rafmacalaba/datause-encoder-data huggingface dataset:
Azerbaijan, response rates are higher for these questions (about 95 percent) and about 2 in 5 report themselves at a lower rung of the ladder relative to where they considered themselves to be four years ago with only about 1 in 4 reporting upward movement. For both Armenia and Georgia, the perceived downward move is a bit less pronounced in comparison to the earlier result, but still quite high, especially for Armenia.

Figure 3: Proportion of population characterized by perceived movements in the 10-step ladder of wealth <!-- Start of picture text --> 60<br>48<br>50<br>42<br>39 40<br>40<br>28 28<br>30 26<br>21<br>20 16<br>10<br>0<br>Armenia Azerbaijan Georgia<br>Down Same Up<br><!-- End of picture text --> _Source_ : LiTS (2016) _Note_ : The bars for each country may not add up to 100 percent because responses that were “Don’t know” and “Not applicable” are not included.

Together, these results on perceived movements provide a number of interesting observations to help anchor a more in-depth exploration of economic mobility in the three countries in the subregion. First, there is a sizeable variation in the perceived movements across the three countries. Second, to some extent the broad patterns are consistent with the level and pattern of economic growth realized in the two countries. Georgia has witnessed a faster recovery and better shared growth than Armenia and that is somewhat reflected in a higher proportion of Georgians reporting upward movements and a higher proportion of Armenians reporting downward movements. Even in Azerbaijan, where we are not able to explore actual mobility, the perceived movements appear somewhat consistent with the recent oil price related economic shocks the country has witnessed. However, the question of the extent to which these perceived movements are anchored in actual realized movements in welfare and living standards is not immediately 9
as developed or underdeveloped and their structure as bank-based or market-based.<sup>(See</sup> Table 5.1 0.) Table 5. _1_ 0

|**C**<br>|**ountryClassification**<br>**(Based ondatacoll**<br>|**ofFinancialStructure**<br>**ected**in**the1990s)**<br>||
|---|---|---|---|
|**Financiallyunderdevelo**|**pedeconomies**|**Financiallydevelope**|**d economies**|
|**Countryname**|**StructureIndex**|**Countryname**|**StructureIndex**|
|<br>**_Bank-based economies:._**||**_Bank-basedeconomies:_**||
|**Indonesia**|**-0.50**|Austria|-0.73|
|Argentina|-0.25|Belgium|-0.66|
|India|**-0.14**|Italy|**-0.57**|
|Ireland|-0.06|Finland|-0.53|
||=|___<br>_<br>_Norway|-0.33|
|||NewZealand|-0.29|
|**_Market-based economie_**|**_s:_**|**Japan**|**-0.19**|
|Denmark|0.15|France|-0.17|
|Chile|0.25|Germany|-0.10|
|Philippines|0.71|||
|||**_Market-basedeconomie_**<br>|**_s:_**<br>|
|||Netherlands|0.11|
|||**Thailand**|**0.39**|
|||Canada|0.41|
|||**Australia**|**0.50**|
||=|____<br>**_Korea**|**0.89**|
|||Sweden|0.91|
||_**_**_|__<br>GreatBritain|0.92|
|||**Singapore**|**1.18**|
|||<br>UnitedStates<br>|1.96|
|||Switzerland|2.03|
||I__<br>__|<br>**Hong Kong**.|**2.10**|
||_ _<br>_|<br>___<br>**Malaysia**|**2.93**|
|Notes:||||

(1) A country is defined as having an underdeveloped financial system if both of the' following hold: (1) _Claims of deposit money banks on the private sector/GDP_<sup>is less than</sup> the sample mean and (2) _Total value traded as a share of GDP_ is<sup>less</sup> than<sup>the sample mean.</sup> (See Table S.16 in Statistical Appendix.) Thus, a country's financial system is classified as underdeveloped only if it has poorly developed banks and markets. (2) Countries are ranked along the spectrum from bank-based to market-based,<sup>wherehigher values of</sup> _Structure_ indicate higher levels of stock market development relative to banking-sector<sup>development.</sup> Countries that have above the mean values of _Structure_ are then classified as<sup>market-based.Countries</sup> that have below the mean values of _Structure_ are classified as bank-based. **_Source: Demirg() -Kunt and Levine (2001a: Table 3.12, 121)_** Demirgii9-Kunt and Levine developed a conglomerate index of financial structure that is based on measures of size, activity, and efficiency. These measures when combined into a "Structure Index" (see above table) identify a country's<sup>place on a</sup> market-based versus bank-based spectrum. Countries are also distinguished<sup>between</sup> 72
- 4 Nowcasting monetary poverty in Syria: Challenges and sensitivity analysis

