rafmacalaba/datause-encoder-data huggingface dataset:
for KUR but had been rejected, others had applied for KUR but had withdrawn their application before receiving KUR, and others did not apply for KUR. These interviews covered the following topics: perception and information received about KUR, KUR loan application process, reasons and notification of KUR loan rejection or withdrawal, alternative financing sources available, adjustments to the business plan or scale after rejection or withdrawal, the impact of COVID-19 on the business, and suggestions for improving the KUR program. As with the qualitative data collection of KUR borrowers, interviews were conducted over the phone, and interviewers had the flexibility for which topics to explore in more depth with each respondent.

# **3. Results**

3.1 KUR’s subsidized credit reaches many first-time borrowers, helping them enter the formal financial sector and invest in their firms KUR primarily targets borrowers who are excluded from the formal financial system: most KUR borrowers (61%) had never received any type of loan, including loans from friends or family, prior to accessing KUR. These borrowers typically relied on personal savings and reinvested profits to meet both their investment and working capital needs before accessing KUR (Figure 4). In fact, only a small percentage of borrowers relied on formal financing arrangements, such as commercial banks, microfinance institutions, and input suppliers. KUR also finances business plans that may not be implemented at the same scale otherwise. Qualitative interviews with rejected KUR applicants reveal that less than half were able to find alternative financing for their projects, and those who did typically reduced the scope of their investment.

Figure 4: Most KUR borrowers relied on savings to finance business investments and working capital prior to receiving KUR

**Main source of capital prior to receiving KUR**

<!-- Start of picture text --> 38%<br>Personal savings 35%<br>21%<br>Savings from business/reinvested profits 37%<br>Friends or family other than spouse (loans or gifts) 6% 8%<br>6%<br>Spouse (loans or gifts) 7%<br>Commercial banks 4%<br>4%<br>Microfinance institutions 3%<br>3%<br>Input supplier 1% 4%<br>0% 10% 20% 30% 40%<br>% firms citing as main source<br>Main source of capital investments Main source of working capital<br><!-- End of picture text --> Source: Author calculations using survey of KUR borrowers Although most KUR borrowers were previously unbanked, there are some borrowers who previously accessed unsubsidized commercial lending and switched to KUR. Between business creation and receipt of

**14**
21 the country sources with transactions under the floor prices, the protection augmentation effect for the rest of the world is slightly higher than the corresponding effect for Argentina and Brazil, except for the MEP system in 1986. But when one considers only the goods in which Argentina and Brazil or the rest of the world have transactions below the floor prices, the augmentation eff-a for goods in which all transactions under the floor prices come from the rest of the world is systematically higher than the corresponding augmentation effect for the transactions in which all transactions under the floor price come from Argentina or Brazil. When one considers the MEP system alone, the impact on imports from the rest of the world is unambiguously more severe than the impact on imports from Argentina and Brazil. This apparently paradoxical result is explained by the fact that Argentine and Brazilian exporters are more aware of the loopholes of the Uruguayan MEP system-that is, its capacity to generate price alignments and rents-than are exporters from the rest of the world.

Table 3. Uruguay's Use of Reference Prices on Selected Imports from Argentdna and Brazil versus the Rest of The World 1986-1989

|-<br>**MYPE**<br>OF PROTECTION**AND YEAR**|**All**PRODU|CIS<br>|.<br>**-**<br>.|_<br>_.<br>***COMMON**|.<br>.<br>**- PRODUClS**W|.|
|---|---|---|---|---|---|---|
|l_-||**S**|<br>pea|--<br>AU|A-_|PM|
||**Itd**<br>_|**ad**|Ofi|trade||ofs|
|_________________|purbms|Ba_|**Wom**|**paz-**|**Bol**|_**Wui**|
|**REFERENCE**<br>**PRICE**<br>SYSTEM|||||||
|**1986**|1.63|1.37|**1.84**|**1.50**|.42|I.d|
|**1917**|1.62|1.53|1.57|**1.75**|1.61|1.57|
|**l191**|**1.51**|1.38|1.68|1.37|1.36|1.60|
|**1919**|1.46|1.34|1.40|**I.39**|1.32|1.44|
|**A-srqe**|1.56|1.41|**1.62**|**1.50**|1.43|1.55|
|l**MINIMUM IPOR**<br>**PRICE**SYSTEM|||||||
|**196**|1.13|1.15|1.12|1.28|1.21|1.22|
|**1917**|1.09|1.09|1.34|1.11|1.09|**1.47**|
|**191**|1.22|1.19|1.48|1.12|1.15|1.42|
|1989|1.19|1.20|1.46|1.17|**1.19**|**149**|
|**Avq**|**1.16**|1.16|1.35|1.17|1.16|1.40|
|GLOBAL<br>AVERAGE|1.36|**1.28**|**1.49**|1.34|1.29|**1.4**|

