rafmacalaba/datause-encoder-data huggingface dataset:
total consumption. It is also consistent with the interpretation that we are capturing net sellers with our urban proxy, and net sellers are harmed more by price increases.

The national level evidence indicated that households are not experiencing as large of a decline in caloric intake as they are in the value of food consumption. They appear to be trading off quality for quantity. This tradeoff seems to exist in urban and rural areas, but takes different forms. For urban households, we observe no statistically significant decline in calorie intake for urban households, despite the large reduction in the value of food consumption. For rural households, we observe a decline of -0.0744 percent for a one percent increase in wheat flour prices (slightly larger than the national estimate). To understand these differences, we turn to changes in the level of dietary diversity and expenditure shares. The results show that urban households experience a large decline in dietary diversity, approximately three times that of rural areas; that is, urban households make relatively large changes in the composition of their diets in order to buffer the shock to calorie intake. The results on expenditure shares reveal that the national level result of an increase in expenditure share on grains is largely driven by urban households (Table 8).

We next turn to the differential impact of increasing food prices on households that produce wheat (denoted as ‘wheat households’) and those that have access to agricultural land (denoted as ‘agricultural households’). (Results tables are in a working paper version available at www.ssrn.com.) We observe patterns similar to the rural-urban results, though with smaller magnitudes for the food consumption and food consumption score regressions and with no statistically significant differential impact in protein intake for either group relative to their counterparts. In the regression analysis, the rural-urban distinction is the most striking and best captures the differential impact of the price shock. As we reclassify households based on other characteristics (wheat-producing, access to agricultural land), the proxy variable then includes more and more rural households in the base category, thus dampening the significant differences between the two groups. Therefore in the Afghanistan context, we view urban households as the best proxy for net buyers.

19
(CCCU, 2019). By examining converging responses of participants, dominant patterns in the data could be identified and effects of individual factors minimized.

An “interpretive-descriptive” approach (Thorne 2008) to interpreting the data was adopted. This approach involves accurately describing what has been understood and reconstructing data into a ‘recognizable reality’ for the participants of the research. It requires selection and interpretation of data by weaving respondents’ words, field note quotations, and evaluator’s interpretations into a “rich and believable descriptive narrative”. This approach is particularly suitable for this comprehensive review of TEIP since it allows all significant stakeholders a voice that is blended into a series of themes and related issues that are shared by a number of people. It also adheres to the advice of Maykut and Morehouse (1994: 18) who suggest that "the task of the researcher is to find patterns within words and to present those patterns for others to inspect while at the same time staying as close to the construction of the world as the participants originally experienced it”.

The interview data were supplemented by reference to a wide range of documentary reports, research studies and articles. This entailed the scrutiny of student teacher and teacher portfolios at different points during the project, with final portfolio samples compared to earlier samples. In addition, observations of teachers and student teachers at work in their classrooms and of training sessions in operation were conducted by CCCU team members at key points during the project. Finally, the contents of field notes that had been collected from the beginning of CCCU involvement in the project also contributed to the data sets that were analyzed.

Critical outputs and outcomes from the PRESET and INSET Components of TEIP are presented in summary form hereunder.<sup>14</sup>

# **5.2 TEIP Outputs: PRESET and INSET**

Significant outputs include:

- The _PTPDI_ which identified the competences to be mastered at each stage of a teacher’s professional development and which acted as an instrument to guide all TEIP activities.

- Extension of the PRESET _Teaching Practicum_ from 4 to 14 weeks of mentored teaching and the integration of entire programs around the fulcrum of the _Teaching Practicum_ (both subsequently mandated by AQAC for all PRESET programs).

- Development of a series of five university-based courses/seminars (80 hours) to support the different stages of the _Practicum._

- Practicum and Mentor Training Handbooks.

- Integration of CK and PCK in the development of ten PRESET and nine INSET modules.

- A system of portfolio development and evidence-based evaluation linked to the PTPDI competences for both PRESET and INSET, along with Handbooks for same.

- Final output from TEIP’s PRESET programs (still in progress) expected to be 2,352 (PCU Reports 2018, 2019).

- Total throughput of UQTs in the INSET component was 3,987.

> 14 For further details on project impact, see TEIP-related documents on the MoEHE website: www.palpcu.ps.

20
economists (Alexeev et al. 1992). The mathematical economics (called _economic cybernetics_ ) that emerged in the late 1950s in the USSR consisted in regrouping elements of control and information theory relevant to socialist planning (Lange 1970). It focused on the economics of central planning and state-owned enterprises and relied on the methods of linear programming and optimization techniques of socialist production (Brue and MacPhee 1995). The paramount task of the Soviet economics was to provide a scientific basis for the policies of the Soviet Communist Party. Soviet economists had very little interaction with Western colleagues. A limited number of copies of a heavilyedited and censored translation of Samuelson’s _Economics_ was published in Moscow in 1966 to be used only for “research” purposes (Gerschenkron 1978), and no translations of significant works in economics were published in the Soviet Union up until the late 1980s.