# 4.1 Data challenges

As discussed in Section 2.1, national accounts – the comprehensive economic statistics that measure economic activity in a country – become an input for poverty projections for years in which no survey has been conducted. The typical (distribution neutral) approach applies per capita growth rates of GDP or private consumption (household final consumption expenditure in WDI) to the baseline mean of per capita expenditure from a household survey to extrapolate poverty beyond the survey period. Besides concerns pertaining the validity of the assumption that each household’s consumption expands/contracts at the same rate of the overall economy, the reliability of NA statistics can pose further challenges. In the case of Syria, _the base year for NA estimation is 2000_ . Hence, NA statistics published by the national statistical agency available up to 2021 assume that the structure of the economy has not changed since 2000, an assumption clearly problematic given the substantial economic changes associated to economic liberalization between 2000 and 2010 and conflict thereafter.<sup>21</sup> In what follows, we discuss possible options for the use of NA statistics in poverty projections taking into account the challenges posed by data quality and data reliability issues in the context of Syria.

## 4.1.1 Measuring “growth”

Should poverty projections be based on GDP or private consumption data from national accounts? Private consumption is generally preferred as it captures a set of goods and services that more closely mirrors consumption from household surveys. In practice however, considerations such as the availability and quality of GDP and private consumption data, as well as the strength of correlations between data from national accounts and household survey data typically influence the choice.

Prior to conflict, for the years in which both NA and survey data are available, the average ratio of average per capita consumption from household survey to average private consumption per capita from NA was 0.93, whereas the corresponding ratio using GDP per capita was 0.61 (Table 8).<sup>22</sup> _Table 8:Ratio of survey household consumption to Private Consumption and GDP_

||**Ratio of average pc consumption from HH**<br>**survey to average pc private consumption**<br>**from NA**|**Ratio of average pc consumption from HH survey**<br>**to pc GDP**|
|---|---|---|
|**2003**|1.18|0.71|
|**2007**|0.84|0.56|
|**2009**|0.79|0.57|
|**Average**|0.93|0.61|

_Source_ : World Bank staff calculations, based on WDI and CBS data

> 21 The international recommendation is to update the base year at least every five years.

> 22 The lower ratio when using GDP is expected because GDP includes more than private household consumption.
learning proficiencies that should arise from undergraduate economics education: (1) access existing economics knowledge; (2) display understanding of economic knowledge; (3) interpret and evaluate economic knowledge; (4) interpret and manipulate economic data; (5) apply economic knowledge to an analysis of an economic problem; and, (6) create new economic knowledge.

Taking these proficiencies as the goal also of development economics education, what is taught and how it is taught can then be related back to the extent to which students are then able to perform these tasks. Doing so requires that students have knowledge of a range of key topics, an understanding of theory, and the ability to be critical readers and users of data analysis.

# **2.4 Implications**

Based on the above, we should expect undergraduate development economics courses that are closest to the frontier in the profession to have a stronger focus on empirical methods and interpretation of data, along with applied theory, rather than a study of development as stylized facts, case-studies and descriptive patterns. They should include more of the more recent topics, and test and assess students in a way that allows them to demonstrate the proficiencies outlined above.

# **3. Data Collection**

We collected data on development economics courses via two sources: an online survey of instructors, and collection of course syllabi. We gathered information on both undergraduate and masters level development economics classes in developing countries. A comparison of the content of the undergraduate and masters level courses provides one way of seeing whether the education level of the students is a key factor in determining what is taught.

# **3.1 Online Survey**

Our initial approach was to field an online survey directed at instructors of development economics classes, asking questions on the range of development economics classes offered at their institution, their background and on the specific development economics courses they teach. The online survey was available in English and French. For the identification of the relevant institutions we relied on the economics departments listed in the _Index of Economics Departments, Institutes and Research Centers in the World_ provided by Repec. Whenever Repec provided a ranking of 9
1.00, −0.23, − 0.79 for the ADLR. Thus, even though we have a short sample, the choice of model has little impact on the key coefficients.

