rafmacalaba/datause-encoder-data huggingface dataset:
|,<br>_f t_<br>_UERAT_|unemployment rate for factor_f_|
|---|---|
|_t_<br>_WALRAS_|variable check on Walras’ law (which is satisfied if value is zero)|
|,<br>_f t_<br>_WF_|economywide wage of factor_f_|
|,<br>_f t_<br>_WFAVG_|average wage for factor_f_(in_FCAPNG_)|
|, ,<br>_f a t_<br>_WFDIST_|wage distortion factor for factor_f_in activity_a_|
|,_f t_<br>_YF_|income of factor_f_|
|_t_<br>_YG_|government current revenue|
|,_i t_<br>_YI_|income of (domestic non‐government) institution_i_(in_INSDNG_)|
|,<br>,<br>_i f t_<br>_YIF_|income of institution_i_(in_INSD_) from factor_f_|

**Table A.1.4. Latin letter parameters**

|**Name**|**Description**|
|---|---|
|,_c f_<br>_capcomp_<br>|quantity of commodity_c_per unit of new capital stock_f_|
|,<br>_c h_<br>_cwts_|weight of commodity_c_in consumption basked of household_h_|
|,<br>_f t_<br>_depr_|rate of depreciation for capital stock_f_|
|_t_<br>_drf_|change in foreign reserves (FCU)|
|_c_<br>_dwts_|weight of commodity_c_in the DPI (PDS‐based producer price index)|
|_a_<br>_c_<br>_ica_ ,|quantity of intermediate input_c_per unit of activity_a_|
|'<br>,_c_<br>_c_<br>_icd_|input of_c_for trade and transportation per unit of commodity_c_’<br>produced and sold domestically|
|, '<br>_c c_<br>_ice_|transactions input of_c_per unit of commodity_c_’ export|
|, '<br>_c c_<br>_icm_|transactions input of_c_per unit of commodity_c_’ imports|
|, ,<br>_f i t_<br>_invshr_|share for capital stock_f_in investment spending of institution_i_(in<br>_INSNG_)|
|,_i t_<br>_mpsb_|baseline marginal propensity to save for domestic non‐gov’t institution<br>_i_ (in_INSDNG_)|
|_t_<br>_ndfg_|net domestic financing to government (indexed to numéraire) (FCU)|
|,_i t_<br>_nff_|net foreign financing to institution_i_(in_INSD_) (FCU)|
|,<br>_ac t_<br>_pop_|population of_ac_(household_h_in_H_or country total)|
|,<br>_c t_<br>_pwe_|export price for commodity_c_(in foreign currency)|
|,<br>_c t_<br>_pwm_|import price for commodity_c_(in foreign currency)|
|, ,<br>_c i t_<br>_qdstk_|change in stock (inventories) of_c_for institution_i_(in_INSD_)|
|,<br>,<br>_i f t_<br>_qfinsb_|endowment for institution_i_(in_INSD_) of factor_f_(in_FOTH_)|
|,_c t_<br>_qgb_|baseline quantity of government consumption of commodity_c_|
|,_c t_<br>_qg01_|0‐1 parameter turning on‐off potential scaling of gov consumption of_c_|
|_c_<br>_qinvb_|base‐year quantity of investment (GFCF) demand for_c_|

‐34‐
|**Table 4: Training Content**<br>**Study**|Separating household and business finances|Accounting|Financial Planning|Product ideas|Marketing|Pricing and Costing|Inventory Management|Customer Service|Business Investment & Growth Strategies|Employee Management|Savings|Debt|Using Banks|Quality|Kaizen/5S/Lean|Aspirations/Self-esteem|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Berge et al. (2012)|X|X|X|X|X|X|X|X||X|X|X|X|X||X|
|Bruhn and Zia (2012)|X|X|X||X||||X||||||||
|Calderon et al. (2012)||X||X|X|X||X|X||||||||
|De Mel et al. (2012)|X|X|X|X|X|X|X|X|||||||||
|Drexler et al. (2012)|||||||||||||||||
|"Standard"|X|X|X||||X||||X|X|||||
|"Rule-of-thumb"|X|X|||||||||X|X|||||
|Field et al. (2010)|X|X|X|||||||||||||X|
|Giné and Mansuri (2011)|X|X||X||X|||||||||||
|Glaub et al. (2012)||||||||||||||||X|
|Karlan and Valdivia (2011)|X|X|X|X|X|X|||||||||||
|Klinger and Schündeln (2011)||X|X||X|X|X|X|||||||X|X|
|Mano et al. (2012)|X|X|||X|X|X||||||X|X|X||
|Premand et al. (2012)||X||X|X|X||||||||||X|
|Sonobe et al. (2011)|||||||||||||||||
|Tanzania||X|||X|X|X||||||X|X|X||
|Valdivia(2012)<br>Notes: based on training descr|iptio|X<br>ns pr|ovided|X<br>in r|X<br>esear|X<br>ch stu|dies|X||||||X||X|

