rafmacalaba/datause-encoder-data huggingface dataset:
# **3 Background and Setting**

## **3.1 Macroeconomic Context**

We conduct a natural field experiment with credit card customers from a large commercial bank in Malaysia. Malaysia is representative of many small, open economies in that inflation has been stable and low in recent decades, whereas the exchange rate has been volatile at times. Figure 2 presents historical data to illustrate these points. The figure first shows the evolution of the consumer price index over the last four decades. The country experienced a short period of high inflation in the early 1980s. However, since then, inflation has been generally moderate and stable at around 2% to 5% annually. Figure 2 also shows the evolution of the nominal exchange rate with respect to the U.S. Dollar. The exchange rate has been markedly more volatile than the rate of inflation. Indeed, the difference in volatility would be even more pronounced if we looked at weekly rather than yearly data, as inflation is stable over the year, while the exchange rate is characterized by sharp changes at shorter time intervals. Exchange rate volatility was most pronounced during the Asian financial crisis that began in 1997, and during which the Malaysian Ringgit depreciated by more than 50% against the U.S. Dollar. Following this experience, the Malaysian Ringgit was pegged to the U.S. Dollar at a rate of 3.20 MYR/USD between 1998 and 2005. Since the end of the currency peg, the exchange rate has fluctuated between 3.08 and 4.45 MYR/USD. Several large exchange rate fluctuations have occurred in recent years, triggered by external and domestic events, such as oil price shocks and political instability surrounding national elections.

Because of its greater volatility and the relatively high share of imported goods in total consumption, it seems plausible that in our setting, the foreign exchange rate plays a more important role in consumer decisions than the rate of inflation. As our theoretical framework in Section 2 highlights, consumers can partially offset the effect of higher inflation by shifting their consumption towards durable goods. We therefore predict that a consumer who expects higher inflation will increase the relative share of durable goods in credit card spending to insure against inflation risk. However, given that inflation in our setting is typically between 2 and 5 percent, this is unlikely to be very meaningful for the consumer’s overall finances. In contrast, when considering the purchase of tradable goods, such as consumer electronics or a car, the same consumer might be substantially affected by exchange rate fluctuations due to the magnitude in our setting. If the consumer, for example, expects a 20–25% exchange rate depreciation, the timing of such a purchase could amount to substantial savings.

To examine whether, in our context, changes in the exchange rate are in fact more salient to households than changes in the inflation rate, we exploit data on online searches and newspaper articles. Figure 3 plots data from Google Trends that tracks the frequency of online searches. These data have been used in several previous studies to measure public interest in specific 8
desirable and are capable of yielding interesting results as shown by those studies that were more current.

Table 11: Geographical coverage of regression studies

|Authors of<br>study and|Study<br>number|Final<br>year of|Unit of<br>analysis if|Number o|f countries|in each|World Ba|nk region b|y study|
|---|---|---|---|---|---|---|---|---|---|
|<br>date|as in|<br>data set|<br>not countr|Latin|Eastern|South|East|Middle|Sub-|
||<br>tables<br>6 and 7|<br>used|y|<br>America<br>and the<br>Caribbean<br>LAC<br>(30)*|<br>Europe<br>and<br>Central<br>Asia<br>ECA<br>(23)|<br>Asia<br>SA<br>(8)|<br>Asia<br>and<br>the<br>Pacific<br>EAP<br>(23)|<br>East<br>and<br>North<br>Africa<br>MNA<br>(12)|Saharan<br>Africa<br>SSA<br>(42)|
|Andrés,<br>Foster, &<br>Guasch.<br>2006.|1|2006|Distribution<br>utilities|116|<br>0|0|<br>0|<br>0|0|
|Ba &<br>Gasmi.<br>2011|2|2005||25|1|3|5|2|5|
|Balza,<br>Jimenez &<br>Mercado.<br>2013|3|2010||18|0|0|0|0|0|
|Cubbin &<br>Stern 2006.|5|2001||21|0|2|2|0|5|
|Erdogdu<br>2011b.|8|2008||18|18||13|0|11|
|Erdogdu<br>2014b.|10|2010||0|18|0|0|0|0|
|Gassner,<br>Popov &<br>Pushak<br>2009.|12|2004|Distribution<br>utilities|155|56|3|2|2|27|
|Nagayama<br>2010|16|2007||21|27||13|0|1|
|Nepal &<br>Jamasb<br>2012b|17|2008||0|27|0|0|0|0|
|Sen &<br>Jamasb<br>2012|20|2007|States in<br>India|0|0|19|0|0|0|
|Sen, Nepal<br>& Jamasb<br>2016|21|2013||0|0||17|0|0|

