rafmacalaba/datause-encoder-data huggingface dataset:
corruption. The first is the degree to which culture impacts expectations of achievement (what Hofstede terms the masculinity dimension), and secondly, amoral familialism (which Hofstede terms as power distance), and finds empirical support from the World Values Survey and the CPI. Additionally, Swamy et al. (2001) find that female representation in the labor force and in political institutions has a negative effect on corruption.

Fisman and Miguel (2007) exploit a unique dataset on parking tickets issued to diplomats in New York to find a positive relationship between CPI and corrupt behavior. They observe the number of unpaid parking tickets for diplomats under two enforcement regimes, with zero enforcement (1997 – 2002), and with legal enforcement (2002 – 2005). In the zero-enforcement period, vehicles with a diplomatic status could be ticketed (and indeed, were ticketed quite frequently), but were not punished for non-compliance and non-payment. By the end of 2002, however, the “State Department gave permission to New York City to revoke the diplomatic plates of vehicles with three or more parking tickets” (Fisman and Miguel 2007). Exploiting this natural experiment, the authors show that the number of unpaid tickets by diplomats is strongly correlated with the CPI in their home countries. The authors argued that cultural norms are persistent, and that individuals carry their norms to new environments. This provides the first unambiguous evidence for the persistence of corruption norms.

In each of the studies above, culture is used to refer to multiple underlying elements (such as paternalism, familialism, uncertainty avoidance, etc.), which tend to overlap. These elements influence, and are influenced by both norms and institutions. Due to the superior control of lab experiments, institutions can be held constant, while norms are allowed to vary so as to capture behavior independent of the institutional channel. Alternatively, institutions can be created in the lab to test out the influence of differing social norms on behavior when reacting to the institutional arrangements.

# _Experimental Studies on Culture and Corruption_

Experimental research on corruption has grown considerably in recent years. Early studies (Frank and Schulze 2000, Abbink et al. 2002, Abbink and Hennig-Schmidt 2006, Schulze and Frank 2003) set about using the lab to study corruption and the effects of various anti-corruption policies on behavior. Abbink (2006) provides a useful summary of experimental work on 10
# **Table A.8. The effect of reference group, RLMS 1994-2017**

|_Main variables_|**Pooled Ordered Logit**|**BUC**|
|---|---|---|
|Lo of household income|0.484***|0.282|
|g|_(0.021)_|_(0.213)_|
|Log of household size|-0.241***<br>_(0.034)_|0.007<br>_(0.117)_|
|Children*Lo of household size|0.028**|0.003|
|g|_(0.011)_|_(0.022)_|
|_Relative income (base – 1_<sup>_st_</sup>_quartile)_|||
|2<sup>nd</sup>artile|0.161***|0.071|
|qu|_(0.021)_|_(0.084)_|
|3<sup>rd</sup>til|0.310***|0.203|
|quare|_(0.025)_|_(0.144)_|
|4<sup>th</sup>quartile|0.477***<br>_(0.033)_|0.323<br>_(0.234)_|
|_Number of observations_|140,026|366,511|
|_Log pseudolikelihood_|-237,167|-135,630|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

43
This paper evaluates the role of these two channels in supporting the performance of indigenous firms across the developing world. Employing a firm-level dataset for 122 countries, it finds that, on average, domestic firms do not capture FDI spillovers. This implies that the average firm does not have the required absorptive capacities to recognize the value of new information, assimilate it, and apply it to improve production processes. Therefore, the competition channel balances out the potential for knowledge spillovers. However, high-growth firms, identified as those enterprises with the highest job creation rates in each country, do capture FDI spillovers. The more powerful transmission channel is contractual linkages with MNCs. The base specification indicates that an increase of 1 percentage point in the share of inputs that are sourced domestically by MNCs in the sector is associated with a 0.51 unit increase in the measure of output growth of domestic highgrowth firms. Investment in developing countries from MNCs embedded in global value chains creates larger FDI spillovers to high-growth indigenous firms than investment from MNCs that seek to serve the host economy. There is no empirical evidence that natural-resource seeking investment creates positive FDI spillovers.

