rafmacalaba/datause-encoder-data huggingface dataset:
suspending federal transfers, not guaranteeing new loans, or explicitly determining situations in which local governments can default on their liabilities. Overall, the FD Law implicitly reduced the incentives of banks to lend to local governments. Evidence displayed in Figure 3 is consistent with this hypothesis—after the FD Law is introduced, banks reduce their share of lending to the government (Panel A) and the risk premium of loans to private firms vis-a-vis loans to the government drops (Panel B).

# **3. Data**

The datasets used in the analysis come from seven sources. The first data set is the _Single Public Registry_ , provided by the Ministry of Finance. This data set reports the debt of subnational governments (at the state and municipal levels) decomposed by credit sources on a quarterly frequency. In addition to total public liabilities, the data set also includes the lending terms contracted by local governments such as interest rates and maturity. Based on this data, we construct the ratio of public debt to freely disposable income of local governments in March 2016—one month prior to the enactment of the Law—to measure the ex-ante indebtedness of subnational governments. This ratio is highly persistent over time and strongly associated with the public-debt-to-net-income financial health indicator that local governments were required to report in the alert system from 2017 onwards (correlation of 0.88).

The second data set consists of a state-year panel containing the annual public expenditures and public revenues of each state in Mexico from 2014 to 2018. This data set is publicly available from the National Institute of Statistics (INEGI). On the expenditure side, we use information on the total expenditure of state governments as well as on certain public expenditure categories. The first category— _transfers, subsidies, and other aid_ —is the largest expenditure group of state governments and represents almost 60 percent of expenditure of states. 76 percent of expenditures within this category comprise funding for health, education and social security programs and institutions. The second category corresponds to _public infrastructure_ spending which represents 5 percent of state expenditures.<sup>22</sup> Public investments on construction, housing, and infrastructure projects (e.g., roads, school buildings, hospitals, sewer systems) are grouped under this category.

> 22 The remaining 35 percent of expenditures are related with salaries and other remunerations of state employees, which tend to be very stable across time.

11
## **<mark>Box 6.2 Kinh Nghiem Joint Stock Company and Dao Tao Ltd.: Private Sector Solutions to Private Sector Problems</mark>**

<mark>Kinh Nghiem Joint Stock Company is a large private shoe manufacturer located on the outskirts of Hanoi. About 80 percent of Kinh Nghiem Joint Stock Co.’s 1,600 employees have come to the capital from the countryside specifically to work for the company. Workers are generally educated at most to the high school level and have limited job experience, so training is very important and time consuming. State-run training schools do not offer suitable courses on shoe manufacturing, so training must take place on-site at the factory. Kinh Nghiem’s recent move up the value-added chain from CMT (cut-make-trim) production—where workers piece together imported, pre-cut materials—to more comprehensive, more profitable FOB (freight-on-board) production—where the company sources its own raw materials and workers must do initial cutting in addition to piecing together—has increased the company’s need for skilled workers dramatically, and with it the importance of high quality worker training.</mark> After numerous years of negotiation with local officials, the owners of Kinh Nghiem were this <mark>year able to secure the rights to land adjacent to their existing factory. Kinh Nghiem’s owners have decided to use this land not only for additional factory space and living quarters, but also for housing something highly unusual in Vietnam: an on-site training center. The training center is being set up as a separately-owned business, Dao Tao, Ltd,. with its own separate financial statements. The training center aims to provide not only high quality training to meet Kinh Nghiem’s FOB training needs, but to also develop a reputation for these services that will allow it to win bids to train workers for other shoe manufacturers in the area. Dao Tao’s owner, Mr. Nguyen Giao Duc, expects his firm to be a model for the profitability of investing into training— aiming not only to increase the skills of workers, but to also raise the professionalism of the entire sector.</mark>

# . **Table 6.2 Chief Limitation on Firm’s Ability to Hire Workers (%)**

||**Industries**|||**Total**|
|---|---|---|---|---|
||**Manufacturing Othe**|**r industry **|**Services**||
|Difficult to find quality workers|<br>70.6|<br>50.0|80.7|73.0|
|<br>Bad reputation of private sector among|11.8|16.7|17.5|14.6|
|workers|||||
|High costs of labor|10.3|16.7||6.6|
|<br>High costs associated with hiring more|7.4||1.8|4.4|
|workers|||||
|Other||16.7||1.5|
|**_Total_**|**_100.0_**|**_100.0_**|**_100.0_**|**_100.0_**|
|Source: World Bank Survey, 2001|||||

**70.** Limitations in the hiring process are partly caused by the informal mechanisms used in recruiting labor.  Over 60 percent of workers are hired through word of mouth through friends, family or existing employees.  Only about 14 percent of workers are hired through advertisements in the newspaper.

