rafmacalaba/datause-encoder-data huggingface dataset:
Rosenzweig, M. And K. Wolpin. 1982. "Govermnent Interventions and Household Behavior in a Developing Country: Anticipating the Unanticipated Consequences of Social Programs." Joumal of Development Economics 10:209-225.

Rosenzweig, M. And K. Wolpin. 1986. "Evaluating the Effects of Optimally Distributed Public Programs: Child Health and Family Planning Interventions." American Economic Review 7(3):470-482.

Rutstein, Shea Oscar, 1984. 'Infant and Child Mortality: Levels, Trends, and Demographic Differentials," World Fertility Surveys Comparative Studies No. 43. Voorburg, Netherlands: International Statistical Institute.

Sastry, Narayan. 1995. "Community Characteristics, Individual and Household Attributes, and Child Survival in Brazil," Rand Labor and Population Programn, Working Paper Series 95-13.

Stansfield, Sally K., and Donald S. Shepard, 1993. "Acute Respiratory Infection,' in Disease Control Priorities in Developing Countries, Jamison et al eds.

Strauss, John. 1990. "Households, Communities, and Preschool Children's Nutritional Outcomes: Evidence from Rural Cote d'Ivoire." Economic Development and Cultural Change 38:231-261.

Strauss, John, and Duncan Thomas, 1995. 'Human Resources: Empirical Modeling of Household and Family Decisions," in J. Behrman and T.N. Srinivasan,, eds., Handbook of Develoment Economics, Vol 3.: Amsterdam: North Holland Press: 1883-2023.

Subbarao, K.Y. and Laura Raney, 1995. "Social Gains from Female Education: a Cross-National Study," Economic Developnmet and Cultural Change, 44:105-128.

Taylor, Carl E., and R.D. Singh, N.D. The Narangwal Poaulation SXudv Integrated Health and Family PlanIng Seriges, Narangwal, India: Rural Health Research Center.

Tendler, Judith, and Sara Freedheim, 1994. "Trust in a Rent Seeking World: Health and Government Transformed in Northeastern Brazil," World Deye1men, 22(2): 1771-1791.

Thomas, Duncan, and John Strauss. 1992. "Prices, Infrastructure, Household Characteristics, and Child Height." Journal of Development Economics 39:301-331.

Thomas, Duncan, and John Strauss, and M.H. Henriques. 1990. "Child Survival, height for age, and Household Characteristics in Brazil." Journal of Development Economics 33:197-234.

Thomas, Duncan, Victor Lavy, and John Strauss. 1996. "Public Policy and Anthropometric Outcomes in Cote d'Ivoire." Joumal of Public Economics 61:155-192.

Vicusi, Kip, 1993. "The Value of Risks to Life and Death," Joumal of Economic Literature, 31(4):1912-46.

Walsh, Judith a., Chris N. Feifer, Anthony R. Measham, and Paul J. Gertler, 1993. "Maternal and Perinatal Health," in Disease Control Priorities in Developing Countes, Jamison et al eds.

World Bank, 1993. rld )develoment Report 1993: Investing in HUM. New York: Oxford University Press.

