rafmacalaba/datause-encoder-data huggingface dataset:
**Table F7: Relationship between food security measures and weight-for-age z-scores (Nepal): Expanded Controls Version**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|||Weig|ht-for-age z-sc|ores|||
|Per capita kcal|0.019<br>(0.022)|||||||
|1-SSR||0.017<br>(0.029)||||||
|1 – SSEXR|||0.057**|||||
||||(0.026)|||||
|FCS||||0.068**<br>(0.027)||||
|DDS|||||0.029<br>(0.026)|||
|PC consumption||||||0.139***<br>(0.036)||
|Food share|||||||-0.075**<br>(0.032)|
|Age (in months)|-0.014**|-0.014**|-0.014**|-0.013**|-0.013**|-0.013**|-0.013**|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|
|Age squared|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Child is female|-0.010|-0.010|-0.012|-0.015|-0.016|-0.012|-0.012|
||(0.044)|(0.044)|(0.044)|(0.044)|(0.044)|(0.043)|(0.044)|
|Household size|-0.018**|-0.018**|-0.017*|-0.022**|-0.020**|-0.016*|-0.021**|
||(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|
|Share of kids under 5|0.048|0.023|0.012|0.050|0.048|0.076|0.067|
||(0.199)|(0.200)|(0.199)|(0.199)|(0.199)|(0.198)|(0.199)|
|_Characteristics of the HH Head_||||||||
|Literate, or < 5 years of sch.|0.032|0.031|0.028|0.010|0.014|0.014|0.009|
||(0.060)|(0.060)|(0.060)|(0.060)|(0.060)|(0.060)|(0.061)|

65
**Table 28. Governance-related drivers of legal and regulatory obstacles: Accessibility of laws**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0402|0.0284|||0.0785|0.0557|||
||(0.0614)|(0.0605)|||(0.111)|(0.113)|||
|Export % of sales|-0.000745|0.000439|-0.000707|0.000479|-0.00124|0.000957|-0.00116|0.000947|
||(0.000777)|(0.000795)|(0.000800)|(0.000805)|(0.00138)|(0.00148)|(0.00149)|(0.00157)|
|Share of sourcing via imports|0.000702|-1.07e-05|0.00102|0.000172|0.00112|0.000134|0.00169|0.000501|
||(0.000840)|(0.000825)|(0.000862)|(0.000845)|(0.00149)|(0.00153)|(0.00157)|(0.00162)|
|High-income source country|-0.0484|0.00565|-0.0631|-0.000746|-0.0845|0.0217|-0.116|0.00760|
||(0.0902)|(0.0905)|(0.0899)|(0.0904)|(0.164)|(0.172)|(0.169)|(0.179)|
|>250 employees|-0.0460|-0.0356|-0.0655|-0.0527|-0.0723|-0.0573|-0.111|-0.0877|
||(0.0738)|(0.0742)|(0.0734)|(0.0737)|(0.131)|(0.139)|(0.134)|(0.143)|
|Cumulative inv. >US$10M|0.0661|-0.0128|0.0546|-0.0254|0.119|-0.0239|0.0987|-0.0468|
||(0.0719)|(0.0724)|(0.0714)|(0.0716)|(0.129)|(0.135)|(0.132)|(0.138)|
|Years in country|-0.00101|-0.00226|-0.000183|-0.00146|-0.00142|-0.00470|6.18e-06|-0.00316|
||(0.00220)|(0.00221)|(0.00219)|(0.00224)|(0.00375)|(0.00399)|(0.00388)|(0.00420)|
|Level of foreign ownership (%)|-0.00177|-0.000492|-0.00184|-0.000286|-0.00296|-0.000940|-0.00326|-0.000672|
||(0.00123)|(0.00132)|(0.00117)|(0.00125)|(0.00215)|(0.00240)|(0.00209)|(0.00235)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.006|0.069|0.031|0.096|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Accessibility of laws and regulations]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