34
# **Is Dirt Cheap?**

# **The Economic Costs of Failing to Meet Soil Health Requirements on Smallholder Farms**

Sydney Gourlay<sup>‡</sup> and Talip Kilic<sup>†1</sup>

# **JEL** : O13, Q12, Q18.

**Keywords** : Soil, agricultural productivity, crop yields, technical efficiency, household surveys, smallholders, Uganda, Sub-Saharan Africa.

> 1 ‡ Economist, Living Standards Measurement Study (LSMS), Development Data Group, World Bank, Rome, Italy. <u>sgourlay@worldbank.org.</u><sup>†</sup> Senior Economist, Living Standards Measurement Study (LSMS), Development Data Group, World Bank, Washington, DC, USA. tkilic@worldbank.org. This paper was produced with financial support from the 50x2030 Initiative to Close the Agricultural Data Gap, a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030. For more information on the Initiative, visit 50x2030.org.
**Table 11: Determinants of energy intensity (1999-2001, CRS, Pooled effects)**

|**Dependent**<br>**variable=ln(energy/output)**|**Four indust**|**ries**|**Pulp and**<br>**indus**|**Paper**<br>**try**|**Cement ind**|**ustry**|**Iron and**<br>**indus**|**Steel**<br>**try**|**Aluminum i**|**ndustry**|
|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|P-value|Coef.|P-value|Coef.|P-<br>Value|Coef.|P-<br>Value|Coef.|P-<br>Value|
|**Constant**|-1.4609|0|-1.7028|0|0.1924|0.481|-0.399|0.26|-0.916|0.016|
|**Ln(price of energy/price of**<br>**output)**|-0.4991|0|-0.371|0.006|-0.152|0|-0.7829|0|-0.063|0.741|
|**Ln (technology development)**|-0.0265|0|0.0128|0.3|-0.008|0.041|0.0135|0.478|-0.012|0.491|
|**Ln(Price of energy/price of**<br>**output)*Ln(technology**<br>**dl**|-0.0075|0.108|0.022|0.086|-0.0007|0.847|0.0183|0.327|-0.01|0.595|
|**eveopment)**<br>**Foreign capital intensity**|0.2682|0.504|-0.4612|0.629|0.044|0.893|7.2048|0.05|5.2162|0.003|
|**Foreign capital**<br>**intensity*Ln(technology**<br>**dlt**|-0.0227|0.282|0.0311|0.575|0.0077|0.672|-0.7852|0.033|0.0512|0.658|
|**eveopmen)**<br>**Collectives**|-0.6779|0|0.1165|0.571|-0.404|0|-1.458|0|-0.196|0.412|
|**Foreign**|-0.6576|0.009|0.4423|0.244|-0.27|0.25|-1.0436|0.09|-3.886|0|
|**Hong Kong SAR, China; Macao;**<br>**Taiwan, China**|-0.5208|0|-0.0959|0.597|0.0964|0.351|-0.5563|0.111|-1.065|0|
|**Shareholding**|-0.2648|0.001|0.1605|0.281|-0.1647|0.007|-0.4114|0.068|-0.537|0.003|
|**Private**|0.0574|0.807|(omitted)||-0.1934|0.112|(omitted)||(omitted)||
|**Other**|0.2943|0.642|(omitted)||0.0162|0.96|(omitted)||(omitted)||
|**North**|0.5462|0.028|(omitted)||-0.233|0.388|-0.5075|0.154|-0.607|0.129|
|**Northeast**|1.3922|0|0.9264|0.001|0.0552|0.84|(omitted)||(omitted)||
|**East**|0.6389|0.007|0.009|0.965|-0.4532|0.091|-0.4557|0.149|-1.271|0|
|**South**|0.6292|0.009|0.4762|0.013|-0.4825|0.073|-0.8097|0.026|-0.936|0.011|
|**Southwest**|(omitted)||(omitted)||(omitted)||-0.6082|0.287|-1.195|0.003|
|**Year 2000**|0.069|0.379|-0.016|0.897|0.049|0.411|-0.0612|0.743|0.1147|0.477|
|**Year 2001**|0.0698|0.377|-0.0355|0.776|0.0532|0.374|-0.113|0.551|0.1058|0.523|
|**R**<sup>**2**</sup>**(obs.)**|0.3690 (770|)|0.2740 (14|6)|0.2725 (342|)|0.4048 (21|0)|0.6740 (72)||

