rafmacalaba/datause-encoder-data huggingface dataset:
liberalization are dramatically smaller than the welfare gains, lobbying interests are much stronger among those who anticipate a negative adjustment. Thus, our results show why there can be a political economy appeal to regionalism, despite the larger net gains of multilateral reform.

We have also assessed the impact of reform of domestic regulations that impose costs on both domestic and foreign suppliers of business services in a non-discriminatory manner. We found that while the discriminatory barriers are important and are the focus of international negotiation, the regulatory barriers that impose costs on domestic supplier and foreign suppliers of services in a non-discriminatory manner are quantitatively more important. The larger gains from the reduction of non-discriminatory barriers are due to the larger bases from which the barriers are reduced. The non-discriminatory barriers affect all providers of services, so the bases of the recaptured rents and the distortion triangles are larger.

In our central scenarios, we assume that the barriers result in costs that are dissipated and liberalization releases labor and capital for productive purposes in the domestic economy. In our sensitivity analysis, we have shown that the magnitudes of the gains are in some cases very strongly impacted by this assumption. If rents are captured by domestic agents, the gains are reduced by as much as 80 percent in one case.

# **8. References**

Anderson, James and J. Peter Neary (1996), "A new approach to evaluating trade policy," _Review of Economic Studies_ , 63 (1), 107-125.

- Anderson, James and J. Peter Neary (2003), “The Mercantilist index of trade policy,” _International Economic Review_ , 44, 627--649.

- Baldwin, Richard E., Forslid, Rikard and Haaland, Jan (1999), "Investment Creation and Investment Diversion: Simulation Analysis of the Single Market Programme," in R. Baldwin and J. Francois (eds.), _Dynamic Issues in Applied Commercial Policy Analysis_ , Cambridge: Cambridge University Press.

- Balistreri, Edward J. and James Markusen (2009), “Sub-national differentiation and the role of the firm in optimal international pricing,” _Economic Modeling_ , 26(1), 47 - 62.

31
treatment per capita, we use service provision information from the hospital inventory per group (uninsured, children under 6, other insured patients), divided by the estimated number in the group (i.e. uninsured and insured patients in the respective district). As mentioned above, the latter was estimated from the Vietnam Household Living Standards Surveys in the corresponding years.

# **_Descriptive statistics_**

Table 2 provides descriptive statistics of the samples used for various estimations.

Because of the low response problem of hospital surveys and the fact that not all hospitals can be matched with Living Standards Surveys, the number of observations varies by outcomes. The full set of data contains 3,818 hospital-year observations spanning the period of 7 years (20052011). Missing data is a particular problem for quality measures and for indicators denoting service intensity per capita (which requires merging hospital with household data). For most outcomes, a natural log transformation was performed to address the skewness in the distribution of the variables. Table 2 shows mean and standard deviation values in unlogged form for ease of interpretation as well as the natural log form for most variables (which is the form used in the estimation).

As shown in Table 2, an average hospital in our sample has 97 beds and 106 staff. The staff and bed sizes vary greatly among hospitals. Drug expenditure accounts for 45% of total recurrent expenditure per case (63 versus 140 VND). The overall C-section rate in the sample is nearly 11% and average length of stay is six days. Domestically produced drugs account for roughly 70% of the value of drugs used in the hospitals.

Among the equity measures, the differences in service intensity between the insured and uninsured are noteworthy. On average, an insured person (who is not among the poor or children 16
4 markets when their host country returns are correlated with the nominal exchange rate. The availability of forward markets ham the potential to increase investment by host country firms in export-oriented industries as well as encouraging multinational investments in host country projects.

The emphasis in this section is on the importance of the relationship between nominal exchange risk and real exchange rilsk in determining the benefits of forward markets. The gains from hedging in forward markets for the multinational firm are shown to be smallest when purchasing power parity holds, and where the firm is primarily engaged in production for the export market. Data on correlations between nominal exchange rate changes and relative price level changes for a number of countries are presented to illustrate the magnitude of gains from hedging. The section concludes with- a simulation illustrating how the level of investment responds to the presence of markets for hedging risk under alternative assumtpions about the correlation between nominal exchange rate changes and relative price level changes across countries.

