rafmacalaba/data-use-mentions-extended huggingface dataset:
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- [74] Coad,
0|58.2|
|Orissa|105.9|75.6|81.0|59.1|
|Punjab|123.5|130.4|133.5|91.8|
|Rajasthan|87.4|125.7|132.3|94.3|
|Tamil Nadu|71.6|110.8|132.1|72.6|
|Uttar Pradesh|99.5|94.3|97.0|69.2|
|Uttarakhand|99.0|118.7|122.1|96.7|
|West Bengal|90.4|85.3|87.8|65.9|
|All India|90.2|93.1|101.5|68.9|

_Notes:_ MGNREGS wage rates estimated as total expenditure on wages (excluding skilled or semi-skilled) divided by total number of person days of employment for FY 2009/10 (April 2009 to March 2010). Casual wages for June 2009 to July 2010 period, based on NSS 66<sup>th</sup> round survey. ―All India‖ includes smaller states not reported. Note that we do not report the female wage rate for Jammu & Kashmir, as we found it was based on a sample of only 7 observations and was not reliable. (The sample estimate of 206.5 was also implausibly high relative to the male wage). _Sources_ : Casual wages from _Key Indicators of Employment and Unemployment in India, 2009/10_ , NSSO, Govt. of India (June 2011). MGNREGS expenditure and employment data are from the state-wise Monthly Progress Reports (www.nrega.nic.in).

29
# **Impact of Climate Change and Aquatic Salinization on Mangrove Species and Poor Communities in the Bangladesh Sundarbans**

**Susmita Dasgupta* Istiak Sobhan David Wheeler**

## **JEL Classification:** Q23; Q25; Q54; Q57

**Keywords:** climate change; aquatic salinization; mangrove; Bangladesh: Sundarbans

**This research was conducted under the South Asia Water Initiative - Sundarbans Landscape.**

*Authors’ names are in alphabetical order.

The authors are respectively Lead Environmental Economist, Development Research Group, World Bank; United States; World Bank Consultant, Bangladesh; and Senior Fellow, World Resources Institute.

We are grateful to Zahir Iqbal, Bangladesh Forest Department for data on mangrove species in Sundarbans. We are thankful to Yunus Ali, Chief Conservator of Forest, Bangladesh Forest Department for his expert opinion. We would like to extend our special thanks to Lia Sieghart, Glenn-Marie Lange, Mainul Huq and Michael Toman for their review comments on this paper.
October 1997 [the Ecinf 1997], we introduce a time dimension in our analysis by considering firms created before and after the new legislation, which was December 1996_ .” Fajnzylber et al. considered November 1996 as cutoff instead. According to them (2011, p. 263): “ _In November, the Brazilian Government implemented a new simplified tax system for micro and small firms, the SIMPLES.”_ In other words, Monteiro and Assunção (2012) used December 1996, the month the law was approved in the Brazilian Congress, as cutoff. Fajnzylber et al. (2011) used November 1996 instead, the month the federal government drew up the provisional measure creating the new program.<sup>6</sup> Since ECINF 1997 indicates the number of months a firm is in business, Monteiro and Assunção (2012) specify their DID regression to obtain estimates comparing outcomes of eligible and ineligibles firms created before and after December 1996. Their estimates include firms from 0 to 20 months old. That is, firms that started business between February 1996 and October 1997.

On the other hand, Fajnzylber et al. (2011) use an RD design to compare eligible firms created after November 1996 with eligible firms that were in business already. Like Monteiro and Assunção, Fajnzylber et al. (2011) also employed a DID approach, but they used a different sample of firms and gave more weight to observations close to the cutoff point.

> 4 This section is brief summary of section 2 in Monteiro and Assunção (2012).

> 5 The exchange rate at that time was 1.04 Brazilian Real per 1 U.S. dollar. See www.bacen.gov.br

> 6 Although the provisional measure gave firms the right to formalize under the simplified tax program, Congress would still have to approve it to turn it into a law.