35
- 102 -

assets, and types of construction in the ESC countries, and applies the historical hurricane intensity distributions and frequencies by individual country and country groupings in order to estimate expected "portfolio" losses as a percentage of total value, under pooled and non-pooled scenarios. The international reinsurance industry in effect already pools risks at a global level and thus achieves the lowest possible EPL coefficient in order to optimize the use of risk capital. However, while this practice is beneficial at the reinsurer level, it is conversely sub-optimal at the primary insurer level since proportionately more capital would need to be made available in a smaller more localized risk area such as a single hurricane prone island. In the East Caribbean, EPL percentages are in the range of 15%-25% with the average primary insurance company allocating its risk capital based on a 20% portfolio estimated expected loss or EPL. However, as seen earlier, reinsurers' EPLs are generally below 10%, and even in hurricane prone states such as Florida in the U.S., insurers' calculated EPLs are closer to 5% given the difficulty of a major hurricane hitting more than a fraction of the state at one time. The analysis below shows the EPL % loss as a function of the probability of various events of differing intensities, with the lowest probability shown to the right of the chart, reflecting an unlikely but very high intensity catastrophic event which corresponds to a higher EPL.

<!-- Start of picture text --> Effect of Risk Pooling on Loss Distribution Functions<br>20.0%<br>18.0%<br>16.0%<br>14.0%<br>i 12.0%/ . .. :<br>U -4-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~Pooled<br>S~~~~~~~~~~~~~~~~~~~~~~OC<br>r 10.0s9 . -U-OEC<br>B~-arbados<br>8.0%<br>,A-<br>4.0,h<br>2.0%E<br>Event Intensity Probability<br>Source: Benfield Greig, Ltd.<br><!-- End of picture text --> As can be observed from the chart, a single country such as Barbados which is rated similarly to medium risk hurricane prone islands such as St. Lucia or Dominica, has a much higher potential portfolio loss for all event intensities and probabilities. Even when the six OECS countriesl<sup>8</sup> which have roughly three risk zones (north EC, mid EC, and south EC) with the northern zone being most susceptible to hurricane losses, have their risks pooled under one portfolio, the potential EPL for the group of countries as one 'portfolio' is substantially reduced. Intuitively this is due to the fact that in any given Is Antigua & Barbuda, Dominica, Grenada, St. Kitts & Nevis, St. Lucia, and St.Vincent & the Grenadines.
3 Using a unique follow-up data set for Northeast Brazil, Gomes-Neto and Hanushek (1991) examine the causes and effects of grade repetiton. They find that student achievement levels are very important, as is the availability of advanced grades. Jamison (1978) examined the determinants, among other things, of student failure and repetition in Nicaragua. Through an evaluation of the Radio Mathematics Project, and using maximum likelihood methods, he found that males are less likely to fail, that the number of times repeated has negative impact on failing during the current year, and that student achievement on test score are significant factors.

The costs of repetition are very high for a developing country. Estimates for Brpzil show that the cost of repetition represents an amount equivalent to the entire federal govemment contribution to first level schooling (World Bank 1986). Policies to reduce repetition and dropout are advocated for the more efficient use of education resources. Cuadra and Fredriksen (1992) estimated efficiency gains as3ociated with reducing repetition and dropout through a simulation exercise. They found that reductions in repetition and dropout rates would result in more students completing the basic cycle of education at a lower input per graduate. Less repetition frees places and lowers the cost of graduating a cohort, while reduced dropout results in more students completing the cycle. Furthermore, less repetition may mean less dropout as the foregone earnings associated with schooling, which are greater when repetition is higher, decrease as the rate of repetition falls.

High levels of grade repetition do not only have cost and efficiency implications, but also affect the foregone earnings associated with attending school, outcomes, and the returns to education. It has been found that repetition rates affect the rate of return to education. Assuming it takzs on average two extra years to complete the basic education cycle, and more for those from.- lower socioeconomic backgrounds, the returns to schooling are found to drop significaitly in Brazil, from 13 percent to 8 percent, after repetition is factored into the calcw.ations (Jallade 1977). Also, Behrman and Deolalikar (1991), using Indonesian data, find that failure to account for repetition and drop out leads one to overestimate the returns to schooling.