The collapse of the Soviet Union was followed by a rapid transformation of the system of Russian economic thought and economic education (Kovzik and Watts 2001). Russian universities began reforming their curricula by introducing courses on market economics<sup>3</sup> . Different fields of economics were reformed at a different pace. Poverty research, completely neglected in the Soviet era, lacked the negative heritage of Marxist ideology. As they entered this, new for Russia, field, Russian scientists could rely directly on the methodology of a fully-developed research discipline, strictly adhering to the principles of scientific methods. Have they utilized this opportunity to break out from the past? The next section provides an answer.

# **4. Do poverty studies in Russia follow the general principles of scientific method?**

We believe that any study of poverty: quantitative or qualitative, causal or descriptive, economic, sociological or even philosophical can and should be pursued rigorously. The relative weights of components of the scientific method will be different for different forms of inquiry. Empirical economic studies of poverty might put more emphasis on strong theoretical models and econometric techniques of analysis of large random

> 3 The Russian edition of the principles of economics textbooks in macro and micro-economics were published in Russia with a circulation of more than 500,000 copies in 1992 (McConnell and Brue 1992). These volumes became the leading textbooks for economic courses throughout Russia.

7
**Table 4: Real Minimum Wage rate Indices** **<u>of Workers in Wage Boards and Government Employees</u>**

|December<br>1978 = 100|Agric|ulture|Indust<br>com|ry and<br>merce|Serv|ices|All C<br>Gover<br>Empl|entral<br>nment<br>oyees|
|---|---|---|---|---|---|---|---|---|
|Month<br>2|006/07|2009/10|2006/07|2009/10|2006/07|2009/10|2006/07|2009/10|
|7|80.7|82.2|55.6|71.8|39.9|54.1|163.7|156.5|
|8|81|82.4|55.8|72.1|40.1|54.2|164.4|156.9|
|9|80.2|82.2|55.2|71.9|39.7|54.1|162.7|156.5|
|10|78.7|81.9|54.2|71.6|39|53.9|159.8|155.9|
|11|75.4|81.3|51.9|71.1|37.3|53.5|153|160.9|
|12|73.7|80.3|54.1|70.2|36.5|52.9|149.5|159|
|1|72.7|112|54|69.3|36|52.1|172.4|156.9|
|2|73.7|111.7|54.7|69.1|36.5|52|174.7|156.6|
|3|74.9|112.8|55.6|69.8|37.1|52.5|177.7|158.1|
|4|75|114|55.7|70.5|37.1|53.1|177.9|159.7|
|5|73.5|112.2|78.5|69.4|55.3|52.3|172.7|157.3|
|6|89.3|111.3|76.1|68.9|53.5|51.8|171|156|

Source: Tables 86 and 87 in 2008 and tables 7.12 and 7.13 in 2011 publications of Economic and Social Statistics of Sri Lanka, Central Bank of Sri Lanka.

In order to incorporate the wage data in our consumption model, we construct a wage rate index that assigns 100 to the wage rate for the 2009/10 public sector. The obtained wage rate index is shown in Table 4. To control for heterogeneity between employed and self-employed workers and urban and rural areas and estates, we followed the following steps:

1. We identified seven industry clusters that covered most workers (4 clusters for those employed in public, agriculture, industry and commerce, and service sectors, and 3 clusters for those selfemployed in agriculture, industry and commerce, and service sectors) in both the HIES and LFS data using individual questions on economic activities and main industries.

2. Using HIES 2009/10 data, the 2009/10 wage rate index for the seven industry clusters in three areas (urban and rural areas and estates) were derived by using the median wage income for individuals who mainly engaged in each of seven clusters in each area, as shown on the third column in Table 5. This step is necessary since the minimum wage indices in Table 4 do not indicate the difference in wage levels across sectors but only reflect changes within a sector across time.<sup>8</sup> 3. The 2006/7 wage rate index for the seven industry clusters in three areas was calculated using the obtained wage rate index in 2009/10 and the across-time changes in Table 4.

4. After merging the obtained wage rate index with the individuals in HIES or LFS data, the household-level wage index is calculated by assigning the sector-specific value of the index to each household member based on which sector he or she worked in and summing them up for each household. Finally, the per capita wage index is calculated by dividing it by a household size.

> 8HIES data are used for this purpose since HIES data include both employed and self-employed income, but LFS data include employed income only.  Also, we did not calculate the wage levels using 1978 wage level data since the available 1978 wage data are old to be unreliable.

