rafmacalaba/datause-encoder-data huggingface dataset:
pocket the profits (Van der Zwan, 2014). Thus, even though wage stagnation builds the backbone for low inflation rates and thus monetary stability (Hassel, 2006), it leaves lower income individuals at disadvantage, leading to rising inequality.

Second, there exists a strong relationship between credit markets and welfare policy. In fact, in several country cases, redistributive policies have increasingly been outsourced into financial markets (Crouch, 2009; Bohle, 2014; Ahlquist and Ansell, 2017). A prominent example of such a mechanism have been mortgage subsidies that allow households easier access to mortgages. As many of these programs come at a fraction of costs that would arise from new public housing construction (Hoek-Smit, 2008; Doling and Ronald, 2010; Bohle, 2014), they have become popular instruments to garner political support without incurring rising public debt levels.<sup>8</sup> Finally, from a political perspective, shifting gears towards an asset-based welfare system might produce perverse e↵ects and lead to further rounds of welfare state retrenchment. For example, Barth, Finseraas, and Moene (2015) show for a selected number of OECD countries how inequality leads to a shift of party politics towards more welfare cuts. Similarly, Ansell (2014) analyzes survey data from the United Kingdom and the United States and finds that homeowners are less likely to vote in favor redistributive policies as they can ‘self-insure’ against adverse economic shocks such as unemployment. Furthermore, enhancing private contributions into pension funds — which have been commonly used to relieve government-run pension funds from financial pressures — might further strengthen individuals’ leaning towards welfare and regulatory policies (Barth, Finseraas, and Moene, 2015; Alt and Iversen, 2017; Pagliari, Phillips, and Young, forthcoming). Pagliari, Phillips, and Young (forthcoming) show for US survey data that individuals with larger asset positions are more likely to be in favor of looser financial regulations. Taken together, we expect that these reinforcing mechanisms lead to an amplification of the initial e↵ect of CBI on income inequality dynamics.

> 8In many instances, governments use credit guarantee schemes or deploy only a small subsidy on loans. In the case of a guarantee, these do not have to be included at face value of the loan in budgetary balances, e↵ectively masking a government’s real financial exposure (Hoek-Smit, 2008; Bachmair, 2016).

9
Table D10: Ethnicity randomization checks, before 2011, 3

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Masai|Judge plur. Meru|Judge plur. Mijikenda|
|Pla. plur. Masai|0.000167<br>(0.000473)|||
|Def. plur. Masai|0.000227<br>(0.000414)|||
|Pla. plur. Meru||0.00370<br>(0.00939)||
|Def. plur. Meru||-0.0123<br>(0.00906)||
|Pla. plur. Mijikenda|||-0.0129<br>(0.0129)|
|Def. maj. Mijikenda|||0.000787<br>(0.00325)|
|DV mean|0.000966|0.0599|0.0167|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|3106|3106|3106|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 1976-2010. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

46
encourages better performance. Imports are negatively correlated with firm productivity and exports do not appear to matter at all. The results on imports differ from the conclusions of Pavcnik (2002) for Chile and Fernandes (2007) for Colombia, but are in line with the findings of Arnold et al. (2006) for the Czech Republic.

# **6.5 Robustness Checks**

We subject our results to several robustness checks. Our analysis is conducted for both measures of TFP and yields conclusions highly consistent with each other. In what follows, to save space we only report the results from using TFP estimated following Levinsohn and Petrin (2003) as it captures technological changes and scale effects on TFP as well and is regarded a superior to the multilateral TFP index.

First, we consider possible outlier issues. Bucharest as the capital of Romania has disproportionate concentration of economic activity and wealth. It produces about 20% of the country’s GDP while only accounting for 10% of total population.<sup>11</sup> To check whether our results are affected by the special case of Bucharest, we exclude observations from Bucharest and perform the benchmark analysis. As evident from Table 11, all coefficients on the presence of global chains remain positive and significant at the 1 percent level and have the same magnitudes. It indicates that our results are not driven by the observations from Bucharest.

Second, we estimate our model in first, second and long differences, instead of levels with firm fixed effects. As pointed out by Katayama, Lu, and Tybout (2006), there are several difficulties involved in using TFP to capture productivity improvements. Substitution of the data on sales revenues, depreciated capital spending and real input expenditure for information on the physical quantities of output, capital and intermediate inputs may lead to confounding higher productivity with higher markups. In our case, this is less of a concern as global retail chains are likely to press suppliers to lower their markups. Therefore, if our TFP measure is subject to the above problem, it will work against us finding a positive relationship between expansion of global chains and TFP of food supplying sectors. Nevertheless, as Katayama et al. (2006) argue that the problems with using TFP are reduced in difference specifications, we check whether our results are robust to doing so.

