rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: The Sources of Misallocation – Continued

|||||In Isolation|Other|Factors<br>W|ith Interactio|ns|
|---|---|---|---|---|---|---|---|---|
||Adjustment Costs|Uncertainty|Correlated|Permanent|Transitory|Correlated|Permanent|Transitory|
|**TWN**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._00|0_._02|0_._23|0_._32|0_._00|0_._22|0_._32|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(1%)|(4%)|(40%)|(56%)|(0%)|(39%)|(56%)|(0%)|
|Effect on TFP|0_._00|0_._01|0_._10|0_._14|0_._00|0_._10|0_._14|0_._00|
|**THA**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._01|0_._05|0_._24|0_._59|0_._00|0_._24|0_._59|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(1%)|(5%)|(28%)|(67%)|(0%)|(27%)|(67%)|(0%)|
|Effect on TFP|0_._00|0_._02|0_._11|0_._26|0_._00|0_._10|0_._26|0_._00|
|**TUR**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._01|0_._05|0_._14|0_._37|0_._00|0_._12|0_._37|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(1%)|(10%)|(25%)|(67%)|(0%)|(22%)|(67%)|(0%)|
|Effect on TFP|0_._00|0_._02|0_._06|0_._16|0_._00|0_._05|0_._16|0_._00|
|**JPN**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._02|0_._01|0_._07|0_._32|0_._06|0_._08|0_._32|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(5%)|(3%)|(16%)|(73%)|(14%)|(18%)|(73%)|(0%)|
|Effect on TFP|0_._01|0_._01|0_._03|0_._14|0_._03|0_._03|0_._14|0_._00|
|**USA**|||||||||
|Effect on_σ_<sup>2</sup>(_arpk_)|0_._05|0_._03|0_._06|0_._29|0_._03|0_._08|0_._29|0_._00|
|% of total_σ_<sup>2</sup>(_arpk_)|(11%)|(7%)|(14%)|(65%)|(6%)|(17%)|(65%)|(0%)|
|Effect on TFP|0_._02|0_._01|0_._03|0_._13|0_._01|0_._03|0_._13|0_._00|

_Notes_ : The contribution of adjustment costs is calculated under the assumption that they are the only source of dispersion in _arpk_ , i.e. with all other factors set to zero. The contribution of uncertainty is computed under the same assumption. The contributions from other factors are calculated in two ways. The first (columns ‘In Isolation’) assumes that all other forces are set to zero while the second (columns ‘With Interactions’) holds adjustment costs/uncertainty at their estimated values. For each country, the first row reports the effect of each force on _σ_<sup>2</sup> ( _arpk_ ) ; the second row expresses these effects as a percentage of the observed _σ_<sup>2</sup> ( _arpk_ ) ; the third row calculates the implied TFP losses.
**Figure 2.  Real Exchange Rate and Growth Gaps 1990-2012**

<!-- Start of picture text --> 1<br>0.5<br>HI<br>SSA<br>0<br>LAC<br>EAP<br>MNA<br>ECA<br>-0.5<br>-1<br>-7 -5 -3 -1 1 3 5 7<br>GDP per capita Growth<br>Real Exchange Rate<br><!-- End of picture text --> ♦LAC 1990-2012 Average ●Other countries per Period ♦ Other groups of countries 1990-2012 Average Sources: Authors' calculations based on UN, WDI and Institutional Investor data. Notes: This figure shows the real exchange rate and GDP per capita growth gaps for the whole sample. Each period is a three-year average. See Section 3 for the details of how the benchmarks are calculated.

