reforms to remove barriers to entry, and regulations to protect investment.

> 2 Source: World Bank Group Investment Climate Department; see www.investmentclimate.org

2
income refers to gross income minus production cost such as wagebill for hired labor used in planting and harvesting crop; expenditure on transportation, seed, fertilizer, and land rental; expenses incurred to feed, vaccinate and look after livestock as well as transportation of livestock and byproducts. (3) On-farm labor is the total days of (own, hired and shared) labor deployed for agricultural activities on own farm.<sup>4</sup> (4) Gross o-farm income is income from employment for wages/salaries and as a casual laborer; and gross return from non-farm enterprise.<sup>5</sup><sup>_,_</sup> (5) Net o-farm income encompasses the incomes from wage/salaried employment and casual labor; and net income from enterprises. (6) O-farm labor refers to the total number of days spent on wage/salaried and casual employment as well as own and hired labor-days spent on running non-farm enterprise.<sup>6</sup><sup>_,_7</sup> To convert agricultural production into revenue and labor days into farm wagebill, we used median prices of outputs and wage rate at the lowest admin division (`kebele' ), whenever data is available. Otherwise, median prices and wages in higher admin divisions like `woreda' /district, zone and even region have been adopted. This price and wage conversion applies only to agricultural incomes and expenses that are used to construct total household incomeagainst which share of o-farm income is imputed. This construction of revenue and wage bill is not applied to o-farm income. Therefore, it does not aect the o-farm return analysis. Value of land is imputed based on farmers' response to a hypothetical question regarding rental value of their own farmed parcel(s), and rental cost of land rented from others.

> 4Labor accounting is also based on aggregating labor days by men, women and children at equal weight. Labor data on livestock is not detailed enough to properly account for labor spent to looking after livestock.

> 5In Ethiopia, o-farm income includes income from working under the public safety-net program (PSNP).

> 6For Ethiopia, labor allocated for PSNP is included under o-farm labor.

> 7In Uganda, we do not have information on days allocated to operating non-farm enterprises.

6
# **_3. The data_**

Our empirical analysis relies on three different datasets. Our _de jure_ indicators of employment protection legislation are drawn from the World Bank _Doing Business_ Database, which provides detailed information on the labor legislation in more than 140 countries around the world. Information on employers’ perceptions on labor regulations and on they responses is drawn from two large surveys – the _World Bank Business Environment_ surveys and the _Investment Climate surveys_ .

_Measuring the stringency of employment protection legislation_ We focus on two separate aspects of employment protection legislation: that concerning regular contracts and that pertaining to temporary contracts.<sup>10</sup>

# **Regulation of regular employment**

Drawing from the _Doing business_ database of the World Bank, we focus on the following variables to characterize individual protection against dismissal for workers with regular contracts:

- _Procedural requirements._ They refer to the process that has to be followed from the decision to lay off a worker to the actual termination of the contract.  They include: 1) the grounds for dismissal  -- whether it is unfair to terminate the employment contract without cause; whether there is a list of “fair” grounds for dismissal; and whether redundancy is considered a “fair” ground for dismissal; 2) the delay before the notice of dismissal can start (for example, because there has to be a series of previous warnings); 3) whether a third party must be notified or consulted; and whether dismissal cannot proceed without the approval of a third party; 4) whether the law mandates retraining or replacement prior of dismissal; 5) whether there are priority rules to dismissal or layoffs; 6) and whether there are priority rules applying to re-employment.

- _Notice period_ required by law for the dismissal of one redundant worker in manufacturing with twenty years of tenure.

- _Severance payment_ (including mandatory indemnity) for the dismissal of one redundant worker in manufacturing with twenty years of tenure.

