rafmacalaba/data-use-mentions-extended huggingface dataset:
.085|0.042|-2.030|0.042|7|
|Interest rate risk|0.082|0.043|1.930|0.053|8|
|Opportunity cost of excess bank reserves|0.055|0.032|1.720|0.085|9|
|Inflation|0.005|0.017|0.290|0.770|10|

Source: Author’s calculations.

# V. CONCLUSIONS AND POLICY RECOMMENDATIONS

This paper provides empirical evidence on the determinants of banks’ interest spreads in Tajikistan using a panel dataset for the period 2011Q1 – 2022Q4. To this end, we specify an empirical model constituting an extension of the model developed by Ho and Sanders (1981), which we estimate using Beck and Katz’s (1995) OLS-based PCSE procedure. As predicted by the empirical model, all the explanatory variables, except for inflation, are significant and have the expected effect on banks’ net interest spreads.

variation of interest rate spreads. Operational efficiency is a significant driver of interest spreads in Tajikistan. Higher operating costs are reflected in higher interest spreads. In addition, we find that higher perceived credit risk translates into higher net interest spreads as banks compensate for the increased risk of loan default. This signals the importance of legal and institutional frameworks for lending and debt resolution. The opportunity cost of holding cash and balances at the central bank is another significant determinant of net interest spreads, reflecting limited interbank market activity and underdeveloped capital markets.

the existence of room for consolidating the banking sector in Tajikistan to remove diseconomies of scale for small banks. Risk aversion is also associated with high spreads, whereby higher capital adequacy ratios are compensated for by higher spreads and margins. We also find that higher market power

> 8 Tajikistan’s estimate of the Regulatory Quality index in 2022 (the latest available data point) was –1.198.

> 9 The economic significance coefficients are obtained by
two data sets, i.e. the US IO1997 table with the EU-RoO index by Cadot et al. (2005). Using these databases, for each intermediate good we create two measures: a simple and a weighted average of the RoO index faced by the associated final goods. We call these measures the IO-RoO Index and the weighted IO-RoO Index respectively. For each intermediate, the simple average is constructed by considering all final goods to which this intermediate contributes. This measure, called IO-RoO, is thus computed as follows: where _j_ and _i_ refer to the intermediate and final good respectively, and _RoOij_ is the RoO index at the level of the final good, _i_ , associated to each intermediate, _j_ . In equation (1) we sum the RoO index, _RoOij_ , across all final goods into which an intermediate _j_ enters, and we divide it by the number of these IO relations, _ni_ . The drawback associated to our measure in equation (1) is that it gives all products using a specific intermediate the same importance. Therefore, to account for the heterogeneity in the use of the intermediate, we also construct a weighted measure where we exploit the direct input-output coefficient requirement which assigns a higher weight to final goods served most prominently by an intermediate good. This weighted measure, called _weighted_ IO-RoO, is then computed as: where _drij_ refers to the direct requirement coefficient.<sup>6</sup> This _weighted_ IO-RoO Index is our preferred measure. We believe that it better reflects the production process common across countries and is less tied to US specificity, since it provides a more accurate proxy of the production technology.<sup>7</sup> Table 1 presents descriptive statistics for the I-O relations according to the restrictiveness of the RoO index faced by final goods according to the sector of the intermediate goods and the resulting sector-level averages of our IO-RoO Indexes. Columns 1 to 7 present the share of intermediate goods hit by these sourcing restrictions.<sup>8</
spillovers (Ethier 1986; Fosfuri, Motta & Rønde 2001; Havr ́anek & I ̆sov ́a 2012; Rodr ́ıguez-Clare 1996), suggests that the desire for technological and knowledge transfer could motivate emerging market firms to acquire firms in an advanced economy. At the same time, emerging-market firms may have specialized managerial and operational expertise which the firms could spillover to markets very similar in nature to their home markets. This _technology transfer_ hypothesis argues that it is not only the home country’s innovative capacity that may influence M&A choices, but also that of the host country.

Finally, political and policy factors may play a role in international M&A as well. Possibly the most likely channel where public policy could affect M&A deal flows is the residual accumulation of reserves as a consequence of existing trade patterns. In addition, policy factors that may affect M&A could take other forms, such as the presence of bilateral investment treaties (BITs) (B ̈uthe & Milner 2008; Neumayer & Spess 2005), or risks associated with economic policy or political conditions (Busse & Hefeker 2007). Accordingly, we cluster these variables into the class of _political economy_ explanations, which we consider in our empirical work.

