rafmacalaba/datause-encoder-data huggingface dataset:
open countries, but not in the group of less ...nancially open countries. Further, the common factors play a larger role in the former countries than in the latter. This applies to both in‡ows and out‡ows, and it is primarily due to the variance contribution of the respective global factors, which is consistently bigger among the former group than among the latter.

In turn, Figure 9 highlights the role of the exchange rate regime using the de facto classi...cation compiled by Ghosh, Ostry and Qureshi (2015), which distinguishes between ...xed, intermediate, and ‡oating regimes. For the purposes of the ...gure, we consolidate the three-way classi...cation into two groups, again depending on how each country’s average degree of ‡exibility over each window compares to the full-sample median; this yields two groups of countries that, in a slight abuse of language, we label ’pegs’and ’‡oats’. The graphs show that the in‡ows and out‡ows of countries on pegged regimes consistently re‡ect the action of common factors to a greater extent than do the in‡ows and out‡ows of countries on ‡oating regimes. This is particularly the case for the global factors, whose variance contribution is up to twice as big in the former group than in the latter. The di¤erence between the two groups along this dimension is especially large in the case of gross out‡ows. Moreover, while the trend of rise and fall of the quantitative role of the common factors around the global crisis a¤ects both country groups, it is much more pronounced among countries with pegs than among those with ‡oating regimes.

Finally, Figure 10 turns to ...nancial depth, classifying the countries into high and low ...nancial depth groups following the same procedure as in the preceding ...gures. Greater ...nancial depth is associated with a bigger role of common factors, for both in‡ows and out‡ows, which again is primarily due to the larger variance contribution of the global factors –although the di¤erence between both country groups along this dimension seems to have narrowed after the global crisis.

These ...gures highlight some ingredients that shape the e¤ect of common factors across countries. However, since they consider one ingredient at a time, they may not convey an accurate picture of the respective role of each one of them. An alternative way to do this is by regressing the estimated factor loadings, which capture the impact of the factors on capital ‡ows, on suitable measures of countries’policy and structural features.<sup>27</sup> In principle, we could run cross-sectional regressions using as dependent variable the fullsample estimates of the loadings. However, these pertain to the full 37-year sample, a time span over which any candidate explanatory variables have surely undergone major changes, which would then obscure their relationship with the loadings in a pure cross-section. To remedy this, we opt instead for using the estimates of the loadings obtained from the moving-

> 27Cerutti, Claessens and Puy (2016) report a similar exercise for portfolio and banking in‡ows to emerging markets.

21
Chesbrough and Teece 2002; Ulrich and Ellison 2005). In fact, despite strong arguments for the advantages of outsourcing, previous studies show few positive or else contradictory effects of outsourcing on performance (Bengtsson et al. 2009<sup>4</sup> ).

In addition to the abovementioned advantages and disadvantages that innovation outsourcing shares with general outsourcing, it also has certain differentiating characteristics, such as a diverse motif and risks, which influence the particular way it affects productivity.

Unlike general outsourcing, in most cases innovation outsourcing activities are identified for strategic reasons, which is likely to produce a different effect on firm output. Firms choose to outsource innovation in areas where they do not have in-house ability and use innovation outsourcing as an instrument to acquire external knowledge, which is subsequently integrated into a firm's own knowledge base, and to access suppliers' competencies. Therefore, innovation outsourcing could potentially improve a company’s capacity to stay current and innovate by interacting with “more advanced knowledge sources.” Also, firms may need to outsource innovation to gain the capacity to produce unique products or improve quality. In fact, product innovation is conducted mainly for these strategic reasons; firms may also benefit from having ability to offer a variety of diversified products they otherwise do not have the capacity to produce. More firms now outsource globally to tap into the richer external source of knowledge, probably because rents from acquired new products or processes may significantly outweigh costs of innovation outsourcing (Quinn 1999; Chesbrough 2003; Fifarek et al. 2008).

