rafmacalaba/datause-encoder-data huggingface dataset:
during a financial crisis. Moreover, welfare gains from policy may become negative. Empirically, I present evidence from Brazilian data that expectations of economic fundamentals respond to capital control announcements: first, the paper uses a highfrequency strategy to show that such announcements tend to affect daily forecasts of GDP collected by the Central Bank of Brazil; second, the paper constructs an econometric model to nowcast Brazilian GDP with real-time macroeconomic data, and shows that such announcements reduce the forecast errors of the model.

In the theoretical section, I incorporate learning from policy into a standard model researchers use to study capital controls in emerging market economies. In this twosector small open economy model, borrowing by domestic agent is limited by tradable and nontradable income through a collateral constraint. This environment features a pecuniary externality because individual agents do not internalize the effect of their borrowing decisions on the value of collaterals and hence on borrowing capacity. Capital controls are usually deployed by the government to mitigate the effect of overborrowing by agents arising from the pecuniary externality. I incorporate three novel features in this setting to model learning from policy: first, the government knows more about future economic fundamentals than the representative agent. Second, the agent knows that the government is levying capital control taxes according to a simple policy rule which reveals useful information previously unknown to her. Last but not least, the agent learns from policy by completing a Bayesian updating of her information set when she observes policy changes.

A version of my model is calibrated to the Brazilian economy and the quantitative model features unintended consequences of capital controls when people learn from policy. I choose the Brazilian economy between 2006 and 2013 because the country experienced large and volatile capital inflows during the period and actively used capital controls to manage such inflows with some success. I solve the calibrated model for the decentralized equilibrium<sup>2</sup> , the equilibrium with policy interventions but no learning from policy and most importantly, the equilibrium with both. When people learn from policy, the domestic economy borrows more from foreign countries and experiences more severe financial crisis in the sense of larger consumption drops,

> 2Equilibrium with no policy intervention

2
Table 4: Determinants of Level of Income. Instrumental Variables estimations. Fixed (F.E.) and Random Effects (R.E.)

||4_._1|4_._2|4_._3|4_._4|
|---|---|---|---|---|
||_Income_|_Income_|_Income_|_Income_|
||R.E.|F.E.|R.E.|F.E.|
|Openness|0.313|0.394|0.396|0.465|
||(0_._083)_∗∗∗_|(0_._094)_∗∗∗_|(0_._075)_∗∗∗_|(0_._086)_∗∗∗_|
|Investment|1.105|0.969|1.075|0.934|
||(0_._254)_∗∗∗_|(0_._256)_∗∗∗_|(0_._258)_∗∗∗_|(0_._256)_∗∗∗_|
|Government Exp.|-0.506|-0.198|-0.496|-0.203|
||(0_._271)_∗_|(0.283)|(0_._275)_∗∗_|(0.283)|
|Human|0.202|0.180|0.198|0.166|
||(0_._022)_∗∗∗_|(0_._070)_∗∗∗_|(0_._022)_∗∗∗_|(0_._070)_∗∗_|
|Natural Resources (<sup>_XNR_</sup><br>_Y_<br>)|0.471|0.398|||
||(0_._192)_∗∗_|(0_._209)_∗_|||
|Natural Resources II (<sup>_XNR_</sup><br>_TX_ <sup>)</sup>|||-0.027<br>(0.083)|0.007<br>(0.087)|
|Terms of Trade|0.833|0.707|0.707|0.572|
||(0_._326)_∗∗_|(0_._333)_∗∗_|(0_._326)_∗∗_|(0_._324)_∗_|
|R2 within|0.39|0.39|0.39|0.40|
|Observations|336|336|336|336|

Standard errors in parentheses; _∗_ significant at 10 percent ; _∗∗_ significant at 5 percent; _∗∗∗_ significant at 1 percent. All the random effects regressions are estimated with regional dummies for African and Latin American countries.

21
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foreign firm prefer acquisition to direct entry.16 Thus both, in north-east region ([A;E]) and the south-west region ([E;A]) of figure 3 there is room for government intervention. In the south-west region, a restriction on acquisitions may induce entry by the foreign firm (as in our benchmark model of domestic monopoly). Similarly, in the north-east region, restricting direct entry induces an acquisition by the foreign firm and thereby improves domestic welfare.

Policy interventions, both in terms of foreign equity restrictions and direct entry may then be justified in oligopolistic markets. Note that the objective of these policies interventions is not to restrict access by foreign firms into the domestic market, but rather to induce a different mode of entry.