Table 3: Cointegrating vector

||Lao PDR|Thailand|Vietnam|
|---|---|---|---|
|Long-run coefficients, βs|1.000|-0.195|-0.836|
|||[-3.016]|[-14.140]|
|Exclusion restrictions||||
|LR statistic|17.831|3.906|20.048|
|_p_-value|0.000|0.048|0.000|
|Adjustment coefficients, αs|-0.249|0.012|0.072|
||[-4.316]|[0.197]|[2.501]|
|Test of significance||||
|LR statistic|19.777|0.038|6.692|
|_p_-value|0.000|0.846|0.010|

Note: t-values in brackets. See Table 2 for model specification.

14
2. _Balanced Sample:_ This sample includes the 535 matched households as well as the 472 households in the TUP survey who report not owning any phone. It excludes households that own a phone on a different phone network than the one who provided CDR. The motivation for this sample is to provide an indication of targeting performance in a regime in which CDR can be used to target all phone-owning households. In addition to applying sample weights from the survey, households that do not own a phone are downweighted so that the balance of phone owners to non-phone-owners (with sample weights applied) is the same as in the baseline survey as a whole (with sample weights applied, 84% phone owners).

3. _Full Sample:_ All 2,814 households in the TUP baseline survey for which asset and consumption data are available, with sample weights applied.

Note that the quota used to evaluate targeting changes for each sample, based on the number of households that are ultra-poor in the sample. For the matched sample, the targeting quota is 27.29%; for the balanced sample and full sample the quotas are 5.47% and 6.02%, respectively.

# **3 Results**

## **3.1 Performance of Targeting Methods**

Our first set of results evaluate the extent to which different targeting methods can correctly identify ultra-poor households. This analysis compares the performance of CDR-based targeting methods to asset-based and consumption-based targeting, using the sample of 535 households for which survey data and CDR data are both available.

An overview of these results is provided in Figure 1. Figure 1a shows the distribution of assets and consumption, as well as the distribution of predicted probabilities of being nonultra-poor generated by the CDR-based and combined methods, separately for the ultra-poor and non-ultra-poor. The dashed vertical line indicates the threshold at which point 27% of households are classified as ultra-poor; we use this quota because 27% of households in this sample were designed as ultra-poor by TUP. Figure 1b provides confusion matrices that compare the true status (rows) against the classification made by each method (columns). These confusion matrices are also used to calculate the measures of precision and recall reported in Table 2 Panel A.

11
# **Figure 5 – Labor Tax Wedge**

<!-- Start of picture text --> Belgium<br>France<br>Germany<br>Austria<br>Italy<br>Hungary<br>Sweden<br>Slovenia<br>Finland<br>Czech Republic<br>Estonia<br>Spain<br>Denmark<br>Greece<br>Netherlands<br>Turkey<br>Slovak Republic<br>Portugal<br>Norway<br>Malaysia<br>Luxembourg<br>Poland<br>Iceland<br>Singapore<br>Mongolia<br>United Kingdom<br>China<br>Canada<br>United States<br>Japan<br>Vietnam<br>Australia<br>Ireland<br>Philippines<br>Myanmar<br>Switzerland<br>Korea<br>Israel<br>Lao PDR<br>New Zealand<br>Brunei<br>Mexico<br>Thailand<br>Indonesia<br>Cambodia<br>Chile<br>0% 10% 20% 30% 40% 50% 60%<br><!-- End of picture text -->

**Sources: World Bank (2014a). Notes: Data are for 2010.**

Figure 5 depicts the labor tax wedge in Vietnam and selected countries in the OECD and the EAP region. The figure shows that, at slightly below 30 percent, Vietnam’s labor tax wedge is lower than for instance’s China’s, similar to the levels observed for Australia and Ireland and higher than those found in the Philippines and Malaysia. Overall, Vietnam is placed around the mid-point of the regional distribution of the labor tax wedge. However, its wedge is relatively high when compared to those of other countries with a similar level of economic development. Moreover, as Vietnam has few active labor market policies, and the country’s unemployment insurance system has only been in place for a few years, workers might not assign much value to the interventions financed through the labor tax or might doubt whether benefits will be available when they are needed. Thus one might conjecture that they are more likely to consider mandatory contributions as a tax rather than as part of their