However, unlike general outsourcing, there is a risk of information leakage from firms that receive innovation outsourcing services, and therefore, firms’ innovation outsourcing is at a less than optimal level where information leakage cannot be monitored (Lai, Riezman, and Wang 2009<sup>5</sup> ). According to a survey conducted by the Shared Services and Business Process

> 4 . Related literature is cogently summarized in Bengtsson, et al. 2009.

> 5 . Lai, Riezman, and Wang (2009) provides an analytical framework related to the cost-saving aspects of innovation outsourcing using a principal-agent framework to analyze whether a production firm under

7
<!-- Start of picture text --> 20<br>4. 0199182<br>5200001<br>10 B2007<br>5<br>0<br>DarEsSalaam Other Urban Rural Mainland<br>Tanzania<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>30<br>ne 199182]<br>0 A200001<br>15 2007<br>10<br>5<br>;<br>DarEsSalaam OtherUrban = Rural Mainland<br>Tanzania<br><!-- End of picture text -->
Europe which faced prolonged episodes of financial market stress, including a high degree of foreign ownership and greater reliance of cross-border funding in many countries. As a result, the slowdown of credit growth in the ECA region was generally driven by foreign banks, as they initiated efforts to repair their balance sheets and retrench to home markets.

Concerns with a dramatic credit crunch in the ECA region led to a concerted effort by regulators and multilateral institutions to preserve minimum levels of cross-border funding, particularly funding by (Western European) parent banks. This effort – labeled as the Vienna Initiative – appears to have succeeded in avoiding a very severe credit contraction in the participating countries (de Haas et al, 2012). Yet, the mere fact that this outcome required a concerted effort by many actors revealed the potential downside risks associated with foreign banks and crossborder funding, and triggered a renewed debate about the benefits and costs associated with foreign banks, cross-border finance, and financial integration.

The crisis triggered an increasing research effort to disentangle the factors that drove the more pronounced and protracted fall in post-crisis credit growth in most ECA countries. This research effort is warranted, as some ECA countries with a high degree of foreign ownership but less reliance on foreign funding seem to have been less affected (e.g. Czech Republic, Poland, Slovakia), while other ECA countries with a lower degree of foreign ownership but strong reliance on cross-border funding were very affected (e.g. Kazakhstan, Slovenia, Ukraine).

Research based on bank-level data confirms that foreign banks did contract local credit provision more than domestic banks in emerging countries, but also that balance sheet structures matter, including funding structures. Countries that had a strong presence of foreign banks but also relied more on their domestic deposit base suffered a much less severe credit slowdown. The balance sheet strength of parent banks also seems to matter—host countries with stronger parent banks also experienced a less severe credit slowdown.

These results suggest the need to put more emphasis on the discussion of business models, regulatory standards, and supervisory arrangements, rather than foreign ownership _per se_ . One 3
Table 8 contains estimates of inclusionary and exclusionary targeting error of the dibao program calculated using local dibao thresholds as the targeting criterion. Targeting errors have declined over the three years. For example, based on estimates using the county average dibao expenditures, from 2007 to 2009 inclusionary error declined from 94% to 86%, and exclusionary error from 94% to 89%. Despite these improvements, the overwhelming majority of dibao beneficiaries had ex ante incomes higher than the local dibao thresholds. Moreover, the dibao program reached only a small proportion (11% or less) of individuals with ex ante incomes below the dibao thresholds. In all years, then, it appears that the vast majority of eligible individuals did not benefit from the program.

By comparison, for China’s urban dibao program Chen, Ravallion and Wang (2006) report an inclusionary error of 43% and an exclusionary error of 71%. Although based on data for earlier years, their estimates suggest that the targeting performance of China’s urban dibao program is markedly better than that of the rural dibao program. Weaker performance of the rural dibao program is not surprising given the uneven capacity and resources of local governments in rural China, as well as the difficulty of measuring rural incomes.

