rafmacalaba/datause-encoder-data huggingface dataset:
sectors. Indeed, most workers have moved from agriculture to wholesale and retail trade and community, social and personal services, which are also among the least productive sectors, due to a high level of informality. Because of the flow of labor from agriculture to other lowerproductivity sectors, the contribution of structural change to productivity growth was small, though positive. Structural change has contributed about 1 percentage point per year on average to labor productivity growth in Guinea. This is similar to what McMillan and Harttgen (2014) have found on average for a number of African countries over the period 2000-10.

**Figure 3.** Guinea: Decomposition of labor productivity growth

<!-- Start of picture text --> 2011-15<br><!-- End of picture text --> <!-- Start of picture text --> 2006-10<br><!-- End of picture text --> <!-- Start of picture text --> 2006-15<br><!-- End of picture text --> <!-- Start of picture text --> Within<br><!-- End of picture text --> <!-- Start of picture text --> Structural change<br><!-- End of picture text --> <!-- Start of picture text -->
-2<br><!-- End of picture text -->

<!-- Start of picture text -->
-1.5<br><!-- End of picture text -->

<!-- Start of picture text -->
-1<br><!-- End of picture text -->

<!-- Start of picture text -->
-0.5<br><!-- End of picture text -->

<!-- Start of picture text --> 0<br><!-- End of picture text --> <!-- Start of picture text --> 0.5<br><!-- End of picture text --> <!-- Start of picture text --> 1<br><!-- End of picture text --> <!-- Start of picture text -->
1.5<br><!-- End of picture text -->

Source: Authors’ calculations based on data from the INS.

Note: The “within” component stands for productivity growth due to capital accumulation, technology change or reduction of resources misallocation across firms, while the “structural change” component measures productivity growth due to labor flow from low to high-productivity sectors.

While structural change has made a small and positive contribution, the data show a large negative contribution of the within component to productivity growth in Guinea during the period 2006-15. From that perspective, the finding in Guinea is different from many developing countries, where the “within” component generally contributes positively to productivity growth. This highlights a lack of capital accumulation, lower technological adoption and higher resources misallocation within firms, weighing on productivity growth.<sup>8</sup>

> 8 Along the same vein, the global innovation index ranked Guinea 119th of 126 countries in 2018. More information on the index can be found at: <u>https://www.wipo.int/publications/fr/details.jsp?id=4330</u>

14
Table E1: Credit Behavior <u>(Year</u> 1)

||School funding|sources (Y/N)|HH|borrowing (|Y/N)|HH loan value|
|---|---|---|---|---|---|---|
||(1)<br>Self-fnanced|(2)<br>Credit|(3)<br>Any|(4)<br>Formal|(5)<br>Informal|(6)<br>Any|
|High|-0.007|0.002|-0.010|0.020|-0.033|1,063.0|
||(0.01)|(0.01)|(0.05)|(0.02)|(0.05)|(15,092.8)|
|Low Treated|-0.0004|-0.006|-0.039|0.010|-0.053|17,384.2|
||(0.01)|(0.01)|(0.05)|(0.02)|(0.05)|(29,982.8)|
|Low Untreated|-0.002|-0.011|-0.005|0.035*|-0.055|13,611.9|
||(0.01)|(0.01)|(0.04)|(0.02)|(0.04)|(21,581.8)|
|Baseline|0.078|-0.017|0.080**|0.208***|0.003|0.064*|
||(0.09)|(0.01)|(0.04)|(0.05)|(0.04)|(0.03)|
|R-Squared|0.03|0.02|0.04|0.14|0.02|0.03|
|Observations|795|795|784|784|784|784|
|# Schools (Rounds)|795 (1)|795 (1)|784 (1)|784 (1)|784 (1)|784 (1)|
|Mean Depvar|0.99|0.02|0.23|0.02|0.21|44,782.7|
|Test pval (H=0)|0.48|0.88|0.83|0.23|0.47|0.94|
|Test pval (_L_<sup>_t_</sup>= 0)|0.97|0.68|0.45|0.64|0.27|0.56|
|Test pval (_L_<sup>_t_</sup>=H)|0.53|0.56|0.60|0.65|0.69|0.60|