30
Estimates from (10’) are presented in the second column of Table IV. Note that tariff _changes_ in high import-penetration sectors continue to have economically and statistically significant ˆ effects of magnitude quite similar to those obtained from estimation of (10) (ˆ _αδ_ 1 + _αδ_ 2 = - 0.092, with an estimated standard error of 0.045). More specifically, a five reduction in tariffs increases _σε_<sup>2fromameanlevel of</sup><sup>_._008to</sup><sup>_._0126,whichintermsofthestandarddeviation</sup><sup>_σε_</sup> amounts to an increase from _._ 089 to _._ 1122 (a twenty five percent increase). Interestingly, the coefficient _ατ_ is now significant. However, the effect of the tariff _level_ on income risk is now given by ( _ατ_ + _φe_ ∆ _e_ + _φgg_ ). After substituting in the mean values of ∆ _e_ and _g_ from the ̄ sample, this estimated sum revealed to be insignificantly different from zero ( ˆ _ατ_ + _φ_<sup>ˆ</sup> _e_ ∆<sup> ̄</sup> _e_ + _φ_<sup>ˆ</sup> _gg_ = 0.02, with an estimated standard error of 0.02). Thus, we are again unable to reject that the _mean_ of the level on income risk is zero.<sup>25</sup> Consider now our estimates of how the tariff level alters the effect of macroeconomic variables on income risk. The coefficient on real exchange rate depreciation, _βe_ , is estimated negative and significant as is the coefficient on GDP growth, _βg_ , while the coefficients _φe_ and _φg_ relating to the interaction terms, (1 + _τ_ )∆ _e_ and (1 + _τ_ ) _g_ , are both positive and significant. The extent to which the tariff level alters the effects of exchange rate changes on income risk is given by _φe_ . As reported in Table IV, this parameter is estimated to have a mean value of 0.54 and an estimated standard error of 0.18. Consider a real exchange rate appreciation of ten percent under two scenarios _−_ when the tariff rate is ten percent and when the tariff rate is five percent. If the tariff rate is ten percent, our estimates indicate that an exchange rate appreciation of ten percent (in the preceding year) raises _σε_<sup>2from0.008to0.0108(an</sup>

> 25Our estimates of the timing and magnitude of the effect of trade policy changes on measured income shocks (i.e., large changes in the year following policy changes and zero mean effects) also indicate that our results are not being driven by other “unobserved” factors such as skill _and_ sector biased technical changes that are possibly correlated with trade policy changes. More specifically, we would expect any such changes in technology to impact income in a gradual manner taking several years for its full impact to be realized. Note also that our own estimates of the returns to education suggest a striking similarity across manufacturing sectors in Mexico, which provides indirect evidence against the view that technological progress in Mexico during the relevant sample period was both skill and sector biased.

20
TABLE 10 THE DOLLAR-LINKED COMPONENT OF OUTSTANDING BANK CREDIT (in percent of each source, end of year data)

|||All Sources|||Free Credit||
|---|---|---|---|---|---|---|
|Year<br>-----------------|total<br> <br>economy<br>-------------------------------------|agriculture<br>--|industry|total<br>economy|<br>agriculture<br>----------------|industry|
|1971|35|7|43|27|__<br>1|14|
|1974|42|15|54|54|37|46|
|1979|51|39|67|68|68|64|
|1984|51|44|67|60|39|46|
|1986|36|28|54|30|9|22|
||S|hort-Term Dire<br>|cted<br>w||Earmarked Loa|ns|
||total|||total|||
|Year|economy|agriculture|industry|economy|<br>agriculture|industry|
|1971|<br>52|9|63|NA|NA|NA|
|1974|<br>41|4|60|NA|NA|NA|
|1979|<br>79 fr||84|24|11|19|
|1984|<br>91|85<br>|91|34|36|45|
|1986|100|ite-|100|27|24|39|

Source: Annual Bank Statistics, various issues.

move, however, was costly to the borrowers: free credit carried extremely high real interest rates (see below) while linked credit was usually supplied at "civilized" rates. Restricted to export financing, directed short term credit was 100 percent linked in 1985 and 1986.

# 4. Term Structure

- It was repeatedly shown that agriculture relied on short term

- credit to finance production assets (Lowe 1955; Sadan and Kadishay 1967; Sarig Committee 1979). It was also often claimed that the mismatch in the term structure of investment and its finance and the creation of the "financing gap"~ (between terms of credit and the productive life of assets) was the result of government policy of

29
income gap score; and (ii) the higher is the state’s Gini coefficient. In other words, we observe that low inequality in access (by income-level) is correlated with lower economic inequality in the state (lower state-level Gini coefficient).

**Figure 10: Correlation Between Access to Goods and Services and Absenteeism**

<!-- Start of picture text --> Figure 10A: Gender Gap in Access vs Figure 10B: Income Gap in Access vs<br>Outcome (Literacy Gap) Outcome (Gini Coefficient)<br>1 1<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0 0<br>7.5 12.5 17.5 22.5 27.5 0.24 0.29 0.34 0.39<br>Literacy Gap in Percentage Gini Coefficient<br>Gender Gap in Access Income Gap in Access<br><!-- End of picture text --> <u>Note: Gender Gap and Income Gap data are from Columns III and IV of Table 6. Literacy Gap data is sourced from</u> Planning Commission (India). Gini Coefficients from Chadha and Nandwani (2016, Table 4).