4
standard errors clustered by respondent. The multi-wave survey contains a wide variety of questions covering topics ranging from demographics to experiences in the United States and Guatemala; specific wording for relevant survey questions is included in the online appendix.

Additionally, we conducted 18 semi-structured interviews with deported migrants from our original sample. To recruit interview participants, we divided all baseline survey respondents who reported using a coyote to migrate into four different groups depending on what we theorize to be two important dimensions of variation: (a) their intention to remigrate and (b) whether they experienced extortion. From each of these four groups, we selected a random sample of respondents and interviewed between 4 and 5 respondents per group. Interviews lasted on average 30 minutes, and participants were compensated with 50 Quetzales of phone credit. For more information about the final number of interviews from each group of migrants, see Table A1.

Before proceeding to a discussion of the specific variables used in the analyses below, it is important to briefly discuss a few ethical considerations, especially given the vulner11
” include the results from the regressions with the dichotomous variable that identifies exposure to a shock in the current period (irrespective of potential exposure to past weather shocks). The columns that are labeled as “2” show the results from the regressions with the dichotomous variables that identify exposure to a shock (i) _only_ in the current period and (ii) in consecutive periods. We report the results for each variable using these two specifications, first for the IHPS sample and then for the FIAS sample.

> 21 Full results are found in Online Appendix 5.

28
TABLE A9: Impacts of text messages on socio-emotional outcomes for children aged 1-3 years

||(1)<br>Behavioral index|(2)<br>Control|(3)<br>Affect|(4)<br>Ability|
|---|---|---|---|---|
|Panel A: Equation 1|||||
|ITT|0.02|-0.14|0.04|0.09|
||(0.08)|(0.09)|(0.09)|(0.09)|
|Observations|717|717|717|717|
|Panel B: Equation 2|||||
|Nutrition & Stimulation|- 0.01|-0.16|0.01|0.10|
||(0.11)|(0.11)|(0.11)|(0.11)|
|Nutrition|0.07|-0.27<sup>_∗∗_</sup>|0.11|0.22<sup>_∗∗_</sup>|
||(0.11)|(0.11)|(0.11)|(0.11)|
|Stimulation|0.02|0.05|-0.00|0.01|
||(0.12)|(0.11)|(0.11)|(0.11)|
|Observations|717|717|717|717|
|Panel C: Equation 3|||||
|ITT Leader|0.02|-0.01|0.04|0.00|
||(0.10)|(0.09)|(0.09)|(0.11)|
|Observations
practices. Durnev and Kim (2005) find that firm-level governance and disclosure is positively related to a firm‟s growth opportunities, need for external financing, and concentration of cash flow rights. The market seems to recognize and reward better disclosure practices since in their sample, firms with higher governance and transparency rankings are valued higher in stock markets.

A second group of studies have proxies for the quality of disclosure practices. In his study on the East Asian financial crisis, Mitton (2002) uses two measures of disclosure quality. The first is that a firm has a listed American Depositary Receipt (ADR). Listing as an ADR can improve disclosure quality as a result of having to meet disclosure requirements mandated by the SEC and the listing exchange.<sup>16</sup> Moreover, the larger pool of potentially more sophisticated investors also increases disclosure quality. The second measure of disclosure quality is that the firm‟s auditor is one of Big Six international accounting firms. Prior research has associated the Big Six Auditors with higher audit quality and greater transparency (e.g. Reed et al. 2000; Titman and Trueman, 1986). Mitton also shows that higher disclosure quality is associated with a better stock price performance during the crisis period.

Finally, in the third group of studies largely focused on US data, a large literature has used the number of analysts following the firm and the accuracy of analyst forecasts as measure of the information environment of the firm (see Lang and Lundholm, 1996; Healy, Hutton, and Palepu, 1999; Gebhardt, Lee, and Swaminathan, 2001, Lang, Lins, and Miller, 2003). International finance scholars have in turn used analyst coverage in the US as a measure of the quality of information available on foreign firms listing in the US as ADRs (Baker, Nosfinger, and Weaver, 2002; Lang , Lins, and Miller, 2003; Bailey, Karolyi, and Salva, 2006).