Notes: * p<0.10, ** p<0.05, *** p<0.001 a) This table looks at credit behavior of school owners in year 1 to understand whether the treatment simply acted as a substitute for other types of credit. Data for columns 1-2 are from the school survey and from the school owner survey for cols 3-6. The dependent variables in col 1-2 are indicators for whether a school reports financing school expenditures through fees or owner income or through a formal or informal financial institution, respectively. Col 3 reports whether the household of the school owner has ever borrowed any money for any reason. Cols 4-5 disaggregate this household borrowing into formal and informal sources. Col 6 examines total borrowing by the owner’s household for any reason. If the owner household did not borrow, the loan value is coded as 0. Schools that closed or refused surveying are coded as missing for credit behavior.

b) Regressions are weighted to adjust for sampling and include strata and round fixed effects, with standard errors clustered at the village level. The number of observations and unique schools are the same since we use one round of data. Observations may vary across columns due to attrition and missing values. The mean of the dependent variable is the follow-up control mean.

c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.

38
# **4.4 Determining urban risk**

Building upon the first three modules of hazard, exposure, and vulnerability, we determine the probability of mortality and economic losses from catastrophic events for each city. The vulnerability coefficients are used as weights that are combined with both the exposure data per city and the city-specific hazard severity. For example, for each city _i_ that is in an earthquake-prone area, we compute the city-specific earthquake mortality rate _Mice_ by multiplying the cluster-specific earthquake mortality rate _rce_ by the city population _Pi_ and the city-specific earthquake severity Wie.

To compute a weighted multi-hazard index value for mortality that reflects total estimated impacts from all disaster types for a city, we follow this method for each hazard _h_ . Since the degree of hazard (hd) for each of the five hazards is measured on a different scale (for example, frequency counts for cyclones versus probability index values for landslides), the accumulated mortality numbers are not easily comparable across hazards and simply adding the resulting values would result in an index unduly dominated by a hazard type _h_ that happens to be measured on a scale with larger values. Before combining the hazards into a multi-hazard index, we apply a uniform adjustment by deflating the weighted hazard-specific mortality figures, so that the total mortality in each region adds up to the total recorded in EM-DAT.

where n is the number of cities per cluster and _M’ih_ is the hazard-specific city mortality rate (hd Pi r).

We calculate the combined, mortality-weighted multi-hazard city risk value _Yi*_ as the sum of the adjusted individual hazard mortality estimates for a given city: 16
# References

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- Argentieri Mariani, L. (2021). Government-Owned Banks and Development: On Unintended Consequence of Bank Privatization.

- Bertay, A.C., Gong, D., & Wagner, W. (2017). Securitization and economic activity: The credit composition channel. _Journal of Financial Stability_ , 28(February), 225–239.

- Bircan, Ç., & Saka, O. (2021). Lending Cycles and Real Outcomes: Costs of Political Misalignment. _The Economic Journal,_ 131(639), 2763–2796.

- Brei, M., & Schclarek, A. (2013). Public bank lending in times of crisis. _Journal of Financial Stability_ , 9(4), 820–830.

- Carvalho, D. (2014). The Real Effects of Government-Owned Banks: Evidence from an Emerging Market. _The Journal of Finance_ , 69(2), 577–609.

- Coleman, N., & Feler, L. (2015). Bank ownership, lending, and local economic performance during the 2008–2009 financial crisis. _Journal of Monetary Economics_ , 71, 50–66.

- Cull, R., & Martínez Pería, M. S. (2013). Bank ownership and lending patterns during the 2008–2009 financial crisis: Evidence from Latin America and Eastern Europe. _Journal of Banking & Finance_ , 37(12), 4861–4878.

- Duprey, T. (2015). Do Publicly Owned Banks Lend Against the Wind? _International Journal of Central Banking_ , 11(2), 65–112.

- Gerschenkron, A. (1962). _Economic backwardness in historical perspective, a book of essays_ . Harvard University Press.

Jiménez, G., Ongena, S., Peydró, J.L., & Saurina, J. (2014), Hazardous Times for Monetary Policy: What Do Twenty-Three Million Bank Loans Say About the Effects of Monetary Policy on Credit RiskTaking?. _Econometrica_ , 82: 463-505

- Khwaja, A.I., & Mian, A. (2005). Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market. _The Quarterly Journal of Economics_ , 120(4), 1371–1411.