In the first and second difference specification, we drop firm age but we still include

> 11 Calculated according to Eurostat REGIO database.

17
# **Combating Invoice Seller Fraud**

In addition, serious tax frauds so called “invoice sellers”<sup>41</sup> who established a bogus company and issued fictitious invoices to sell to businesses have not disappeared but widespread since 2000 as shown in Table 1. Since 2003, the NTS had reinforced tax investigation against invoice seller frauds through its Dedicated Cross-regionaltracking Investigation Team under the annual invoice sellers investigation plan (the NTS, 2003 & 2004). Cases of invoice seller frauds peaked in 2005 and started to decrease gradually, but they were unacceptably high in 2007 before the compulsory ETI was introduce the following year.

Table 1: Number of Invoice Seller Charges

||**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|
|---|---|---|---|---|---|---|---|---|
|**Number of**|||||||||
|**invoice sellers**|637|1,065|1,129|2,108|3,698|3,725|1,836|1,702|
|**charges**|||||||||

Note: Data compiled by the author from the NTS press release (the NTS, 2003) and a document submitted to the National Assembly by the NTS (the NTS, December, 2008) As noted above, the NTS computerized transaction information and cross-checked sales and purchase of taxpayers to identify any suspicious transaction even before the introduction of the mandatory ETI. But it took more than one and a half year after the tax return to identify suspicious transactions. Therefore, the old system of crosschecking taxpayers’ tax return by computerizing the summary lists of invoices was no longer effective against invoice sellers who typically sold a large amount of invoices in a very short period of time and disappeared. In contrast, ETIs were seen as a powerful tool to digitalize each transaction carried out among taxpayers in real time and cross-check transactions between sellers and purchasers even before the VAT return. It computerizes not simply the aggregate result of transactions and taxable amount as in e-filing but also all the evidences of the e-filing, which will 41 A basic “invoice seller” fraud is shown in the following diagram. One or more buffer companies can be inserted between an invoice seller and Co.2 in a more complex fraud scheme.

<!-- Start of picture text --> 3. Provide fee 4. Supply<br>Invoice for invoices Co. 2 goods with invoices Co. 3<br>seller<br>2. Sell<br>fictitious<br>invoices<br>6. Disappear before NTS 5. Claim input<br>detect the frauds 1. Supply goods tax credit with<br>without invoices<br>fictitious<br>at a lower price invoices<br>Co. 1<br>NTS<br><!-- End of picture text --> 19
of the land. These results are consistent across the three databases we use in this work. Similarly, we find that the interaction term between the distance to the hypothetical road network and connection to submarine internet positively correlates with the interaction term between the distance to the real road network and connection to submarine internet, and we also find that lightning intensity negatively correlates with the latter variable.

For our three samples, a strong and significant correlation is demonstrated between the instruments used and the endogenous variables of our model. The Cragg-Donald Wald and Anderson-Rubin Wald statistical tests show that the chosen instruments are strong enough. Certainly, the results of these tests allow us to reject the null hypothesis of weak identification. Also, results of the KleibergenPaap test allow us to reject the null hypothesis of under-identification. Our first stage is, therefore, robust across the three samples used, with the theoretical model well fitted to the data.

# **4.2 Average Complementary Effect of Roads and Internet**

This subsection presents the average, complementary effect of access to submarine internet and road networks on employment and occupation types.

## **4.2.1 Employment**

Table (3) displays the coefficients resulting from the estimation of equation (2). In columns (1) and (2), using the DHS sample, we regress two measures of the odds that an individual is employed on the infrastructure variables and on all our controls including year, country and grid-cell fixed effects. In the third column, we use the LSMS data to compute the effect of infrastructure on the number of hours worked, using the same identification strategy as in the first two columns.<sup>14</sup> The last three columns display the estimations computed from the WBES, with the dependent variables being the number of hours of operation per week in the last fiscal year; the number of permanent, full-time employees at the end of the

> 14Information on the number of hours worked is not collected in the DHS.