Our results have some further implications. In industries with more complex technologies we should observed both a higher cost of technology transfer ('r) and a more concentrated market structure, as the cost of entry should increase with the level of technological complexity. In terms of figure 3, industries with complex technologies (services) will lie in the north-east region of the diagram, whereas industries with less complex technologies (manufacturing) will be lie in the south-west region of the diagram. If so, our analysis indicates that governments may have incentives to restrict different modes of entry by foreign firms depending on the industry. In services one should observe restrictions in terms of the number of foreign firms allowed in the market, whereas in manufacturing acquisition would be restricted in order to promote direct entry by foreign firms. This is broadly consistent with the evidence provided for different regions in OECD (2001) and UNCTAD (2000), where it is reported that the ratio of foreign mergers and acquisition with respect to greenfield FDI is much larger in services than in manufacturing.

> 16 Note that welfare may be higher under acquisition even if direct entry leads to more technology transfer because of the fact that producers are better off under acquisition.

21
assessing the quality of the data, conducted a Post-Enumeration Check Survey (PECS) that found that there was under coverage both in urban and rural areas, but this was more prevalent in urban areas. BBS thus used a two-step approach to adjust the 2011 Census of Population and Housing estimates. First, it reclassified urban and rural areas using the concepts of: (i) growth centers, (ii) urban agglomerations, and (iii) other urban areas. Second, it inflated all urban and rural counts from the 2011 Census of Population Areas to align with the PECS results. These two adjustments estimated the share of the urban population at 28 percent, which is the number that BBS has been using since then in order to produce official population projections and statistics.<sup>30</sup> To address this mismatch between the original HIES 2016/17 sampling and the official population statistics, we adjust our _ex-ante_ sampling weights in two ways. First, we re-classify all selected urban/rural PSUs using the new “growth centers” categorizations, which affects 24 out of the 64 _zilas_ in the sample. This adjustment brings the urban share from 23 percent to 26.6 percent. Second, we use the official 2016 population projections estimated at the _zila_ level to inflate the adjusted sampling weights; that is, for each _zila_ we divide the official 2016 population projections by the adjusted 2011 population projections used for the original sampling design.<sup>31</sup> For this exercise, we use the number of households in the original 2011 census frame (i.e. enumerated households) instead of the adjusted number of households (i.e. the number adjusted after the PECS). After all these adjustments, the final sampling weights for the HIES 2016/17 yield an urban share of 28 percent, and the sum of the sampling weights by _zila_ match the official BBS 2016 population statistics.

> 30 <mark>The BBS official population projections are available at the following link:</mark> <u><mark>http://www.bbs.gov.bd/WebTestApplication/userfiles/Image/PopMonographs/PopulationProjection.pdf</mark></u> 31 To convert the adjusted number of households to adjusted population counts for estimating the final poststratification factor by zila, we use the official household size reported in the 2011 Census for Population and – Housing for rural, urban, and City Corporations areas 4.49 (rural), 4.45 (urban), 3.98 (City Corporation). 35
What really sets our dataset apart from the earlier survey based work, however, is that our data has bilateral contract information, allowing us to control both for buyer and supplier firm characteristics and analyze both sides of the buyer-seller transaction, whereas earlier work only analyzed one side of the buyer-seller transaction. An equally important distinction with previously used survey data is that we have multiple contracts for the same buyer and supplier firms, rather than a firm-average response. This allows us to include firm fixed effects in our empirical analysis, thereby correcting for time-invariant firm characteristics that might determine the choice of credit terms.

The limitations of our data set are that the number of buyers is small (a total of 56), which limits our analysis of buyer characteristics, and that we have relatively little information about the sellers in our sample. For example, we do not have information on the industry of the seller. Unfortunately, we have no way of getting more data. Because our buyers are mostly large firms we are less likely to pick up the financing motive as strongly as in earlier work (although we do have a mix of investment and non-investment grade buyers), but this allows us to focus on other non-financing motives.

We start by summarizing typical trade credit contracts terms, such as number of discount days, discount rate, and net days, and analyze how they relate to buyer and seller characteristics. We then turn to analysis. We argue that while it seems hard to conclude that trade credit is primarily a cheap way for suppliers to provide buyers financing in our sample (most of our buyers are larger and better rated than our suppliers, and still get credit), non-financing motives seem to be operative. In particular, large, investment-grade buyers get long terms from small suppliers. We believe this is consistent with relatively untrusted suppliers extending longer terms to buyers to guarantee product quality. However, this leaves suppliers exposed to riskier credits. This is where discounts help. Riskier buyers are offered discounts to repay early so that suppliers can offer warranties about the quality of the product supplied even while containing the credit risk in their trade credit portfolio.

The paper continues as follows. In section 2 we review theories of trade credit, and we describe our data set in section 3. In section 4, we present the empirical results, and we conclude in section 5.