# **5. Quality of Service Delivery**

This section measures several aspects of quality:

- The number of months needed to process an application for a good or service

- The number of documents needed to file an application for a good or service

- The number of trips made to a government office to complete an application

- The total costs of filing an application

- And the overall ease of applying for a good or service

34
|Figure 13: Poverty Projections at LIC extreme poverty line ($2.15, 2017 PPP) and growth based on GDP|
|---|
|current prices (CPI adjusted) and 2009 poverty baseline using different passthrough rates .................... 25|
|_Figure 14: Poverty Projections at LMIC international poverty line ($3.65, 2017 PPP) and growth based on_|
|_GDP current prices (CPI adjusted) and 2009 poverty baseline using different passthrough rates_............. 25|
|Figure 15: Benchmarking of poverty estimates for Syria, 2020 .................................................................. 26|
|Figure 16: Trends in night-time light emissions .......................................................................................... 27|
|Figure 17: Poverty trends 2009-2022 ......................................................................................................... 28|
|Table 1: Official poverty rate and national poverty lines ............................................................................. 6|
|Table 2: Poverty estimates using income class international poverty lines ................................................. 7|
|Table 3:Comparative Overview of Post-Conflict Poverty Estimation Studies in Syria ................................ 9|
|Table 4: Expenditure module of the 2021 and (revised) 2022 Demographic and WASH survey ............... 14|
|Table 5: Analysis of relation between food share and total household expenditure ................................ 15|
|Table 6: Spatial price deflator based on WFP Standard Food Basket (SFB) ................................................ 16|
|Table 7: National poverty estimates based on nominal and spatially adjusted welfare ............................ 17|
|Table 8:Ratio of survey household consumption to Private Consumption and GDP ................................. 19|
|Table 9: Average annual growth rate 2003-2009 ....................................................................................... 20|
|Table 10: Comparison between baseline poverty estimates and projections in 2009 ............................... 24|
- 18 -

which investors perceive the Fed to smooth interest rate swings. If a positive nominal rate shock is in excess of inflationary expectations, it translates into a positive real rate shock and commodity prices would be expected to fall for two important reasons. First, investors adjust their portfolio by holding more money and fever commodity contracts. Also, for storable comodities, real interest rate surprises 1/ are important since interest rates are a major cost component in storage. An unexpected rise in real l tes makes it more costly to hold inventories. In the short-run, traders will get rid of their inventories and cut further demand for them. This action will, in turn, cause commodity prices to fall. Chambers (1984, 1985) provides theoretical evidence for this reasoning. However, if positive nominal interest rate shocks are not in excess of increases in expected inflation, real rates fall and commodity prices rise. Also, if investors have **any** reason to believe that the Fed might smooth out interest rate swings by counteracting wide unanticipated interest rate movements, commodity prices might react in one direction in day t and in an opposite direction in day t + 1 to shocks occurring in day t. This kind of behavior is observed.

1/ Barnhart provides empirical evidence on the important impact on commodity prices of unexpected changes in announced discount rates. The present study considers the impact of daily interest rate surprises on commodity markets. In this way one can capture the full effect of surprises from the credit markets on the commodity markets. Also, a higher discount rate will most likely translate into a higher market rate and hence the impact of unexpected changes in discount rates are also captured in this way.
# **Corruption and Productivity Firm-level Evidence from the BEEPS Survey**

## Donato De Rosa

_The World Bank, Washington DC, USA_

## Nishaal Gooroochurn

_London Metropolitan University, UK_ Holger Görg _Kiel Institute for the World Economy and University of Kiel, Germany, and CEPR_ The authors are grateful to Irena Grosfeld, Erich Gundlach, Mariana Iootty, Jorge Pena, Ekaterina Zhuravskaya, and seminar participants at the Kiel Institute and the World Bank for the many helpful comments received.
WV. Rimvestment and Gjperagniag Performance The various occupational pension funds appear to adopt different investment policies and are characterized by large variations in their asset allocations (Table 4). There are significant differences between large pension funds (those with more than MUR 100 million in assets), medium funds (those with between MUR 30 and 100 million under management) and small funds (those with less than MUR 30 million in assets). Most funds invest heavily in equities and also have substantial assets in foreign securities, but some focus more heavily on housing loans and real estate.