# **4 Data Description and Econometric Methodology**

## **4.1 Data sources and description**

The cross-border M&A investment database used for this paper was compiled from a variety of sources. The primary data for M&A deals were drawn from a larger dataset compiled by Thompson-Reuters SDC Platinum, which covers all publicly disclosed cross-border transactions for which the ultimate acquiring company was based in an emerging-market country, and the immediate target company was located in a country other than that of the ultimate acquirer. Transactions that were included involved either two or more companies pooling their assets to form a new entity (a _merger_ ), or a foreign company gaining a portion of a domestic company (an _acquisition_ ). All completed and partially
# **Table 4. The Effect of Land Title on Investment: Instrumental Variable Regressions**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from the first and second stage of instrumental variable regression of investment on Titling Firm dummy variable. In Column (1) and (2), the results of the first stage estimate are presented. In Column (3) and (4), the results of the second stage estimate are presented. The instrument we use in (3) is whether the Shenzhen government was the controlling shareholder of the firm when it was first established. The instrument we used in (4) were the shares of Shenzhen government as the percentage of total shares of the firm when the firm was first established. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that is White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||Titling<br>First sta<br>(1)|Firm<br> <br>ge OLS<br>(2)|<br>∆(Investme<br>IV: Shenzhen<br>government as<br>controlling<br>shareholder when<br>established<br>(3)|nt/PP&E)<br>IV: Shareholding<br>percentage of<br>Shenzhen<br>government when<br>established<br>(4)|
|---|---|---|---|---|
|Shenzhen gov’t
further details on how we collected baseline and follow-up data.

We were able to re-interview 724 of the 803 individuals in the sample, achieving a 90.2% response rate. An attrition rate of 9.8% compares very favorably to the attrition rates found in other vocational training studies<sup>14</sup> or even in labor market administrative surveys for developed countries.<sup>15</sup> Response rate for the treatment group was 93.3%, while for the control was 87.0%. small in Table 1 shows that this group Although magnitude, difference across treatment groups is statistically significant. We deal with this differential attrition in several ways. First, in Table 3 we show that the sample of non-attriters is extremely similar in pre-treatment observable characteristics to the whole sample. Moreover, we show that pre-treatment covariates remain balanced between treatment groups for the sample of non-attriters: all the differences between treatment and control groups show roughly the same magnitudes and statistical significance when compared to the whole sample. Second, we investigate whether any pre-treatment observable characteristic correlates with the probability that an individual attrits the sample. We estimate a linear probability model in which we regress a dummy that takes the value of 1 if individual attrits, 0 if she remains in sample on treatment status, randomization strata fixed effects, and baseline characteristics.<sup>16</sup> Figure 3 shows the point estimates and confidence intervals for each variable in such regression. In this figure, we can observe that the only variable that significantly predicts attrition is treatment assignment, with no systematic correlation with any other pre-treatment characteristic.

Given that the differential response rate is small in magnitude, that it is not correlated with pre-treatment characteristics, and that the non-attriters sample shows the same balance as the whole sample, our main estimates do not adjust for attrition. Reassuringly, however, in section 4.7 we show that these estimates are robust to adjusting for differential attrition. We do so by implementing bounds by
Policy Research Working Paper 10297

# **Abstract**

This paper extends recent research on satellite-based carbon dioxide measurement to an easily updated template for tracking changes in carbon dioxide concentrations at local and regional scales. Using data from the National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2 satellite platform and a large sample of urban areas, a comparison of trend estimation models suggests that the template can use a simple model that estimates trends directly from satellite data pre-filtered to isolate local concentration anomalies. Illustrative applications are developed for a long-period trend model and a short-period model focused on change in the most recent year. In addition, the paper estimates carbon dioxide emissions for thousands of urban areas and identifies cities whose emissions performance is above or below expectation. Although the tracking model is “simple,” it requires software and hardware that are beyond the means of many interested stakeholders. For this reason, the World Bank’s Development Economics Vice Presidency has established an open web facility that pre-filters data from the National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2satellite and publishes monthly mean concentration anomalies for all terrestrial cells of a 25-kilometer global grid. The website will also publish annual carbon dioxide tracking reports for urban areas and provide information that links the 25-kilometer global grid cell IDs to IDs for urban areas and national administrative units (levels 0, 1, and 2).

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers
with the first having the strongest interpretation as capturing amenities. The correlation with wages for this measure is 0.37, while the correlation with the INEGI’s welfare index is 0.77.