30
The conceptual framework developed by Wells and Wint, 1990 and revised in 2001, suggests that an IPA's effectiveness is influenced by its institutional structure and reporting mechanisms. Although we tested several internal characteristics using both dummy variables and interactive **tenns** associated with the IPA budget variable, only three of them seem to have significant effects on the effectiveness of IPAs across countries (Table 3)17. First, FDI flows are significantly lower in countries where the IPA is part of a ministry in contrast to be an autonomous body or a joint private-public institution. Second, the effectiveness of IPAs is enhanced when the agency reports to a supervisory board that includes representatives of the private sector --- the higher the number of private members, the greater IPA effectiveness<sup>18</sup> . Finally, IPA effectiveness<sup>is</sup> enhanced when it reports directly to a country's president or a prime minister.

Table 5: The influence of IPA characteristics on FDI Inflows (T-statistics in parenthesis)

|DependantVariable/ExplanatoryVariable|FDI|FDI|FDI|
|---|---|---|---|
|Publicagency*dummy|-.79<br>(-2.43)|||
|NumberofprivaterepresentativesintheBoard *Budgetc/||.052<br>(2.72)||
|PrimeMinister/PresidentDummy|||.53<br>(1.80)|
|IPABudget|.21<br>(1.95)|.22<br>(2.01)|.25<br>(2.29)|
|Observations<br>|56|36|55|
|AdjR<sup>2</sup>|0.71|0.72|0.68|

Notes: a/We omit to report the results associated with the constant term, GNI per capita, the Heritage Index that wei e also included in the above regressions.

b/All variables are in logs, except for Heritage Index and the dummy variables.

c/ The number of representatives starts from 1 to the maximum registered in our survey.

17 The methodology was twofold. First, we used dummy variables and, second, an interactive term multiplied to the IPA budget. The first approach captures the possible effect of the EPA characteristics on the FDI flows, independently of the promotion effort. The second approach assumes a linear relationship between the EPA budget and the EPA characteristics. These two approaches have been detailed **in** the precedent technical appendix.

**18** An agency with **I** private representative would report an elasticity coefficient equals to 0.22, while an agency with 8 private sector representatives in its Board will see its effectiveness increase to about 0.32.

17
**Table S1. Test of dominance between concentration curves**

||Data 1<br>|Data 2<br>|R|ule<br>|
|---|---|---|---|---|
||Survey<br>Year|Survey<br>Year|mca|iup|
|Stunting|2000|2014|non-dominance|non-dominance|
|Wasting|2000|2014|2014 dominates 2000|non-dominance|
|Underweight|2000|2014|2014 dominates 2000|non-dominance|
|Measles Vaccination|2000|2011|2014 dominates 2000|curves cross|
|Full Immunization|2000|2011|non-dominance|curves cross|
|Contraception|2000|2014|2014 dominates 2000|non-dominance|
|ANC4+|2000|2014|2014 dominates 2000|non-dominance|
|SBA|2000|2014|2014 dominates 2000|2014 dominates 2000|

Note: Test of dominance for concentration curves Figures 2a-d based on O’Donnell (2006) and O’Donnell et al. (2008). Significance level is 5% and the number of evenly spaced quintiles points is 19 (from 5% to 95%). The test is not applicable to the mortality concentration curves we developed in this study. However, curves cross in all illustrations of mortality concentration curves.

# **References**

Ásgeirsdóttir, T. L., and D. Ó. Ragnarsdóttir. 2013. “Determinants of relative and absolute concentration indices: evidence from 26 European countries.” _International Journal for Equity in Health_ , _12_ , 53.