The targeting performance of the rural dibao program can also be evaluated relative to the poverty line so as to ascertain the extent to which the program benefited the poor versus nonpoor. Table 9 shows the shares of the poor and nonpoor who received dibao benefits. These shares are calculated using the three poverty lines and ex ante incomes. In all cases, less than 10% of the poor received dibao transfers. A higher proportion of the poor than nonpoor, however, were dibao recipients. For example, based on the 2011 official poverty line, the percentage of the poor receiving dibao benefits in 2009 was 8%, versus less than 3% of the nonpoor. Also, the proportion of the poor who received dibao benefits increased over time. For example, based on the official poverty line, the share of the poor receiving dibao benefits increased from 4.7% in 2007 to 8.0% in 2009.

How well does the dibao program target poor households? Table 10 shows the inclusion and exclusion errors calculated using ex ante incomes in relation to the official poverty line. The inclusion error is between 64 and 75%, depending on the estimate and year. That is, between 64 17
Figure 3: Prova Brasil-based management index x WMS management index <!-- Start of picture text -->
-2 -1 0 1 2<br>Management Index, WMS<br>1<br>.5<br>0<br>-.5<br>Management Index, Prova Brasil<br>-1<br><!-- End of picture text -->

Note: This graph is a binned scatter plot. Each circle represents the average of 5 schools. The sample contains 262 schools which have data for both Prova Brasil and WMS in 2013. Correlation of 0.19 (p-level:0.00).

35
# 5. International comparisons

Brazil has one of the highest social expenditure and fiscal revenue levels for an upper-middle income country in the sample of available CEQ assessments (Figure 15). The direct transfer expenditure as a percentage of GDP is the highest (7.9%) in the sample, even greater than the total social expenditure of at least five countries. Only Argentina also dedicates more than 7% of its GDP to direct transfers, and the size of direct transfers in Brazil is more prominent in 2019 than in the previous fiscal incidence analysis relative to 2009 (5.4%). On the other hand, public education expenditure is the fourth highest in the sample, only trailing Costa Rica. It is similar to that of South Africa and Argentina, and is slightly higher than that for Brazil in 2009. Government health spending is above the average, and in the top 7 among 18 countries, behind Latin American countries like Argentina, Costa Rica, and Colombia.

Tax revenues are the highest in the sample of countries (Figure 16). Only Belarus approaches Brazil, followed by Argentina and Brazil 2009 estimates. The top component of Brazil's government revenues are the indirect taxes, which are well above the average of other countries in the sample, surpassed only by countries like Namibia, Belarus, and Botswana. The value of indirect taxes as a percentage of GDP is slightly above that of Argentina and Brazil's 2009. Social contributions (11.6%) are also much higher than in almost all other countries in the sample, whose average is 3.9% of GDP. Only social contributions from Argentina, Brazil (in 2009 and 2019), and Belarus exceed 8% of GDP. Third, the direct taxes revenues in Brazil were also above average in 2019, with a value very close to the 2009 estimate, and only behind the level of South Africa, Namibia, Georgia, and Botswana. Finally, Brazil's 2019 non-tax revenues, also above the sample average, are in third place, only below Ecuador and Botswana.

**Figure 15. Components of social spending, upper‐middle‐income countries (% of GDP)**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Direct transfers Health Education<br><!-- End of picture text --> Source: Authors based on CEQ Data Center.

36
labor market changes were contemporaneous with rising food prices, they are worth taking seriously as possible confounding factors. Given my estimation strategy, however, NREGA will only affect the results insofar as the local expansion of the program was systematically related to the change in the agricultural price index or, rather, to the instrumental variable for this change.

Based on 7-day employment recall information in the NSS-EUS, I compute the population weighted mean of the fraction of days per week spent in public works employment (both NREGA and other) for rounds 61 and 66.<sup>29</sup> Including the 2004-09 change in this public works employment variable (∆ _PW_ ) in regression (8) results in no appreciable changes in my estimates of _γ_ (compare cols. 5 and 7 of Table 3). Of course, the coefficient on ∆ _PW_ does not necessarily reflect the _causal_ impact of NREGA or any other public works employment program in India on rural wages; this specification merely serves as a robustness check.

