rafmacalaba/datause-encoder-data huggingface dataset:
Nevertheless, if there is misreporting in yi in equation (2) so that y<sup>Standard</sup> and y<sup>Experiment</sup> are systematically different from each other, and the same is observed for at least some proxies (x), then β will be biased as well. Following our results in Table 5 and Table 6, it would seem reasonable to assume that the misreporting in x and y are correlated, and have means different from zero. With measurement errors on both sides of the regression, there are no boundaries on direction or size of bias in β (Bound, 2001). Although direct measurement of consumption in household surveys is often considered as the best approximation for true consumption, we can only note that the propensity for reporting consumption is sensitive to questionnaire design, and that consumption regressions from such surveys could be biased due to misreporting.

In future methodological experiments, comparable questionnaire modules could be assigned different orders for different random subsets of the samples that receive experiment versus standard questionnaires, holding the content of the modules, the order of questions in each module, and the interview mode constant. This would, in turn, provide an opportunity to assess whether the reporting differences hold uniformly irrespective of module placement. Similar exercises could be carried out to assess the effect of the order of key questions, holding the content of the modules, the order of modules, and the interview mode constant in alternative questionnaire instruments. These efforts could be complemented by the applications of pretesting techniques, such as cognitive interviews and behavior coding, that could help illuminate cognitive and behavioral processes that play out in answering the same questions as part of different questionnaires (Presser et al., 2004). Moving forward, household survey operations designed for proxy-based poverty measurement should, prior to full roll-out, consider piloting their instruments in parallel with the questionnaire instruments from which they have evolved. This methodological exercise could be designed as a randomized household survey experiment to test whether the data for poverty predictors differ depending on whether they were solicited in an experiment versus a standard questionnaire.

15
## SPLIT DECISIONS

Table 6 shows the impacts on household decision-making by applicants. Household survey respondents were asked who bears the principal responsibility for household decisions in five areas. They could answer themselves, another identified member, or shared decision-making by themselves and another identified member. The outcome variable in this table is an indicator for whether or not the job applicant is a principal or shared decision-maker in each area.

Migration by the applicant causes important changes in how household decisions are made. It causes decision-making by the applicant to decline in all areas, though these declines are only statistically significant in the case of “major purchases” and “weekend activities”. The lower half of the table considers only applicants who were married at baseline. For them, the magnitude of the declines is much larger, and they are statistically significant for “childcare”, “major purchases”, and “weekend activities”. A graphical representation of the “childcare” row is shown in the lower-right panel of Figure 3.

# 6.3. Effects on individual children

Table 7 shows the effects on children. The table considers household members under age 18 who are the children of the applicant or the applicant’s spouse.

Considering only those of schooling age (⩾ 6), migration does not affect school enrollment. This is to be expected given that over 98% of children in this age group are already enrolled. Migration does cause a 41 percentage point increase in the probability that a child is in private school (from a base of 28 percent). Migration also causes a 30 percentage point increase in the fraction of children who are receiving awards at school. This could be due to better performance in school, or due to a different propensity for private schools to give awards.

Considering children of all ages, migration by the applicant does not cause changes in the probability that these children visited a health facility in the previous month. There is suggestive evidence that it causes more of those visits to be at private health facilities, but this is imprecisely measured (ITT _p_ = 0 _._ 16, TOT _p_ = 0 _._ 18). Migration by the 19
all. Unfortunately, typically little if any effort is devoted to estimating, let alone reporting, the substantial margins of error in any other governance and/or investment climate indicators – objective or subjective, aggregate or individual. A key advantage of the WGI is that we are explicit about the accompanying margins of error, whereas in most other cases they are often left implicit or ignored altogether.<sup>3</sup> Despite these margins of error, the WGI are sufficiently informative that many cross-country comparisons result in statistically (and likely also practically) significant differences in estimated governance. In comparing governance levels across countries, for example, we document that over 64 percent of all cross-country comparisons using the WGI for 2008 result in statistically significant differences at the 90 percent significance level, and nearly 74 percent of comparisons are significant at the less stringent 75 percent significance level. In assessing trends over time, we find that 35 percent of countries experience significant changes (at the 90 percent level) over the decade 1998-2008 in at least one of the six indicators (roughly evenly divided between significant improvements and deteriorations). This highlights the fact that governance can and does change even over relatively short periods of time. This should both provide encouragement to reformers seeking to improve governance, as well as warn against complacency in other cases, as sharp deteriorations in governance are possible as well.

