rafmacalaba/data-use-mentions-extended huggingface dataset:
implicit assumption here is that Chinese producers take account of changes in the bilateral exchange rate and average domestic inflation to determine export prices. However, there could be alternative ways Chinese producers and exporters determine their destination-specific export prices. Chinese producers could be influenced just by the nominal bilateral exchange rate ( ) or by the real bilateral exchange rate ( ), with and denoting prices in importing country and China respectively. The specifications corresponding to these two ways of measuring the exchange rate are in columns [1] and [2] (for the VBI) and columns [6] and [7] (for the CBI). In both cases, the results are robust, although the magnitudes decline relative to the core specification.

Yet other models of pricing behavior could involve Chinese producers looking at changes in their multilateral competitiveness in determining destination-specific export prices. In this case, the relevant exchange rate is not destination specific but a multilateral one that is identical across all importers ( ) where stands for China’s multilateral exchange rate and hence without a subscript).<sup>13</sup> We re-estimate the core regression to cater to these possibilities by using the IMF’s effective exchange rate as the relevant measure with the nominal rate in columns [3] and [8], and the real rate in columns [4] and [9]. Again, the coefficients are correctly signed and significant at the 1 percent confidence level. Interestingly, these coefficients are substantially greater than for the core specification.

In all these specifications, exchange rates are measured as the relative price of two different currencies. An alternative way of measuring real exchange rates—sometimes called the internal real exchange rate—is as the relative price of tradables to nontradables within a country. This exchange rate is available from the Penn World Tables from the series that measures the price level of GDP.<sup>14</sup> In columns [5] and [10], we use this measure of China’s
**NUTS 3 regions: uneven development** . Figure 13 gives a simple description of labor market conditions in Latvia’s regions in 2002. The Riga region is well ahead, while Latgale seems to be depressed, and differences between the other three regions are relatively small; somewhat higher wages in Kurzeme have to be seen against lower unemployment rates in Vidzeme and Zemgale. Some recent, unfavorable, developments in Vidzeme are, however, documented in Section 3, below.  On the other hand, unemployment rate in Latgale dropped from 21 percent in 2000 to 17 percent in 2002 (Appendix Table 9).  Nevertheless, the difference in unemployment rates (both LFS-based and registered) between the Riga region and Latgale is striking given the small size of the country. This is especially true when combined with the 60% wage advantage in Riga (Figure 13) and a much higher share of discouraged workers in Latgale (Appendix Table 11).  The fact that State Employment Service was until recently only available to jobseekers at their official place of residence clearly added to labor market rigidities. Still, the problems of Latgale, a relatively remote region, would not be solved by commuting or (at least in the medium term) by migration. Thus, job creation in this region is essential.

**Figure 13.  Unemployment Rates (left scale, percent) and Relative Average Wages (right scale) in Latvia’s Regions, 2002**

<!-- Start of picture text --> 20 120<br>100<br>15<br>80<br>10 60<br>40<br>5<br>20<br>0 0<br>Riga region Kurzeme Zemgale Vidzeme Latgale<br>jobseekers (LFS based unemployment rate, age 15-74)<br>registered unemployment rate<br>long-term registered unemployment rate<br>gross wages (Riga region = 100)<br><!-- End of picture text --> _Source_
Table 1. FDI vs. FAI - Stylized Comparison

||UP|to H|UP to IP|IP|to H|Total|
|---|---|---|---|---|---|---|
|FDI|2||1|1||4|
|FAI|3||0|0||3|

of equity directly into the affiliate in the host country, and $1 of equity into it indirectly through the affiliate in IP, possibly through a loan.

Total inward FDI in country H is then $3, comprising $1 FDI from country IP and $2 FDI from country UP. But there is also another $1 of FDI recorded as a flow into country IP from country UP, even though those funds only pass through to H, without any real activities taking place in IP. Aggregate FDI is thus $4, double counting all the cross-border funds that pass through intermediaries.

In contrast, the total amount of inward FAI in H will be between $0 and $3, depending on how much these funds are invested in physical capital goods—and it is that invested amount which is recorded in the FAI data. All the FAI would be recorded in country H, with FAI in country IP being zero. Supposing, for instance, that all the funds go to real investment, FAI will be recorded as $3 in H and zero elsewhere.