55
|Table1:Descriptive statistics||Ob|servations fro|m all, maj|or, and mino|r refugee-hos|ting round|-EAs||Mean|
|---|---|---|---|---|---|---|---|---|---|---|
|||All|||Major host|s||Minor host|s|Difference|
||N|Mean|SD|N|Mean|SD|N|Mean|SD|Major-Minor|
|**Panel (a): Outcome variables**|||||||||||
|Total consumption expenditure|13391|6255.255|6210.662|5331|6195.617|6546.654|8060|6294.700|5978.154|(-)|
|Food consumption expenditure|13391|4645.919|5019.261|5331|4522.487|5449.857|8060|4727.559|4711.468|(-)**|
|Education expenditure|13391|143.623|745.448|5331|193.004|825.692|8060|110.961|685.347|(+)***|
|<br>Other non-food expenditure|13391|1465.713|2391.347|5331|1480.126|1954.538|8060|1456.181|2640.931|<br>(+)|
|Consumption poverty (=1)|13391|0.347|0.476|5331|0.351|0.477|8060|0.344|0.475|(+)|
|Wealth|13925|0.008|2.881|5590|0.693|3.244|8335|-0.451|2.507|(+)***|
|Wealth poverty (=1)|13925|0.393|0.489|5590|0.301|0.459|8335|0.456|0.498|(-)***|
|<br>Employed (=1)|33691|0.194|0.395|14845|0.195|0.396|18846|0.193|0.394|<br>(+)|
|<br>Permanently employed (=1)|33858|0.105|0.306|14931|0.119|0.324|18927|0.093|0.291|<br>(+)***|
|Hours worked, permanent|33853|171.321|597.256|14931|204.571|653.410|18922|145.084|547.499|(+)***|
|Temporarily employed (=1)|33738|0.096|0.294|14869|0.082|0.275|18869|0.106|0.308|(-)***|
|<br>Days worked, temporary|33725|5.052|24.946|14866|4.811|25.415|18859|5.243|24.569|<br>(-)|
|Owns an NFE (=1)|13889|0.337|0.473|5588|0.369|0.483|8301|0.315|0.465|(+)***|
|Number of NFEs owned|13889|0.406|0.699|5588|0.488|0.769|8301|0.350|0.641|(+)***|
|Plans to open a new NFE (=1)|13811|0.199|0.399|5581|0.208|0.406|8230|0.192|0.394|(+)**|
|<br>Worked for other HHs for free (=1)|33741|0.232|0.422|14861|0.207|0.405|18880|0.251|0.434|<br>(-)***|
|# of other HHs worked for|33740|1.015|2.865|14861|0.859|2.584|18879|1.137|3.062|(-)***|
|A fall in the prices of food items (=1)|13924|0.029|0.168|5590|0.038|0.191|8334|0.023|0.150|(+)***|
|A rise in the prices of food items (=1)|13923|0.214|0.410|5590|0.248|0.432|8333|0.191|0.393|(+)***|
|<br>A rise in the prices of agri. inputs (=1)|13924|0.094|0.292|5590|0.105|0.307|8334|0.087|0.282|<br>(+)***|
|<br>**Panel (b): Measures of refugee intensit**|**y**||||||||||
|Refugee intensity|1198|954.556|1639.084|488|1626.140|2404.409|710|492.960|202.379|(+)***|
|<br>Refugee intensity (future)|1198|1796.334|2890.862|488|2622.614|4267.171|710|1228.413|899.452|<br>(+)***|
|<br>**Panel**(**c): A measure of receptivity**|||||||||||
|Receptivity|1198|621.251|325.743|488|911.738|242.303|710|421.592|202.027|(+)***|

38
Page 27

# **Table IV.2: Key Infrastructure built by AUDA between 1999 and 2010**

|**Infrastructure**|**Proposed Length/**<br>**Coverage**|**Expenditure**<br>**Rs (Cr)**|
|---|---|---|
|Roads|1472 km|825|
|Water Supply Network|144 sq km|80|
|Water Treatment Plant<br>(Not Included in D.P)|275 MLD capacity at<br>Jaspur|106|
|Sewerage Network|144 sq km|83|
|Sewerage Treatment Plant<br>(not included In D.P)|At Vasna|106.9|
|Sewerage Treatment Plant<br>and terminal station|At Vinzol|36.8|
|Storm Water Disposal|144 sq km|101|

Source: Urban Planning Cell, Ahmedabad Urban Development Authority, Ahmedabad

# T **able IV: 3 Land Appropriated in all TP Schemes in Ahmedabad, 2010** .

|No.|Land Allotted to<br>Appropriate<br>|AMC<br>||AUDA<br>||
|---|---|---|---|---|---|
||Authority<br>|Total 101 Nos. of T<br>sqkm|.P.S for 148.7|Total 103 No<br>sqkm|s. of T.P.S for 153.8|
|||Total Area in sq<br>km.|%Age<br>(AMC)|Total Area<br>in sq.km.|% Age (AUDA)|
|1|Roads|17.78|11.69|21.79|14.2|
|2|Schools|0.85|0.57|1.01|0.7|
|3|E.W.S.H|2.89|1.94|4.53|2.9|
|4|Salable Plots|3.64|2.45|12.06|7.8|
|5|N.C./ P.U. / Others|5.54|3.72|4.69|3.1|
|6|Gardens, O.S., PG|2.57|1.73|4.45|2.9|
|7|Total Land to A.A.|33.27|22.38|48.53|31.6|
|8|Private F.P. Area|115.4|77.6|105.3|68.4|