Cutler, D.M, A.S. Deaton and A. Lleras-Muney. 2006. “The determinants of mortality.” _J Econ Perspectives,_ 20: 97–120.

<mark>Federal Ministry of Health (FMOH) (2013), Annual Performance Report-2012/13. Addis Ababa, Ethiopia: Federal Ministry of Health.</mark> ICF International Inc. 2013. _Trends in Demographic and Reproductive Health Indicators in Ethiopia_ . Calverton, Maryland, USA: ICF International Inc.

Kakwani N., A. Wagstaff and E. van Doorslaer. 1997. “Socioeconomic inequalities in health: measurement, computation and statistical inference.” _J Econ_ , 77: 87–103.

- Kruk, M. E., M. R. Prescott and S. Galea. 2008.  “Equity of Skilled Birth Attendant Utilization in Developing Countries: Financing and Policy Determinants.” _American Journal of Public Health_ , 98(1):142–147.

39
# **2. Study Area**

Upon consultation with experts from Bangladesh, three areas were selected for analysis: (i) the foreshore of polder 35/1 (ii) land area adjacent to Polder 40/2, and (iii) a location at the southern foreshore of the Polder 40/1 in the Sundarbans and the Central coast of Bangladesh.<sup>8</sup> These study areas were selected considering their vulnerability to storm surge and tidal characteristics, such as mud flat accretion/erosion and availability of adequate mangrove afforestation area in the foreshore of the coastal polders. The study areas are in Bagerhat and Borguna districts where the incidence of poverty is high and natural disasters, especially recurrent cyclones in recent years, have been cited as a major reason behind the poverty.<sup>9</sup> As cyclones can make landfall anywhere and it is not possible to predict a future cyclone track with certainty, our analysis covered severe cyclone SIDR (2007) and seven sites selected in the three aforementioned study areas. Table 1 presents a few key characteristics of the study areas and Figure 2 displays the study sites.

> 8 Sundarbans coast and central coast of Bangladesh were struck by 10 severe cyclones and cyclones during 1960-2016. Records indicate an increase in the frequency of severe cyclones striking the region over time. Cyclone Sidr in 2007, cyclone Rashmi in 2008 and cyclone Aila in 2009 are examples of recent devastating cyclones.

> 9 The poverty maps developed by the Bangladesh Bureau of statistics, World Food Program and the World Bank estimates total poverty population with food expenditures are at or below the food poverty line established by the Bangladesh Bureau of Statistics for Bagerhat and Barguna districts are 619,480 and 168,098 respectively in 2011. These numbers include extremely poor households who do not meet the basic needs of food expenditure World Bank (2014a, b).

6
Figure A3).<sup>10</sup> A combination of these two components has the best predictive ability in the Goldstone et al. study and, moreover, reflects the degree to which the political system allows societal actors to translate their preferences into policy with the help of peaceful mechanisms such as voting and elections. The upshot is that meaningful elections will decrease the appeal of violent means of political contestation.

Further, we use several measures from Fearon and Laitin (2003) to capture the degree of fractionalization of societies - ethnic ( _ef_ ), religious ( _relfrac_ ), and linguistic ( _numlang_ ) – as well as Reynal-Querol’s index of language fractionalization ( _Qlf_ ). Collier and Hoeffler (2004 a) find that ethnic dominance increases the chances of civil war and we employ two measures of ethnic and religious dominance (dummy variables that take the value of 1 if the largest ethnic / religious group is between 45 and 90 percent of the population). For the purposes of creating instruments for the different types of organized political violence we use the partition of social fractionalization along ethnic and language lines, which were the strongest predictors of civil war onset.<sup>11</sup> The results from the multinomial model of organized political violence are shown in Table 1. The results are largely consistent with theoretical expectations: Low income countries are more likely to experience coups and civil wars (insignificant income coefficient for riots). Oil rich countries are more prone to have civil wars; compared with autocracies, full democracy reduces the chance that contestation of political power will involve violent means (negative and significant democracy coefficients for our three outcomes - riots, coups, and civil wars). Of the hybrid, anocratic regimes – partial democracies and partial autocracies - only partial democracies that develop factional politics increases the risk for all three of our violent outcomes, while partial autocracies increase the risk of riots and coups, but not the risk of civil war. The reference category for social fractionalization variables is our cross-cutting category that is countries with moderately high ethnic and language fractionalization. The results show that social fractionalization increases the likelihood of war onset: when compared to moderately divided countries, homogenous societies have a lower risk of civil war onset and extremely divided societies have a higher risk.