- 15 -
|**Table 3. Paramet**<br>|**ric specificati**<br>|**on**<br>|
|---|---|---|
|**_Aggregate parameters_**|**_Parameter_**|**_Value_**|
|Discount factor|β|0.95|
|Fraction of steady state hours worked|N|0.33|
|Labor share|α|0.7|
|Depreciation rate|δ|0.06|
|Leading edge technology drift|μζ|0.045|
|**_Plant level parameters_**|||
|St. deviation of shock to incumbents|σθ|0.06|
|St. deviation of shock to startups|σ|0.06|
|**_Simulation parameters_**|||
|Leading edge technology shock|εζ|0.045|
|**Efficient economy – US**|||
|Recovery rate (development level)|s|0.775|
|Entry barrier (policy regulation)|π|0|
|**Developing economies**|||
|**Median LDC – Egypt**|||
|Recovery rate (development level)|s|0.175|
|Entry barrier (policy regulation)|π|0.2 (0.74 GDP pc)|
|**Median LAC – Brazil**|||
|Recovery rate (development level)|s|0.125|
|Entry barrier (policy regulation)|π|0.15 (0.52 GDP pc)|

The link between the recovery rates from _Doing Business_ and the parameter _s_ in the model is direct and correspond one-to-one. Both represent the fraction of initial investment that is not lost when the firm closes. On the other hand, the connection between the entry barrier data from _Doing Business_ and the parameter π in the model is more involved. First, we need to convert the two measures (fees and time) into the same unit. As an approximation, we do it by assuming that the fraction of days in a year that takes to open a business corresponds to the fraction of GDP per capita lost in the process. Then, we add this measure to the fees, already expressed as ratio to GDP per capita. The second step is to transform this cost as ratio to GDP 23
# **5 Empirical Strategy and Results**

Since treatment status was randomly assigned, our empirical strategy is straightforward and we estimate treatment regressions of the form: where _yil_ is the outcome of interest for loan officer _i_ and loan _l_ , _Til_ is a vector of treatment dummies for the incentive schemes being compared to the baseline. In all regressions, we use the low-powered baseline incentive **w** B = [20 _,_ 0 _,_ 10] as the omitted category. We additionally control for loan officer fixed effects, _θi_ , loan file fixed effects _θl_ , and individual controls **X** _il_ , including loan officer age, seniority, rank, education, and include dummies for whether the loan officer has management and business experience. Finally, we include dummies for the randomization strata, **R** _il_ . Standard errors are clustered at the loan officer-session level, the same level at which the treatment is assigned.

Our data set includes 14,369 lending decisions, representing 206 unique subjects, with three key treatment conditions: (1) Low-powered incentives, which we use as the baseline throughout the empirical analysis; (2) High-powered incentives, which reward loan officers for approving loans that perform and penalizes the origination of loans that default; and (3) Origination bonus, which rewards the loan officer for every originated loan.<sup>17</sup> In addition to these incentive vectors, we vary conditions under which incentives are paid. In 369 randomly selected sessions (2,214 loan evaluations), we defer incentive payments by 3 months, rather than paying immediately. In further 163 sessions (978 evaluations), we relax the participant’s limited liability constraint by providing an initial information endowment of Rs 200 (US$ 4.5), which can be lost if a loan officer makes a series of unprofitable lending

> 17Regressions using all data we collected, which includes the performance bonus schemes which pay only if a loan performs, along with the appropriate treatment dummies, are reported in the supplementary appendix.