3
connections has increased from 2.4 million in 1996 to 4.2 million by the end of February 2004. Over 700 IT companies had been established by 2003. At the same time, the fixed line and mobile deregulation policies have been recently introduced in order to enhance competition and attract private investment (MoF, Government of Pakistan, 2003-04).

There remains, however, significant work ahead in broadening the use of information technology and electronic commerce to expand trade in the region. The number of internet hosts in the region relative to others, for example, is low and has significant room for expansion. The number of individuals with access to the internet also needs to be expanded, including those in government and private sector engaged in trade transactions. Figure 7 and 8 show that the numbers of internet hosts and users per 10,000 in 2003 for East Asia are higher than those for South Asia.

In particular, “India still has a fraction of the number in the Philippines, Thailand, or Malaysia.”(source, World Bank). In considering the number of main telephone lines per 1000 people – both fixed and cellular – India trails behind Malaysia, Thailand, Philippines, China and Indonesia.”(source, World Bank). Miller (2001) analyzes the reasons why the internet has not spread as widely in India. First, the country is not economically developed enough so that the cost of access is relatively high. Second, telephone connections are not well established for wide-spread internet use. Third, tariff rates on phone calls are high. Lastly, the lack of modern physical infrastructure networks to deliver products to customers imposes limits on expansion of e-commerce (Miller, 2001).

Figure 7: Internet Hosts per 10,000 Inhabitants in 2003 <!-- Start of picture text --> 300<br>276.3 Bhutan<br>250 India<br>Maldives<br>200<br>Nepal<br>150 Pakistan<br>Sri Lanka<br>90.0<br>100<br>South Asia Average<br>50 East Asia Average<br>13.91 18.55 5.9<br>0.82 0.391.010.98 Global Average<br>0<br><!-- End of picture text --> Source: Authors’ calculation based on International Telecommunication Union (2004) data Improvement in information technology services is as equally important as development of physical infrastructure. Biggs et al. (2000) examines the case in Nepal where service is poor and hinders business activities. For example, phone lines usually do not work well outside of the cities. Services are not available all day, and when available, voice quality is often poor (Biggs et al., 2000). Continued efforts to deregulate telecommunications, open markets to competition and investment, and expand financing for infrastructure 17
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**<u>Table 3. Estimated demand function: Baseline</u>**

||Withou|t OD dummies|With O|D dummies|
|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|
|lnP|-0.026|(0.016)|-0.021|(0.017)|
|Trip purpose: Work|0.322|(0.061) ***|0.265|(0.060) ***|
|Business|0.158|(0.086) *|0.115|(0.083)|
|School|0.348|(0.061) ***|0.298|(0.060) ***|
|Shopping|0.117|(0.060) *|0.063|(0.060)|
|Personal|0.037|(0.062)|-0.011|(0.061)|
|Family visit|-0.017|(0.062)|-0.064|(0.062)|
|Hobby|0.097|(0.084)|0.042|(0.085)|
|lnTIME|-0.031|(0.010) ***|-0.033|(0.011) ***|
|lnACC|0.008|(0.010)|0.011|(0.011)|
|lnEGR|-0.008|(0.008)|-0.007|(0.008)|
|dTRF|-0.059|(0.039)|-0.072|(0.041) *|
|lnTRF|0.066|(0.025) ***|0.065|(0.025) ***|
|dSUBU|0.008|(0.018)|0.016|(0.021)|
|dBOTH|-0.084|(0.032) ***|-0.067|(0.035) **|
|dCUA|-0.022|(0.015)|0.008|(0.062)|
|dUNSAFE|-0.035|(0.019) *|-0.035|(0.020) *|
|dUNCOMF|0.007|(0.018)|0.007|(0.018)|
|Individual effects:|||||
|Income: 200-300k|0.065|(0.019) ***|0.066|(0.020) ***|
|300-400k|0.089|(0.020) ***|0.090|(0.021) ***|
|400-500k|0.051|(0.026) **|0.058|(0.026) **|
|500-750k|0.109|(0.028) ***|0.110|(0.029) ***|
|750k-1 mil|0.099|(0.037) ***|0.106|(0.038) ***|
|> 1 mil|0.162|(0.040) ***|0.168|(0.040) ***|
|dFEMALE|-0.029|(0.016) *|-0.028|(0.016) *|
|dAGE56|-0.074|(0.028) ***|-0.087|(0.029) ***|
|lnSIZE|-0.019|(0.016)|-0.021|(0.017)|
|dHHH|-0.055|(0.019) ***|-0.047|(0.019) **|
|Occupation: Employed|0.031|(0.018) *|0.027|(0.018)|
|Unemployed|-0.043|(0.020) **|-0.045|(0.020) **|
|Other|-0.012|(0.030)|-0.021|(0.030)|
|dCAR|-0.071|(0.031) **|-0.064|(0.032) **|
|dMOTO|0.014|(0.017)|0.013|(0.018)|
|dBIKE|-0.033|(0.020) *|-0.031|(0.020)|
|constant|1.510|(0.133)***|1.490|(0.139)***|
|Obs|3,133||3,133||
|F-statistics|24.19||57.05||
|R squared|0.180||0.210||
|No. of OD dummy variables|0||96||
|Ramsey RESET test|3.92|***|1.93||