> 10 See Polity IV data at: http://www.systemicpeace.org/polity/polity4.htm

> 11 From our typology the following are diverse countries: Angola, Burkina Faso, Cameroon, Central African Republic, Chad, Ethiopia, Gabon, Gambia, Ghana, Guinea-Bissau, Ivory Coast, Indonesia, Kenya, Lebanon, Liberia, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, Sierra-Leone, Sudan, South Africa, Papua New Guinea, Tanzania, Uganda, Togo, Zambia.   Homogenous societies: Albania, Armenia, Denmark, Egypt, Greece, Honduras, Hungary, Ireland, Italy, Jamaica, Japan, South Korea, Libya, Norway, Poland, Portugal, Tunisia.

12
_Railway Reforn In the Central and Eastern European (CEE) EconomMa 5_ Figure 7 <!-- Start of picture text --> Rail ParsKilometers per Capita (000)<br>(1988)<br>cam i - 1.28<br>POLAND 1.28<br>HUNOAf R?<br>AUgrRA 0,97<br>1 8ALY 17 C 2019<br>8WEEN - CQ72<br>W.IANYAAW CX64<br>HMXAND 'C_c 84<br>IaKM -Q_Q6<br>YL4MGAV- A<br>SPAIN - l Q30<br>0 0 .2 0Q4 OSC 1 12 0a 0A 8S<br><!-- End of picture text -->

**Fi8ure 8**

**Figure 9**

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**Auto8/COpep;**
| POLICY RESEARCH WORKING PAPER 2029

# Summary findings

Dasgupta, Lucas, and Wheeler use new data from Brazil and Mexico to analyze relationships linking economic development, the size distribution of manufacturing plants, and exposure to industrial pollution. For lack of data, prior work in this field has been limited largely to water pollution and medium-size plants. This study examines air pollution and encompasses small plants (with 1 to 20 employees) as well as medium-size and large plants.

Four main questions are addressed (with answers from plant-level data): _* Are small plants more pollution-intensive than large facilities?_ Clearly, yes.

_* Are there proportionately more small plants in lowincome regions?_ The answer is yes, in thousands of Brazilian municipalities. Small plants dominate poor regions and are a relatively low source of employment in high-income areas.

* _Is industry more pollution-intensive in low-income regions?_ In Brazil, yes. For each municipality, the authors estimate the share of the six most pollution-intensive ("dirty") sectors in total industrial activity. They find that the dirty-sector share declines continuously with increases in municipality income per capita.

* Do _poor areas suffer more than wealthy areas from industrial air pollution?_ Paradoxically, no. The risk of mortality from industrial air pollution is much higher in the top two income deciles among Brazil's municipalities and the great majority of projected deaths is attributable to emissions from large plants. The scale of large-plant emissions dominates all other factors. Lower-income areas suffer much less from industrial air pollution in Brazil, despite the greater emissions-intensity of smaller plants and the prevalence of smaller plants in lower income areas.

- This paper-a product of Infrastructure and Environment, Development Research Group is part of a larger effort in the group to understand the economics of industrial pollution control in developing countries. The study was funded by the Bank's Research Support Budget under research project "Small Plants, Pollution, and Poverty" (RPO 682-66). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Yasmin D'Souza, room MC2-622, telephone 202-473-1449, fax 202-522-3230, Internet address ydsouza@worldbank.org. The authors may be contacted at sdasgupta@worldbank.org or dwheeler@worldbank.org. December 1998. (21 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
We use the 2001 edition of Amadeus and limit our sample to the years 1998 and 1999.<sup>10</sup> There are two reasons to limit our analysis thus. First, there is the potential problem of survivorship: As companies exit or stop reporting their financial statements, Amadeus puts a "not available/missing" for 4 years following the last included filing. Firms are not removed from the database unless there is no reporting for at least 5 years (i.e. 1997 or earlier). So the data for firms from 1997 as reported in the 2001 database will not include firms that exited in 1997 or before. To avoid this potential survivorship bias, we restrict our attention to 1998 and 1999. A second reason is that efforts were made in 1998 to expand the coverage for Central and Eastern European countries allowing us to include more countries, but making the prior data less comparable.<sup>11</sup> As shown in Table 1, Column 1, we start with a sample in Amadeus of about 3 million annual observations over the years 1998-1999. We then impose a number of restrictions on the data. First, we require reporting firms to have some basic accounting information in their accounts over the years (i.e., data on total assets, sales, profit before tax, or employment). The reason for dropping those that do not report is that there may be country differences in the criteria for including firms with no information on their accounts. In addition, this criterion excludes any “phantom” firms established for tax or other purposes.