46
pensions are treated as deferred income, the scale of inequality-reducing redistribution through the tax and transfer system in Serbia of 0.034 Gini points is lower than in Albania (0.052), Montenegro (0.072), Romania (0.071), and Croatia (0.102).<sup>11</sup> It is also lower than in Armenia, Poland, Belarus, the Russian Federation, Ukraine, and several Latin American countries with similar income level as Serbia. Strict comparability across countries can be difficult due to the use of income versus consumption as the main welfare aggregate to represent disposable income.

As previously mentioned, the main analysis in Serbia treats pensions as deferred income since Serbia has a pay-as-you-go pension system and the entitlement to pensions is linked to payment of contributions during employment. Figure 2 also shows the impact on inequality when pensions are treated as government transfers instead of deferred income. In that scenario, market income inequality in Serbia amounts to 0.482, which is somewhat lower than in Albania (0.49), Croatia (0.51), and Montenegro (0.53). The overall reduction in inequality due to fiscal intervention in Serbia is equivalent to 0.131 Gini points from market income to final income when old-age pensions are treated as transfers but again lower than in most of the countries in Figure 3 (0.24 in Poland, 0.22 in Croatia, 0.17 in Romania, 0.15 in Albania).

**Figure 3. Decline in inequality (Gini coefficient) from market to final income**

<!-- Start of picture text --> 0<br>-0.05<br>-0.1<br>-0.15<br>-0.2<br>-0.25<br>-0.3<br>Pensions as deffered income Pensions as transfers<br><!-- End of picture text --> <mark>Source: CEQ Standard Indicators. https://commitmentoequity.org/datacenter/</mark><sup>11</sup> 11 Commitment to Equity Institute Data Center on Fiscal Redistribution, 2019. Based on information from: ALBANIA (Davalos et al., 2018); ARGENTINA (Rossignolo, 2018); ARMENIA (Younger et al., 2019); BELARUS (Bornukova et all., 2017); BOLIVIA (Paz Arauco et al., 2014); BRAZIL (Higgins, Pereira and Cabrera, 2014); BURKINA FASO (World Bank, 2018); CHILE (Martinez-Aguilar et al., 2018); COLOMBIA (Melendez and Martinez, 2019); COLOMBIA (Melendez and Martinez, 2019); COMOROS (World Bank, 2017); COSTA RICA (Sauma and Trejos, 2014); CROATIA (Inchauste and Rubil, 2017); DOMINICAN REPUBLIC (Aristy-Escuder et al., 2019); ECUADOR (Llerena et al., 2015); EGYPT (Lara Ibarra et. Al., 2019); EL SALVADOR (Beneke, Lustig, and Oliva, 2018); ETHIOPIA (Hill et al., 2017); GEORGIA (Cancho and Bondarenko, 2017); GHANA (Younger et al., 2018); GUATEMALA (Icefi, 2017a); GUINEA (World Bank, 2019); HONDURAS (Icefi, 2017b); INDONESIA (Jellema, Wai-Poi, and Afkar, 2017); IRAN (Enami, Lustig, and Taqdiri, 2017); JORDAN (Alam, Inchauste, and Serajuddin, 2017); KENYA (Pape and Lange, 15
surveys. We therefore report results for two samples, the pooled sampyle that contains 980 observations from 1999/2000 plus 371 observations from 1992 (columns 1 and 2) as well as the "panel" with information on 371 communities in both periods (columns 3 aLnd 4). As indicated in the bottom row, about 44% of communities had at least one household affected by civil strife (column 1) while on average only about 8% of the population was affected by this pbenomerion (columnns 2-4). In addition, we use the fact that we have a panel to estimate a random effect tobit model (column 4). Furthermore, column _5_ gives the rneans of the independent variables used in the regression. As the regressions do not indicate significant changes over time in the coefficients for individual right hand side 'ariables, we report the regression where only regional dummies are interacted with the year dummy.