10
NO2 may also be interpreted as a new variant in big data efforts to quantify cyclical fluctuations. For example, across countries in Sub-Saharan Africa, Buell et al. (2021) present a suite of high-frequency indicators, including Google search trends and mobile payments. Similarly, across ZIP codes in the United States, Chetty et al. (2020) build a database that uses anonymized data from private companies to proxy consumer spending, business revenues and employment rates nearly in real-time. Similarly, Goolsbee and Syverson (2021) and others rely on mobility data. The advantages of air pollution data over any of these alternative sources is that satellite NO2 readings have global coverage, are available in real-time, are entirely open access, and are now reaching their maturity, with comprehensive data back over 15 years.

**Outline of the Paper.** The paper proceeds as follows: Section 2 provides background on satellite measurements of NO2. Section 3 describes the data in further detail, including summary statistics and other basic characteristics. In Section 4, we describe the methodology, including our objective procedure for identification. Section 5 showcases the baseline results, and Section 6 implements the methods to applications. Section 7 concludes.

# **2 Satellite Measurement of Nitrogen Dioxide**

**Background.** We use air pollution as a measure of economic activity. However, not all air pollution data, including that measured at ground stations, is suited to such a task. First, ground-level air pollution monitoring stations have an uneven spatial distribution. It is not the same to measure pollution in an urban intersection or in the countryside. Additionally, these stations are sparsely located, particularly in developing countries, where grid cells may contain just one station or none at all. Second, even when measured, these readings may subsequently be doctored due to political motives, such as meeting mandatory targets of energy intensity (Ghanem and Zhang, 2014).

These features are some of the primary reasons why “top-down” measurement of air pollution from satellites has become prevalent in scientific studies (Lin and McElroy, 2011; Wang et al., 2012). NO2 specifically has been of keen interest to scientists, and top-down measurements have proven fruitful in several contexts. For example, to infer atmospheric densities over China, where monitoring station data is suspect (Richter et al., 2005). An air pollutant with serious implications for the environment and health, near-Earth NO2 is primarily due to human economic activities involving combustion (e.g., vehicles, power plants, and factories).

Still, the availability of satellite readings of other air pollutants more familiar to some people, including carbon dioxide (CO2), begs the question of why satellite readings of air pollution have

> (2020) note a correlation between NO2 and economic activities in select U.S. cities during the COVID-19 crisis. No other work generalizes these results globally, develops an NO2-based measure of economic activity, or considers our applications.

4
20 The final variable which is statistically significant in Table 4 is country credit rating, which is negatively related to international migration. This result means that countries with a higher (i.e. better) credit rating produce a lower share of international migrants. One way to interpret this finding is that countries with better macro-economic management are able to achieve a higher credit rating in the international marketplace. This in turn enables them to attract more foreign and domestic capital to create more jobs at home and reduce the need (incentive) for people to migrate abroad.

# 6.  Conclusion

This paper has used a new data set of 74 low- and middle-income developing countries to examine the impact of international migration and remittances on poverty. Five key findings emerge.

First, international migration – defined as the share of a country’s population that is living abroad – has a strong, statistical impact on reducing poverty in the developing world. On average, a 10 percent increase in the share of international migrants in a country’s population will lead to a 1.9 percent decline in the share of people living on less than $1.00 per person per day.

Second, as might be expected, distance to a major labor-receiving region (the United States, OECD (Europe) or Arab Gulf) has an important effect on the level of international migration. On average, the results suggest that a 10 percent increase in a country’s distance to a major labor-receiving region will reduce the share of migration from that country by between 9.5 and 15.3 percent. This result is sensible because those countries which are located closest to the United States – like Mexico and Jamaica – and
**Table C1: Google Mobility Indices for Workplaces and Retail & Recreation**

|||_N_|_Mean_|_SD_|_Min_|_Max_|
|---|---|---|---|---|---|---|
||Workplaces|26797|-23.77|12.06|-58.40|10.80|
|Wave 1|Retail and<br>recreation|26797|-36.76|18.11|-81.00|-1.60|
||Workplaces|22930|-20.69|10.06|-52.71|8.14|
|Wave 2|Retail and<br>recreation|22930|-23.02|18.22|-69.71|8.29|
||Workplaces|24321|-23.58|9.17|-44.57|-2.43|
|Wave 3|Retail and<br>recreation|24321|-17.71|17.56|-59.29|16.00|
||Workplaces|24423|-21.07|9.85|-41.88|0.88|
|Wave 4|Retail and<br>recreation|24423|-16.48|14.66|-56.00|10.00|
||Workplaces|23320|-18.12|9.98|-52.75|7.00|
|Wave 5|Retail and<br>recreation|23320|-21.28|13.64|-65.25|-3.25|
||Workplaces|23616|-18.99|9.16|-41.22|3.89|
|Wave 6|Retail and<br>recreation|23616|-22.08|12.19|-57.67|2.22|
||Workplaces|29805|-16.70|9.36|-34.83|8.92|
|Wave 7|Retail and<br>recreation|29805|-20.23|14.00|-55.08|5.83|