# **4 Food Prices and Welfare**

## **4.1 Welfare elasticities**

Now consider a rural household embedded within the economy sketched out in Section 2. It has an endowment of farmland and of manual labor that it can supply to its own farm (insofar as it has land) or to other farms, or to firms in the services or manufacturing sectors. Household income is simply _y_ in equation (5) with aggregate endowments replaced by household endowments. Indirect utility is a function of this income and of prices, _PM , PS,_ and _Pj_ , _j_ = 1 _, ..., c_ . Following the conventional derivation, the percent change in

> 29This is essentially the same variable considered by Imbert and Papp (2012). Across districts, the mean days per week of public-works employment reported in 2004-05 was 0.0022, increasing to a still rather minuscule 0.0144 days per week in 2009-10. Note, however, that NREGA employment is concentrated in the agricultural off-season.

21
experience losses in cattle value (which can also be inferred from the median value of cattle even rising for coffee farmers), i.e. there is no indication of desperation-led selling of cattle; and second, the value of cattle appears to fall significantly for the most specialized coffee farmers.

Table 10: Changes in cattle endowments by farm type, 1995-1999

|_Household Type_|_Share of_<br>_with cat_|_farmers_<br>_tle (%)_|_Valu_|_e of cattle (_|_in 1989 prices)_|
|---|---|---|---|---|---|
||_1995_|_1999_|_19_|_95_|_1999_|
|Non-coffee|16|21|196|328|297653|
|farmers|_12_<br>_19_|_12_<br>_23_|_146024_|_246632_|_249418_<br>_345887_|
|Cff frmr|21|28|258|471|221835|
|oee aes|_17_<br>_25_|_17_<br>_31_|_101795_|_415148_|_180119_<br>_263551_|

Source: Authors’ calculations.

Note: 95 % confidence intervals in italics (standard errors corrected for survey design).

This evidence is supported by qualitative questions in the 1999/2000 survey, in which households are asked to assess the availability of different types of assets at the date of interview compared to 1992. Table 11 shows that farmers who grow coffee on more than 60 percent of their cultivated land report declining livestock assets. Under the plausible assumption that the evaluations of farmers mainly refer to recent changes in asset availability, this fits well with the quantitative evidence.

Table 11: Coffee farmers’ assessment of change in livestock assets between 1992 and 1999

|_D egree of_<br>_specialization,_<br>_coffee area as_<br>_share of cultivated_<br>_land_|_E valuation of_<br>_change in_<br>_livestock assets,_<br>_1992-99_|
|---|---|
|no coffee|2.84|
|< 0.2|3.29|
|< 0.4|3.35|
|< 0.6|3.05|
|< 0.8|2.89|
|> 0.8|1.89|

Source: Authors’ calculations. Note: 3 equal, > 3 more, < 3 less in 1999 than in 1992.

27
|ImprovingPovertyM&E Systems in Guatemala|Pedro Olinto|
|---|---|
|ImprovingPovertyM&E Systems in Guatemala|Pedro Olinto|
|ImprovingPovertyM&E Systems in Guatemala|Pedro Olinto|
|ManagingHealthcare Waste and PCBs|Africa Eshogba Olojoba|
|ManagingHealthcare Waste and PCBs|Africa Eshogba Olojoba|
|ManagingHealthcare Waste and PCBs|Africa Eshogba Olojoba|
|ManagingHealthcare Waste and PCBs|Africa Eshogba Olojoba|
|ManagingHealthcare Waste and PCBs|Africa Eshogba Olojoba|
|ManagingHealthcare Waste and PCBs|Africa Eshogba Olojoba|
|ManagingHealthcare Waste and PCBs|Africa Eshogba Olojoba|
|ManagingHealthcare Waste and PCBs|Africa Eshogba Olojoba|
|ManagingHealthcare Waste and PCBs|Africa Eshogba Olojoba|
|ManagingHealthcare Waste and PCBs|Africa Eshogba Olojoba|
|Corporate Plan for Ghana Statistical Service - Part II|John Y. Ngwafon|
|Corporate Plan for Ghana Statistical Service - Part II|John Y. Ngwafon|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|INDIA - FinancingEnergyEfficiencyat SMEs|Sitarama Krishna Addepalli|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
|Coal-Fired Generation Rehabilitation|Ashok Sarkar|
maintain the full variation of household budget shares. It is this heterogeneity in budget shares that explains horizontal inequity and reranking.