Source: Drawing Branch, Ahmedabad Municipal Corporation, Ahmedabad Drawing Branch, Ahmedabad Urban Development Authority, Ahmedabad EWSH-Economically Weaker Section Housing, NC-Neighborhood Center, PUPublic Utilities, OS-Open Space, PG Playground, AA-Appropriate Authority, ie, Municipal Corporation, Development Authority or other agency designated by the Town Planning Act.
**Table 1. Trends of the Decentralized Reforms in the 1980s**

||_80_|_81_|_82_|_83_|_84_|_85_|_86_|_87_|_88_|_89_|
|---|---|---|---|---|---|---|---|---|---|---|
|Marginal retention rate|.11|.12|.11|.14|.17|.17|.19|.23|.26|.27|
|Autonomy of production decisions|.07|.08|.10|.14|.25|.35|.40|.53|.64|.67|
|Share of output under mandatory<br>plan of the govermnent||Not av|ailable||.64|.62|.60|.58|.57|.57|
|Share of finrs with wage discretion|.01|.01|.01|.02|_.05_|.09|.12|.20|.32|.35|
|Share of fims under Contract<br>Responsibility System|.00|.00|.00|.01|.02|.04|.08|.42|.83|.88|
|Ex ante firm-level pay sensitivity<br>for firms under CRS|To|o few ob|servatio|ns|.51|.39|.41|.46|.43|.42|
|Share offirms with management<br>turnover<br>I|.09<br>|.01|.06|.10|.16|.14|.07|.15|.10|.09|

Note. All numbers come from the author's computation based on _A Survey of State Enterprises: 1980-89._ share of output under the government's mandatory plan, 8which was reduced from 64% in 1984 to 57% in 1989 for the firms in our sample.

3. More discretion for managers of state enterprises to determine employees' wages. Traditionally the government set an employee's wage as an almost deterministic function of his or her age, education, location, tenure and gender, leaving managers with little leverage to induce employee effort. This was reinforced by the employment guarantee.<sup>9</sup> To give more discretionary power to managers of state enterprises, the Chinese government granted some firrns _managerial wage discretion,'_<sup>_0_</sup> controlling only the growth rate or aggregate amount of wages for the firm. The proportion of firms with the _managerial wage discretion_ mainly increased in the latter 1980s: from 0.5% in 1980, to 5% in 1984, 20% in 1987, and 35% in 1989.

8A state enterprise's output fell into three categories: mandatory plan by the government, which was set up by the government and must be fulfilled, directive plan that was suggested by the government, and own plan which was under the discretions of the manager.

9Part of the reason was that the "fall-back position" for the fired employees would be quite unattractive: it was very difficult for the gurantee to find jobs outside the incumbent firm due to a rigid labor market; in addition, since the social security and welfare function were carried out by the state enterprises rather than the market or the state, the dismissed employees would lose much of their firm-specific benefits such as housing, tenure wage, pension plan if they left the incumbent firms.

10It is called a firm-specific wage scheme in the questionaire.

6
adapt to extreme weather events.

