rafmacalaba/datause-encoder-data huggingface dataset:
# **References:**

Ahmad, Q. K., Chowdhury, A. K. A., Imam, S. H., and Sarker, M. 2001. _Perspectives on flood 1998_ . Dhaka: The University Press Ltd.

Ahmed, A. U. and Mirza, M. M. Q. 2000. ‘Review of causes and dimensions of floods with particular reference to flood ’98: national perspectives’ in Ahmad, Q. K., Chowdhury, A. K. A., Imam, S. H. and Sarker, M. (eds) Perspectives on Flood 1998, University Press Ltd, Dhaka, pp67–84 Auffhammer M., S.M. Hsiang, W. Schlenker, and A. Sobel. 2011 “Global Climate Models and Climate Data: a User Guide for Economists,” _Working Paper_ Banerjee, Lopamudra 2007. “Effects of Flood on Agricultural Wages in Bangladesh: An Empirical Analysis.” _World development,_ 35, no. 11, pp1989-2009 Blankespoor, B. and Yoshida, N. 2010. _Market Accessibility in Bangladesh_ . 16pp. mimeo, Washington DC, World Bank Deaton, Angus 1992. _Understanding Consumption_ Carendon Lectures in Economics, Oxford University press.

Elbers, C., J.W. Gunning, and B. Kinsey 2007. “Growth and Risk: Methodology and Micro Evidence.” _The World Bank Economic Review,_ 21, 1–20.

Ellis, Frank. 2004. “Occupational Diversification in Developing Countries and the Implications for Agricultural Policy” Programme of Advisory and Support Services to DFID (PASS).

Hossain, M. 2004. Rural Non-Farm Economy in Bangladesh: A View from Household Surveys. Center for Policy Dialogue (CPD) Occasional Paper Series no 40, Dhaka: Center for Policy Dialogue.

Islam, M. J., Parul, S. S., Pathan, A. B. M. B. U., Quasem, M. A., & Islam, M. S. 2004. Influence of cracking on rice seasons and irrigation in Bangladesh. Journal of Biological Sciences, 4(1), 11–14.

Jacoby, H.G., and E. Skoufias. 1997. “Risk, Financial Markets, and Human Capital in a Developing Country.” _The Review of Economic Studies_ 64, no. 3: 311–35.

Madhavan, M., Takamatsu S., Yshida N., 2012. “Responses to climate variability with employment in Monga areas” (mimeo) Menon, Nidhiya 2009. “Rainfall Uncertainty and Occupational Choice in Agricultural Households of Rural Nepal” _Journal of Development Studies_ , Vol. 45, no. 6 (July), pp, 864-888.

Menon Nidhiya and Narayanan Subramanian. 2008. “Learning, diversification and the nature of risk” _Economic Theory_ 35: 117–145 Mirza M.M.Q. 2003. “Three Recent Extreme Floods in Bangladesh: A Hydro-Meteorological Analysis”, Natural Hazards Vol. 28, pp35-64.

21
20 that firms have operational problems that affect the quality of service from the consumer’s perspective. But coverage is again an issue. Power transmission and distribution losses for the most recent years are available for about 90 of 153 developing countries. For earlier periods coverage drops even more.

In telecoms, we rely on telephone faults as our proxy for technical quality. Telephone faults are defined by ITU as “reported faults per one hundred telephone mainlines.” Compared with ITU’s indicators of access and affordability, coverage of telephone faults is rather low. We have data for about 60 of 153 developing countries. An additional problem with phone faults is that they include only _reported_ faults, which is sometimes misleading (especially in countries with bad reporting systems).

Regarding water, no technical quality indicators are available at the country level. The few available indicators from the consumer’s perspective (like hours of water supply) have very low coverage. Unfortunately, due to the lack of data, we were not able to analyze the impact of reform policies on water service quality.