# A. Investment Levels with No Forward Markets

Consider a risk averse multinational firm that is allocating investments between its home country and a host country.<sup>4</sup> The host country is BAPAIN and the source country (home of the multinational) is JAPAUS. The firm has an initial stock of capital, W, which it must allocate between home country and foreign country projects. The stock of capital is measured in terms of the JAPAUS consumption good, which is the deflator for source country nominal income. The real retuzn from source country projects is assumed to be a certain return, r, which is constant and independent of the quantity invested.<sup>5</sup>

> An investment in BAPAIN of amount K yields a return ao(K), where 4 is a
consequences of Americans' judgments of the seriousness of global warming using data from two surveys. They find that judgments about global warming are a function of beliefs about the existence of global warming, attitudes toward it, the certainty with which these beliefs and attitudes are held, and beliefs about human responsibility for causing global warming and people's ability to remedy it. They also find that beliefs about whether global warming is a problem are a function of relevant personal experiences (with the weather) and messages from informants (in this case, scientists), that attitudes toward global warming are a function of particular perceived consequences of global warming, and that certainty about these attitudes and beliefs is a function of knowledge and prior thought. More specifically they observe that people stopped paying attention to global climate change when they realize that there is no easy solution for it. Instead they note that many people judge as serious only those problems for which they think action can be taken.

Sandvik, Hanno 2008 Public concern over global warming correlates negatively with national wealth Climatic Change 90(3): 333-341 The author builds on work by Norgaard and others to assert that public awareness and concern regarding climate change is not a function of scientific information alone, but psychological and sociological factors as well. Sandvik examines data on public concern for climate change from 46 countries, with results pointing to a negative association between concern and national wealth (GDP) and carbon dioxide emissions and a “marginally significant” tendency that nations’ per capita carbon dioxide emissions are negatively correlated to public concern. Sandvik concludes that such a relationship is “in accordance with psychological findings, but poses a problem for political decision-makers.” Sterman, John and Linda Sweeney 2007 “Understanding Public Complacency About Climate Change: Adults Mental Models of Climate Change Violate Conservation of Matter” Climate <u>Change</u> 80: 213-238.

Stoll-Kleemann, Tim O’Riordan and Carlo Jaeger “The Psychology of Denial Concerning Climate Mitigation Measures: Evidence from Swiss Focus Groups” Global Environmental <u>Change 11: 107-117.</u> See above Norgaard, Kari Marie. 2006a. "'People Want to Protect Themselves a Little Bit': Emotions, Denial, and Social Movement Nonparticipation." Sociological Inquiry 76:372-396.

Emotions can be a source of information and an impetus for social action, but the desire to avoid unpleasant emotions and the need for emotion management can also prevent social movement participation. Ethnographic and interview data from a rural Norwegian community describes how people avoided thinking about 57
# **<u>MOODY’S</u>**

|||**Developing**<br>**countries**<br>**weighting**|
|---|---|---|
|**1.**|**Operating environment**|**60%**|
||GDPper capita USD(threeyears average in PPP terms)|50%|
||GDP volatility (%)|25%|
||Government Effectiveness Index|25%|
|**2.**|**The institutional framework that determines RLG powers and**<br>**responsibilities**|**10%**|
||Predictability, stabilityand responsiveness|50%|
||Fiscal flexibility (gougingseparatelyrevenue and expenditure flexibility)|33.4%|
||Fiscal adequacy|16.6%|
|**3.**|**Financialposition andperformance**|**7.5%**|
||Interest Payments / OperatingRevenues(%)|25%|
||Cash FinancingSurplus / Total Revenue(%)|25%|
||Gross OperatingBalance / OperatingRevenue(%)|25%|
||Net WorkingCapital* / Total Expenditures(%)|25%|
|**4.**|**Debtprofile**|**7.5%**|
||Net Direct and Indirect Debt/OperatingRevenues(%)|50%|
||Short Term Direct Debt /Direct Debt(%)|25%|
||Four Year Trend in Net Direct and Indirect Debt / OperatingRevenue(%)|25%|
|**5.**|**Governance and managementpractices**|**7.5%**|
||Fiscal Management|40%|
||Investment and Debt Management|20%|
||Transparencyand Disclosure – financial statement disclosure|15%|
||Transparencyand Disclosure -audit|15%|
||Institutional Capacity|10%|
|**6.**|**Economic fundamentals**|**7.5%**|
||GDPper capita(USD,PPP),Estimated||