The aggregate indicators that we report constitute a useful way of organizing and summarizing the very large and disparate amount of information on governance embodied in all of our underlying data sources. The specific aggregation procedure we use also allows us to calculate explicit margins of error to capture the inherent uncertainties in measuring governance. At the same time, we recognize that for some purposes the information in the many individual underlying data sources can be of interest to users. For example, several of these provide highly specific and disaggregated information about particular dimensions of governance that could be of interest for monitoring particular reforms. For this reason, we report country scores on the individual indicators underlying our aggregate governance indicators, on the WGI

> 3 The only other governance-related indicators that we are aware of that now report margins of error are the Transparency International Corruption Perceptions Index and the Global Integrity Index.

3
asset ratio of 13.2%, compared with 11.7% for non-African banks. African banks were also substantially smaller. The median bank held $455 million in assets in 2020, compared with $1.2 billion for the median non-African bank. African banks also lent less: non-interest income was 24.9% of total income for the median African bank and only 13.9% of the income of the median non-African bank. These comparisons give a picture of African banks that are well-capitalized, over-liquid, and provide only limited lending to the real economy, as highlighted previously by Honohan and Beck (2007), Beck et al. (2011), and Beck et al. (2019). Moreover, there has been improvement among African banks on some of these indicators – in 2010, the median African bank held only $302 million in assets, non-interest income was 36.5% of total income, and liquid assets were 26.7% of total assets. There was not enough progress, however, to close the gaps with banks in non-African developing economies.

The bank-level data also permit an analysis of the drivers of high operating costs in African banking. More specifically, Table 1 uses regression analysis to relate bank-level variation in overhead costs to bank- and country-level characteristics and compares banks in Africa with banks in non-African developing countries. For a cross-country sample of banks, overhead costs in 2020 are regressed on (1) the share of non-interest income, (2) the equity-asset ratio, (3) the liquidity ratio, (4) loan growth over the previous year, (5) the log of total assets, (6) the inflation rate and (7) the Kaufmann, Kraay and Mastruzzi (2011) indicator of Rule of Law. The results in Table 1 indicate the extent to which these different factors contribute to substantially higher overhead costs of banks in Africa (3.44% in the regression sample) than in banks outside Africa (1.87%). Relatively high reliance by African banks on non-interest income and their smaller size can explain 3 and 54 basis points, respectively, of the difference in overhead costs. Higher 12
the identification assumption that capital adjusts with a lag to productivity.<sup>15</sup> The consistent LP production function coefficient estimates are shown in Table 2.

To implement the second step, we use those production function estimates to compute firm-level TFP estimates as residuals from Eq. (1). We average the firm-level TFP estimates at the sector- _comuna_ level using firm-level employment shares as weights, and compute the corresponding growth rate between 1992 and 2004 to obtain sector- _comuna_ TFP growth.

# **3. Empirical Specification and Local Economic Structure Measures**

Our empirical specification relating TFP growth with the local economic structure pools across Chilean sector- _comuna_ cells and is given by: _TFPg jrt_  _T_   0   1 _Conc jrt_   2 _Div jrt_   3 _Comp jrt_   4 _AvgS jrt_  _TFPjrt_  _I j_   _jrt_ (2) where _TFPg jrt_  _T_<sup>is TFP growth of sector</sup><sup>_j_in</sup><sup>_comunar_between 1992 (</sup><sup>_t_) and 2004 (</sup><sup>_T_),</sup> _Conc jrt_ , _Div jrt_ , _Comp jrt_ , and _AvgS jrt_ are indices capturing local concentration, local diversity, local competition, and local average firm size in 1992, and  _jrt_ is an i.i.d. residual. _TFPjrt_ is the TFP level of sector _j_ in _comuna r_ in 1992 that allows to capture possible convergence to the mean for sector- _comuna_ cells initially lagging behind. Sector fixed effects _I j_ account for sectoral unobserved factors driving growth between 1992 and 2004 possibly correlated with the local economic structure in 1992: e.g., demand shocks experienced by some sectors and _comunas_ that may be correlated with the local economic structure.

> 15 We assume that productivity follows a Markov process and capital does not adjust to the unexpected component of current productivity. The reader is referred to LP for technical details.