# **6.2 Basic data analysis**

The evolution of technical quality between 1990 and 2002 differs from electricity to telecoms (Table 9). In electricity, average losses increased by 13 percent, while in telecoms average faults dropped by 57 percent. However, it is important to keep in mind that these two indicators are reported by firms. Improvements in reporting and auditing systems may increase the reported figure, even if the quality of the service has not deteriorated.

**<u>Table 9 Evolution of average technical quality, 1990–2002</u>**

||**Average**|**Standard**<br>**deviation**|**Number of**<br>**observations**|
|---|---|---|---|
|**Power transmission and distribution losses (percentage**<br>**ofoutput)**||||
|1990|15|9|72|
|2002|17|11|89|
|**Telephone faultsper 100 mainlines**||||
|1990|98|86|45|
|2002|42|39|62|

Source: Authors’ calculations using data from WDI.

Reform policies, such as IRAs and PPI, may have influenced the service quality and/or the efficiency of reporting systems. Table 10 relates technical quality to reform policies by presenting averages for different country groups according to whether or not reforms were adopted. There seems to be no difference in power losses between countries that committed fully to reforms and those that did not (columns 1 and 4). In contrast, telephone faults are 50 percent lower in countries that have reformed than in those that lack an IRA and in which the telephone company remains state-owned. Regarding countries that have partially committed to reforms (columns 2 and 3), in both electricity and telecoms, those that have an IRA but not PPI show poorer quality performance than
support for rebellion, rekindling of old rivalries, and dissatisfaction of the population with the status quo helped lead to the expansion of the conflict from northern to central Mali.

In 2013, after northern rebels and their international violent extremist allies had conquered much of northern Mali a French-led military intervention, Operation Barkhane, pushed them out of the area. Violent events spiked in 2013, especially in northern Mali as the French fought with violent extremists and their local allies. That violence subsided somewhat after 2013 due to the deployment of French troops and a UN peacekeeping mission MINUSMA (ACLED, 2020; Nomikos, 2019). The early wave of fighting in 2012/13 produced a wave of displacement, but much of it was short-lived as the French intervention pushed the violent extremist connected groups out of the major population centers of the north, Gao and Timbuktu. Hoogeveen et al. (2019) show great heterogeneity in those who decided to return to their hometowns from this initial wave of displacement.

While the French-led intervention tamped down the immediate conflict, the violence started to spread to other countries (Niger and Burkina Faso) and other parts of Mali. We show in figure 1 the location of violent events in Mali as measured by ACLED for the period of this study. What is obvious is the spread of violence from being mostly in the north in 2014 to expanding into more parts of the country by 2017 and increasing in intensity (as seen by larger/darker circles on figure 1).

Starting in 2014 the violence that starts to pick up in central Mali becomes in part driven by long-simmering issues between ethnic groups where the spread of violence is pushed by discontent with the status quo, deficiencies in national security, and justice institutions. The violent extremist groups provide the promise of personal security and rudimentary justice for rural residents who do not see the government as able to provide that. In opposition to the violent extremist groups and their local allies, many villages developed their own selfdefense or communal militias as their own response to the inadequacy of government justice and security institutions. These self-defense groups along with the violent extremists have raised levels of inter-communal violence and have been a major source of fatalities, especially 4
indices, that are explained within the model and their values are endogenously changed (determined).

# **4. Data Source and Model Calibration**

In the CGE models, a set of mathematic equations translates the structure of an economy and describes the behaviour of all agents and the equilibrium conditions of all markets. A calibration procedure fixes the parameters for the model’s equations (called benchmark equilibrium) and, then the model can be solved for an alternative scenario associated with any changed policy regime. A comparison between the counterpart or comparative scenario and the benchmark scenario makes it possible to assess effects on allocation and income distribution. CGE model is designed to analyze “What if ” question. For example, what is the impact of foreign aid ($1000 million US dollars) on receipt country’s income, output, welfare and other key economic indicators? The second advantage of CGE model is that it catches the sectoral and regional linkage effects. While partial equilibrium model are unable to provide the overview of the counterpart scenarios.