23
# **Table 7**

|**Category**|**Dataset**|**Driver**|**Definition**|**Available**<br>**in NNS**<br>**dataset?**|**Required**<br>**for**<br>**adequacy**<br>**in**<br>**domain?**|
|---|---|---|---|---|---|
|||**Access to**<br>**improved**<br>**drinking**<br>**water**|**Main source of drinking water is**<br>**piped, from a borehole, from a**<br>**protected dug well or spring,**<br>**rainwater, bottled water or truck**|**Yes**|**Yes**|
|Household facilities that|NNS Form<br>1.2|**Access to**<br>**improved**<br>**sanitation**|**Toilet facility is water sealed (either**<br>**flush or pour with septic tank)**|**Yes**|**Yes**|
|support hygiene,||Access to|Uses clean cooking fuel (electricity,|||
|sanitation and disease<br>prevention||clean cooking<br>fuels|LPG or natural gas) and does not burn<br>garbage|Yes|No|
|||Exposure to<br>farm animals|Household member owns or manages<br>livestock|Yes|No|
||Not in<br>questionnaire|Handwashing<br>facilities|Availability of handwashing facilities,<br>water, soap & detergent|No|No|
|Community-level<br>environment|NNS Form<br>1.2|**Community**<br>**level**<br>**sanitation**|**At least 75% of households in**<br>**municipality have improved**<br>**sanitation**|**Yes**|**Yes**|
|Behaviors that support<br>hygiene, sanitation and|NNS Form<br>1.2|Safely<br>manages<br>drinking water|Boils, chlorinates or filters water or<br>purchases bottled mineral water|Yes|No|
|<br>disease prevention|Not in<br>questionnaire|Feces disposal|Child’s stools are disposed into an<br>improved sanitation facility|No|No|

Access to improved WSH infrastructure is in general very high: 95 percent of children under-2 have access to improved drinking water, 85 percent have access to improved sanitation and 82 percent live in a municipality where at least three-quarters of the households have improved sanitation. Nationally, close to 3 in 4 children under-2 live in a household with adequate WSH infrastructure.

Several indicators associated with the household environment but not related to water and sanitation were identified in the NNS data. Whether the household has access to electricity or uses improved cooking fuel and does not burn garbage has a bearing on indoor air quality, and how efficiently food can be prepared. Whether there is a functioning refrigerator has a bearing on the household’s ability to preserve foods that are prone to spoilage, including meat, eggs, animal milk, and breastmilk. Nationally, only about 1 in 3 children under that age of 2 live in households that use improved cooking fuels and or have a functioning refrigerator. Use of improved cooking fuel and refrigerator ownership are even lower for the poorest quintile, 3 and 8 percent, respectively, compared to 56 and 79 percent for the richest quintile. And of all the indicators considered, the urban-rural difference is largest for the use of improved cooking fuel: Urban households are five times as likely to burn cleaner fuels for 38
via an out-grower tobacco scheme.<sup>6</sup> Farmers in the study were organized by the tobacco companies into clubs of 10-15 members and all had group liability production loans from OIBM prior to enrollment in the study.

Table 1 presents summary statistics of baseline household and farmer club characteristics. All variables expressed in money terms are in Malawi Kwacha (MK145/USD during the study period). Baseline survey respondents own an average of 4.7 acres of land and are mostly male (only six percent were female). Respondents are on average 45 years old. They have an average of 5.5 years of formal education, and have low levels of financial literacy: 42% of respondents were able to compute 10% of 10,000, 63% were able to divide MK 20,000 by five and only 27% could apply a yearly interest rate of 10% to an initial balance to compute the total savings balance after a year.

Sixty three percent of farmers at baseline had an account with a formal bank (mostly with OIBM).<sup>7</sup> The average reported savings balance at the time of the baseline in bank accounts was MK 2,083 (USD 14), with an additional MK 1,244 (USD 9) saved in the form of cash at home.

Figure 1 presents the timing of the experiment with reference to the Malawian agricultural season. The baseline survey and interventions were administered in April and May 2009, immediately before the 2009 harvest.

# **_Financial Education Session_**

After the baseline was administered, all clubs (treatment as well as control) attended a financial education session that reviewed basic elements of budgeting and explained the benefits of formal savings accounts, in particular how they could be used to set aside funds for the future (such as for school fees or agricultural inputs). The full script of the financial education session can be found in Appendix A.

The financial education session was deliberately provided to both treatment and control groups so that treatment effects could be attributed solely to the provision of the financial products, abstracting from the effect of financial education (for example, strategies for improved budgeting) implicitly provided during the product offer. For this reason, we can estimate neither

> 6 Tobacco is central to the Malawian economy, as it is the country’s main cash crop. About 70% of the country’s foreign exchange earnings come from tobacco sales, and a large share of the labor force works in tobacco and related industries. Despite its importance as a cash crop, the World Bank does not encourage its production (cf. OP4.76).