The results can provide insights as to more genera] patterns and are generally in line with the predictions from our model, even though more in-depth case study may be needed to adapt these to the specific conditions of Uganda. Higher human capital endowments by households are estimated to significantly reduce the likelihood of civil strife. The quadratic specification suggests that higher levels of education decrease individuals' propensity to engage in civil strife at a declining rate up to an absolute minimum is between 8.1 and 5.9 years of schooling per household, dLepending on the specification. With a mean attainment of less than 5 years, there appears scope for reducing the potential for violence by further increasing aggregate levels of human capital. At the same tfime, the irapact is estimated to be greatest in communities where current endowments with human capital are very low. For example, increasing the level of education by a community in the second decile from 2.5 years to the mean of 4.9 would, according to the probit regression, reduce the risk of civil conflict by than 10.5% while a similar addition of 2.4 years for a community at the mean would reduce the risk of conilict only by 4.8%.16 In addition to human capitaL, endowments with physical capital are also estimated to be important in the tobit regression with panel communities (though not in the pooled tcbit). The presence of coffee at the community level, as a immobile asset the returns of which can be expropriated in the short, is very 20
# **3. Empirical Results**

# **3.1 Data and Sample**

The data we use are from the Annual Survey of Industrial Firms (ASIF) of National Bureau of Statistics of China for 1998 to 2007. ASIF includes _all_ SOEs, and all non-state firms with sales exceeding five million _yuan_ . Since our research is on the decentralization of SOEs, we only keep the SOE sample (with share of state ownership exceeding 50%).<sup>9</sup> We do no classify firm ownership based on a firm’s registered ownership type because some former SOEs do not change their registered ownership type after ownership restructuring (Dollar and Wei, 2007).<sup>10</sup> The oversight government (for SOEs), from more centralized to more decentralized, can be the central, provincial, municipal (or prefecture), county, and township. We delete from our sample SOEs those observations that have missing or “others” values for the oversight variable (10,133 firms). We further restrict our sample in the following ways. First, we delete those SOEs which lie at the bottom of the hierarchy: those firms whose oversight government is at the county level or below (44,905 firms). These SOEs by definition could not be further decentralized. Second, we drop those enterprises without at least three continuous years of data (22,115 firms). Finally, we delete those enterprises whose oversight relationship had changed more than twice (470 firms).<sup>11</sup> Our final sample consists of 14,420

> 9 In other words, we only keep the periods of data for SOEs who were either non-decentralized or just decentralized.

> 10 In the data, every firm is given a unique firm code. A small number of firms may change their firm codes within the sample period but remain in the sample.  To solve this issue, we follow Bai et al. (2009) and Brandt et al. (2011), and obtain firms’ unique codes relying on the firm’s name, address, zip code, telephone number, and legal representative so that those firms with changed codes can be allocated unique codes throughout our sample period.   The data cleaning includes four steps.  First, if the firm code is the same for year t and t+1, we keep the original code.  Second, for firms that appear in year t but not in t+1, we find firms with identical firm names and given the year t+1 observation the original firm code of the firm with the same name in year t.  Third, repeat step 2, but relying on zip codes and the names of legal representatives to identify unique firm. Fourth, repeat step 2, but now rely instead on zip codes and telephone numbers.

> 11 Retaining them does not change out key results.