The AIDCGEM model is calibrated for the year 2001 using the GTAP data base, version 6 and foreign aid data is mainly extracted from OECD STAT<sup>*</sup> .

GTAP data base, around which the model has been built, is a cross-section data of international trade flows and national input-output tables. All the information in the data base is reported in values converted to US dollars. The behavioral parameters utilized in the GTAP model are described in Dimaran _et al._ (2006). They define the magnitude of behavioral responses to changes in relative prices. In particular, there are four sets of behavioural parameters in GTAP data base: (i) elasticities of substitution, in both consumption and production; (ii) transformation

> ***** Foreign aid data set is extracted from OECD STAT

> ( Available at http://www.oecd.org/document/21/0,3746,en_2649_34665_43230357_1_1_1_1,00.html)

**12**
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**Table 1. Quarterly Trends in Underlying Proritability**

|Sector|1989:1V|1990:1|1990:11|1990:111|1990:1V|1991:1|
|---|---|---|---|---|---|---|
|Metallurgy|37.8|35.8|28.5|20.2|12.9|16.5|
|Electro.iachincry|34.7|20.9|18.8|23.1|17.5|2.2|
|Chemical|31.2|18.7|16.6|13.0|18.8|8.9|
|Light|36.7|19.7|8.9|6.7|4.1|-2.0|
|Food|18.2|16.3|22.4|23.2|22.4|18.2|

**e:** Underlying profitability is underlying profit/net sales %. See Glossary.

# **Other Income**

As Table 2 shows, other income (subsidies, but ma:.. y asset leasing and sales) was significant in electromachinery and light manufacturing, helping to boost pre-tax profit in 1991:1. For the other sectors, except food processing, the trend was less pronounced or downwards.

**Table** 2. Other Income to Net Sales Percentage

|Sector|1989:1V|1990:1|1990:11|1990:111|1990:1V|1991:1|
|---|---|---|---|---|---|---|
|MetaUurgy|**3.3**|0.6|0.2|**_0.5_**|**0.4**|1.3|
|Electromachinery|5.1|2.4|3.5|3.6|1.3|9.8|
|Chemical|5.7|5.4|3.5|3.8|2.2|2.2|
|Light|2.6|2.7|2.6|2.7|3.3|**_7.5_**|
|Food|**3.1**|1.6|1.3|2.5|0.8|**_5.8_**|

# **Cost dynamics**

Figs. 3-7 capture underlying profitability and cost trends by sector for materials and energy, depreciation, interest and wage costs (wage bill x 1.65 to reflect taxes, social insurance, etc.), as a share of net sales:

* the rapidly shrinking mark-up over costs (the clear portion of the bar charts) attests to two facts: (a) firms were unable to follow "cost plus constant mark-up" pricing rules; and (b) the exchange rate anchor was obviously beginning to bite. In short, the inflation process was approaching what would be expected in a small open economy, i.e., prices being constrained by international price parity, rather than being driven by cost-push alone.

* the squeeze on value added (crudely, net sales minus materials and energy costs, i.e., everything except the darkly shaded portion of the bar charts) and profit margins is evident, indicating that firms were caught between the fixed exchange **rate** and rising costs, with no clear cost minimizing responses in evidence.

* the loss of implicit CMEA subsidies is captured by the upward trend in materials and energy costs for the three "heavy" sectors, metallurgy, chemicals and electromachinery.
cross-country variation in the degree of financial openness has widened quite significantly. Lastly, commodity intensity (shown in the bottom panel of Figure 5) also shows increasing cross-country variation over time. Its median value remained roughly constant since the 1960s until the early 2000s, and fell afterwards to almost zero in 2010. In contrast, the 75<sup>th</sup> percentile more than doubled over the sample period, while the interquartile range rose threefold.