The Sugar Industry Pension Funds are heavily engaged in the latter two areas. Apart from the funds administered by SICOM and some of the smaller self-administered funds, the large occupational pension funds invest small amounts in government securities. Rather surprisingly given that they are managed by large insurance companies, the insured funds have a small proportion of their assets invested overseas. The CEB funds are predominantly invested in loans to the sponsoring employer and housing loans to members but several smaller funds also have large exposures to their sponsors. Some of the medium and small pension funds invest increasingly in mutual funds, which are classified as other assets in Table 4. The category "other assets" is relatively large for several pension funds and insurance companies. In addition to investments in mutual funds, it also includes "loans to shareholders", which are important for some insurance companies.

Table 4: Asset Allocation of Pension Funds, 2001

||Govt<br>Sec|NonGvt<br>Bonds|Comp<br>Shares|Bank<br>Deps|Hsng<br>Loans|Loans<br>Spons|Real<br>Estate|For<br>Assets|Other<br>Assets|
|---|---|---|---|---|---|---|---|---|---|
|LargePension Funds|0.4|0.6|24|9|23|25|6|11|1|
|MediumPensionFunds|11|17|26|12|8|4|2|13|7|
|SmallPensionFunds|18|6|22|13|0.4|6|1|13|20.6|
|TotalRegistered|3|4|24|10|19|20|5|11|4|
|SugarIndustry||2|9|5|37||32|14|1|
|SICOM|43|2|11|1|26||2|15||
|Insured|9|6|20|13|30||6|5|11|
|Totaloccupational|21|3|15|6|27|4|8|12|4|
|NPF|58|5|7|11|8||2|4|5|
|TotalPension**Fnds**|43|4|10|9|16|2|4|7|5|
|InsuranceCompanies|15|8|17|9|24||6|9|12|
|NSF|82||3|6|9|||||
|Total|34|5|12|9|18|1|_5_|8|8|
|DoubleCounting|32|3|13|5|28||3|12|4|
|GrandTotal|35|6|12|10|17|1|5|7|7|

> Source: Estimated on the basis of data collected by the FSC, NPF and Registrar of Associations.

Registered pension funds invest small amounts in government securities. This is especially true for the larger funds and is attributed to the short maturity of government debt. Insurance companies hold about 7 percent of outstanding domestic government debt while the NPF and NSF account for a combined 28 percent of total domestic public debt.

10
_Has actual liberalization been preferential in nature_ ? In theory, liberalization commitments made under PTAs tend to be for the exclusive and preferential benefit of PTA partners. However, a review of the aforementioned PTAs and their rules of origin for financial services (especially those for Colombia and Costa Rica) suggest that the benefits of regional integration will not necessarily accrue solely to PTA counterparts (i.e. the US). While some real liberalization commitments – for example, the abolition of an economic needs test – are country-specific, most of them require new ‘horizontal’ regulations or laws that would presumably apply to the entire industry and could actually benefit financial established third country suppliers – for example, opening up Costa Rica’s insurance market, or permitting branching and certain outsourcing services for collective investment schemes in Colombia. This would seem to suggest that there might not be important first-mover advantages or serious economic distortions created by using PTAs to promote market opening in financial services, although much depends on the specific nature of liberalization commitments _per se_ .

_Is a GATS- or NAFTA-type ‘architectural’ model preferable for liberalization purposes_ ? As mentioned previously, the choice of modality used to schedule liberalization commitments can be an important contributor to the actual degree of liberalization attained. GATS- and NAFTA-type models both have their advantages and drawbacks, and can theoretically generate the same liberalization outcome. A simple review of the PTAs analyzed in this study would seem to favor the negative list approach and broader rules and disciplines embedded in NAFTA-type agreements on transparency grounds. However, this result can be largely attributed to the involvement of the US in such agreements, which tends to distort the results in favor of the NAFTA model. In addition, one could argue that the direction of causality between scheduling approaches and levels of liberalization commitments can run both ways – for example, a country with a very open financial system would feel more comfortable using a negative list approach (and vice versa). Finally, even some NAFTA-type agreements tend to use a hybrid lists for financial service commitments. For example, the US-Chile FTA comprised a positive list for commitments in insurance and insurance-related services and a negative list for commitments in banking and other financial services; a hybrid approach was also used for different supply modes. In fact, both models have introduced new features in recent years that borrow from each other, showing some signs of convergence around a more hybrid-type approach.