**roe: Cenual Bak** of **ruguay**

Now* : [a) Ptoducu an *common **whr impot** Gm coming fiom botb **bourcs, tht** is **ArgentinaaBazil** and the 'mm of X **worodd.** (b] The 'on of th world' Is dfined as al tade puine of UrugUy, Oxcia1.1tg Argentgn and Bzil.
those still residing in their original village. Most female migration is within the same district, and only 0.5% (11 women) of the female sample lives outside Pakistan.

# **II. MEASURING SKILLS**

In our 2018 survey, we measured two types of skills that, following the literature, we refer to as cognitive and socioemotional (SEM) skills. Here, we describe the instruments we used to measure each type of skill and the reliability of the respective measures (Table A4 provides an overview of the instruments).

# **II.1. COGNITIVE SKILLS**

Our measurement strategy aimed to capture two sets of cognitive skills. First, we measured English, Mathematics, and Urdu skills that are commonly taught in schools. Second, we measured respondents’ proficiency in everyday arithmetic and literacy skills (which may not necessarily be taught in schools; see Banerjee et al., 2022).

Our measures of commonly taught cognitive skills include the same tests used previously in the LEAPS project. These tests are norm rather than criterion-referenced and designed to cover a wide range of topics. Andrabi et al. (2002) and Bau, Das, and Yi Chang (2021) show that the LEAPS tests satisfy the requirements of horizontal and vertical linking (that is, the function that relates a respondent’s ability to their likelihood of correctly answering a question is stable across test takers and over time).<sup>8</sup> In the limited number of items where vertical linking does not function well, eliminating the unstable items does not lead to any appreciable difference in the test scores.

As the original LEAPS tests targeted primary-school-age children, we worried that ceiling effects would censor the cognitive skills distributions of the resurvey. Therefore, we worked with an educational organization to design an adaptive test administered on tablets. Each assessment started with a set of simple questions; the difficulty of the following items increased or decreased based on the respondent’s performance. The test classified respondents into six levels (Level 1 corresponding

> 8 Here, we do not vertically link the scores as we use results from an additional adaptive test as well. Andrabi et al. (2002) discuss test construction and assess the psychometric properties of the original test administered in 2003 sample. Bau, Das, and Yi Chang (2021) further assess vertical linking in the LEAPS test and demonstrate that there was limited differential item functioning between 2003 and 2011. That is, the function relating the latent variable (ability or knowledge) to the likelihood of answering the question correctly remained stable across the years.

10
# **Table A.10. Predicted Poverty Rates Based on Imputation, from NPS 2019/20 to TA 3 using 2019/20 Poverty Line, Tanzania (percentage)**