# **Firm attributes and adaptive capacity**

# _Firm size_

Consistent with the Schumpeterian ‘cleansing hypothesis’ (Schumpeter et al., 1939), evidence from the Hurricane Katrina, which hit the United States in 2005, reveals that firms with larger _initial size and productivity_ had a lower impact on their survival and revenue and also recovered more quickly (Basker and Miranda, 2018). Nevertheless, evidence also showed that the advantages offered by external firm attributes such as firm size dissipate over time.<sup>1</sup> Firm size proxies for access to capital markets, better management and technical capabilities and hence heightened productivity and profitability. For instance, a firm’s scale of operations will affect the profitability of adopting various adaptation strategies. Many adaptation strategies are lumpy such that they require bearing a fixed cost. Such lumpy investments feature scale economies so that larger firms will be more likely to adopt these measures Graff Zivin et al. (2018). Firms that do not have access to capital will be less likely to have access to lumpy adaptation strategies. Workers at these firms will face more risk and will demand to be paid higher wages.

# _Manager quality_

Larger and more productive firms have better financial and managerial capabilities Bloom et al. (2010). For instance, European firms with more _intangibles assets_ (e.g., patents and trademarks) experienced higher post-flooding employment growth and lower productivity decline (Leiter et al., 2009). The most vulnerable firms are less likely to engage in adaptation measures perhaps due to lack of _managerial capability_ to explore alternative business opportunities ( _structural adjustments_ ) or rational inattention of managers to productivityenhancing climate change ( _sectoral_ ) adaptations. In Uganda, firms with higher ability managers are more likely to adapt to pollution by protecting their workers through the provision of equipment and flexibility in work schedule, that is via _within sector adaptation_ mechanisms rather than avoiding locating in well-connected polluted areas ( _spatial adjustments_ ) (Bassi et al., 2021).

# _Firm ownership structure_

‘Footloose’ multinationals or foreign-owned plants are more likely to exit the market

> 1The authors studied 12,300 geocoded establishments in Mississippi from the Census Bureau’s Longitudinal Business Database (LBD), including over 1,500 businesses in four counties that experienced significant storm damage as determined by the Federal Emergency Management Administration (FEMA).

5
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month period, and finally no payments at all for the rest of the spell. There is no reason to believe that the behavior of this worker will remain unchanged during the ten months that she is unemployed, so we distinguish between these three different scenarios in the empirical analysis. In order to do this, we transform the original Unemployment Registry data by dividing each unemployment spell in what we called (shorter) pseudo-spells that account for changes in the worker's unemployment insurance status.

73. This methodology is best understood through an example. The first part of the table below contains benefit information for three different individuals, as it appears in the Unemployment Registry. All three individuals become unemployed at the same time (t=l), but their spells differ in length. Moreover, the first individual does not receive unemployment insurance, while the other two do. Finally, individuals 1 and 2 find a job in the private sector, but individual 3 does not.

# **Original data.**

|**ID**|**Entry**|**Exit**|**Received**<br>**UI**|**Duration**<br>**U**I|**Duration**<br>**high**RR'|**Duration**<br>**low**RRtA|**Exit-to-**<br>**emp**|
|---|---|---|---|---|---|---|---|
|**1**|1|12|No|-|-|-|Yes|
|**2**|**1**|14|Yes|6|3|3|Yes|
|**3**|**1**|5|Yes|2|2|0|No|

# **Auxiliary data.**

|**ID**|**Entry**|**Exit**|**Receiving**<br>**UT**|**Receiving**<br>**highRRA**|**Receiving**<br>IOWRRA|**Exhausted**<br>U|**Exit-to-**<br>**emp**|
|---|---|---|---|---|---|---|---|
|**1**|**1**|**12**|No|||No|Yes|
|2|**1**|3|Yes|Yes|No|No|No|
|2|_3_|6|Yes|No|Yes|No|No|
|2|6|14|No|||Yes|Yes|
|3|1|2|Yes|Yes|No|No|No|
|3|2|5|No|||Yes|No|

Replacement ratio.

74. The lower part of the table shows this data in a new format. Individual l's record is identical to the previous one since she does not experience any changes in unemployment insurance status during her time unemployed (i.e. she never receives unemployment insurance). In contrast, individual 2's history is now captured through three pseudo-spells. The first one corresponds to the period when the worker receives high-level unemployment insurance, between months 1 and 3. Similarly, the second and third ones correspond to lowlevel unemployment insurance and no unemployment insurance respectively. Because the individual finds a job only after 14 months, 'No' is recorded in the exit-to-employment column in the first and second pseudo-spells. Finally, changes for the third individual are made following the same criteria.