16
40

|**Country**|**Securities'**|Insurance'|**Real Estate**<sup>**3**</sup>|**Bank Investments in**<br>**Industrial Firms**<sup>**4**</sup>|**Industrial Firm**Investment<br>**in Banks**|
|---|---|---|---|---|---|
|_Hong Kong_|Permitted, subject to limits<br>based on the capital of the<br>bank|Permitted, subject to limits<br>based on the capital of the<br>bank|Permitted, subject to limits<br>based on the capital of the<br>bank|Permitted, subject to limits<br>based on the capital of the<br>bank|Permitted, subject to<br>regulatory consent based on<br>suitabt<br>of the shareholder|
|_Indonesia_|Permitted through subsidiaries|Permitted through<br>subsidiaries__|Not permitted|Not permitted|Permitted<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|
|_Korea_|Perrnitted through affiliates<br>___________|Permitted through affiliates<br>____________________|Generally limited to holding<br>bank premises and to 100%<br>of bank capital|Subject to prior approval for<br>investments in excess of 15%<br>____________________________|Permitted, up to 1 00% of the<br>bank's capital, but subject to<br>prior approval based on<br>suit<br>f the shareholder|
|_Singapore_|Banks mnayhold equity<br>participation in stockbrokering<br>firms with MAS approval|Locally incorporated banks<br>may own insurance<br>companies with MAS<br>approval|Limited in the aggregate to<br>40% of bank's capital<br>(excluding<br>ses used<br>for banking<br>ess)|Limited in the aggregate to<br>400% of the bank's capital<br>_<br>_L:__<br>_|Acquisitions of 5% or_moe_<br>requires regulatory approval|
|_The_<br>_Philippines_|Permitted; expanded<br>commercial banks may engage<br>in securities activities directly<br>or through a subsidiary;<br>regular commercial banks may<br>engage in securities activities<br>_through<br>subsidiaries<br>only<br>I|Insurance agency and<br>brokerage permitted for<br>unibanks through<br>subsidaries<br>|Permitted for unibanks<br>through subsidiaries|Permitted for unibanks with<br>limitations|Permitted with limitations|

1. Securities activities include underwriting, dealing and brokering all kinds of securities and all aspects of the mutual fund business.

2. Insurance activities include underwriting and selling insurance as principal and as agent.

3. Real estate activities include real estate investment, development and management.

4. Including investments through holding company structures.

5. Restrictions on the business of securities subsidiaries will be abolished by March 2000. Selling of securities investment trusts (mutual funds) by banks was permitted on December 1, 1997.

6. Banks will be permitted to enter the insurance business through subsidiaries by 2001. Selling of insurance policies by banks will also be permitted by 2001 with some restrictions.

7. Bank holding companies and their subsidiaries are allowed to hold in the aggregate up to 15% of the total shares of non-financial companies. _Source:_ Institute of International Bankers. Regulatory and Market Developments. Global Survey 1998. New York, September 1998.
persons per square km), and high-density districts are those in the top quartile (over 693 persons per square km).

Table 6 presents the results of interacting the three regressors from our typical approach with indicator variables for the various density bins. The one exception is that all of the nodal districts are above the median density for India, and so we do not have a low-density nodal-district effect. Effects continue to be measured against districts farther than 50 km from the GQ network.

The results in Table 6 suggest that the GQ upgrades have increased new entry the most in high- and medium-density districts that lie 0-10 km from the GQ network. For instance, moderate-density districts, like Surat in Gujarat or Srikakulam in Andhra Pradesh, that lie on the GQ highway registered more than 100% increase in new output and new establishment counts after GQ upgrades. On the other hand, the GQ upgrades are not linked to heightened entry or performance in low-density areas. One interpretation of these results is that the improved connectivity enables manufacturing establishments to efficiently locate in intermediate cities, but that localization economies prevalent for the sector continue to preclude entry in low-density places.<sup>12</sup> Our findings are similar to Baum-Snow et al. (2012) who identify how infrastructure aided the decentralization of industrial production and population in Chinese cities from 1990-2010. Henderson et al. (2001) similarly find that industrial decentralization in Korea is attributable to massive transport and communications infrastructure investments in the early 1980s. These and similar studies form the foundation for development recommendations with respect to infrastructure found in the World Bank’s (2012) Urbanization Review Flagship Report and comparable policy reports.<sup>13</sup>

# **Section 5: Conclusions**

This paper evaluates the impact of a large-scale highway project on economic activity in the Indian manufacturing sector using establishment-level survey data from 1994-2007. The Golden Quadrilateral highway project of India upgraded the quality and width of 5,846 km of highways linking four major hubs in India. In the process, this upgrade improved the connectivity and market accessibility of districts lying close to the highway compared to those more removed. Non-nodal districts located within 0-10 km

> 12 For example, Duranton and Puga (2001, 2004), Rosenthal and Strange (2004), Ellison et al. (2010), and Gill and Goh (2010).

> 13 Henderson (2010) provides a broader review of the role of cities in development.

20