|<br>**Mthd**||||**T**<br>**Mdl**|**A 3**|**Mdl**|||
|---|---|---|---|---|---|---|---|---|
|**eo**|**Model 1**|**Model 2**|**Model 3**|**oe**<br>**3.2**|**Model 4**|**oe**<br>**4.2**|**Model 8**|**Model 9**|
|1) Nrmal linar rrin mdl|**16.6**<sup>**a**</sup>|**17.2**<sup>**a**</sup>|37.3|**18.1**<sup>**a**</sup>|23.6|**18.3**<sup>**a**</sup>|**17.8**<sup>**a**</sup>|**18.9**<sup>**a**</sup>|
|o e egesso oe|(2.2)|(2.3)|(2.4)|(2.0)|(2.5)|(2.2)|(2.3)|(2.4)|
|2 Eiil diibi f h|**16.6**<sup>**a**</sup>|**17.0**<sup>**a**</sup>|37.7|**17.7**<sup>**a**</sup>|23.1|**18.0**<sup>**a**</sup>|**17.6**<sup>**a**</sup>|**19.0**<sup>**a**</sup>|
|) mprca struton o te error terms|(2.2)|(2.3)|(2.4)|(2.0)|(2.5)|(2.2)|(2.3)|(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>|0.39|0.54|0.89|0.89|0.67|0.67|0.54|0.43|
|N1(base survey)|1,179|1,179|1,179|1,179|1,179|1,179|1,179|1,179|
|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 adjuste<br>linear regression model with the theoretical distribu|d for complex<br>tion of the err|survey design.<br>or terms and M|All estimates<br>ethod 2 uses|17<br>(1<br>are obtained<br>the empirica|.7<br>.9)<br>with the popul<br>l distribution o|ation weight<br>f the error te|s. Method 1 u<br>rms. Both me|ses the normal<br>thods employ|

**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 2019/20. 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.5.

54
period 1972 to 1977 when such decisions were based on political, rather than academic, considerations. We define father to be a high administrator when his highest achieved administrative status was at the level of division manager ( _chu_ ), bureau director ( _ju_ ) or above.

To illustrate the changes in the intergenerational correlation of educational attainment associated with the Cultural Revolution, in Figure 4, for each city we plot partial correlation coefficients for fathers’ and children’s years of schooling by birth cohort.<sup>9</sup> In each city, the correlation falls during the Cultural Revolution years and recovers thereafter, with the exact timing and magnitude of these changes varying across cities. The Cultural Revolution thus appears to have succeeded in temporarily weakening the strong correlation between the educational attainment of fathers and their children that is found in most societies. This finding echoes that of Deng and Treiman (1997), who analyze census data to examine trends in the intergenerational correlation in educational attainment for households in which parents co-reside with their adult children.

To examine these relationships more rigorously, we estimate a binary response probit model, _G_ ( ⋅ ) , where the determinants of the probability of individual _i_ from city _j_ and cohort _t_ to complete a certain level of education, _ED_ ijt, is modeled as follows: Here _eshock_ jt is a city-cohort specific shock to education (alternatively _hshock_ jt, _pshock_ jt or _sdshock_ jt) that is interacted with father’s years of education, _fed_ ijt, and also separately interacted with whether father held high administrative status, _fad_ ijt. The interacted variables _fed_ ijt and _fad_ ijt are included among a vector of individual, family, and school characteristics,

> 9 The partial correlations control for differences in fathers’ administrative status

14
(E) we interact deep TA with a services trade dummy, an indicator variable if one of the signatories of the TA is a high-income country, a G7 country, a member of the EU or the United States. All columns include importer-broad sector-time, exporter-broad-sector-time, importer-exporter-broad sector fixed effects, and time-varying border controls to account for general trends in globalization. Standard errors are clustered threeway for importer, exporter, and year.<sup>_∗_</sup> for _p <_ 0 _._ 1,<sup>_∗∗_</sup> for _p <_ 0 _._ 05 and<sup>_∗∗∗_</sup> for _p <_ 0 _._ 01.

IV
71 _τ_ periods before period _t_ . Note that _ban_<sup>_τ_</sup> _ct_<sup>is always equal to zero for products whose imports were</sup> never banned.

We estimate the evolution of the ban’s effects on two alternative outcome variables ( _yct_ ): an “extensive-margin” dummy indicating non-zero imports and an “intensive-margin” variable consisting of the log of import values. To define our extensive-margin dummy variable, we expand our original HS8 product-month level import data by adding an entry with zero imports for each month (within the January 2017-October 2022 period) when imports of a product are not reported.

The sample period used in the estimation ranges from January 2019 to September 2022. For presentational purposes, the figures with our import results (Figure I and Appendix Figures A5, A6, and A7) only show coefficients for the four months prior and twelve months subsequent to the ban, even though the sample period used in the estimation is longer.