# m. A Bird's Eye View of Repetition in Latin America

Table 1 presents key information on primary schooling in Latin America, as well as conservative estimates of the indigenous population in some countries. While many children do not start school on time - according to the official primary school entry age -, most do, however, eventually enroll. Primary school coverage is complete in the Anglophone Caribbean region and Mexico, as well as for a number of Central and South American nations. The lowest levels of primary school coverage occur in Haiti, Guatemala, El Salvador, the Dominican Republic, and Nicaragua.
**<u>Table 4. Relationship Between Alternative Measures of Payment Systems Development and Credit</u>**

**Dependent variable: Annual Credit over Annual GDP ratio**

||(1)|(2)|
|---|---|---|
|Credit Transfers per Inhabitant|0.0046***<br>(0.0013)||
|All Non-Cash Payments per Inhabitant (a)||0.0022*<br>(0.0009)|
|Year Fixed effects|x|x|
|One year Lagged Control Variables (b)|x|x|
|R-squared|0.89|0.79|
|Nbr. obser.|35|38|

Note: Robust clustered standard errors in parentheses. The sample includes all countries covered previously except Bulgaria. (a) Including direct debits and cheques. (**), (***) Stands for significance at 5 per cent and 1 per cent level, respectively. (b) All control variables as listed in Table 2

**<u>Table 5. Difference-in-Difference Estimates</u>**

# **Dependent variable: Annual Credit over Annual GDP ratio**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||**_two lags excluding_**<br>**_non-reform_**<br>**_countries_**||**_two lags excluding_**<br>**_non-reform_**<br>**_countries_**|
|**Post-Reform Trend Break**|**0.073**<br>**(0.010)**|**0.069**<br>**(0.006)**|**0.109*****<br>**(0.016)**|**0.108*****<br>**(0.012)**|
|**(Trend Break)*(Pre-reform number of**|||||
|**credit transfers per inhabitant)**|**-0.190****|**-0.184****|**-0.125*****|**-0.122*****|
||**(0.070)**|**(0.069)**|**(0.029)**|**(0.028)**|
|**(Trend Break)*(Pre-reform average**<br>**credit/gdp ratio)**|||**-0.048*****<br>**(0.008)**|**-0.049*****<br>**(0.009)**|
|Inflation|0.005|0.006**|0.004*|0.005**|
||(0.002)|(0.002)|(0.002)|(0.001)|
|Inflation*(High Inflation dummy)|-0.004*|-0.005**|-0.003*|-0.004**|
||(0.002)|(0.002)|(0.002)|(0.001)|
|High Inflation dummy|0.024*|0.025|0.022*|0.024|
||(0.013)|(0.020)|(0.010)|(0.016)|
|Expenditure over GDP ratio|-0.922*|-0.632|-0.952|-0.708|
||(0.469)|(0.685)|(0.539)|(0.809)|
|Log real GDP per capita|0.802**|1.294***|0.768**|1.222***|
||(0.348)|(0.165)|(0.331)|(0.164)|
|Banking reform index|-0.582*|-0.625**|-0.701*|-0.741**|
||(0.304)|(0.251)|(0.321)|(0.286)|
|Banking crises|-0.017|0.018|-0.068|-0.051|
||(0.069)|(0.081)|(0.069)|(0.092)|
|Public Banks' Assets Share|0.004|0.004|0.004|0.004|
||(0.003)|(0.002)|(0.003)|(0.003)|
|Foreign Banks' Assets Share|-0.002|-0.001|-0.0002|0.0003|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Foreign Exchange reserves/GDP|0.007***|0.006***|0.006***|0.006***|
||(0.002)|(0.001)|(0.002)|(0.002)|
|Country specific trends|x|x|x|x|
|Year effects|x|x|x|x|
|Country effects|x|x|x|x|
|Seasonal Dummies|x|x|x|x|
|Within R-squared|0.90|0.90|0.90|0.91|
|Nbr. obser.|400|288|400|288|