Note: The dependent variable is ln _Q_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
in each case. Section 3 describes the way the database of knowledge products was constructed and how the key attributes of each document were defined. Descriptive statistics from this database are used in Section 4 to show that the universe of knowledge products is broader than that typically receiving the attention of World Bank management, with both sets even displaying opposite trajectories over time. In section 5, knowledge products are classified in several relatively homogeneous categories; for each of them a statistical analysis akin to the estimation of production functions is conducted. The main patterns emerging from these exercises are summarized in Section 6. Building on them, Section 7 concludes by offering several practical recommendations to strengthen analytical work at the World Bank.

# 2. Previous studies

Based on their unit of observation, previous assessments of knowledge work at the World Bank can be classified in four groups. A few of them have focused on _research products_ , the more academic end of the spectrum. Others, more operational in nature, have tried to distill patterns from _budget codes_ that are deemed to generate analytical content. Several studies have put emphasis on the _work processes_ through which analytical products are prepared. And a couple of them have elicited and analyzed _user views_ in relation to World Bank knowledge products.

Evaluations of _research products_ tend to focus on academic recognition, with citations being an obvious measure of the World Bank’s influence on development thinking. One study in this group applied the citations metric to some 8,100 journal articles, around 6,800 books and chapters in books, and close to 3,000 working papers that had been put in the public domain by the World Bank over several decades. The set was assembled by combining records from an internal database of documents and from various external bibliographical repositories (Ravallion and Wagstaff 2011).

Using citation data from the Google Scholar platform, this study revealed the heterogeneous quality of the research products selected. A large share of them – including several _World Development Reports_ – had little discernable influence. However, within specific geographic areas such as Africa, China and India, and specific topics such as education, growth, inequality, health, and poverty, World Bank research products were ranked highly. And a few of them were among the most cited articles in several of the top economics journals. However, beyond noting that ratings tend to rise with years of publishing, this study did not attempt to explain what drove these stark differences in visibility.

6
between 1982 and 1986 were not of school age in 1999. We compared their exposure to that of those born between 1977 and 1981 in war and non war districts.

The results in table 7 show that cohorts who were not supposed to be of primary school age during the conflict years but born in conflict affected districts do not show a different primary school completion rate as compared to the same cohorts born in unaffected districts. This gives support to the fact that our identifying assumptions are satisfied.<sup>25</sup>

# **5. Empirical results**

# **5.1. School attendance in 2001**

Table 8 shows the results for the impact of the two channels of violence exposure in 1999 on school attendance in the 1999/00 and 2000/01 academic years. As discussed previously, we are mostly interested in looking at the effects of the violence on school attendance in the post-conflict academic year (i.e. 2000/01). Table 8 reports the estimates from a fixed effect model applied to our ̳ _ad hoc’_ panel dataset derived from the TLSS2001. For sake of simplicity, we have estimated a linear probability model. The analysis is disaggregated by gender and age groups. The results highlighted in bold are our main coefficients of interest.

The results show a negative and significant impact of displacement on school attendance in 2001 for the overall sample. The impact on girls is larger than that on boys but the difference is not statistically significant (panel A, table 8). We observe also that household displacement is associated with more substantial decreases in primary school attendance among younger individuals. This effect is stronger for girls (columns 4-6). Among older individuals, boys seem to be more affected than girls. We do not find any statistically significant effect of house destruction on primary school attendance in 2001 (panel B, table 8), in line with the descriptive analysis in section 4.2.

The impact of these two channels of violence exposure becomes clearer once we estimate a triple interaction equation in order to isolate the impact for those that have been only displaced, those with only the house damaged and those affected by both shocks (panel C, table 8). We find that being affected only by displacement decreases school attendance on average, with stronger effects for boys. Interestingly, exposure to house destruction is associated with increases in school

> 25 There could still be unobservable errors in the identification of districts most affected by the violence. We have used an instrumental variable methodology to estimate the impact of violence on school completion in order to minimise the bias and found no difference in the results discussed in the sections below.

26