These results have important implications for policy design as governments often direct public funding to support linkages programs to connect domestic suppliers with MNCs in the developing world. The evidence presented here indicates that these programs should include a targeting mechanism to identify high-potential domestic suppliers, rather than being directed to the whole group of small and medium-sized enterprises. Because some of these programs are very costly (e.g. supplier development programs), policy interventions have better odds to succeed if public interventions are aimed at indigenous firms with high absorptive capacities. More research is needed about the characteristics of these enterprises, as are rigorous impact evaluations about linkages programs.<sup>15</sup> Regardless of the distributional impact of MNCs on domestic firms, countries open to multinational production tend to experience aggregate productivity gains. This paper shows that part of these benefits is accrued by local high-growth firms via the assimilation of positive FDI spillovers, which increase their sales and productivity. However, the increased competition that MNCs bring to the

> 15 Reyes (2017) reviews the economic rationality for public interventions and provides an overview of the different support programs to promote linkages.

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Figure 1: Vietnam: Schooling and Earnings Differences by Region <!-- Start of picture text --> Eaings ('000s VND)<br>150<br>100<br>50<br>0<br>North South<br><!-- End of picture text --> <!-- Start of picture text --> Schooling (Years)<br>8-<br>64-<br>2<br>0<br>North South<br><!-- End of picture text --> Source: VLSS 1992-93 Estimating earnings functions separately for workers in the North and the South results in rates of return to schooling that are similar in both regions (Table 8). On average, an additional year of schooling will increase earnings by 8 percent in both regions. Experience, however, is rewarded more in the South than in the North. Also, hours worked have a slightly higher payoff in the South.

Table 8: Vietnam: Earnings Functions by Region

|Variable|North|South|
|---|---|---|
|Constant|0.515|0.782|
|Years of schooling|0.082|0.078|
||(6.8)|(12.0)|
|Years of experience|0.041|0.078|
||(4.1)|(10.2)|
|Experience-squared|-0.001|-0.001|
||(3.0)|(8.5)|
|Log hours worked/week|0.639|0.698|
||(6.7)|(9.4)|
|R-squared|0.113|0.177|
|N|788|1,471|

Source: VLSS 1992-93 19
bring to the domestic market counteracts the FDI benefits that the average firm is able to internalize. Second, the low absorptive capacity of the average firm prevents it from capturing FDI benefits.<sup>12</sup>

**<u>Table 3: FDI Spillovers and Firm Performance</u>**

|VARIABLES|[1]|[2]|[3]|[4]|[5]|[6]|[7]|
|---|---|---|---|---|---|---|---|
|Linkages Channel||-0.023||-0.028|-0.053||-0.053|
|||(0.051)||(0.052)|(0.050)||(0.051)|
|Demonstration Channel|||0.010<br>(0.015)|0.011<br>(0.019)||0.002<br>(0.019)|0.006<br>(0.019)|
|High-growth Firm|||||0.207*** <br>(0.019)|0.207*** <br>(0.021)|0.190***<br>(0.021)|
|X Linkages Channel|||||0.678***<br>(0.190)||0.568***<br>(0.201)|
|X Demonstration Channel||||||0.171***<br>(0.061)|0.109*<br>(0.063)|
|Log Age|-0.068*** <br>(0.005)|-0.068*** <br>(0.005)|-0.068*** <br>(0.005)|-0.068*** <br>(0.005)|-0.061*** <br>(0.005)|-0.061*** <br>(0.005)|-0.061***<br>(0.005)|
|Exporter|-0.001<br>(0.010)|-0.001<br>(0.010)|-0.001<br>(0.009)|-0.001<br>(0.010)|-0.003<br>(0.010)|-0.003<br>(0.010)|-0.003<br>(0.010)|
|Log Labor Productivity|0.082***<br>(0.005)|0.082*** <br>(0.005)|0.082*** <br>(0.002)|0.082*** <br>(0.005)|0.083*** <br>(0.005)|0.083*** <br>(0.005)|0.083***<br>(0.005)|
|Constant|-0.783*** <br>(0.059)|-0.784***<br>(0.059)|-0.785<br>(0.551)|-0.786*** <br>(0.059)|-0.813*** <br>(0.060)|-0.816*** <br>(0.059)|-0.816***<br>(0.059)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|33,305|33,305|33,305|33,305|33,305|33,305|33,305|
|R-squared|0.165|0.165|0.165|0.165|0.174|0.174|0.174|

Note: The dependent variable is total sales. The sample excludes foreign firms. Standard errors (in parentheses) are clustered at the country-sector level.

*** p<0.01, ** p<0.05, * p<0.1

Source: Author computation using data from the World Bank Enterprise Surveys.