> 7 This number includes a number of “payroll” accounts opened in a previous season by OIBM and one of the tobacco buyer companies as a payment system for crop proceeds, and which do not actually allow for savings accumulation. Our baseline survey unfortunately did not properly distinguish between these two types of accounts.

6
where "n;c is the import demand elasticity of good n in country c. Thus, for a given year, the OTRI of a country depends on the current year import ‡ow and tari¤ of the goods and the corresponding import demand elasticity.

When comparing the OTRI of a country across two years using (2), we would keep the trade ‡ow data and elasticity estimates constant (at base year), so that changes in the OTRI within the country across two years are purely driven by policy changes and not due to changes in trade ‡ows associated with shifts in preference or income. In the current context, we use the trade ‡ow information in 2008 to construct the OTRI of the countries in 2009: In this way, the di¤erence in the OTRI of a country between 2008 and 2009 only captures trade policy changes, and does not re‡ect the collapse of trade during the crisis period.

As shown in Kee, Nicita and Olarreaga (2009), the OTRI can be further decomposed into the import weighted average tari¤, t<sup>�</sup> c, and the covariance between the tari¤ and the import demand elasticity, cov (tn;c; ~"n;c): with ~"n;c denotes the elasticity of good n in country c rescaled by the import-weighted elasticity across all goods in country c. The higher the import weighted average tari¤ or the covariance between the tari¤and the import demand elasticity, the higher the OTRI. Thus, the OTRI increases if a country levies higher tari¤ on goods that have a larger import, and if the goods are very responsive to tari¤ changes.

In our empirical exercise below, we present the OTRI estimates of countries, and decompose 6
investment choices across cities. Similar regressions for floods and cyclones did not generate statistically significant estimates.

Private investment in mitigation is capitalized into property values _._ Just as natural disaster risk reduces property values, investments in mitigation such as earthquake proofing structures are likely to be recovered through increases in property prices. Nakagawa et al. (2007) use a 1998 hazard map of the Tokyo Metropolitan Area to examine the extent to which housing rents reflect earthquake risk as well as earthquakeresistant materials used in construction. The study exploits the fact that the Building Standard Law was amended in 1981 to enhance the earthquake-resistant quality of buildings, and that a building constructed after 1981 needed to conform to the new standard of earthquake-resistant quality. The authors find that the rent of houses built prior to 1981 is discounted more substantially in risky areas than that of houses built after 1981. In Tehran, Willis and Asgary (1997) interview real estate agents to examine the capitalization of investments in Earthquake Risk Reduction Measures (ERRMs) on property values and assess if home buyers are willing to pay for ERRMs incorporated into a house. The estimates suggest significant price differences between earthquakeresistant and nonresistant houses, across all districts in the city – however, the limited adoption of ERMMs in Tehran may be due to limited public information of earthquake risks.

# **Spatial distribution of hazard risk**

There is heterogeneity in how households respond to information on risk _._ When facing risk from natural disasters, people can move out of harm’s way; they can self-protect by building structures less vulnerable to damage; or they can insure. Empirical research on how people and housing markets responded to Hurricane Andrew -- one of the largest natural disasters to affect the United States, shows that the economic capacity of households explains most the differences in how different groups respond to the hurricane damage (Kerry Smith et al. 2006). In 2000, households with annual incomes over USD 150,000 were the only group likely to be attracted to areas with a comparable “type” of household—i.e. to areas where the same income level households lived in 1990.

18
b Based on multilevel logit models with interaction terms; 95 percent confidence intervals are in parentheses (* _P_ <0.05).

# **<<B>>** Quality of Care

Anecdotal evidence suggested that the SPs used income generated from the HealthyBaby voucher program to upgrade facilities and improve service provision. These improvements included the opening of a maternity ward, hiring of additional staff, improving equipment, and bringing volunteers onto the payroll. However, results from observations of CPIs (based on the quality-ofcare model of structure, process, and outcome of care) showed that although voucher facilities performed better in terms of adequately creating a rapport with clients (e.g., greeting clients and ensuring privacy and confidentiality) during ANC consultations compared with nonvoucher facilities, no significant difference was seen in the proportion of SPs that adequately took into account the general or medical history of clients. Similarly, no significant difference was seen in the proportion of SPs from voucher and nonvoucher facilities that adequately created a rapport or took into account the general history of PNC clients (table 7).

30