Overall, the literature on disclosure practices
|> 30,000|3.6|
|Memo: Mean loan size (baht)|15,790|
|Memo: Standard deviation of loan size(baht)|8,335|
|Source: Thailand Socio-Economic Survey 2009||

**<mark>Table 9.  Maximum loan sizes for regular and emergency loans</mark>**

|Maximum size for regular|% breakdown|Maximum size for emergency|% breakdown|
|---|---|---|---|
|loan(‘000 baht)||loans(‘000 baht)||
|< 20|9.9|<3|7.7|
|20|73.2|3|25.3|
|-30|8.8|-5|34.4|
|-40|2.4|-10|16.3|
|-50|5.2|-20|11.2|
|>50|0.5|>20|5.1|
|Sample size|2,811|Sample size|1,501|
|Mean interest rate|6.1|Mean interest rate|7.8|

Note: * This includes non-rounded amounts (such as 20,500, etc.). Source: Village Fund Survey, 2010 This is indeed what we observe. Many village funds distinguish between their ordinary loans, and emergency loans; the latter tend to be smaller, and represent a modest part of the portfolio, with relatively low minimum amounts, as Table 9 shows. The median number of emergency loans approved was 14 per village fund per 11
Instruments for lagged income have included lagged expenditure (e.g. McCulloch and Baulch, 2000), subjective measures of well-being (Luttmer,

> 1 An alternative method used in developed countries has been the use of administrative data from payroll records, where measurement error is likely to be much less (e.g. Aaberge et al., 2002). Such data is much less common in developing countries, and does not allow analysis on the dynamics of earnings of the self-employed, or of total household income.

2
- Beaubien, J. (2019, June 3). They Pump $15 Billion A Year Into Bangladesh’s Economy — But At What Cost? Retrieved June 16, 2019, from NPR.org website: <u>https://www.npr.org/sections/goatsandsoda/2019/06/03/722085193/they-pump-15billion-a-year-into-bangladeshs-economy-but-at-what-cost</u>

- Beegle, K., De Weerdt, J., & Dercon, S. (2010). Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey. _The Review of Economics and Statistics_ , 93(3), 1010–1033. <u>https://doi.org/10.1162/REST_a_00105</u>

- Benjamini, Y., Krieger, A. M., and Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. _Biometrika_ , 93(3), 491-507.

- Benhabib, J., & Jovanovic, B. (2012). Optimal migration: A world perspective. _International Economic Review_ , 53(2), 321–348.

- Brown, R. P. C., & Leeves, G. (2007). Impacts of International Migration and Remittances on Source Country Household Incomes in Small Island States; Fiji and Tonga(No. 07–13). Retrieved from Agricultural and Development Economics Division of the Food and Agriculture Organization of the United Nations (FAO - ESA) website: https://ideas.repec.org/p/fao/wpaper/0713.html

- Casey, K., Glennerster, R., and Miguel, E. (2012). Reshaping institutions: Evidence on aid impacts using a preanalysis plan. _The Quarterly Journal of Economics_ , 127(4), 1755-1812.

- Cho, Y., Denisova, A., Yi, Soonhwa, & Khadka, U. (2018). _Bilateral Arrangement of Temporary Labor Migration:
**Figure 5 Gini Coefficient for LAC Countries, Actual vs. Projected**

<!-- Start of picture text --> 5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>2017 0.00<br>-1.00<br>-2.00<br>ARG BOL BRA CHL COL CRI ECU SLV HND PAN PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>2018 0.00<br>-1.00<br>-2.00<br>ARG BOL BRA COL CRI DOM ECU SLV HND MEX PAN PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>2019 0.00<br>-1.00<br>-2.00<br>ARG BOL BRA COL CRI DOM ECU SLV HND PAN PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>2020 0.00<br>-1.00<br>-2.00<br>ARG BOL BRA CHL COL CRI DOM ECU MEX PER PRY URY<br>Old - With Re-scaling New - With Re-scaling New - No Re-scaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