# **B.B Stylized Fact 3**

In Section IV.C, we estimate the fertilizer import ban’s downstream effects on agricultural exports. We follow a firm-level approach, focusing on firms who exported agricultural products in the preban 2017-2019 period. For each firm _f_ , we define the following exposure measure _Uf_ : where _f_ indexes firms and _v_ crops, _FICv_ is fertilizer intensity (in kg/ha) of crop _v_ , and _Xfv_<sup>2017</sup><sup>_−_2019</sup> is total exports of crop _v_ by firm _f_ in 2017-2019. The summation in equation (42) only considers the 23 crops for which we have fertilizer intensity data (see Appendix A.E). This exposure measure is the weighted average of fertilizer intensity across all crops that a firm exports (with weights given by each crop’s share in the firm’s export portfolio).

We then use a firm-quarter panel and the dynamic DID estimator from De Chaisemartin and d’Haultfoeuille (2022) once again to estimate the ban’s impact on firm exports. The treatment group consists of firms above the upper quartile of fertilize exposure variable _Uf_ , with the remaining firms in the sample serving as the control group. The estimating equation is as follows: where _f_ indexes firms, _t_ indexes quarters, _Xft_ is agricultural exports, Ω _t_ and Ω _f_ are quarter and firm fixed effects, respectively, _T_ 0 is the quarter when the fertilizer import ban was introduced (i.e. Q2/2021), and _μft_ is an error term.

The sample period used in the estimation ranges from January 2019 to September 2022. For
The proportion of ozone uptake attributed to surface liquid dynamics decreases progressively as a surface liquid thins and its reactivity with ozone diminishes, so that the highest epithelial tissue dose is predicted for the terminal bronchiole-respiratory bronchiole region, which is, indeed, a site of damage in ozone-exposed animals. Bronchoscopic sampling along airways also indicates that a substantial fraction (35 percent) of orally inspired ozone is taken up in the upper airway and trachea and that ozone in exhaled air is limited to the initially expired volume representing airways dead space (Bromberg 1999).

The toxicity of ozone inhalation in large airways is supported by evidence of ciliated cell loss and increased epithelial mitotic index in small animals, netrophilic inflammation in humans, increased bronchial artery blood flow in sheep, and by the symptoms of cough and substernal pain exacerbated by deep inspiration in humans (Bromberg 1999).

# **Development of Exposure-Response Models for Mexico City**

# **_Meta-analysis_**

Although the number of published studies on the health effects of air pollution has grown during the past decade, specific studies of the ZMVM are still limited. We, therefore, decided to summarize relevant international and national published reports through a metaanalysis, which combines the results from various studies to identify consistent patterns. Due to the rapid growth of the field of epidemiology since the 1960s, the number of publications is overwhelming and a classical narrative review is no longer appropriate for summarizing findings. Despite limitations, statistical analysis of compiled published results has become more common when dealing with an extensive offering of differing and inconclusive results.

# **_Identirication, Selection and Classfication of Bibliographical Information_**

The meta-analysis involved an exhaustive search of published studies on human health effects due to exposure to ozone and PMIo using Medline, Pubmed, Biomed-net and Aries databases. Manual library searches examined mainly Mexican publications. Not all the bibliographic material collected was appropriate for the statistical analysis.

## Criteria for inclusion were

- peer-reviewed published papers evaluating the association between exposure to ozone or particles and clinically identifiable human health effects (biochemical and molecular effects were not included); and

- papers that quantified any type of particles: Total Suspended Particles (TSP), black smoke (BS), coefficient of haze (CoH), or any PM.'<sup>0</sup>

0 We used the approach of Dockery and others (1993) to convert air quality expressed in these metrics into PM1O concentrations.

_21_
# Results

# **Duration of Hospital Stay**

# K1n

Figure 1 shows the average total patient stay in each of the four Kenyan hospitals.<sup>5</sup> For D&C patients, the average duration of stay ranged from 40.9 to 100.7 hours. In contrast, the average stay of MVA patients ranged from 18.8 to 23.9 hours. At the two hospitals where both D&C and MVA data were collected, differences in average total stay per patient were notable. In Hospital KENI, total stay for MVA patients was 49% shorter than for D&C patients -- 20.7 versus 40.9 hours. In Hospital KEN2, total stay was 76% shorter for MVA patients -- 23.9 versus 100.7 hours. For all sites studied **in** Kenya, the hospital with the longest average stay for patients treated with MVA (23.9 hours) was 42% shorter than the hospital with the shortest average D&C patient stay (40.9 hours).