75. Although, in principle, the social assistance/support system is as complex as the unemployment insurance one, some of its features facilitate the analysis substantially. In particular, the fact the amount of the payments is either fixed (i.e. maternity allowance or child allowance) or designed to bring household income to the MSL level, eliminates much 20
|This table presents several tradin<br>the average percentage of assets<br>by asset class. Column (2) presen<br>average across PFAs of the diff<br>traded portfolio.|<br> <br>TradingStatistics<br>g statistics during the entire sample period (July 1996 to Decem<br>that a PFA trades, as a share of the total amount of assets held in<br>ts the average across PFAs of the lagged weight of the traded po<br>erence in weights (contemporaneous weight using lagged price<br>**Table 8**<br>**Average Percentage of Assets Traded by a PFA**|<br>ber 2005). Column (1) presents<br>its portfolio, over all assets and<br>rtfolio. Column (3) presents the<br>s minus lagged weights) for the|
|---|---|---|
||Average Percentage of Assets<br>Traded Relative to Assets Held<br>Average Lagged Weight of<br>Traded Portfolio|Average Weight Difference of<br>Traded Portfolio|
||(1)<br>(2)|(3)|
|**All Asset Classes**|11.0%<br>21.7%|4.1%|
|**Domestic Assets**|||
|Former Pension System Bonds|5.9%<br>0.2%|0.0%|
|Corporate Bonds|7.2%<br>0.5%|0.1%|
|Financial Institutions|34.6%<br>1.9%|0.4%|
|Government Paper|9.5%<br>2.6%|0.8%|
|Investment and Mutual Funds|6.4%<br>0.1%|0.1%|
|Equity|37.4%<br>9.0%|1.4%|
|Mortgage Bonds|13.5%<br>3.3%|0.4%|
|**Foreign Assets**|||
|Fixed Income|37.2%<br>0.5%|0.2%|
|Investment and Mutual Funds|47.6%<br>4.2%|0.9%|
|Equity|54.2%<br>0.1%|0.0%|
# **Table 1. The correlation between conflict-related indicators from diverse sources is substantial**

||Casualties|Incidents|Terrorism Casualties|Theft/Violence|Insecurity/Violence|Fear for Safety|Bad Security Condition|
|---|---|---|---|---|---|---|---|
||(per 1,000<br>inhabitants)|(per 1,000<br>inhabitants)|(per 1,000 inhabitants)|(% Households victim at<br>the provincial level)|(% Households victim at<br>the provincial level)|(% Households<br>Responding)|(% Households<br>Responding)|
|Casualties||||||||
|(per 1,000 inhabitants)|1|||||||
|Incidents||||||||
|(per 1,000 inhabitants)|0.8377|1||||||
|Terrorism Casualties||||||||
|(per 1,000 inhabitants)|0.5255|0.581|1|||||
|Theft/Violence||||||||
|(Households victim, % of province)<br>Insecurity/Violence|0.1824|0.1474|0.1618|1||||
|(% Households victim at the provincial<br>||||||||
|level)|0.2867|0.1742|0.1627|0.2568|1|||
|Fear for Safety||||||||
|(% Households Responding)|0.4648|0.4733|0.1698|0.0563|0.2278|1||
|Bad Security Condition||||||||
|(% Households Responding)|0.6797|0.6877|0.3327|0.1346|0.3034|0.6027|1|

_Sources:_ The first three indicators are from the provincial database; the subsequent two indicators are from the NRVA survey; and the two last indicators are from the Asia Foundation Survey of the Afghan People. Correlation coefficients are computed for 2011.