Note: actual data excludes COVID-19-related mitigation measures when the
,

|

| 3

# Introduction

|

|

_ The investigation of the institutional and political roots of government decisions has accelerated rapidly in the last twenty or more years. However, cross-country empirical work has been handicapped bya lack of detailed data on the political and institutional characteristics of countries, and their change over time. Comparative political economy, therefore, has been restricted to small-sample case studies, important in their own right, but imposing unavoidable limits on researchers seeking to generalize their results beyond the case study sample. |

The lack of data has been a serious obstacle to better understanding of numerous areas. in political economy, including the political conditions under which governments promote rather than retard economic development. Despite evidence that institutions underpin economic development, we are still far away from the answers to two fundamental questions: Which political institutions are most conducive to development and reform? And under what conditions do such institutions emerge? Unfortunately, cross-country data that could help to answer these questions, data that provided a disaggregated picture of a country’s political institutions and operations, have been scarce. In this article we introduce a new data set that fills many of these data gaps, the Database on Political Institutions (DPI). It has been compiled in the Development Research Group of the World Bank, but it rests on an intellectual foundation laid by a large body of political economy research.

| |

The DPI contains 113 variables (detailed in Annex 1) for 177 countries over the years 1975-95. The variables provide details about elections, electoral rules, type of political system, party composition of the opposition and government coalitions, and the extent of military influence on government. The DPI also contains a number of new variables, compiled from
the inputs. Confirmation percentage is defined as the proportion of the reviewed studies showing positive and significant relationship between the specific input and test scores. At the primary level, it is clear that class instructional time, school library, textbooks, and class frequency of homework have the highest confirmation rates at 73.1 - 88.9 percent. On the other hand, teacher's salary level and school teacher/pupil ratio have the lowest confirmation rates at 36.4 and 34.6 percent. More recent studies also tend to stress the effectiveness of improving of physical facilities. Relating the cost of these inputs to their marginal effects on test scores, available estimates further show that in fact textbooks and other educational materials along with improvement of physical facilities have much higher cost-effectiveness than increased teacher salary, years of experience and teacher/pupil ratio.

|**Table 1. Confirmation Percentages of Various Educational Inputs Sorted by Direct**|
|---|
|**Importance to Teacher Utility**|

||Number of<br>studies|Positive and<br>sianificant relation|Confirmation<br>Percentaae|
|---|---|---|---|
|**Primary**Schools:||||
|Teacher's salary level|11|4|36.4|
|School teacher pupil ratio|26|9|34.6|
|Teacher's years of schooling|18|9|50.0|
|Teachees experence|23|13|56.5|
|Class instructional time|17|15|88.2|
|Class frequency of homework|<br>11|9|81.8|
|School library|18|16|88.9|
|School textbooks|26|19|73.1|
|**Secondary Schools:**||||
|Teachers salary level|11|2|18.2|
|School
# 7 Final thoughts

This paper has estimated tax multipliers for a large group of countries following the narrative approach. Speci...cally, based on a novel dataset on VAT rates in 51 countries (20 industrial and 31 developing) and contemporaneous economic records and sources, we have identi...ed 96 tax rate changes and classi...ed them into endogenous and exogenous to current (or prospective) economic conditions. The analysis has made clear the critical importance of relying on a narrative approach as opposed to the much more common (due to its considerable ease of implementation) Blanchard-Perotti approach. In terms of the bias, the Blanchard-Perrotti approach tends to overestimate the size of the tax multiplier in our global sample due to the numerous tax changes that have responded procyclically to the business cycle. In contrast, the true e¤ect of tax changes on output would be underestimated in advanced economies like the U.S. which typically follow countercyclical tax policies by increasing (cutting) taxes in good (bad) times.