> 10 In the _Doing Business_ database, EPL regulations are defined on the basis of a representative worker.  The work has the following characteristics: (i) he is a non-executive full-time employee who has been working in the same firm for 20 years; (ii) his salary plus benefits equal the country's GNP per worker during the entire period of employment; (iii) he has a non-working wife and two children, and the family has always resided in the country’s most populous city. The employer has the following characteristics: (i) it is a manufacturing company wholly owned by nationals; (ii) its legal domicile and its main place of business is the country’s most populous city; (iii) it has 250 workers; and (iv) it abides by every law and regulation, but does not grant workers more prerogatives than are legally mandated.  See World Bank (2004c) from more details and <u>http://rru.worldbank.org/DoingBusiness/.</u>

9
-9-

Table 1: KEY FINANCIAL VARIABLES

|Date|RM|Ml|OMD|M2|NFABI|INTEB|DISC|SBPU|SBI|
|---|---|---|---|---|---|---|---|---|---|
|||(in Rp|. trill|ion)|||(in|%)||
|1982.1|4.01|<br>6.78|2.23|9.00|4.10|17.1|-|-||
|1982.2|3.91|<br>7.17|2.34|9.50|3.25|18.0|-|-||
|1982.3|4.08|<br>7.59|2.43|10.02|3.20|17.3|-|-||
|1982.4|4.00|<br>7.12|2.54|9.66|3.65|16.3|-|-||
|1983.1|4.07|<br>7.38|2.78|10.16|2.96|17.9|-|-||
|1983.2|4.27|<br>7.51|3.42|10.93|3.78|10.5|-|-||
|1983.3|4.71|<br>7.72|3.98|11.70|4.14|10.6|-|-||
|1983.4|4.89|<br>7.57|4.80|12.37|4.78|13.7|-|-||
|1984.1|5.34|<br>8.05|5.28|13.33|6.24|16.4|17.5|-||
|1984.2|5.51|<br>8.18|5.87|14.05|7.20|14.0|16.5|-||
|1984.3|5.54|<br>7.96|5.81|13.77|6.80|46.8|26.0|-||
|1984.4|5.47|<br>8.58|6.40|14.98|7.60|9.6|21.0|-|16.7/17.5|
|1985.1|5.83|<br>8.99|7.14|16.13|7.87|13.2|21.0|20.5|17.0|
|1985.2|6.14|<br>9.43|7.98|17.41|7.98|9.2|21.0|19.5|-|
|1985.3|6.14|<br>9.41|9.16|18.57|8.02|10.2|18.5|17.0|-|
|1985.4|6.44|<br>10.10|9.67|19.77|8.48|11.5|18.5|17.0|15.0|
|1986.1|7.55|<br>10.47|9.89|20.36|8.16|15.2|18.5|17.0|15.0|
|1986.2|7.09|<br>10.35|10.63|20.98|7.69|12.7|18.5|17.0|15.0|
|1986.3|7.26|<br>11.19|11.61|22.80|11.76|12.9|18.5|17.0|15.0|
|1986.4|7.81|<br>11.67|11.39|23.07|8.34|14.8|18.5|17.0|15.0|
|1987.1|7.99|<br>11.50|12.42|23.92|9.30|15.0|18.5|17.0|15.0|
|1987.2|7.79|<br>11.59|12.79|24.38|9.14|17.8|19.5|19.0|15.0|
|1987.3|7.89|<br>11.97|15.37|27.34|10.88|16.1|20.3|||
|1987.4|8.65|<br>12.68|16.54|29.22|11.38|11.9|18.7|||
|Glossary:|RM|-<br>reserv|e money|||||||
||Ml|-<br>curren|cy and|demand de|posits|||||
||QMD<br>M2<br>NFABI<br>INTEB<br>DISC|-<br>saving<br>(exclu<br>=<br>M1 + Q<br> -<br>net fo<br> -<br>weight<br>-<br>Bank I|and ti<br>ding fo<br>M<br>reign a<br>ed-aver<br>ndonesi|me deposi<br>reign cur<br>ssets of<br>age inter<br>a's basic|ts<br>rency dep<br>Bank Indo<br>-bank rat<br>discount|osits)<br>nesia<br>e<br>rate||||
||SBPU|-<br>discou|nt rate|on SBPU||||||
||SBI|-<br>cut-of|f rate|on 90-day|s SBI auc|tions.||||

7. The Policy Debate. The rise in the real interest rate has generated a serious concern about its adverse effect on private investment. Reflecting this, there is an ongoing policy debate in Indonesia concerning ways the real interest rate could be brought down. The debate is particularly intense