- Kornai, J. (1979). Resource-Constrained versus Demand-Constrained Systems. _Econometrica_ , 47(4), 801– 819.

- La Porta, R., Lopez-De-Silanes, F., & Shleifer, A. (2002). Government Ownership of Banks. _The Journal of Finance_ , 57(1), 265–301.

17
32

**Figure 4: Development of institutional infrastructure across transition economies**

<!-- Start of picture text --> 8<br>7<br>6<br>Strength of legal rights<br>5<br>index (0-10)<br>4 Depth of credit<br>information index (0-6)<br>3<br>Strength of investor<br>2 protection index (0-10)<br>1<br>0<br>200420052006200720082009201020112012<br><!-- End of picture text --> Source: Doing Business, own calculations

**Figure 5: Development of institutional costs across transition economies**

<!-- Start of picture text --> 40<br>35<br>30<br>Cost (% of claim)<br>25<br>20 Recovery rate (cents<br>on the dollar)<br>15<br>Cost (% of property<br>10 value)<br>5<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Source: Doing Business, own calculations In addition, the financial possibility frontier has also been pushed by improvements in macroeconomic stability, socio-political stability in many though not all transition economies and by foreign bank entry. While often controversial, the evidence on the effect of foreign bank entry on the efficiency, breadth, and stability of banking systems in transition economies has been overwhelmingly positive. Perhaps the most important impact of foreign 32
# **Table A.13. Predicted Poverty Rates Based on Imputation, from NPS 2020/21 to TA 3 using 2019/20 Poverty Line, Tanzania** **<u>(percentage)</u>**

|<br>**Method**||||**TA**|**3**||||
|---|---|---|---|---|---|---|---|---|
||**Model 1**|**Model 2**|**Model 3.1**|**Model 3.2**|**Model 4.1**|**Model 4.2**|**Model 8**|**Model 9**|
|1 Nl li i dl|**19.1**<sup>**a**</sup>|**19.7**<sup>**a**</sup>|35.7|**17.3**<sup>**a**</sup>|28.5|**19.0**<sup>**a**</sup>|**20.5**|**20.9**|
|) orma near regresson moe|(2.3)|(2.4)|(2.5)|(2.0)|(2.7)|(2.3)|(2.4)|(2.4)|
|2) Empirical distribution of the error|**18.5**<sup>**a**</sup>|**19.3**<sup>**a**</sup>|36.7|**16.4**<sup>**a**</sup>|28.4|**18.7**|**20.2**|**20.6**|
|terms|(2.3)|(2.4)|(2.4)|(1.9)|(2.7)|(2.3)|(2.4)|(2.4)|
|_Control variables_|||||||||
|Food expenditures|||Y|Y|||||
|Non-food expenditures|||||Y|Y|||
|Utilities: water, kerosene, lighting|||||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.34|0.46|0.84|0.84|0.66|0.68|0.47|0.37|
|N1(base survey)|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|
|N2(target survey)|698|698|698|698|698|695|698|698|
|**True poverty rate in TA 1**<br> <br>**Note**: The standard errors in parentheses are adju<br>linear regression model with the theoretical distr<br>cluster random effects. Models 3.1 and 4.1 emplo<br>Food and non-food expenditures, household siz|sted for comp<br>ibution of the<br>y non-standar<br>e, and age in b|lex survey des<br>error terms an<br>dized variable<br>oth surveys,|ign. All estima<br>d Method 2 us<br>s. Models 3.2 a<br>are transformed|17.<br>(1.9<br>tes are obtained<br>es the empiric<br>nd 4.2 employ<br>to normality|7<br>)<br>with the popu<br>al distribution<br>variables in TA<br>using the Box|lation weights.<br>of the error ter<br>3 standardize<br>-Cox method b|Method 1 u<br>ms. Both me<br>d by those in<br>efore standa|ses the normal<br>thods employ<br>NPS 2020/21.<br>rdization. The|

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Models 3.1 and 4.1 employ non-standardized variables. Models 3.2 and 4.2 employ variables in TA 3 standardized by those in NPS 2020/21. Food and non-food expenditures, household size, and age in both surveys, are transformed to normality using the Box-Cox method before standardization. The imputed poverty rates for TA 3 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.6.