13
Kempel, S and Aung Thun. “Ward and Village Tract Administrator Elections 2016: An Overview of the Role, the Laws and the Procedures”, January 2016. <http://www.themimu.info/sites/themimu.info/files/documents/Report_Ward_Village_Tract_Ad <u>ministrator_Elections_2016_NPA_Jan2016.pdf> (accessed 22 November 2016).</u> Kurosaki, Takashi. “Crop Choice, Farm Income, and Political Control in Myanmar”. _Journal of the Asia Pacific Economy_ , Vol. 13, No.2, March 2008.

Malena, Carmen, Reiner Forster, and Janmejay Singh. “Social Accountability: An Introduction to the Concept and Emerging Practice”. World Bank Social Development Papers. No. 76, Dec 2004.

Mod, Craig. “The Facebook-Loving Farmers of Myanmar: A dispatch from an Internet revolution in progress”. _The Atlantic,_ 21 January 2016.

Dr Zaw Oo, Dr. Aniruddha Bonnerjee, Phoo Pwint Phyu, Giles Dickenson-Jones and Paul Minoletti. “Making Public Sector Finance Work for Children in Myanmar” UNICEF and MDRI-CESD, 2014.

Nash, Manning. _The Golden Road to Modernity: Village Life in Contemporary Burma._ New York: John Wiley and Sons, 1965.

Nixon, Hamish and Cindy Joelene. “Fiscal Decentralization in Myanmar: Towards a Roadmap for Reform”. Subnational Governance in Myanmar Discussion Paper Series. No. 5, June 2014.

Paung Ku. “Reflections on the Giri Response”. November 2010.

<http://www.scribd.com/doc/67205436/Paung-ku-reflections-on-giri>. (accessed 22 November 2016).

Pederson, Morten B. “Myanmar’s Democratic Opening: The Process and Prospect of Reform”. In _Debating Democratization in Myanmar,_ edited by Nick Cheesman and Nicholas Farrelly. Singapore: The Institute of Southeast Asian Studies, 2014.

QSEM. “Livelihoods and Social Change in Rural Myanmar”. QSEM Series. No.1. World Bank, October, 2012.

________ “Livelihoods and Social Change in Rural Myanmar”. QSEM Series. No.2. World Bank, June, 2013.

________ “Livelihoods and Social Change in Rural Myanmar”. QSEM Series. No.3. World Bank, December, 2013.

________ “Livelihoods and Social Change in Rural Myanmar”. QSEM Series. No.4. World Bank, December, 2014.

________ “Livelihoods and Social Change in Rural Myanmar”. QSEM Series. No.5. World Bank, January, 2016.

________ “Livelihoods and Social Change in Rural Myanmar”. QSEM Series. No.6. World Bank, forthcoming.

Thawnghmung, Ardeth. “Rural Perceptions of State Legitimacy in Burma/Myanmar”. _Journal of Peasant Studies_ 30(2): 1-40, 2003.

19
Table A.8: Neonatal EFM as a function of ultrasound availability and firstborn sex

|**Dep var: Death within 1 month of birth**|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_Firstborn girl_ * _Female_|1.299***|1.247***|1.190***|0.985*|
||(0.393)|(0.389)|(0.386)|(0.492)|
|_Firstborn girl_ * _Female_ * _Post1_|-0.620|-0.573|-0.564|-0.266|
||(0.458)|(0.460)|(0.457)|(0.553)|
|_Firstborn girl_ * _Female_ * _Post2_|-0.600|-0.591|-0.570|-0.448|
||(0.403)|(0.398)|(0.385)|(0.442)|
|N||328,0|72||
|_Female_ * _Post1_ and _Female_ * _Post2_|x|x|||
|_Firstborn Girl_ * _Post1_ and _Firstborn Girl_ * _Post2_|x|x|||
|_Xijt_||x|x|x|
|_Firstborn Girl_ x Birth year FE|||x|x|
|_Female_ x Birth year FE|||x|x|
|_Female_ x State FE|||x|x|
|_Female_ x Birth order FE|||x|x|
|Birth order x Birth year FE|||x|x|
|Birth order x State FE|||x|x|
|State x Birth year FE|||x|x|
|_Firstborn girl_ x State FE|||x|x|
|_Firstborn girl_ x Birth order FE|||x|x|
|Mother FE||||x|