In graphs C1 and C2 below we show the variation in mobility for the two indices separately over time and by region. Countries in Sub-Saharan Africa and Asia Oceania had higher movement in retail and recreation relative to some of the other regions and remained approximately 20 percentage points below pre-pandemic levels of movement throughout the survey period. In South Asia and Latin America there is a greater increase in the level of movement in retail and recreation in the countries over time. In the countries in South Asia during wave 1 the movement in retail and recreation was -60 percentage points lower than prepandemic levels which increased over time to -20 percentage points lower than pre-pandemic levels by wave 6.

53
Policy Research Working Paper 8562

# **Abstract**

This paper utilizes data for African countries to analyze the extent to which financial development affects the dynamics of the relationship between exchange rate flexibility and economic growth. The findings indicate that financial development exerts a positive influence on the relationship between exchange rate flexibility and GDP growth as well as total factor productivity growth. The paper also documents a positive impact of trade openness on the relationship between exchange rate flexibility and growth. Moreover, the results show a strong and positive association between exchange rate flexibility and financial development. The findings, therefore, suggest that discussions and decisions on exchange rate policy should be undertaken with consideration for structural policies that address the development of the financial sector. In addition, the paper asserts that policy makers should adopt a stance that facilitates some flexibility in exchange rates to foster development of the financial infrastructure in these economies.

This paper is a product of the Office of the Chief Economist, Africa Region. 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/research. The authors may be contacted at wkassa1@worldbank.org or elartey@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 carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
strongly significant also after the inclusion of the relevant control variables. SPREAD2, which represents our most accurate spread measure, appears to be bear a relatively stronger relation to the indicators of property rights than SPREAD1. Among the control variables only the level of inflation and quasi fiscal restrictions associated to the level of required reserves appear to have an impact on banks’ spreads.

Some further robustness tests are carried out (columns 4 to 8 in Tables 3 to 6) by adding to the baseline specification indicators of bank concentration, activity restrictions, entry restrictions, extent of state ownership, and presence of a public credit register. None of these additional controls appears to have a significant effect on banks’ spreads nor on the explanatory power of the property rights proxies.

Finally, we test whether countries with a significant level of dollarization may have induced a downward bias in the average level of interest rates due to the presence of significant share of bank intermediation carried out in foreign currency and presumably with smaller spreads. As we can see in column 8 (Tables 3 to 6) the exclusion of countries with an extensive degree of dollarization – defined as a dollarization ratio in excess of 50 percent – does not alter our main conclusion as our proxies of property rights remain significant. The coefficients of the same variables, though, appear to be systematically smaller than in the larger sample, indicating that on average the use of foreign currencies does not bring with it smaller spreads and that, quite the contrary, dollarized economies are plagued by higher spreads between banks’ borrowing and lending rates.

Apart from the effect associated to the exclusion of the dollarized economies, the coefficients of the proxies for property rights have shown a remarkable stability across 14
and their subsidiaries, (b) estimate the correlation between the distance to default of the parents and their subsidiaries, and (c) investigate the factors that affect the correlation between the default risk of parent banks and subsidiaries. Section 4 presents results from our econometric analysis. Section 5 concludes.

# **2. Data**

We assembled an original and extensive database of stock market prices and balance sheet characteristics for both publicly traded parent banks and their publicly traded subsidiaries in developing countries. Our sample consists of 93 publicly listed foreign subsidiaries, operating in 36 host developing countries and owned by 41 parent bank groups, headquartered in 24 home countries. Our period of analysis is the peak of the global crisis: from September 2008 to December 2009. Table 1 presents a list of all the parent and subsidiary banks we consider in our analysis.

Even though the presence of foreign banks has increased in recent decades, the final sample of subsidiaries that we were able to include in the analysis is smaller as the result of two constraints. First, most foreign subsidiaries are not listed in the stock market, since they are privately held.<sup>3</sup> Second, of the ones that are listed in host countries’ stock markets, there are several cases that are not traded often, since parent banks control most shares (e.g., 98 percent or more ownership). In this context, we identified about 167 banks in about 44 host countries where foreign banks held important ownership stakes, but we found enough information for only 93 cases. The median ownership stake in the sample is about 61 percent.<sup>4</sup> The limitations to

> 3 As a robustness check, we extend the analysis to non-traded banks following the approach outlined in Falkenheim and Pennachi (2003). See section 4.1 and Table 7.

> 4 In 28 of the 93 foreign subsidiaries included in the sample, the identified parent banks seem to directly control less than 50 percent of the shares. We include them in the analysis because the identified parent banks are portrayed as

5