When properly identi...ed, we show, as in RR, that the tax multiplier is negative, indicating that tax hikes reduce economic activity and tax cuts boost output. Why? Because increases (decreases) in the VAT rate reduce (increase) the incentives to consume, invest, and work. However, we found –both for our global sample and for RR’s U.S. dataset –that such “average”multiplier hides important nonlinearities. In line with theoretical models, which are based on distortionary and disincentive-based type of arguments, we ...nd that the tax multiplier is essentially zero under relatively low/moderate initial tax rate levels but markedly negative when initial tax levels are high. By the same token, for a given initial tax rate, larger changes in the tax rate have larger tax multipliers. These ...ndings have important policy implications given that the initial level of taxes varies greatly across countries and thus so will the
*3**<br>**Poverty estimation using the HNAP 2022 Demographic and WASH Household Survey**................. 13|
|**3.1**<br>**Improvements to the survey’s expenditure module**................................................................ 14|
|**3.2**<br>**Monetary poverty estimates based on the HNAP 2022 Demographic and WASH survey**...... 15|
|**4**<br>**Nowcasting monetary poverty in Syria: Challenges and sensitivity analysis**................................... 19|
|**4.1**<br>**Data challenges**.......................................................................................................................... 19|
|4.1.1<br>Measuring “growth” .....
# **5. Changes in Debt Attributes during Crises**

Next, we connect the two main findings documented above (that firms with demand for credit switched debt markets during crises and that debt characteristics systematically vary across markets) to analyze the effects of those switches on debt issuances. We study first whether the switches across markets offset (in terms of amount raised) the decline in financing in certain markets. Then, we study how debt maturity and currency denomination changed during crises within and across debt markets (total debt). We report the findings first for the GFC, separately for advanced and emerging economies, and then for domestic banking crises.

To study the issuance activity around the GFC, we estimate regressions similar to the logit analysis used earlier. We focus on the five-year window period around the GFC: the pre-crisis period (2005-2007) and the crisis period (2008-2009). For the analysis of domestic banking crises, we consider the complete 1991-2014 period and follow the definition of crisis and non-crisis years described in the previous sections. We run separate regressions for each debt market individually and for total debt by restricting our data to: (i) domestic bonds, (ii) cross-border bonds, (iii) total bonds (domestic + crossborder bonds), (iv) domestic syndicated loans, (v) cross-border syndicated loans, (vi) total syndicated loans (domestic + cross-border loans), (vii) total domestic debt (domestic bonds + domestic loans), (viii) total cross-border debt (cross-border bonds + cross-border loans), and (ix) total debt (which aggregates all types of debt). The estimations for total debt capture changes both within and across markets.

We estimate the following regression specification for each debt market defined above:<sup>19</sup>

> 19 The methodology is similar to that used by Khwaja and Mian (2008), who study the transmission of bank liquidity shocks to the real economy using the amount of bank credit.

23
|74.54***|12.77***|76.11***|11.76***|1.786***|
||(0.911)|(1.380)|(1.060)|(0.687)|(0.493)|(0.179)|
|Observations|7,508|2,757|2,757|7,499|7,499|1,390|
|R-squared|0.023|0.021|0.021|0.038|0.045|0.004|
|Sector fixed effects<br>Standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1|Yes|Yes|Yes|Yes|Yes|Yes|

_Source:_ Calculations on World Bank Enterprise Surveys (2018).

_Note:_ The regression equation is specified as follows: x� �α�p�β�FCS �p�γ�δ� �ε� where x� indicates the figures in columns reported by firm _i_ ; p� is a binary indicator for pioneering; FCS is a binary indicator for operating in a fragile state, and δ� are sector fixed effects. Coefficient β reported under ‘Pioneers’ captures the average difference between pioneers in non-FCS and others i.e. non-pioneers operating anywhere, while controlling for the sector of economic activity. Coefficient γ reported under ‘Pioneers in FCS’ captures what is distinctive about pioneers in FCS, relative to pioneers in non-FCS i.e. the effect of fragility on pioneering.

14