The two sets of statistics diverge further if there is local financing of real investment since, as noted, real investment supported by a local injection of funds is included in FAI but not in FDI. Local borrowing in H to finance real investment of $0.5, for example, increases FAI into H by $3.5 but leaves measured FDI unaffected. On the other hand, any changes in the amount of retained earnings in the host affiliate that is not invested in real assets would change
short time sample covering a period during which a country could have gone through major structural changes in the financial sector could be problematic. Similarly, using country specific historical data especially for emerging countries, covers only a rather limited number of crises periods. For these reasons, we find the pooled regression approach preferable for the sake of robustness of the acquired estimates, as lower income countries develop and their own past experience may not be relevant in assessing the impact of possible future crises on their financial system and on the economy. Moreover, if more country specificity is desirable, the pooled regression estimates could be used as priors in a Bayesian estimation of the NPL regression, including also additional country specific variables, possibly also as latent variables (see, Brand, Buncic and Turunen, 2010, on how latent variables can be incorporated in a monetary polciy modeling environment) 21Note here again that we will simply refer to this variable as NPLs (non-performing loans) in the text.

25
.32|3.73|
||ESS18|Loss/Harvest Ratio|884|20|0.63|1.61|
|Note: The table|<br>reports the r|Loss/Harvest Ratio(anyloss)<br>atio of estimated losses to self-reported ha|.<br>rvest on maize and sor|<br>ghum plots for the wh|0.53<br>ole sample|1.16<br>and only|

Note: The table reports the ratio of estimated losses to self-reported harvest on maize and sorghum plots for the whole sample and only for plots who have had any loss, by survey round.

Our graphical analysis is further complemented by Table 3. Generally, there is little variation between rounds in the average percent plant productivity reduction and percent area harvested. However, compared to the two other survey rounds, the Ethiopia Socioeconomic Survey 2015/16 (ESS15) clearly stands out as

- More plots had any plant productivity reduction (irrespective of crop)

- More sorghum plots lost some of the planted area

- The average size of losses for maize (plant productivity loss) and sorghum<sup>31</sup> (plant productivity loss and planted area loss) was notably larger.

Conspicuously though, while our losses proxies tell a clear story consistent with the notion of a large drought in 2015/16, this is not supported by average self-reported or crop cut yields. This finding is not unique to our analysis and is extensively discussed in Sohnesen, (2020) who finds that while meteorological data and self-reported shock reports are consistent with a severe drought, this is not supported by vegetation indices and crop yields. In our case, higher than usual reports of area and productivity reductions combined with yet relatively normal realized production leads the loss-to-production ratio to be considerably larger in ESS15 with a few unrealistically large outliers in loss estimates (cases where the loss per hectare exceeds the highest realized yield on any plot in the sample
We hope this paper will be a useful input for policy makers’ and development partners’ deliberations on how to improve teaching quality.

Box 1. Simulation model: Steps, assumptions, and robustness check

|**A. Seven steps **|
|---|
|Step1. Estimate totalpopulation in EAP MICs|
|•<br>Access World Population Prospects 2022 estimates for each country up to 2040 (https://population.un.org/wpp/)<br>•<br>Use the indicator “population by 1-year age groups and sex”|
|Step2. Estimateprimaryschool-agepopulation in EAP MICs|
|•<br>Confirm education system for each country using indicators “official entrance age to primary education (years)” and “theoretical duration<br>of primary education (years)” from World Bank EdStats (https://datatopics.worldbank.org/education/)<sup>6</sup><br>•<br>Estimate primary school-age populations using data retrieved from World Population Prospects 2022 data|
|Step3. Estimate student enrollment inprimaryschools in EAP MICs<br>•<br>Estimate student enrollment in primary schools<br>•<br>Use the indicator “total net enrollment rate, primary, both sexes (%)” from World Bank EdStats|
|Step4. Estimate teacherposition needsperpupil-to-teacher ratio(PTR)in EAP MICSs<br>•<br>Use the indicator “pupil-qualified teacher ratio in primary education (headcount basis)” to estimate the number of teachers needed for<br>primaryeducation|

5 For instance, see International Task Force on Teachers for Education 2030 2021, UIS 2009 or UIS and EFA-GMR 2014.

> 6 Original data source is UIS.

4
) we report marginal e¤ects from Tobit estimation, but with the dependent variable left-censored at zero. Reassuringly, the estimates remain very similar.

For robustness, Table 9 reports the results of a Heckman sample selection model. In line with Helpman et al. (2008), we use World Bank data on country-speci...c regulation costs of ...rm entry to proxy for ...xed costs of exporting. These indicators consist of the number of procedures and the number of days for an entrepreneur to legally start operating a business.<sup>14</sup> Columns (1) to (3) report the marginal e¤ects of the Probit selection equation. The results in column (1) show that export participation is signi...cantly less likely in markets characterized by a larger number of procedures to form a business, as would be expected. Column (2) shows that the number of days required to form a business also enters negatively the selection equation, but this e¤ect is not statistically signi...cant. In column (3), we specify a binary indicator for both of these costs, which takes the value of one when the sum of the number of days and procedures is above the median. Once again, the e¤ect of this indicator on the probability of exporting is negative, although statistically insigni...cant.