NOTES: This table reports estimates of equation (1) in the text for neonatal mortality of second and higher order births. Neonatal mortality is mortality in the first month of life. Each column is a separate regression. The outcome is an indicator for death within one month of birth. We drop children that are less than 1 month old to allow each child in the sample “full exposure” to the risk of neonatal mortality. We always control for _Female_ and fixed effects (FE) for birth year and birth order and for _Firstborn girl_ and state FE in columns (1)-(3). The vector _Xijt_ comprises mother’s age at birth and, except in column (4), household wealth quintiles, caste, religion, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

55
Table A3 Regional Inequality in the Distribution of Welfare, 2001

||Gini|Atkinson-1|Atkinson-2|Mean LogDeviation|Theil|
|---|---|---|---|---|---|
|Douala|42.46|26.16|39.33|30.33|41.17|
|Yaoundé|42.59|26.00|39.88|30.11|37.79|
|Adamaoua|33.82|16.87|28.81|18.48|20.14|
|Center|34.62|18.55|34.61|20.52|22.06|
|East|34.21|17.66|31.33|19.43|20.26|
|Far North|32.97|16.05|27.77|17.49|18.69|
|Coast|34.19|17.62|31.36|19.39|20.24|
|North|36.16|19.23|31.11|21.36|25.62|
|North West|40.55|24.40|41.68|27.98|29.96|
|West|31.21|14.69|25.49|15.89|17.61|
|South|29.76|13.27|23.19|14.24|15.45|
|SouthWest|38.02|21.41|35.88|24.09|26.81|
|Cameroon|40.41|24.00|38.82|27.45|33.75|

Source: Authors’ Calculations TableA4 Regional Inequality in the Distribution of Welfare, 2007

||Gini|Atkinson-1|Atkinson-2|Mean LogDeviation|Theil|
|---|---|---|---|---|---|
|Douala|33.87|17.07|28.37|18.72|21.72|
|Yaoundé|33.15|16.60|28.02|18.15|21.07|
|Adamaoua|33.75|16.70|27.25|18.27|21.20|
|Center|28.07|11.91|20.72|12.68|14.13|
|East|32.88|15.79|26.63|17.19|18.99|
|Far North|36.52|19.14|30.28|21.24|25.07|
|Coast|31.86|15.33|25.71|16.64|19.26|
|North|35.33|18.22|28.57|20.12|24.65|
|North West|38.24|20.98|33.32|23.54|27.66|
|West|29.73|13.39|23.41|14.37|15.80|
|South|34.58|18.02|29.79|19.87|23.61|
|SouthWest|33.24|16.54|28.67|18.08|19.69|
|Cameroon|38.96|21.94|35.82|24.77|27.88|

Source: Authors’ Calculations Table A5 Shapley Decomposition of Urban- Rural Differences in 1996

|||Difference|Scale|Inequality|
|---|---|---|---|---|
||Headcount|-11.87|-16.12|4.26|
||Poverty Gap|-4.43|-8.51|4.09|
|Urban|Squared Poverty Gap|-2.09|-4.96|2.87|
||Watts|-6.12|-13.24|7.12|
||Headcount|6.36|13.12|-6.76|
||Poverty Gap|2.37|6.91|-4.53|
|Rural|Squared Poverty Gap|1.12|4.10|-2.98|
||Watts|3.28|10.80|-7.52|

Source: Authors’ Calculations 38
as shown in Figure C1(c) and the share of BTF in total transport expenditure also fell as seen in Figure C1(d).

In Table A2, we present the average treatment effects of the scheme on household expenditures, as estimated using equation (7). Across columns 1 to 5, it is evident that households in and Tamil Nadu less on com- Punjab spend considerably transportation pared to their counterparts in neighboring states during the post-treatment periods relative to the pre-period. Specifically, following the scheme implementation, expenditure on BTF as a proportion of total transport expenses for treated households decreased by an average of 6.6 percentage points compared to the control group (column 5).