57
productive sector to more productive sectors, and (ii) “organic” productivity increases in each sector because of technology and institutional effects.

Table 1. Sources and aspects of growth (1978-2004) (average annual increase, in percent)

||1978-93|1993-2004|
|---|---|---|
|GDP growth|9.7|9.0|
|Employment growth|2.5|1.1|
|Urban employment growth|5.2|2.9|
|Labor productivity growth|7.0|7.8|
|From TFP growth|3.7|2.7|
|From increasing K/L ratio|3.2|5.1|
|Memorandum item (in percent)|||
|Investment/GDP ratio|30.2|36.8|
|At end-period|||
|Share employment in agriculture|56.4|46.9|

_Source: NBS, and authors' estimates._ The economy-wide capital-labor ratio should increase along the way because labor moves from a less capital-intensive sector to more capital intensive ones, and the sectors themselves also become more capital intensive. As labor moves out of agriculture, productivity there increases, and the productivity gap between agriculture and the other sectors declines over time. Therefore, as the economy grows, the share of employment in agriculture declines. Using cross-country data, figure 1 confirms the strong association between economic development and the share of total employment in agriculture.

Figure 1. China and other countries: agricultural employment share and GDP per capita, (2001, unless otherwise specified) <!-- Start of picture text --> 80<br>Y axis: Agricultural employment, as share of total<br>70<br>60<br>50 China (2003) Thailand<br>Indonesia<br>Malaysia (1980)<br>40<br>Phillipines<br>30<br>Malaysia (1990)<br>Malaysia<br>20<br>South<br>X axis: GDP<br>Korea<br>per capita,<br>10<br>at PPP,<br>United<br>in 1995 US$<br>States<br>0<br>1,000 10,000 100,000<br>Sources: World Development Indicators and NBS (for China).<br><!-- End of picture text --> 4
While the LSGs are required to fulfill all these responsibilities and manage significant resources, not all LSGs have capability and staff to do these properly, while opportunities for professional development for LSG staff and elected officials are limited. The fiscal relations with the higher levels of government are also not necessarily clear, while transfers not always predictable. As a result, a significant proportion of services provided by the LSGs do not satisfy the population. According to the adopted Program for Development of the Local Self-Government in the Republic of Kyrgyzstan in 2013-2017 prepared by the State Agency for Local Self-Government and Interethnic Relations, this situation occurs due to the absence of a unified system of rules and procedures, insufficient reimbursement for certain services, lack of single set of requirements and responsibilities for local self-government units, etc. A lot of these problems stem from a poor understanding of how the local budgets are formulated, managed and executed and what are exact rights and responsibilities of LSGs by all local stakeholders – citizens, elected and appointed local officials and budget staff working at the local self-government units.

# **3. METHODS**

# **_Life in Kyrgyzstan survey_**

This paper builds upon previous surveys and analysis conducted in the Kyrgyz Republic. One primary example is the research project “Economic Transformation, Household Behavior and Well-Being in Central Asia: The Case of Kyrgyzstan” and its major product, the _Life in Kyrgyzstan_ (LiK) panel survey (Brück et al., 2014). The project was conducted in 2010-2012 by the German Institute for Economic Research (DIW Berlin) and in cooperation with Humboldt-University of Berlin, Center for Social and Economic Research (CASE-Kyrgyzstan), and American University of Central Asia (AUCA). The project collected survey data, investigated well-being and behavior of individuals and households in the Kyrgyz Republic, and improved the research capacity within Central Asia.

The initial sample of the survey - based on the 2009 Kyrgyz population census - included 3,000 households and 8,160 adult individuals in 120 communities. The households in the survey sample were obtained by stratifying two-stage random sampling with probabilities proportional to population size. The strata were formed by the two cities of Bishkek and Osh as well as the urban and rural areas of the seven oblasts, totaling 16 strata. The LiK sample is representative at the national, rural/urban and north/south levels, but not on the oblast level). The 2011 and 2012 waves of the LiK survey maintained a relatively low attrition rate of individuals and households. In the 2011 wave 2,863 households were interviewed; in 2012 this number declined slightly to 2,816 households interviewed.