# **Trade and Investment Variables**

<u>Foreign Direct Investment : This is aggregate per capita foreign direct investment from</u> 1995-1999 reported to the government by the firms investing and was provided by the Central Bank of Mexico. The Federal District (DF)- Mexico City- shows vastly higher rates because much of the FDI destined for other states is registered at the headquarters of the firm in the DF. We include a dummy variable in the regressions to account for this measurement error. As figure 2 suggests, FDI is highly, although not exclusively concentrated along the northern border with the US.

<u>Maquila Value Added: This variable may be seen purely as a proxy for FDI, but since the</u> maquilas are primarily exporters, it can also be seen as a proxy for “maquila exports per capita” which brings it closer to being a trade variable. Since it is collected from industrial surveys, it does not suffer from the “headquarters” effect of the FDI variable. The two variables are moderately correlated (.64) and hence are it may be a serviceable proxy for FDI more generally. On the other hand, the government of Mexico tabulates 12 of the states in an “other” category and the loss of these state implies reduced information and potential selection bias. We run the regressions with the substantially reduced sample.

<u>Exports: This variable is provided by the Ministry of Finance (MOF). As with the FDI</u> data, the exports reported by firm are assigned to the location of the headquarters, often in the DF, which may not be the actual location of production. The MOF therefore reassigns the firm’s aggregate exports proportionally using data on the location of plants and employees of each firm
Contracts vary significantly in the case of force majeure. In the Kosovo airport PPP, the contract asks the parties to agree on the terms of termination. The contracts of road PPPs in Argentina mandate that the government compensate the private party for the whole accumulated investment amount without penalties.

In India (not one of our sample countries), PPP contracts include a compensation scheme that aligns with the PPI database’s conceptualization of PPP financing. Road concession agreements, for example, express compensation as a percentage of debt and equity financing of the project, depending on the source of termination of the contract. In the case of default by the project company, the concession agreements do not foresee any compensation on equity; in the case of default by the public authority, the contract entitles the private sponsor to 150 percent of its equity, which can be seen as an approximation of the return on equity invested, as stipulated in the contracts mentioned before. In the case of a force majeure event indirectly caused by a political event, the private sponsor of an Indian road PPP is entitled to 110 percent of the equity it invested in the project.

When a project is terminated, it is assumed that the government loses all its equity. As a result, equation (4) can be rewritten as follows: E � D D + P + P E �, (5) � ii ii ii ii ii EEiiDDEE where<sup>�</sup> is the predicted probability of early termination of project = PPE �DDeeDDtt EELLEE DDPPiicc E PPiittEE iiP ttee E PPiittEE , EELLEE is the debt of project , D D is the proportion of debt lost because of early termination, P is ii ii PPE ii DDeeDDtt the government equity in project , P is the private equity in project , and ii ii EELLEE DDPPiicc E PPiittEE E is the proportion of private equity lost because of early termination. ii ii iiP ttee E PPiittEE ii EEiiDDEE EELLEE Three scenarios are considered: • Low: D D = 0.793
smaller low-caste population and their lower landownership.<sup>7</sup> All but five of these settlements are located in Southern Punjab or Sindh. Among the low-caste children in our sample, 48% reside in high-caste dominant settlements. By contrast, only 6% of high-caste children live in low-caste dominant settlements.

Qualitative evidence gathered from field interviews documents how caste-based stigma plays out in schools either to exclude low-caste children altogether or to make them unwelcome. The following snippets are typical: “No, there is no school for girls here. Our zamindar got a teacher

> 6Comparable numbers for India put the scheduled castes (dalits) at 15-20% and other backward castes at 20-27% of the population.

> 7Table 2 reports that 17% of all settlements in the village census are low-caste dominant as compared to only 4% of villages. This is further indication of the relative segregation of settlements based on caste in rural Pakistan.

7
U.S. households prefer balancing the sex composition of their children, Korean families prefer sons. They use the first child’s sex as an instrument for fertility and find that having children reduces the labor force participation of married Korean women. More recent papers use different instruments that rely on childless mothers undergoing infertility treatments (Cristia, 2008; Lundborg et al., 2017) or miscarriage in a women’s first pregnancy (Hotz, McElroy and Sanders, 2005) or natural experiments like the use of the contraceptive pill at the state level (Bailey, 2013) or changes in abortion legislation (Bloom et al., 2009; Angrist and Evans, 1996) similarly conclude that fertility has a negative impact on mother’s labor force-participation or earnings.