A concern might be that differences in COVID regulation stringency measures across treatment and control groups could drive changes in household expenditures. Although we have controlled for COVID-19 cases in our main specification, as a robustness check, we control for the share of the state had either recom- additionally days government mended or required closing public transport in a month and the share of days the state government had either recommended or required individuals not to leave the house in a month. We present the results in Appendix Table D6. Estimated coefficients remain robust to adding these controls with magnitudes and standard errors similar to those in the baseline Table A2. Finally, the significance levels of the baseline estimates remain robust when the standard errors are clustered at the district level (see Appendix Table D7).

# **5.2 Evidence from the Delhi Primary Survey**

We next utilize the sample from the Delhi Primary survey data to examine changes in monthly transportation expenditure, including bus expenses, at the _individual_ level.<sup>10</sup> Among non-users, 64% spent |1 _∼_ 1,000 per month on transportation and the remaining spent more than |1,000. Of users, both new and continuous, 55% did not spend any money on transportation, including buses, per month in the period after the scheme’s launch. The remaining 45% spent between |1 and |1,000 on transportation. Specifically, among new bus users (see Figure C2), 82% spent more than |1,000 per month on transportation before the scheme, but this percentage decreased dramatically to 1% after the scheme was implemented. This 81% difference is also statistically significant at a 1% level. Furthermore, while none of the new bus users reported any spending on transportation before the scheme (the |0 category in Figure C2), the proportion increased to

> 10The survey inquires about the monthly travel expenditures of all respondents after the launch of the _Pink Slip Program_ in Delhi. However, it only asks new bus users about their monthly travel expenditures before the program’s launch, that is, only for the new bus users the survey records expenditures before and after the scheme.

10
_26_ **_Polih F;sal Crisis_**

# Table A. 1 (continued)

||Quantity|Price|Value|
|---|---|---|---|
|Accounting Qption 2:||||
|Materials consumed in production|1|20|20|
|HCA profit=sales - cost of materials used in|||40-20|
|producing the units sold|||= 20|
|Stock of materials on 31.12|1|10|10|
||1|30|3|
|Total:|2||40|
|Inventory accumulation, 1.1-31.12|||40-30|
||||= 10|
|Accounting identity: cash flow = HCA profit-|||20-10|
|inventoryaccumulation|||= 10|
|Accounting Option 3#||||
|Materials consumed in production|3|30|30|
|HCAprofit= sales - cost ofmateriVls<br>used in|||40-30<br>|
|producing the units sold|||=10|
|Stock of materials on 31.12||||
||1|10|10|
||1|20|29|
|Total:|2||30|
|Inventory accumulation, 1.1-31.12|||40-30<br>= 0|
|Accounting identity: cash flow=HCA profit-|||10-0|
|inventory accumulation|||-<br>10|
**<u>Table 1. Sample means and standard deviations of outcome variables in treated and control households</u>**

|||**2003/**<br>|**2004**<br>|||**2005/2**<br>|**006**<br>||
|---|---|---|---|---|---|---|---|---|
|**Variable**|**Co**|**ntrol **|**Tre**|**ated**|**Con**|**trol **|**Tr**|**eated**|
|Consumption p.c. (Rs/year)|7920|(4386)|7226|(3820)|8748|(4247)|8582|(4140)|
|Food (Rs/year)|3886|(1768)|3695|(1612)|4298|(1850)|4146|(1796)|
|Non-food (Rs/year)|4034|(3164)|3531|(2794)|4450|(3196)|4436|(3031)|
|Energy intake p.c. (Kcal/day)|2111|(712)|2144|(691)|2343|(979)|2322|(887)|
|Protein intake p.c. (g/day)|49|(18)|51|(18)|49|(19)|50|(18)|
|Total non-financial assetsp.c.(Rs)|3133|(3781)|3506|(4248)|3605|(4671)|4438|(6086)|
|Number of observations|5|26|18|80|5|26|1|880|

_Source:_ Own computation from 2004 and 2006 SERP/CESS impact evaluation surveys. Sample weights applied.

17