10
increased both the use of maternal and child health services and favorable newborn practices, and, to a lesser extent, mother’s knowledge about maternity care and newborn health problems. Comparing urban areas covered by the Bangladesh Smiling Sun Franchise Program to adjacent areas, and one and four years after the program was initiated, Lance, Angeles, and Kamal (2012) find that the program did not have positive effects on the rates of use of modern contraceptives, use of antenatal care services, or vaccination. The authors indicate that the results are due to a growing intensity of other government and NGO maternal and child health service activity in the comparison areas.<sup>18</sup> _Demand_ : Kamal et al. (2016) find a large increase in the use of maternal and child health services over the first decade of the 2000s in both urban and rural areas in Bangladesh, and that the difference by household economic status in the use of such services has narrowed, particularly in urban areas. They also find that the use of private maternal and child health services dominates, although the use of such services is lower among poorer households.

Slum residents in Dhaka city report that they prefer seeking health care from pharmacies and nonformal or traditional healthcare practitioners, and prefer home deliveries by traditional birth attendants (Adams, Islam, and Ahmed 2015). These findings are consistent with other evidence for urban slum and poor households in Bangladesh (for example, see Nahar and Rahman 2010 for Chittagong city). The findings are also consistent with evidence for many other developing countries that the poor tend to obtain care from informal private health practitioners, who often lack required academic and professional qualifications or are unlicensed to practice (Sudhinaraset et al. 2013).

Gazi et al. (2015) examine the reasons reported by nonusers of maternal and child health services provided by NGO health facilities in Sylhet city under the Bangladesh Urban Primary Health Care Project and the Bangladesh Smiling Sun Franchise Program. They find that the main reasons include the lack of awareness of services, the limited range of offered services, and the availability of free services at public health facilities. Using 2006 BUHS data, Islam and Gagnon

> 18 Among other studies, Uddin et al. (2010) use data gathered just before the intervention was initiated and just before it was completed, and find that the Expanded Program of Immunization administered by the Dhaka city-corporation government and NGOs increased the child immunization rate in slum settlements, and that a key factor presumably was longer immunization service hours. Comparing slum settlements in Narayanganj city corporation covered by the Manoshi program to those in another nearby municipal town, Jolly et al. (2016) find higher rates of use of antenatal and postnatal care services in settlements covered by the Manoshi program, although the difference in use rates was smallest among the poorest women.

13
- 28 -

first-order conditions for the optimal<sup>stocks of physical and knowledge capital</sup> turn out to be free of tax parameters.<sup>The derivation of these optimality</sup> conditions can be found in appendix<sup>(4). They are given by:</sup> The left-hand sides of these optimality<sup>conditions represent the expected</sup> net marginal benefits from increasing<sup>the two capital stocks, while their right-</sup> hand sides are the rental prices of<sup>the services from physical and knowledge</sup> capital respectively under the assumption<sup>that there is full short-run forward</sup> shifting of the CIT. In future these<sup>expressions for the rental prices of</sup> capital services will be referred<sup>to as rppt(fs)</sup> [equation (27)] and as rpk(fu) (equation (28)3, where "fsal again denotes full shifting.

As mentioned before, for the Pakistani<sup>textile industry data limitations</sup> prevented us from incorporating R&D and the stock of knowledge capital<sup>into the</sup> model. For that industry the rental<sup>price of the services from knowledge capital</sup> (equation (28)3 is therefore irrelevant.

First-order conditions (27) in the<sup>presence of full tax shifting and (15)</sup> in its absence, both correspond<sup>to equations (7) and (16) of Pindyck and</sup> Rotemberg (1983). The difference<sup>is that our optimality conditions are fully</sup> consistent with rational expectations<sup>on the part of firms, and that our model</sup> incorporates non-separable internal<sup>adjustment costs due to gross investment in</sup> physical and knowledge capital.

At the time of the imposition of<sup>(change in) the CIT the dominant firm's</sup> capital stocks are still the same<sup>as they would be in the absence of the tax.</sup> But over time the firm's optimal capital<sup>stocks K% (fe) and K4 (fs) in the</sup>
in our counterfactuals. Our paper focuses on the entry margin: we ...nd that the reductions in both ...xed and marginal costs induce a lot of entry of Bangladeshi ...rms into the export market, which occurs because ex-ante expected pro...ts shift upward and this increases the equilibrium mass of entrants. This increase is large due to the low estimated elasticity of substitution between goods made by Bangladeshi ...rms. Our estimated elasticities, as we argue below, are broadly in line with those in the literature.

The paper proceeds as follows. Section 2 contains a brief discussion of the empirical application and the data. Section 3 lays out the model with the details of the derivations in the Appendix. Section 4 lays out the estimation outline. The results are presented in Section 5, while policy counterfactuals are presented in Section 6. Section 7 concludes.