Next we delete from our sample firms that report only consolidated statements. For most firms in Amadeus, unconsolidated statements are reported and consolidated statements are provided when available.<sup>12</sup> We use unconsolidated financial statements to avoid double-counting firms and subsidiaries. Using unconsolidated statements has the additional advantage that we can focus on the local operations of firms and not overestimate local employment figures. For example, the consolidated statements of a European multinational include operations abroad and

> 10 Due to lags in data collection, the coverage for the year 2000 is incomplete.

> 11 For example, the coverage of Central and Eastern European firms increased by 16% from 1997 to 1998, but less than 5%, on average, for the following 2 years.

> 12 Although the small number of deleted firms does not creates a significant bias in entry rates based on total number of corporations, this may create some bias in the total employment figures, since consolidated firms tend to be larger.  However, some employment may already be included by other group firms in the sample with unconsolidated statements.

9
service sector, average wage increases are negligible, while high-skill workers gain above 2 percent in the same sector (Figure 6).<sup>18</sup>

**Figure 6:  Changes in Relative Wages from  a Wage Subsidy in Formal Sector**

<!-- Start of picture text --> 4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>Agri-Low skill Agri-Med skill Agri-High skill Indust-Low Indus-Med Indust-High Service-Low Service-Med Service-High<br>skill skill skill skill skill skill<br>-1.0%<br>-2.0%<br>-3.0%<br>-4.0%<br>-5.0%<br>-6.0%<br>Occupational Types<br>Source: Author’s calculations<br>Figure 7: Income Gains from a Wage Subsidy for Workers in Formal Sector<br>12.0%<br>10.0%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>Agri-Low skill Agri-Med skill Agri-High skill Indust-Low Indus-Med Indust-High Service-Low Service-Med Service-High<br>skill skill skill skill skill skill<br>Occupational types<br>Percentage Change in Average Wages<br> Percentage gains from wage subsidy<br><!-- End of picture text --> Source: Author’s calculations Source: Author’s calculations 18 In the CGE model, the average real wage for low- and medium- skilled formal labor type is fixed economy wide, but wage differences exist by activities by activity.

- 21 -
## **2.3.5 Placebo: Simple Reminder**

We assigned 1,000 customers to the _simple reminder_ treatment. Customers in this group received the standard message on the due date and another message two days before the deadline for repayment. This second reminder is similar to the first message sent to all customers on the due date and makes no reference to the moral or financial implications of non-repayment: _The due date of your [name of the card] bill was on [due date] and your payment has not been received yet. Please repay your credit card balance at your earliest convenience. Call [customer service number]._ This treatment tests how receiving a second reminder affects repayment through channels such as limited attention and memory. Comparing its effect to that of moral incentives allows us to distinguish the impact of moral appeals from the effect of receiving additional reminders.

## **2.3.6 Placebo: Religious Message**

Finally, we assigned 1,000 customers to a _religious placebo_ treatment. Customers in this group received the standard message on the due date and a message two days before the repayment deadline, which contained a religious quote from the Prophet Muhammad taken from the _Shahih alBukhari_ . However, in contrast to the moral incentives treatment, this quote was entirely unrelated to financial matters or debt repayment: _The Prophet (Peace and blessings be upon Him) says: “When Allah wishes good for someone, He bestows upon him the understanding of the Book” (Imam al-Bukhari). Please repay your credit card balance at your earliest convenience. Call [customer service number]._ This treatment condition allows us to test whether moral appeals work because they highlight the moral implications of a specific action (non-repayment of debts), or simply because they prime customers on ethical behavior, evoke a religious frame of mind, or remind them of the religious nature of their contract and bank.

# **2.4 Data and Summary Statistics**

The data set combines information on the repayment decisions of individuals participating in the experiment with administrative data on their accounts at the bank, as well as data from a phone survey administered to participants at the end of the intervention.

10