10
# **Table 12: Regression Results:  Frequency of Bribe Payments to Public Utilities**

|||Frequency of Bribe P|ayments|
|---|---|---|---|
|Procedures Getting Electricity|-0.281***||-0.176***|
||(0.050)||(0.040)|
|Ln GNI per capita||0.479***|0.439***|
|||(0.039)|(0.040)|
|Constant|6.398***|0.762*|2.066***|
||(0.30)|(0.35)|(0.50)|
|R-squared|0.171|0.523|0.586|
|Observations|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1||
|||Frequency of Bribe P|ayments<br>|
|Time Getting Electricity (Log)|-0.494***||-0.203|
||(0.115)||(0.103)|
|Ln GNI per capita||0.479***|<br>0.456***|
|||(0.039)|(0.043)|
|Constant|7.085***<br>(0.54)|0.762*<br>(0.35)|1.866*<br>(0.72)|
|R-squared|0.096|0.523|0.538|
|Observations|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1||
|||<br>Frequency of Bribe P|ayments|
|Cost as % of GNI pc Getting Electricity (Log)|-0.220**||-0.060*|
||(0.075)||(0.029)|
|Ln GNI per capita||0.479***|0.406***|
|||(0.039)|(0.053)|
|Constant|6.138***|0.762*|1.728**|
||(0.47)|(0.35)|(0.58)|
|R-squared|0.340|0.523|0.536|
|Observations|139|139|139|
|Standard errors in parentheses|*** p<0.01, ** p|<0.05, * p<0.1||
||-0012***|Frequency of Bribe P|ayments<br>-0005***|
|Countr rank on the ease of ettin electricit|.||.|
|y gg y|(0.001)||(0.002)|
|Ln GNI per capita||0.479***|0.365***|
|||(0.039)|(0.055)|
|Constant|5.909***|<br>0.762*|<br>2.209***|
||<br>(0.13)|<br>(0.35)|<br>(0.59)|
|R-squared|0.382|0.523|0.572|
|Observations|139|139|139|
||*** p<001 **|<005 * p<01||
|Standard errors in parentheses|.,|., .||

43
> where a high value of _Wi c_ , represents high use of wholesale funds. This logarithmic transformation also reduces the role of outliers and is valid for banks with no deposits.<sup>7</sup> Alternatively, I also use other measures of a bank’s dependence on wholesale funds, such as the ratio of total deposits to liabilities, total retail deposits to total loans, and money market funds to liabilities to check the robustness of the results.

Balance sheet information for the different measures comes from the latest balance sheet available before June 30, 2007, of all listed banks reporting to Bankscope. Data on stock market returns of listed banks included in Bankscope come from Bloomberg. Stock market returns correspond to the percentage change in closing price between two consecutive trading days. Only banks from countries with an available FTSE banking industry index were considered. This ensures that countries included are actively part of the international financial system, and that the regressions can control for industry wide effects (industry factor).<sup>8</sup> For each country in the sample, the market and banking industry returns are the corresponding FTSE indexes obtained from Datastream. Finally, data on country level characteristics, such as exposure to US ABS, trade linkages, fraction of US external assets, coverage of deposit insurance and financial system transparency are obtained from various sources detailed in the Appendix.

The final sample of banks with data on the main measure of wholesale fund dependence comprises 662 banks in 44 countries excluding the US. It covers all geographical areas, although most countries belong to Western Europe or East Asia and Pacific (14 and 11, respectively), followed by Latin America, East and Central Asia, Middle East and North Africa, South Asia, North America, and Sub-Saharan Africa (6, 6, 3, 2, 1, and 1 countries, respectively). For obvious reasons, most countries are either high or middle-income (24 and 18, respectively), but two low-income countries are also represented (India and Pakistan). The geographic and income distribution of the sample varies slightly at the bank level. East Asia and Pacific overtakes Western Europe, with 234 and 231 banks, respectively; South Asia surpasses the remaining

> 7 Alternatively one could use log(1  _RDEPLIAB_ ), the log of the fraction of non-deposit liabilities. The only inconvenience of this measure is that since deposits are typically a large fraction of liabilities, it is more negatively skewed than the measure defined above. Nonetheless, results obtained with both measures are virtually identical.

> 8 FTSE indexes include only countries that allow investment by foreign nationals and repatriation of funds. This ensures that countries included are part of the global liquidity market. In addition, the construction of the indexes relies on having banks of appropriate size, capitalization, and liquidity. For further details, see FTSE (2006).

14