# 2 The Empirical Application

We use data on the woven apparel sector in Bangladesh, in particular, the subcategory Mens/Boys Cotton Trousers (HS 620342). Firms that produce garments from woven fabrics typically use imported or domestically produced fabrics to make apparel items, such as men’s cotton shirts or ladies dresses, and export the ...nished products to the EU or US. Given that fabrics make up more than 50 percent of the costs, the origin of the fabric has implications for the tari¤s faced by these products in each market, due to the existence of trade preferences in some markets. We describe the setting in some detail below as it is the basis for how we margin of trade can be found in Yi (2003), who focuses on multi-stage production, and Kehoe and Ruhl (2013) and Debaere and Mostashari (2010), who focus on the new goods margin. Giovanni and Levchenko (2013) argue that reductions in marginal costs would have a far larger impact than reductions in ...xed costs in a world with signi...cant fractions of very large ...rms, i.e., where the size distribution of ...rms has fat tails as in the U.S. data. In such settings, small ...rms contribute little to trade and reductions in ...xed costs, which a¤ect the entry of small ...rms, do little to increase trade. Lincoln and McCallum (2011) look at the U.S. census data and ...nd that in the US, both the number of ...rms and their average size grew from 1987 to 2006, consistent with a fall in marginal costs driving entry and larger size. However, they attribute this to rising incomes in export markets rather than to a fall in ...xed or marginal cost. On the other hand, by focusing on cross sectional data of a wide range of developed and developing countries, Helpman, Melitz and Rubinstein (2008) show that ...xed costs of trade could determine the extensive margin of trade through the selection of ...rms into the export market.

7
-6-

US$31.6 billion, with a GNP per capita of US$1,869. The openness of the economy can be seen from the high ratio of external trade to GNP of 130% in 1987, and the historical sensitivity of the external payments position to changes in the terms of trade (Figure 1).

2. Historically, Malaysia has been considered a model of cautious economic management. The Government has a stated policy of maintaining a surplus in its current account budget, strong external reserves backing for its currency and low inflation. In the 1960s, the country enjoyed a steady expansion in average annual real GDP of 5.2% with inflation of less than 1% annually. In the turbulent 1970s, Malaysia grew by an average of 8% in real terms, with a peak GDP growth of 9.3% in 1979, but inflation rose to an average of 6%, peaking at 11.1% in 1981 (Table 1).

# 1.2 DecLine in growth

3. In the light of its strong financial position and to counteract the impact of the severe international recession of 1980-82, the Government embarked in 1980 on a counter-cyclical policy to build up infrastructure and an industrial base, <!-- Start of picture text --> Malaysia: Fiscal deficit and<br>5an i nM I GNP roWth<br>20<br>10-<br>5<br>-10<br>-15<br>-20- \ X<br>1976 1977 1978 1979 1980 tPt 19t82 1983 1984 1985 1986 1987 1988<br>Your<br>a Flecal deficit/GNP + Naninal GhP growth<br><!-- End of picture text --> Figure 2
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# Table 3: Characteristics of fiscal events