The LiK survey collected information at the individual, household, and community levels. Each of the questionnaires consisted of several topical sections. This paper exploits a rich set of individual, household and community characteristics found in the LiK 2012 survey, as well as lagged community 5
# **4.2 Employment Effects**

The results from the audit experiment reveal three main findings. First, they show that reference letters are valuable in principle: for the _same_ individual, employers more often call back an applicant that attached a reference letter (on average, unconditional on content). Second, this effect is stronger for individuals with higher numeracy and literacy scores on an aptitude test. Third, the content of the letters is informative: employers’ assessment is correlated with an objective measure of ability and higher referee ratings (unless deemed implausibly good) increase the probability of callback from employers.

While novel and informative, the results from Experiment 1 share the main limitations of most audit studies (e.g. Bertrand and Mullainathan, 2004). Specifically, the main outcome is limited to employers’ callback, as opposed to actual employment. Also, the audit framework ignores potential changes in job seekers’ search strategy, given that applications are submitted by the researchers.

## **4.2.1 Effect of reference letters on employment**

Our Experiment 2 allows us to address these limitations and provide a more general contribution. As explained above, Exp. 2 uses a different sample of job seekers and encourages a random half of them to obtain a reference letter. We can then follow their job search behavior and employment outcomes. To test whether the letters increase firm responses and employment when used by job seekers, we estimate the following model: The dependent variable _yij_ is measured for individual _i_ residing in location _j_ . We focus on three key outcomes: (i) number of applications submitted, (ii) job interviews in the last four weeks, and (iii) employment status. _Ti_ captures whether participants were assigned to the treatment group that received the encouragement to obtain a letter. In order to increase precision we control for the baseline value _yij_<sup>_bs_ofoutcomes.Toaccountfor</sup> differences in firm demand across space, we control for location fixed effects _λj_ . Robust standard errors are computed at the individual level. Since the audit study suggest that the effects of reference letters may differ by gender, we also estimate specification 6 separately for women and men.

Table 5 shows the results. Columns 1 to 3 report the intent to treat (ITT) effects after 22
Table 1: Predicted percentage changes in income in Bangladesh and India as a result of the Motor Vehicles Agreement

|Scenarios|Bangladesh|India|
|---|---|---|
|Inefcient MVA|3.5|1.4|
|Efcient MVA|11.3|5.6|

_Notes_ : The table shows the percentage change in worker’s welfare at the country-level. We consider transit from India-Bangladesh only through the entry points outlined in the MVA.

Lower prices of final goods increase the purchasing power of consumers, and cheaper input prices make producers more competitive. The locations experiencing the largest decreases in prices become more competitive, attracting more workers and increasing their economic activity. The reductions in prices, the relocation of economic activity, and potential increases in wages lead to the increase in aggregate income in Bangladesh and India.

The effects of regional integration differ between countries because of differences in the magnitude and nature of the economic shocks on Bangladesh and India. Bangladeshi districts benefit from improved access to Indian markets. Indian states experience both an improvement in access to Bangladeshi markets and a decrease in trade costs to reach other Indian markets, with the former more important than the latter.<sup>3</sup> The gains from regional integration are much larger for Bangladesh than for India, because Bangladeshi markets are relatively small for India and Indian markets are large for Bangladesh. Small countries tend to gain more from regional integration if they remain competitive enough to export to foreign markets.

Both new transit routes and improved connectivity for bilateral trade reduce spatial inequality across states in India and increase it across districts in Bangladesh. Improvement in the connectivity of northeast Indian states with the rest of India and the increase in competitiveness of the states close to Bangladesh leads to a relocation of economic activity across the country and therefore a reduction in wage dispersion across states (Figure 2). The MVA leads to a marginal increase in wage dispersion across Bangladeshi districts, because of the significant comparative advantage of Dhaka and Chattogram districts. The rest of this section discusses the spatial effects under the efficient MVA only; the spatial patterns under the inefficient MVA are the same, with only the magnitude of the impacts changing.

> 3A scenario with no border time for transit and a 55-hour border time for bilateral freight (that is, a scenario in between the inefficient and efficient MVA scenarios) increases income in India by 1.6 percent relative to the baseline. Even if all those gains are attributed to transit, which is unlikely, improved access to Bangladeshi markets accounts for 71 percent of the income gains under the efficient MVA scenario.

12