<!-- Start of picture text --> Figure 1. Aveage Total Patlent Stay In Kenya, by Hospital and Type of Procedure<br>0l Pre-Evacuation 0 Post-Evacuation<br>120<br>100.7<br>100 10. i<br>A 80 .. -. ,t' . ), . N.t,.:<br>.5 40.9 00.2<br>E 40 . X<br>04127 23.9 19.4 18.<br>KENI KEN1 KEN2 KEN2 KEN3 KEN4<br>(n.17) (n=10) (n=5) (n=11) (n t1) (n.45)<br>D&C WA D&C MVA MVA MVA<br><!-- End of picture text --> In Kenya, the pre-evacuation period for D&C patients accounted for most of the total hospital stay. Average pre-evacuation times for D&C patients were 25.5 and 90.2 hours, representing 62% and 90% of the total hospital stay, respectively. For MVA patients, average pre-evacuation times ranged from 9.1 to 11.6 hours (45% - 56% of the total stay). Post-evacuation stay for D&C patients ranged **fron** 10.5 to 15.4 houus; for MVA patients the range was from 9.1 to 13.2 hours.

**5 For all sites, patient stay was divided into pre- and post-evacuation waiting time. "Pre-evacuadon" included elapsed time from admission through the uterine evacuatdon procedure. "Post-evacuation" included elapsed time from the completion of the evacuation procedure until discharge.**

7
|**Appendix table 2: Registration and**|**distance to CBD- lin**<br>**Sp**<br>|**ear regression w**<br>**oradic adjudicat**<br>|**ith block fixed ef**<br>**ion**<br>|**fects**<br>**Systematic**<br>|
|---|---|---|---|---|
||**Total**|**Pre-reform**|**Post-reform**|**Adjudication**|
|Distance from CBD in m (log)|-0.236***|-0.186***|0.0194|0.167***|
||(0.0612)|(0.0495)|(0.0503)|(0.0583)|
|Plot area in m<sup>2</sup>(log)|0.0182***|0.0366***|-0.0268***|-0.00977***|
||(0.00229)|(0.00185)|(0.00189)|(0.00218)|
|N<br>|79,051|79,051|79,051|79,051|
|R<sup>2</sup>|0.00108|0.00541|0.00265|0.000395|

_Note:_ Block fixed effects and constant included throughout. Standard errors in parentheses; * p<0.10, ** p<0.05, *** p<0.010 39
Page 52 The households around the lakes offer a different set of considerations. Since the lake is prone to periodic flooding, it is often assumed that these households should merely be relocated, and the lake infrastructure upgraded as a separate matter. However, experience suggests that it usually makes much more financial sense to reinforce the lakeside infrastructure to reduce flooding and improve these slum neighborhoods in situ to avoid dislocation. According to our rough estimates, less than 10 percent of households would need to be relocated if infrastructure investments are made. One hopes that working with the community, as was the case under the SNP, this number could be reduced even further. This offers a substantial savings—if all lakeside households are relocated, this would add 150 percent to the current estimate of relocation costs city-wide. This incremental sum of Rs. 300-600 crores that could be saved by making the necessary infrastructure investments is quite substantial—representing from 10-20 percent of total project costs.

# **Program Implementation Building from Experience with SNP: Scaling Up by Design**

# **Positive Lessons from SNP**

The Slum Networking Program was started in the mid-1990, and continued for about 10 years. During that time, services were provided to about 14,000 households very cost effectively and made major changes in the neighborhoods of many of the city’s slum pockets—as can be seen in Figure V.1. The homes in these slum pockets, once provided with decent neighborhood infrastructure, greatly increase in value due to the amenities in the environment, and bring households within sight of moving up to middle class housing such as that shown in Figure V. 2 below. A number of lessons were learned in the process and these offer invaluable insights for the design of city scale program.