Source: Moody’s (2008a) 42
between decentralization and growth. Xie, Zou and Davoodi (1999) found a similar relationship for the U.S. after examining time series data from 1948 to 1994. Other studies conflicted these findings by showing evidence of a positive relationship between fiscal decentralization and economic growth. For example, Lin and Liu (2000) found evidence of a positive relationship for the same Chinese provinces used by Zhang and Zou (1998) after taking into account other concurrent reforms. In a pioneering study on fiscal decentralization in Turkey, Neyapti (2005) examined the links between fiscal decentralization and socio-economic indicators in Turkish provinces. Overall, she found a favorable impact of fiscal decentralization, with a positive relationship with the level and growth rate of output. Neyapti’s empirical analysis was constrained, however, by data limitations regarding the fiscal decentralization data that only allowed cross-sectional analyses for the years 1995 and 1998.

In another study, Akai and Sakata (2002) pointed to the importance of controlling for historical or cultural differences between observations and using a period of relatively lower growth in a decentralization study. To improve on the data problems of other studies, they used data from 50 U.S. states for the period 1992-1996. They found evidence of positive contribution of fiscal decentralization to economic growth. In a recent study, Stansel (2005) extended the local government empirical literature by examining the link between local decentralization and local economic growth using a new dataset of 314 U.S. metropolitan statistical areas. He found a negative and significant relationship between the central city share of metro population and population and real per capita income growth and a positive and significant relationship between the number of county governments per resident and population and real per capita income 3
extractive mechanism or a tool to reassert traditional gender identities. Specifically, of the 14 studies analyzed, 11 find that cash transfers are associated with a reduction in the risk of IPV.

However, not all interventions supporting women’s economic empowerment find positive results. Notably, a randomized control trial of employment in the flower industry in Ethiopia found that receiving a job offer had zero impact on the exposure to violence (Kotsadam and Villanger 2020). In Vietnam, women entrepreneurs who participated in gender and entrepreneurship training that increased their profits faced a greater likelihood of victimization, probably because of more frequent conflicts over the gains in income (Bulte and Lensink 2019).

Causal papers that examine the effects of macroeconomic changes on IPV are few. This research is important because many more people are impacted than those participating in interventions. Furthermore, interventions are limited by funding; so, their impact may occur over shorter periods. Using the private responses from the British Crime Survey, Anderberg et al. (2016) find that unemployment among men is associated with decreases in self-reported IPV, while unemployment among women leads to a rise in reported spousal abuse. Aizer (2010) has most broadly explored how labor market changes impact IPV. She focuses on low-skilled workers (who face higher rates of IPV) and finds that an increase in wages among women relative to the wages of men reduces hospitalizations among women in California. On a smaller scale, Munyo and Rossi (2015) also find that IPV rises if the wage ratio increases in favor of men. They use plausibly exogenous changes in the Uruguayan exchange rate that generate opposite effects on the average wages of men and women in 24 police jurisdictions and find impacts on violence concentrated in poorer jurisdictions.

# **Background: The Brazilian Context**

A middle-income economy, Brazil is the fifth most populous country in the world, and, similar to many middle-income countries, women are more well educated than men. Brazil has a relatively high female labor force participation rate, which is closer to that of the United States than that of Mexico, for example. Perhaps because of this economic participation, 24 percent of women ages 15–49 interviewed in a household survey were household heads.<sup>1</sup> Repartnering is common, indicating wide cultural acceptance of union dissolution. In the same survey, about 15 percent of women ages 15–49 separated and

> 1 Data from the 2015 round of the National Household Survey (Pesquisa Nacional por Amostra de Domicílios, PNAD).