High-growth firms behave radically differently from the average firm in their ability to internalize FDI spillovers. Both channels are positively and statistically significantly related to the performance of these firms (specifications 5-7). The results indicate that an increase of one percentage point in the size of linkages (share of inputs sourced domestically by foreign firms) is associated with a 0.515 unit increase in the sales mid-point growth rate of high-growth firms. This impact is statistically significant at the 1 percent level. For the demonstration channel, the impact

> 12 The finding that intra-industry spillover effects are rarely accrued by domestic firms is standard in the literature. Meyer and Ninani (2009) and Gorg and Strobl (2001) provide two meta-analyses reviewing this literature.

- 15 -
# OZIER - REPLICATION REDUX – p. 22 of 41

following exactly the original steps but with corrected data, the 7.5 percentage point change in school attendance, dropping to an imprecise 3.9 percentage points, seems not to hold up.

The twist is that there is no particular reason that 6km is the right radius to check. In the 2004 analysis, it seemed that there was no meaningful difference in either point estimate or precision, whether the 3‐6km spillovers were included or not. However, the step including these spillovers now appears to be a problematic one. The distant spillovers are too imprecisely estimated to add in, so should not have been included in the first place. Even in the original analysis, there must have been insufficient variation in treatment at still greater distances to estimate spillovers from afar, so the line had to be drawn somewhere. Where should it be drawn now, and why? Hicks, et al. (2015) point out that adding imprecisely estimated quantities to precisely estimated ones yields imprecision, so even if adding in the 3‐6km spillover yields an unbiased estimator of the true total effect, the expected distance from such an estimate to the truth (the “mean squared error”) is larger than it would be, had one used the slightly biased but lower variance estimator involving only the direct effect and 0‐3km spillovers. This reasoning requires knowing the magnitude of the bias, which Hicks, et al., take from the relative magnitude of the estimates of direct and 3‐6km spillover effects on worm infections: the 3‐6km spillovers appear to cause relatively little change in worm infection, thereby making it likely that they also cause very little change in school attendance. By this reasoning, the line should be drawn at 3km.

There is a simpler way of looking at this. Without Hicks, et al.’s suggestion of a rule meant to minimize mean squared error, one can take both the original distance thresholds as equally reasonable options for effect summation. Having two statistics to choose from, however, one must confront the problem that usually arises when picking whichever of two specifications yields a more statistically significant effect: “p‐hacking.”
# **Section1. Introduction**

It is challenging to collect critical welfare information in the growing number of conflict-affected regions across the world (e.g., ACLED 2021). Aside from largely not being able to conduct nationally representative household surveys (e.g., Corral et al. 2020), there are also difficulties in coaxing respondents to truthfully respond to sensitive questions. Fully truthful answers about conflict-related issues might be obscured by social desirability bias, where individuals might not like to admit support for certain groups that have been accused of human rights abuses (e.g., Fisher 1993); individuals are often targeted for their opinions and actions that are contrary to the wishes of the official and de facto authorities in fragile and conflict settings (e.g., Human Rights Watch 2021); and even benign information on the humanitarian situation in these settings might be strictly controlled so as to not contradict official humanitarian assessments (e.g., Favari et al. 2022). As the share of the extreme poor becomes more concentrated in fragile and conflict settings (e.g., World Bank 2018), sensitive questions about the ways in which the conflict intersects with individuals’ lives are becoming arguably more important to accurately capture than information used to build traditional welfare measures, such as monetary poverty (e.g., Favari et al. 2022).

We investigate the possibility that anonymous and novel internet-based surveys might better elicit sensitive information in conflict settings. In particular, we investigate surveys using Random Domain Intercept Technology (RDIT), which invites a subset of internet users who reach wrong or dated web pages to complete a short and anonymous survey.<sup>1</sup> Unlike either mobile phone or face-to-face surveys, these internet surveys do not capture any information about respondents. In such a setting, it is possible that respondents might worry less about the social desirability of their answers, being targeted for their opinions, or about any of the other issues that might limit respondents’ willingness to respond to sensitive questions.

Although the internet surveys used here are indeed anonymous, we investigate whether

> 1Specifically, the surveys are performed by a company named RIWI, which purchases the domain names of old websites and those of spellings that are close to popular websites. A proprietary algorithm then determines which of the internet users that reach RIWI-owned websites are offered the chance to participate in an RDIT survey.