Note: Robust standard errors in parentheses. Significant at (*) 10 per cent level; (**) 5 per cent level; (***) 1 per cent level.
One important consideration regarding the cost of each arm is the cost of interviewer travel. The travel cost for any survey will vary significantly depending on the local context, dispersion of the sample, and fieldwork organization. For this survey, the travel costs were relatively low since the sample was restricted to households within urban Hanoi and thus travel time to and between survey areas was low. For national surveys that cover a wider area, travel costs associated with the multiple visits required for T2 and T4 can be substantially higher. The fieldwork organization of the survey also has implications for the cost of each arm. Travel costs will be high if mobile teams of interviewers are moving throughout a wider area to conduct interviews, while travel costs will be minimal if interviewers remain for an extended period in their assigned survey area (i.e. resident interviewers). These details can have a dramatic effect on the cost and feasibility of the different arms examined in this study. For national surveys with mobile survey teams, the cost of T2 and T4 could be prohibitive and the less preferred but still improved T3 would be more practical. However, for a survey with resident interviewers or covering a very small area, the cost of T4 would be reasonable.

When looking at the length of the interview, time is calculated by adding up the time spent on each of the questions in the FAFH section which is automatically calculated by the CAPI software (row 3). Additionally, a row on instruction time is added based on field observations during the training as it took more time to explain and implement arms T2 and T4 relative to the other two. The instruction time spent on diaries is estimated to be 15 minutes, while that on the sheet and scheduling the following visit is estimated to be 7.5 minutes. Finally, the last row presents an alternative interview time estimate, where the time spent on the 24-hour recall is not accounted for. The reason for such estimate is to estimate how much of the difference between T3 and T4 is driven by this additional module.

As expected, the diary module took by far the longest time. It takes on average about 42 minutes (including instruction time) to implement the diary. Furthermore, as shown in Figure 4, this is not driven by extreme outliers: the median time is 36.9 minutes. In contrast, interview time takes slightly over one minute when applying the one-line recall. With regards to the two preferred arms (T3 and T4), time spent on each correlates with their level of accuracy. That is, the improvements achieved through the bounding/salience arm come at a cost. Not only is the mean about twice as high, but the whole distribution is shifted to the right. When the 24-hour recall portion is eliminated, however, time spent comes very close to the individual recall arm, at about 8 minutes on average.

30
# 3.3 Asset ownership among men and women in different occupations

Across men and women in these occupation categories, Table 4 presents the ownership distribution of different types of assets covered in the Cambodia LSMS+, including across exclusive versus joint ownership.<sup>24</sup> Ownership of assets in general varies widely, by class, and women were in general less likely than men to own assets like mobile phones, financial accounts, and motorized vehicles. Mobile phones were owned by 91 percent of men and 77 percent of women, for example, with the widest gender gaps among those not working in any sector. Within these groups, a slightly higher share (47 percent of men and 40 percent of women) owned mobile phones exclusively, although joint ownership of these devices was also substantial. Motorized vehicles were owned by 68 percent of men and 53 percent of women, and only 14 and 12 percent of men and women, respectively, owned a financial account.

Table 4 also shows that respondents who own an NFE are more likely than other groups to have a motorized vehicle, financial accounts and a mobile phone, although women continue to be less likely than men to own these assets. On the other hand, women were significantly more likely than men to own livestock, residential land, as well as (among off-farm workers, and in particular, in wage work) non-residential land. For all assets except for mobile phones, gender differences in ownership tend to be driven by differences in exclusive, as opposed to joint, ownership.

Figure 3 presents, by age and gender, respondents’ self-reported valuation of these assets in Cambodian Riel. For all assets, other than financial accounts, respondents were asked “If you were to sell <THIS ASSET> today, how much would you receive for it?” For financial accounts, respondents were asked about the current value of the account. For assets that were jointly owned, the value of the asset owned by the respondent was calculated by dividing the self-reported asset value by the number of reported joint owners. In general, Figure 3 underscores many of the key gender differences observed in ownership. The value of mobile phones and motorized vehicles owned by women tends to be significantly lower than those owned by men. And this holds true across most age groups. For financial accounts, younger women tend to have higher asset values, but this trend reverses among men and women aged 40 and older. For land and livestock, asset values are also similar among younger age groups, but wider gender disparities emerge among older respondents.

> 24 Exclusive/joint ownership of motorized vehicles was not asked in the survey.  In the case of financial accounts, the exclusive/joint distinction was asked, but nearly all respondents reported exclusive ownership.

16