5
Appendix 2. Definitions and sources of variables (continued)

|Variable|Definition|Source|
|---|---|---|
|Interest rates,c|onfidence,and uncertainty||
|Shor-term<br>interest rate<br>(nominal and<br>real)|Treasury bill rates or money market rates, with the maturity of three months or less. In countries<br>where 3-month rates are unavailable, shorter maturity rates (including overnight rates) are used. Data<br>are at a quarterly frequency and used as annual averages. The real short-term interest rate is the<br>difference between nominal rate and inflation in the following quarter (as a proxy of expected<br>inflation). Data are taken from different sources, and the data series with the longest time coverage is<br>used as the main series. Then, the main series is spliced with those from other data sources. Data for<br>aggregatedgroups are computed with market-weighted output as a weight.|Bank for International<br>Settlements, Haver<br>Analytics, IMF, OECD,<br>World Bank.|
|Business<br>confidence|Business confidence index, originally taken from the European Commission, OECD, and country<br>sources (including statistical offices, central banks, academic institutions, and think tanks). The series<br>is obtained as quarterly data and used as annual averages. Data from different sources are first<br>standardized and converted to an index equal to 100 in 2015-18. Confidence for aggregated groups is<br>computed with market-weighted output as a weight.|European Commission,<br>OECD, country sources,<br>World Bank.|
|Policy<br>uncertainty|Economic policy uncertainty index, which is based on the frequency of articles in domestic newspapers<br>mentioning economic policy uncertainty. Country-level indices are at a monthly frequency and used as<br>annual averages. Data are first converted to a 6-month moving average and then an index equal to<br>100 in 2015-18. Aggregated policy uncertainty is computed with market-weighted output as a weight,<br>based on 20 countries with available data.|Davis (2016); World<br>Bank.|
|Policies|||
|Government<br>expenditure|Government primary expenditure (i.e., government total expenditure excluding interest expense) in<br>local currency, deflated by GDP deflator. The expenditure series is first taken from the IMF and then<br>extended with data in Mauro et al. (2015). GDP deflator is taken from the IMF, PWT 9.1, and the<br>World Bank. Real government expenditure growth in aggregated groups is computed with market-<br>weighted output as a weight.|IMF; Mauro et al. (2015);<br>World Bank.|
|Policy interest<br>rate|Nominal central bank policy rates. Data are at a quarterly frequency and used as annual averages.<br>Data are taken from different sources, and the data series with the longest time coverage is used as the<br>main series. Then, the main series is spliced with those from other data sources. Data for aggregated<br>groups are computed with market-weighted output as a weight.|Bank for International<br>Settlements; Haver<br>Analytics; IMF; OECD;<br>World Bank.|

64
Some of these differences in innovation premium across sectors could be related to having different technological and market structures. For example, there are potentially lower rents created in agriculture due to the production of more homogeneous good or lower bargaining power of workers. But also differences could reflect the fact that the PoTec maybe a less adequate proxy of innovation outside manufacturing, since the PoTec occupations have been selected in relation to their correlation with manufacturing R&D.<sup>9</sup> Table 5: Private Sector: Innovation Premium per sector, 2014-2017

||||Dependent variab|le: _ln_(_wijkft_)|||
|---|---|---|---|---|---|---|
||(1)<br>Agriculture|(2)<br>Agriculture|(3)<br>Manufacturing|(4)<br>Manufacturing|(5)<br>Services|(6)<br>Services|
|Innovation|0.285***|0.0632|0.401***|0.0838|0.109***|0.0686**|
||(0.0515)|(0.0638)|(0.0754)|(0.109)|(0.0121)|(0.0282)|
|Age Squared|0.117***|0.123***|0.319***|0.317***|0.228***|0.211***|
||(0.0115)|(0.0127)|(0.0100)|(0.0102)|(0.00553)|(0.00527)|
|Tenure|0.213***|0.0227***|0.0384***|0.0367***|0.0230***|0.0239***|
||(0.000616)|(0.000690)|(0.000649)|(0.000714)|(0.000646)|(0.000829)|
|Temporary|0.0242|-0.0776|0.143|0.170|-0.00704|0.0479***|
||(0.0629)|(0.0497)|(0.124)|(0.140)|(0.00612)|(0.0104)|
|Firm size|0.0234***|0.0176***|0.0309***|0.0433***|0.0321***|0.0265***|
||(0.00112)|(0.00327)|(0.00124)|(0.00326)|(0.00102)|(0.00146)|
|Individual-Year|Yes||Yes||Yes||
|Individual-Year-Firm||Yes||Yes||Yes|
|Observations|4625738|4618266|22898875|22870154|82711558|82330700|
|R-squared|0.915|0.923|0.945|0.948|0.930|0.940|

_Note:_ Innovative firms is measured by the share of PoTec workers in total employment for firm _f_ in year ** _t_ . Heteroskedasticity robust standard errors clustered at the firm level are reported in parenthesis. ***, and * respectively indicate 0.01, 0.05 and 0.1 levels of significance.

# **4.3 The innovation premium and the large firm wage premium (LFWP)**

A strong implication of the descriptive statistics in Table 3 is the significant difference in terms of firm’s size between innovative and non-innovative firms, which raises the concern that previous results may be only capturing the well-established large-firm wage premium (Oi and Idson, 1999; Bloom et al., 2018). Even though in Table 4 there are controls for firm’s size, as an additional robustness test, we re-estimate the innovation premium dividing the sample according to firm size groups (see Table 6).