The empirical literature from developing countries is relatively small and the evidence found is not unified. Some studies in developing countries have also found negative or no impact. Cruces and Galiani (2007) generalize the results for the U.S. found by Angrist and Evans (1998) to the populations of two Latin American countries (Argentina and Mexico) and find a negative effect of fertility on female labor force participation. Using data from a social experiment in Bangladesh, Schultz (2009) also finds a negative effect of family planning programs on female labor-force participation. Using son-preference as an instrument, Lee (2002) finds no significant effect of fertility on rural female labor supply in China. Ebenstein (2009) also uses son-preference but he reports a negative effect of fertility on maternal labor force participation in Taiwan, China. A more recent study by Ag ̈uero and Marks (2011) uses self-reported infertility as an instrument for family size. Based on data from 26 low- and middle-income countries, their estimation results show that the presence of children affects neither the likelihood of work nor its intensity for women.

However, there is also a scattering of papers in developing countries which show mixed or even positive effects of fertility on labor supply. Based on data
# **Discrepancies distort trade balances between countries.**

The previous subsection highlighted how discrepancies in detailed data have the potential to introduce notable distortions in the import or export structure of reporters. However, we show that reporting discrepancies can distort findings even at the most aggregate level. To prove the extent to which the specific choice of international merchandise trade data impacts its findings, consider the case of trade balances. Trade surpluses and deficits are often important drivers of empirical research and policy design and rely on a simple calculation using exports and imports between a pair of countries. However, both export and import values can be estimated using two separate sources. As suggested by trade balances (Farhad et al., 2023), they can be severely impacted by reporting discrepancies. In calculating the trade balance, it is important to use either the CIF or FOB valuation for both the recorded import and export, so as not to distort the balance with CIF rates that inflate the import value. This means that any calculation of the trade balance using unmirrored (one-sided) data will naturally be biased due to the structural discrepancy that occurs in the valuation method. We empirically demonstrate this point by calculating trade balances for all countries using unmirrored data, i.e., using FOB valuation for exports and CIF valuation for imports.

As an example, consider the trade between Madagascar and France. France has consistently been one of Madagascar’s top export destinations, accounting for 21% of Madagascar’s exports in 2015 and 17.5% of Madagascar’s exports in 2021. In addition, France is the largest European origin of Madagascar’s imports. Even though both countries have consistently reported a trade surplus in favor of Madagascar, they have different accounts regarding its magnitude, as shown in Figure 8. For example, in 2015, Madagascar reported a trade surplus of 253 million USD with France. However, according to France's accounts, the same surplus amounted to only 84 million USD.

27
|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|HH|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.037|0.041|0.046|0.055|0.025|0.265|0.018|0.023|0.021|
|# of EAs|433|290|143|433|290|143|433|290|143|
|N|13519|9731|3788|13519|9731|3788|13436|9664|3772|

_Notes:_ This table provides estimates of the impacts of logarithm of refugee intensity on a binary measure of ownership of an NFE (the first three columns), number of NFEs owned (the middle three columns), and a binary measure of whether a household plans to open a new NFE in the next 12 months (the last three columns). Household sample weights from ESS2 applied. Columns labeled “All,” “Rural,” and “Urban” report results for all, rural, and urban households, respectively. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5% and 1% levels, respectively.

48
represented four distinct quarterly surveys that we merged together. Thus, any seasonality in wages (bonuses or raises) should be averaged out over the course of each survey.

In each survey round, workers were asked the number of hours they had worked over the past seven days. By contrast, the measures of earnings varied from year to year,<sup>4</sup> so to construct our measure of hourly wages:

- In 2003, workers were asked monthly earnings from their main activity in the past year. We calculate each worker’s average hourly earnings by dividing this amount by the hours worked in the past week multiplied by 4.33 (the average number of weeks in a month, across the year).

- In 2005, 2010 and 2013, workers were asked about earnings from the previous week in their primary job, so we divide this by the hours worked in the past week.

- In 2015 and 2016, workers were asked take home earnings for the past one month. We calculate average hourly earnings by dividing this amount by the hours worked in the past week multiplied by 4.33.

In each round, earnings were asked to paid employees: both regular wage workers and well as casual/piece rate workers. While, prior to 2013, self-employed workers were asked about their earnings, for consistency over our period of study, we restrict our sample to paid employees. We deflate all wages to 2010 taka using the consumer price index.<sup>5</sup> There may be differences in the wages calculated across the different survey rounds. The fact that the 2003 survey asked about monthly earnings over the past year presumably means that respondents reported their average monthly income over the year, which reduces variance between workers. Similarly, the average weekly pay over the past month in 2015 and 2016 could display lower variance than in rounds 2005-2013, when workers reported weekly pay directly. However, any level differences between years would be absorbed in the year fixed