The redistributive effect shown in the second column of table 2.5 reveals that the pre-2000 indirect tax system in Australia was slightly regressive overall and for most household types except for couples with no children and at least one retiree and for single retirees. Reranking is more substantial than horizontal inequity. It can be noted that the favorable redistribution towards type 3 and type 10 household would be more significant in the absence of the reranking effect. Reranking reduces redistribution for type 3 by about 32 percent and by 22 percent for type 10. The largest amount of reranking is observed for single parents with one dependent child. The author explains that this reranking is largely due to exemptions working through heterogeneous budget shares.

Table 2.5. The Redistributive Effect of Indirect Taxes in Australia

|Household type|RE|V|V(%)|H(%)|R(%)|
|---|---|---|---|---|---|
|1. All households|-0.0013|-0.0009|71.39|0.66|27.94|
|2. Couple, no children|-0.0021|-0.0017|80.55|0.05|19.39|
|3. Couple, no children, at least one retired|0.0018|0.0025|132.84|1.32|31.53|
|4. Couple, one dependent child|-0.0017|-0.0013|76.29|1.45|25.16|
|5. Couple, two dependent children|-0.0013|-0.0010|75.77|1.51|25.74|
|6. Couple, three or more dependent children|-0.0027|-0.0024|87.56|0.13|12.57|
|7. Single parent, one dependent child|-0.0013|-0.0008|64.17|2.42|38.25|
|8. Single two or more dependent children|-0.0020|-0.0016|79.29|1.92|18.79|
|9. Single, not retired|-0.0038|-0.0032|84.91|0.35|15.44|
|10. Single, retired|0.0029|0.0036|123.46|1.27|22.19|

Source: Creedy (2001) Urban and Lambert (2008) explain that the AJL machinery reviewed above works only when applied to groups of exact equals. They observe that typical real-world datasets rarely contain exact equals. In such circumstances, analysts use groups of close equals to identify the horizontal effect of a tax system. Such groups are defined on the basis of a chosen bandwidth that determines the maximum difference between the pre-tax incomes of any two individuals considered as near equals. Urban and Lambert identify 17
and that this government involvement can cause nonresponse and false responses on politically sensitive questions.<sup>5</sup> This is especially important since the survey includes a set of questions on the operations of firms, firms’ experience in dealing with different government authorities, and the effects of different government policies on firm investment. Later in this paper we outline our strategy to deal with potential nonresponse and false response by including list of randomized experiment questions for our analytical purposes.

Our survey wave includes a total of 1,417 owners or managers spread across all of the six administrative divisions, although the largest percentage of firms (45.07%) are located around the capital, Dhaka, the center of most of the economic activities of the country. The nationally representative firms in this survey, mirroring the universe of firms in Bangladesh, tend to be small, with over half of the firms employing less than 10 workers, and less than 7% employing more than 50 workers. This includes a mix of newer and older firms, where slightly more than 50% of the firms were established before 1999. Finally, the firms in the survey are almost evenly split between firms in urban areas and rural areas (51.67% to 48.33% respectively).

The largest number of these firms are located in the manufacturing sector (33.61%) followed by wholesale and retail trade (21.94%), finance (13.82%), and hotel and restaurant (10.83%). The vast majority of these manufacturing firms are in the textile industry engaging in the production of garments, leather goods, or other textiles.<sup>6</sup> Unfortunately, Bangladesh’s low levels of development and limited success in attracting foreign investment doesn’t allow for sufficient variation in ownership structure

> 5 Jensen, Li, and Rahman (2010). 6 This sector accounts for 23.82% of the firms in our sample, and over 70% of the manufacturing firms.

7