Columns (4) to (6) report the corresponding estimates of the Heckman selection model for the export revenue equation. The estimation can be conducted using full maximum

> 14 Data refer to 2005, come from the WDI of the World Bank, and are available for 159 countries of our sample.

15
to US$0.51 per year at full coverage (Chaitkin et al, 2022). A review on the costs of WASH in healthcare facilities in low- and middle-income countries found 36 studies, with piecemeal information for different service types and levels but limited evidence for giving a picture of the resource needs on the continent (Anderson et al, 2021).

# Study focus on Eastern and Southern Africa

In SSA, there is a lack of routine monitoring on WASH, HCWM and environmental cleaning in healthcare facilities, although it has been improving since the WHO/UNICEF Joint Monitoring Programme (JMP) started compiling statistics in the SDG era. Figure 3 shows the latest data available in the nine countries from Eastern and Southern Africa included in this study. Data on environmental cleaning are available for only 2 of the 9 countries, and there are major data gaps for basic sanitation and basic hygiene. Lack of data would suggest that these basic conditions for patient and health worker safety are largely absent. To provide a better picture of coverage in healthcare facilities, these data compiled by the JMP can be supplemented with other data available from the study countries, although attention is needed on quality and national representation of additional data. For example, in Tanzania between 2005 and 2010, it was found that only 24% of health facility delivery rooms were “WATSAN-safe” (Benova et al, 2014).

6
35 into poverty. The two indicators are therefore complements, and the impoverishment indicator sheds light on the poverty SDG (target 1.1). Both indicators are widely used in the literatures on financial protection in health (Wagstaff and van Doorslaer 2003; van Doorslaer et al. 2006b; van Doorslaer et al. 2007; Wagstaff et al. 2018a; Wagstaff et al. 2018b).

Some studies decide whether health expenditures are catastrophic by comparing them to total consumption, while others compare them to total income. Similarly, some studies decide whether health expenditures are impoverishing by comparing income net of health expenditures and income gross of health expenditures, while others compare consumption net and gross of health expenditures. There is no right or wrong approach. However, it is important to keep in mind that total consumption includes health expenditures, and will therefore increase when a health shock occurs if the household finances part of the health expenditure through borrowing or dissaving rather than entirely through cutting back consumption on other budget items. A household with a large health expenditure may therefore appear to be richer (in terms of consumption) than one that incurs only small health expenditures. As a result, we may end up with the rather perverse result that large health expenditures (possibly catastrophic ones, too) are more common among ‘rich’ households, and less common among ‘poor’ households (WHO and World Bank 2017). By contrast, income is not directly affected by health expenditures. (It may be affected indirectly in the sense that the same health shock that causes the health expenditures may also reduce labor income.) Thus, when computing catastrophic and impoverishing health expenditures, and especially when looking at inequality in catastrophic health expenditures, it is probably preferable to use income rather than consumption (WHO and World Bank 2017).

# <u>Data search and data sources</u>

As with the health equity side of the HEFPI database, our goal is to assemble as large a data set as possible of surveys suitable
<!-- Start of picture text --> Figure 5: Expenditures on Innovative Property (% of GDP)<br>1.35%<br>1.20%<br>1.05%<br>0.90%<br>0.75%<br>0.60%<br>0.45%<br>0.30%<br>0.15%<br>0.00%<br>2000 2002 2004 2006 2008<br>Science and enginnering R&D Mineral exploration<br>Copyright and license costs New product development costs in the financial industry<br>New architectural and engineering designs R&D in social sciences and humanities<br><!-- End of picture text --> Nonetheless, it is striking that even if we assume that Brazil‘s financial services industries spend only half as much as their US counterpart on product development (that is, only 10 percent of costs), total Brazilian spending on new financial products would still be as high as expenditure on R&D across all industries. Further, it is unlikely that we are underestimating R&D given that our data source for R&D expenditures, the PINTEC surveys, are comparable to the innovation surveys used to estimate R&D expenditures in the US and EU. Thus, compared to those advanced economies, R&D in the traditional sense –conducted largely by manufacturing firms – is a smaller component of innovation-related spending in Brazil. For instance, even though manufacturing has a smaller share in GVA in the US<sup>19</sup> (13 percent for US versus 17 percent for Brazil in 2008), total business spending on R&D in the US is considerably larger than that on new financial product development (CHS 2005).