2
**Table 3: Summary statistics for the sample of large states (continued)**

|**Performance**|**Indicator**|**Number of**<br>**observations**|**Mean**|**Standard**<br>**deviation**|
|---|---|---|---|---|
||Share of manufacturing (share of working‐age population)|512|0.06|0.05|
||Share of other industries (share of working‐age population)|512|0.04|0.03|
||Share of services (share of working‐age population)|512|0.13|0.06|
|Economic structure|Share of medium‐size firms (share of firms)|512|0.0055|0.0033|
||Share of large firms (share of firms)|512|0.0008|0.0011|
||Diversification index (index)|512|16.0|6.0|
||Mineral production capacity (million metric tons per year)|512|58.0|482.0|
|Elt tt|Share of self‐employed (share of working‐age population)|512|0.40|0.13|
|mpoymen srucure|Share of wage workers (share of working‐age population)|512|0.27|0.11|
||Primary education (share of working‐age population)|512|0.17|0.07|
|Human capital|Secondary education (share of working‐age population)|512|0.04|0.03|
||Tertiary education (share of working‐age population)|512|0.01|0.01|
||Access to finance (share of total households)|512|0.33|0.14|
|Sil ili|Gender gap in secondary education (percentage point difference)|512|0.03|0.02|
|oca ncuson|Share of scheduled castes (share of total households)|512|0.13|0.23|
||Share of scheduled tribes (share of total households)|512|0.19|0.11|
||Crime rate (incidence per million people)|512|47.0|34.0|
|Governance|Labor regulation index (index)|512|0.03|1.20|
||Land reform index (index)|512|4.51|3.03|

_Source:_ Authors, based on Li et al. (2015).

_Note:_ The 21 largest states or union territories are Andhra Pradesh, Assam, Bihar, Chhattisgarh, Delhi, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, Uttaranchal and West Bengal. km<sup>2</sup> = square kilometer; m = meter; mm = millimeter; Rs = Indian rupees 16
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30
Some implications of Policy Games for High Inflation Economies by Miguel A. Kiguel and Nissan Liviatan

# Table of Contents

- Introduction 1 Part I: Explanations of High Inflation 5 1. Basic Aspects of Policy Games 5 2. The High Inflation Equilibrium 7 3. The Employment Wedge and Monetary 12 Accommodation

- Part II: Stabilization Policies 13 1. Commitments and Crediblill) 13 2. Commitments under Imperiect Information 14 3. ligNhcsations of **Rr1Tn'R** Model of Incomplete 15 Information

- 4. Credibility, Accommodation, and Compromise 18 5. Signalling and Overadjustment 23 6. Compromise and Overadjustmnent 28 7. Concluding Comments 29

- Footnotes 31 32

Bibliography 'We are grateful to Alex Cukierman and participants in the workshops at Georgetown University and the World Bank for comments on an earlier draft. This paper is part of the World Bank research project "Stopping High Inflation' (RPO 674-24).
<!-- Start of picture text --> 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>age 0 age 6<br>age 0 age 6<br>age 0 age 6<br>beginning of TSC ASER tests<br><!-- End of picture text --> Figure 2: Empirical strategy: 2001-2003 TSC intensity matched to six-year-olds’ 2007-2009 tests 36
- 40 -

policy during 1993-96 was becoming less effective as an instrument to deal with overheating and capital inflows (for example, the offset coefficient was increasing, see Annex 2), and was in fact encouraging further inflows and the accumulation of external short-term liabilities.

**Figure 14. Thailand: Growth in Reserve Money and Its Components**

<!-- Start of picture text --> 100 .0%<br>80.0%<br>60.0%0/<br>40.0%<br>20.0%<br>0.0%<br>-20 .0%<br>-40.%<br>-60.0%<br>N IT CN CN V_ N ' I CN<br>a a a a 0 a a a a a CYa a C) a a<br>co co co 0) 0) 0) 0) 0) 0) 0 0) 0 0) C) 0)<br>0) 0) 0 0) 0) 0) 0 0) 0) 0) 0) 0) 0) 0) 0) 0)<br>1-I Fcrdg,ASs EQ| rrsmG~emBt ~n Firei Samcr - FaeEMr M<br><!-- End of picture text -->

Note: Annual growth measured as the percentage changed from the same quarter one year before.

# **c. Fiscal Policy**

Figure 15. The Central Government's Fiscal Position <!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>Q0.1 _<br>0.05<br>0<br>-0.05<br>1987/88 1988/89 1989/90 1990/91 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97<br>E Revenues _ Expenditures Overall Balance<br><!-- End of picture text -->
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28