> 9In the case of services for example, the role of design is as or more important than of R&D. See for example Cox (1990) for a discussion of the importance of design on R&D and innovation.

19
# **Correcting Sampling and Nonresponse Bias in Phone Survey Poverty Estimation Using Reweighting and Poverty Projection Models**<sup>*****</sup>

Kexin Zhang, Shinya Takamatsu and Nobuo Yoshida<sup>a</sup> Keywords: Phone surveys, Weighting, Poverty projections/estimation, Correction of sampling bias and nonresponse bias JEL codes: I32, C83, C81 __________________________________________________________________________________

* The authors would like to thank Kristen Himelein, Xueqi Li, Aziz Atamanov, Christina Wieser, Anna Luisa Paffhausen for their support for this study. The authors also grateful to peer reviewers, Kevin McGee and Daniel Gerszon Mahler, and all participants at the Quality Enhancement Review meeting held at the World Bank on November 2021, UNECE Conference of European Statisticians 2022, and participants at IARIW Conference 2022 for helpful comments.

a) Kexin Zhang, School of Agricultural Economics and Rural Development, Renmin University of China, zhangkexin629@ruc.edu.cn Shinya Takamatsu, Poverty and Equity Global Practice, World Bank, stakamatsu@worldbank.org Nobuo Yoshida, Poverty and Equity Global Practice, World Bank, nyoshida@worldbank.org
Table 2.4: TANZANIA, Percentage of observations lost due to missing values by industry and size

||Size|S|mall|Me|dium|La|rge|T|otal|
|---|---|---|---|---|---|---|---|---|---|
|Industry||#Obs|%Lost<sup>(d)</sup>|#Obs|%Lost|#Obs|%Lost|#Obs|%Lost|
|Food & beverages|Sampling frame<sup>(a)</sup>|105||87||51||243||
||Complete case<sup>(b)</sup>|47|55.2|44|49.4|17|66.7|108|55.6|
||With replacement<sup>(c)</sup>|82|21.9|57|34.5|31|39.2|170|30|
|Textiles & apparel|Sampling frame|33||41||19||93||
||Complete case|10|69.7|14|65.9|5|73.7|29|68.8|
||With replacement|26|21.2|24|41.5|8|57.9|58|37.6|
|Chemicals, rubber &|Sampling frame|23||55||24||102||
|plastics|Complete case|10|56.5|18|67.3|14|41.7|42|58.8|
||With replacement|13|43.5|40|27.3|16|33.3|69|32.4|
|Paper, edition &|Sampling frame|27||39||9||75||
|publishing|Complete case|8|70.4|19|51.3|6|33.3|33|56|
||With replacement|16|40.7|30|23.1|9|0|55|26.7|
|Machinery &|Sampling frame|49||29||9||87||
|equipment|Complete case|14|71.4|6|79.3|6|33.3|26|70.1|
||With replacement|36|26.5|21|27.6|8|11.1|65|25.3|
|Wood & furniture|Sampling frame|133||53||9||195||
||Complete case|52|60.9|13|75.5|3|66.7|68|65.1|
||With replacement|89|33.1|23|56.6|5|44.4|117|40|
|Non-metallic|Sampling frame|11||16||6||33||
|products|Complete case|3|72.7|11|31.3|5|16.7|19|42.4|
||With replacement|9|18.2|12|25|6|0|27|18.2|
|Total|Sampling frame|381||320||127||828||
||Complete case|144|62.2|125|60.9|56|55.9|325|60.7|
||With replacement|271|28.9|207|35.3|83|34.6|561|32.2|

Notes: (a) “Sampling frame” refers to the total number of observations (firms surveyed multiplied by the number of years of information).

(b) “Complete case” refers to the complete case of production function variables (sales, materials, capital and labor), missing values in other IC variables—other than production function— are not considered.

(c) “With replacement” refers to the sample after imputing IC variables according to the ICA Method; missing values in other IC variables—other than production function—are not considered. Notice that only observations with information available in at least one of sales, labor, labor cost, materials or capital, are imputed (d) “Perc. lost” refers to the percentage of observations lost with respect to the sampling frame. Source: Authors calculations with IC data.

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