38
# **Table 3 Returns to Education**

# OLS Models

**Panel A: Returns to Middle School, High School and College Attainment**

|Model<br>Variable|1<br>ln(wage)|2<br>ln(wage)|3<br>ln(wage)|4<br>ln(wage)|
|---|---|---|---|---|
|College|0.498|0.497|0.471|0.420|
||(0.029)|(0.029)|(0.028)|(0.030)|
|High School|0.256|0.245|0.216|0.209|
||(0.027)|(0.027)|(0.028)|(0.029)|
|Middle School|0.254|0.270|0.242|0.234|
||(0.062)|(0.063)|(0.063)|(0.064)|
|Other Included Regressors|||||
|City and Birth Year Dummy Variables|Yes|No|No|No|
|Three Year Birth Cohort x City Dummy Variables|No|Yes|Yes|Yes|
|Height, Female and Height x Female|No|No|Yes|Yes|
|Parent Education Variables and Number of Siblings|No|No|Yes|Yes|
|School Quality and Parent Occupation Variables|No|No|No|Yes|
|N|3614|3614|3611|3611|
|R-Squared|0.282|0.295|0.310|0.324|
|**Panel B: Returns to Years of Schooling -- Linear in Schooling**<br>Model|1|2|3|4|
|Variable|ln(wage)|ln(wage)|ln(wage)|ln(wage)|
|Years of Schooling|0.096|0.096|0.089|0.083|
||(0.004)|(0.004)|(0.004)|(0.004)|
|Other Included Regressors|||||
|City and Birth Year Dummy Variables|Yes|No|No|No|
|Three Year Birth Cohort x City Dummy Variables|No|Yes|Yes|Yes|
|Height, Female and Height x Female|No|No|Yes|Yes|
|Parent Education Variables and Number of Siblings|No|No|Yes|Yes|
|School Quality and Parent Occupation Variables|No|No|No|Yes|
|N|3613|3613|3610|3610|
|R-Squared|0.293|0.306|0.319|0.333|

35
- 3 -

particularly difficult to address in the transport sector because there are few available time series data in the sector.

4. The current paper’s contribution to the literature is at least twofold: First, the paper applies a dynamic panel regression model to obtain an unbiased estimate of the transport connectivity impact on firm agglomeration. In recent years, the instrumental variable (IV) method has been used to deal with the endogeneity issue (e.g., Datta, 2012; Jedwab and Maradi, 2012; Donaldson, 2018; Banerjee et al., 2020). It is shown that quasi-random (or quasi-exogenous) factors, such as geographic conditions, historical events and “unintended” benefits from the network nature of infrastructure, can be good instruments because they are less relevant to the underlying economic performance but may have influenced infrastructure development in subsequent years. Notably, however, the selection of instruments is still dependent on the judgement of researchers and subject to post statistical tests. The current paper applies a dynamic panel data regression model where valid instruments can be explored more systematically.

5. Second, the current paper develops a way of generating detailed spatial data to measure transport connectivity based on micro shipping data. A challenge to apply the above-mentioned dynamic panel data approach is that detailed panel data are needed for a relatively long period of time. A practical norm of the length of time, _T_ , may be five to ten rounds (Labra and Torrecillas, 2018). It means that it takes 5-10 years if annual data are used. This is significantly costly, although some earlier studies collected a series of household surveys. For instance, Khandker et al. (2009) use panel data collected over a period of 7 years. Dercon et al. (2009) analyze five rounds of household surveys carried out for 10 years. However, this approach is costly and often impossible. In the transport sector, there are few data that are regularly collected.

6. In the literature, in addition, potential benefits from transport connectivity are often defined in a dichotomous fashion, depending on proximity (e.g., a distance of 2 km) to road infrastructure (e.g., Dercon et al., 2009; Khandker et al., 2009; IMF, 2020). This binary definition of connectivity is often not realistic particularly when panel data with large _T_ is considered. The potential impacts could be distributed more widely, depending on where people