|||f|iscal eve|nts(95) <sup>a/</sup>||
|---|---|---|---|---|---|
||Av<br>_(aver_|erage level<br>_age annual_|by 5-year<br>_growth i_|period<br>_n Italics)_|change ppa|
||Pre-ev<br>[t-n<br>|ent period<br>-1; t-1]<br>|Post-ev<br>[t<br>|ent period<br>;t+n]<br>||
||(1)|(2)|(3)|_(4)_|(5)=(3)-(1)|
|% of public expenditure in GDP<br>All|25.51|_-1.33_|24.43|_0.73_|-1.08|
|<br>HIc/|29.41|_-0.85_|28.76|_0.82_|-0.65|
|non HI c/|23.53|_-1.58_|22.22|_0.69_|-1.31|
|% of public deficit/surplus in GDP<br>All|-2.92|_0.79_|-0.73|_-0.32_|2.19|
|<br>HIc/|-1.94|_0.69_|-0.25|_-0.50_|1.69|
|non HI c/|-3.44|_0.84_|-0.97|_-0.23_|2.46|
|Expenditures by function|||||Change %|
|<br>_(in % of total public exp.)_|||||<br>(5)=(3-1)/1|
|<br>_Education_<br>All|12.54|_0.16_|13.50|_0.15_|7.7%|
|HI|9.95|_-0.06_|10.17|_0.03_|2.1%|
|non HI|13.80|_0.28_|15.13|_0.21_|9.7%|
|_Health_<br>All|7.13|_0.07_|7.50|_0.07_|5.1%|
|HI|8.70|_0.01_|8.92|_-0.03_|2.5%|
|non HI|6.36|_0.10_|6.80|_0.13_|6.9%|
|_Transport & Communication_<br>All|6.17|_-0.13_|5.46|_0.01_|-11.6%|
|HI|4.83|_-0.18_|4.32|_0.01_|-10.8%|
|non HI|6.80|_-0.11_|5.99|_0.01_|-11.9%|
|_Non-interest public services_<br>All|18.04|_-0.94_|16.78|_-0.40_|-7.0%|
|<br>HI|13.77|_-0.25_|13.64|_0.09_|-1.0%|
|non HI|20.18|_-1.30_|18.35|_-0.65_|-9.1%|
|_Defense_<br>All|9.97|_-0.18_|8.82|_-0.06_|-11.6%|
|HI|9.18|_-0.10_|8.42|_-0.15_|-8.3%|
|non HI|10.37|_-0.23_|9.01|_-0.01_|-13.1%|
|_Housing & community amenities_<br>All|3.09|_0.09_|3.57|_0.12_|15.5%|
|<br>HI|3.03|_-0.02_|2.86|_0.18_|-5.7%|
|non HI|3.12|_0.15_|3.92|_0.09_|25.9%|
|_Recreation, culture,& religion_<br>All|1.41|_0.01_|1.23|_0.01_|-12.8%|
|<br>HI|0.97|_0.00_|0.98|_-0.01_|0.9%|
|non HI|1.62|_0.01_|1.35|_0.02_|-17.0%|
|_Others (residual)_<br>All|41.64|_-_|43.15|_-_|3.6%|
|<br>HI|49.55|_-_|50.70|_-_|2.3%|
|non HI|37.75|_-_|39.44|_-_|4.5%|

Notes: See annex A.1.3, table A.3 for country classification in the sample.

Col. 1: average over 5-year period preceding fiscal event; Col. 3: average over 5-year period after fiscal event a/ Based on the benchmark set of parameters (table A.4, row 1).

b/ Average annual growth rate over the period in parenthesis c/ HI stands for “High-Income” countries as defined by World Bank (July 2007); “HI” sample = 32 fiscal events, “non HI” sample = 63 events.

_Source: Authors’ computation from GFS and PWT 6.2 data, see Annex A.2._ 18
# able: Relative Nag.t04 of 3e.hvez4 4 _logwz4_ szxioltua Llkage IA Regal Afrsia

3e~ag4 LiNkale fm tr_umlt Ford& Ltnae fSm Agurltte Rtatio of Distribution of DistLbutrsX Vogve04 to Zqlmmt Tmlmmt Iqiutted Food Leathawand Za_rsz s_ataz RePSs toot Tota ptooesla Rastaurea Rutabag w*or Stogae Total Ll _kage._ (aIogmmt per 1,06 population) 206ia (1965) DipeAaettlna -- 3.2 -- 3.2 44.0 0.0 -- 0.4 4.71 49.1 15 s I TS. _st 11.000 -- 1.2 -- 1.2 M.5 1.9 -- 0.6 26.6 ".5 55 Wal sm_, 12,000-50,000 -- 1.4 -- 1.4 1i.9 1.3 -- 0.6 26.7' 30.5 3 < bStaloi1l -- 2.7 -- 2.7 56.0 0.2 -- 0.4 7.41 46.0 17 **Slog** Lema (1974) lettlmrts _r 2,000 -- 5.5 5.5 0.0 _bI_ -- -- -- -- -- -- aL TWsN. 5,000-20,000 0.7 -- 0.7 0.0 ;/ -- -- -- -- -- -- (otos_t of Rural Soot _m htertsos)_ 1X_a (1977) Cea PCec i g 0.2 5.5 2.1 7.S 5.9 -- 4.3 **o.** f 5.1 di 19.1 2.5 Al Calated **as** 50 petoma of tot rural ditzlbutlaa md sto*rop. Ibis ame that distribtLom of ruzly pxodooad atloutual **good.** in proportional to thelr abas of total sural cash e.p_itue. **ea** ote 11 fe details. ki Sly baksn mnstated **me** _roe-peeIft activities. Rang is bed emported wbmat flou. I LImo Ialuie pecessng of aguriultural fsors. _ji_ mluie emly fruts e getable.

Soee,s kmhia: Whm MO WIsseha (16)i **Sierra** Lesta Lioeil m bita (1976)t _twas_ Freeman_ ad N_gollffe (196).