It is worth noting also that the three conditioning variables show fairly modest pairwise correlation. While trade and financial openness are moderately correlated (.28), commodity intensity appears virtually uncorrelated with both of them (the pairwise correlations are .16 and - .07, respectively). This suggests that the three variables capture different features of the economies under consideration.

To assess the role of these variables in shaping the response to global shocks, we use the framework of equation (1’). Specifically, we estimate the parameters of the equation letting the set of conditioning variables **z** consist of our indices of trade openness, financial openness, and commodity-intensity (plus a constant).<sup>16</sup> Armed with the resulting estimates of � and �, we can calculate the impulse responses generated by the model for alternative values of the three conditioning variables, and we can also construct the associated decompositions of the variance of the GDP forecast error.

We implement this approach one variable at a time: we let each one of them vary while holding the other two constant – a _ceteris paribus_ exercise. For each value of the conditioning variable of interest, we follow the procedure described earlier to obtain 1,000 identified models

> 16 To minimize potential simultaneity problems, in the estimation we use the lagged values of the conditioning variables **z** . Unavailability of the Chinn‐Ito capital account openness index results in the loss of a few countries for this exercise. They are Cambodia, Angola, Botswana, Ethiopia, Lesotho, Namibia, Albania, Bulgaria and Mongolia.

23
# **3. Methodology**

This section first presents the method used to construct indicators for measuring accessibility, availability and safety of the public transport network (these are the facets of the public transport system that are of interest to us). It then presents the empirical model used to estimate the effect of the constructed transport measures on labor market outcomes.

## **_3.1. Measuring accessibility, availability and safety of the public transport network_**

In this section, we present the definitions for each measure used to capture different aspects of the public transport system as well as the data sources and the methodology used to compute them. It also provides descriptive statistics and maps of the transport measures for each of the three cities. Details for each of the indicators can be found in Annex A.

### _a. Measuring accessibility_

Accessibility focuses on measuring the spatial accessibility of job opportunities throughout the city using either public transport or walking. For each residential location, we compute the percentage of total jobs that are accessible within 60 minutes during peak hours (8am to 9am).<sup>21</sup> The measure of accessibility combines the time taken to walk to a public transport stop, time spent in the public transport vehicle, and the time needed to walk to a job opportunity.

Three sources of data are used to measure accessibility for each household in our sample. First, we use a layer of the street composition for each city. All street grid data is taken from OpenStreetMap. For the three cities, there are no pedestrian restrictions, meaning all streets and links in the network are considered accessible to pedestrians. A pedestrian walking speed of 3.6km/h is used, and the threshold of 20 minutes is used as the maximum walking time _per leg_ of the trip when public transport is being used. Second, we use the public transit network that has been collected. The collected frequency of departure, and speeds by which the public transport vehicles travel on the route are used for the analysis (see 2.1. for details and maps about this data). Third, we use the distribution of employment in each city. We base it on Barzin et al. (2022), which estimates the relative density of employment across the metropolitan area using a machine learning method and remote sensing data.

> 21 A given destination will be considered to be reachable from a residential location if it can be reached in under 60 minutes during at least 50% of the departures within the 8:00 to 9:00 am window.