Our estimates suggest that Brazilian firm expenditures on R&D have risen in the initial years, dropped significantly, and then have been
# 1. **INTRODUCTION**

It is a subject of controversy whether the East Asian crisis-stricken countries are suffering from a credit crunch. While there is anecdotal evidence that even good firms are finding it difficult to obtain credit to finance production and investment, macroeconomic data on monetary and financial developments do not unequivocally support the assertion that a credit crunch is occurring. A better understanding of this issue is crucial for the designing of appropriate policy actions and for the recovery prospects for the East Asian countries. For example, it is widely held that after currency devaluation, recovery will be led by export-driven demand, entailing a relevant transfer of resources from the non-traded to the traded sectors.' Yet, a pervasive credit crunch could retard or even jeopardize such a transfer, thus undermining recovery prospects.

This paper aims to shed some light on whether and to what extent the East Asian countries are currently suffering from a credit crunch. First, we propose a systematic framework to evaluate the occurrence and the magnitude of the credit crunch. In particular, we make reference to well established literature studying the transmission of shocks through the credit channel, often referred to as the credit view.<sup>2</sup> We draw on this literature to help identify the channels through which the negative shocks hitting East Asia may have propagated the credit crunch in these economies. This framework allows us to deploy a consistent approach in analyzing the issue across all countries and to go beyond exclusively relying on macroeconomic indicators and anecdotal evidence, which are often in contradiction. The framework also enables us to assess how credit crunch affects differently across the various sectors of the economy. Second, we apply the framework to five East Asian countries: _Indonesia, Korea, Malaysia, the Philippines,_ and _Thailand._

> See JP Morgan Economic Research (1998) and IMF's World Economic Outlook (1998) for more on this.

> 2 Bernanke and Gertler (1995) provide a comprehensive discussion of the credit channel and its components. This
by incidence, average unconditional amount per hectare, or average conditional amount per hectare. These trends may signal gender differences in FISP fertilizer voucher distribution and redemption outcomes. In fact, the IHS3 data indicates that even though female-headed households are just as likely to receive a fertilizer voucher as their male-headed counterparts, conditional on receipt, the _average number_ of fertilizer vouchers that are received among female-headed households (1.56) is lower than the analogous statistic for male-headed households (1.63), and the difference is statistically

> least one missing value among the independent variables of interest. Finally, top and bottom 1 percent of the distribution of the log of gross value of output per hectare are trimmed, corresponding to 336 plots. These exclusions leave us with the final analysis sample of 16,372 rainy season plots.

> 26 The plot-level gross value of output in Malawi Kwacha (MK) is calculated by first multiplying the kilogramequivalent quantity of production for each crop on a given plot by the median crop sales value per kilogram within the corresponding EA, and then aggregating across values of crop production. The median crop sales value per kilogram is computed within the corresponding EA only if at least 10 values are available from the survey data. Otherwise, the median crop sales value per kilogram is computed at a higher level, in the order of traditional authority, district, region, and country. Our outcome variable is computed by normalizing plot-level gross value of output with GPS-based cultivated plot area.

> 27 The overwhelming majority of the owned plots (83 percent) are acquired through inheritance. Another 12 percent is reported to have been granted by local leaders. The remaining are acquired as bride price (2 percent), purchased with title (1 percent) and purchased without title (1 percent).

10
# **2.3 PHYSICAL BARRIERS**

20. Another factor in access to financial services is the physical accessibility of formal financial sector services for low income clients. The limited economic literature available to date points to location as one determinant of access, while its relative importance compared to income and education is inconclusive<sup>34</sup> . Nevertheless, location is one of the barriers to enter formal financial sector services for low income households. The lack of bank branches in the neighborhood can prevent the unbanked from gaining access to formal financial sector services at all, while remote branch locations increase the costs of access for the poor. The following therefore takes a look at the geographical location of bank branches in Colombia and Bogotá drawing on the limited work conducted to date on this subject.

21. **On a national basis, banks have more branches in higher populated areas** **_._** An analysis by Medina and Steiner<sup>35</sup> based on data from the Superintendencia Bancaria shows that Colombia’s banks focus their branch network on municipalities with more than 20,000 inhabitants. Seventeen out of the 27 analyzed banks by Medina and Steiner do not have a branch office in a municipality with less than 20,000 inhabitants and most other banks have less than 10% of their branch offices in municipalities with less than 20,000 inhabitants. An analysis by the Superintendencia finds that over 70% of total loans are provided to and about 70% of total deposits are collected in municipalities with more than 2 million inhabitants.

22. **Bank branches are located in higher income areas** **_._** Medina and Steiner’s analysis of bank branch distribution by income strata<sup>36</sup> in Bogotá shows that all 27 of the surveyed banks focus on income strata 3 and above (see Figure 2.4). The average income strata for all bank branches is 3.6,