14
combustion, and the power sector accounts about half of the region’s total fossil fuel-based CO2 emissions (IEA, 2015). Most South Asian countries have set targets to reduce their GHG emissions under the Paris Agreement through their nationally determined contributions (NDCs) pledges. India has set a target of reducing its emissions intensity of GDP by 33% to 35% by 2030 from the 2005 level (UNFCCC, 2015). Pakistan intends to reduce up to 20% of its 2030 projected GHG emissions if international financial supports are provided.<sup>9</sup> Bangladesh intends unconditionally to reduce GHG emissions by 5% from its BAU level by 2030 in the power, transport and industry sectors. The reduction in each of these sectors will be increased to 15% if international supports (e.g., grants, technology development and transfer, and capacity building) are provided.<sup>10</sup> Since the power sector is an important contributor to GHG emissions in Bangladesh, India and Pakistan, expanding the clean energy sources, including hydropower for power generation, would be the main strategy to meet their NDCs. Timilsina and Toman (2018) estimate that regional electricity trade that stimulates hydropower development and trade in the region would help reduce power sector emissions by 43%, 9% and 3% in, respectively Bangladesh, India and Pakistan in 2030 from the baseline. For the South Asia region as a whole, the corresponding reduction would be 11%. If a carbon tax is added on top of the regional electricity trading provision, hydropower and other renewable energy resources would further substitute fossil fuels thereby reducing power sector emissions in 2030 by 55%, 16%, 26% and 20% from the baseline in Bangladesh, India, Pakistan and South Asia, respectively. These results suggest the importance of hydropower to mitigate climate change in South Asia.

> 9 Pakistan’s Intended Nationally Determined Contribution (Pak-INDC).

> http://www4.unfccc.int/ndcregistry/PublishedDocuments/Pakistan%20First/Pak-INDC.pdf

> 10 http://www4.unfccc.int/ndcregistry/PublishedDocuments/Bangladesh%20First/INDC_2015_of_Bangladesh.pdf

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# 2 Conceptual framework

This section outlines the conceptual framework that motivates our narrative analysis, which closely follows that of RR. However, unlike RR, we highlight the critical role of procyclical/countercyclical tax policy in determining the sign of the bias of the estimator of the e¤ects of tax changes on output. We ...rst lay out the basic set-up and then illustrate the possible biases.

## 2.1 Basic set-up

To ...x ideas, let us use the simplest speci...cation capturing how tax changes a¤ect real GDP: where yt is the logarithm of real GDP (and �yt is thus the real GDP growth rate, expressed as the di¤erence in logarithms), �t<sup>all</sup> t represents all legislated tax rate changes expressed in percentage points, and "t is the stochastic error, with zero mean and variance �<sup>2</sup> "<sup>.</sup> Tax changes can be broken down into two types: where �t<sup>endog</sup> t (endog stands for endogenous) are changes in tax rates enacted as a result of (i) current or prospective output growth di¤ering from normal and/or (ii) other factors likely to a¤ect output growth in the near future. In contrast, �t<sup>exog</sup> t (where exog stands for exogenous) are changes in tax rates driven by reasons unrelated to developments likely to a¤ect output in the near term. Therefore, while �t<sup>exog</sup> t are orthogonal to "t (i.e., cov(�t<sup>exog</sup> t ; "t) = 0), �t<sup>endog</sup> t are not (i.e., cov(�t<sup>endog</sup> t ; "t) 6= 0). As indicated by RR, examples of exogenous tax changes would be a cut in taxes (i) based on the belief that lower marginal rates will increase long-run growth or (ii) with the hope that lower revenues will eventually shrink the size of the government.

Let us now focus on endogenous tax changes. Without loss of generality, we can write �t<sup>endog</sup> t as where � 7 0 captures how endogenous tax changes respond to output shocks caused by factors other than taxes such as government spending, monetary policy, and terms-of-trade shocks, among many others. For example, suppose that a negative terms-of-trade shock reduces output (i.e., "t < 0). Policymakers may decide to reduce taxes to ...ght o¤ such a recession (i.e., �> 0), or they may be “forced”(due to the ensuing fall in ...scal revenues) to increase taxes (i.e., �< 0).<sup>8</sup> More generally, countercyclical tax policies (aimed at smoothing out the business cycle) imply �> 0; procyclical tax policies (which in principle tend to amplify output volatility) are captured by �< 0; and acyclical

> 8 We should note that these endogenous tax changes are triggered by lower cyclical tax revenues and thus are not the result of inherited ...scal problems. This important distinction will be discussed in detail in Section 4.

7