45
-91-

TABLE 7.8

|Productivity<br>(Value Added, F<br>byMajor Branch<br>---------------------------------------------------------|ormer Nat<br>of Manu|ional<br>Ac<br>facturin|counts<br>Con<br>g,<br>Mexico/|cept)<br>per<br>Brazil,<br>19<br>__------------------__--|Person<br><br>75<br>--|Engaged|
|---|---|---|---|---|---|---|
||at Mex|ican "price|s"|at Braz|ilian<br>"|prices"|
||Mexico<br>1975<br>(1975|Brazil<br>1975<br>pesos)|Mexico/<br>Brazil<br>(%)|Mexico<br> <br>1975<br>(1975 cr|Brazil<br>1975<br>uzeiros)|Mexico/<br>Brazil<br>(%)|
|Food Products|66,029|93,245<br>|70.81<br>|39,827<br>|57,539|69.22|
|Beverages|117,730|200,034|58.85|54,846|87,654|62.57|
|Tobacco Products|136,148|205,618|66.21|82,892|124,628|66.51|
|Textiles<br>and Wearing Apparel|66,951|74,750|89.57|44,665|45,194|98.83|
|Footwear<br>and Leather<br>Products|51,396|70,109|73.31<br>|22,952|30,776|74.58|
|Wood**a-<d**<br>Paper Products|79,719|100,240<br>|79.53<br>|31,238|52,814|59.15|
|Chemical Products|166,865|263,519|63.32|178,583|238,931|74.74|
|Rubber**4..**<br>nd Plastic Products|117,375|250,239|46.91|39,967|84,887<br>|47.08<br>|
|Stone, Clay and Glass Products|87,938<br>|95,560|92.02|59,380|49,347|120.33|
|Metal products|115,973<br>|113,873<br>|101.84|85,737|72,581|118.13|
|Electrical<br>Machinery|83,550|126,462|66.07|51,345|90,579|56.69|
|Machinery and Transport|||||||
|Equipment|108,703|160,766|67.62|50,627|74,265|68.17|
|Other|73,110|96,449|75.80|48,177|55,442|86.90|
|Average for Total<br>|||||||
|Manufacturing|94,060|121,772|77.24|61,982|69,999|88.55|

Source: Value added (former national accounts concept) from table 4.10; employment from table 7.5.
parts: (1) The shift component: a measure of the pull of potential destination districts, and (2) A share component: a measure of the strength of the connection between the source and the potential destination districts. We detail each in turn below and note that both shift and share component are skill-specific, as in Peri, Shih and Sparber (2015).

Our shift component - the proxy for the pull of each (destination) district - is the total immigration into that district in the past five years from all districts (barring the one source district under consideration, that is, a “leave-one-out” measure). It is based on the idea that when a location has many economic opportunities, it will attract migrants from a variety of locations.<sup>22</sup> Our share component - the measure of the migration connections between the source district and potential destinations - consists of the historical shares of various destination districts in the total out-migrants from the source district. Specifically, the connection between a destination district _j_ and source district _i_ in year _t_ is the share of district _j_ in the total number of individuals who migrated out of district _i_ five years or more before year _t_ .<sup>23</sup> This approach of using the historical migrant network as a measure of connection follows Card (2009). It is based on the insight that social networks have a strong influence on the location decisions of migrants, with migrants more likely to go to places to which more individuals from their network have previously migrated (Munshi, 2003; Munshi and Rosenzweig, 2016; Munshi, 2020). Within the Indian context, the role of networks in migrants’ destinations has also been highlighted by Gore (1970) who notes that ‘the nearest town is not always the best option’, referring to the importance of the familiarity of fellow villagers in a new location.<sup>24</sup>

> 22This approach of using total migration flows as the shift component follows studies such as Card (2001), Card (2009) and Goldsmith-Pinkham, Sorkin and Swift (2020) in spirit. The details differ because these studies are interested in arriving at exogenous measures of _immigration_ rather than _emigration_ . In this sense, our approach might be more similar to Anelli et al. (2023) and Madhok et al. (2024) who use destination country/district economic shocks as the shift for out-migration.

> 23In calculating this stock, we consider only male individuals of 20-64 years.

> 24For more recent examples, see among others, Munshi and Rosenzweig (2006); Tumbe (2018).

26
Policy Research Working Paper 5906

# **Abstract**

The purpose of this paper is to stress test the resilience of Croatian households with debt to economic shocks. The shocks not only impact a household’s welfare, but also increase the probability of loan default. As a result, there is a direct link between these stress-testing exercises and financial stability risks. The authors find that very few households are at risk as a result of the shocks experienced over the past few years; new vulnerable households represent about 2 percent of all households, 6 percent of households with debt, and 2–3 percent of aggregate banking system assets. This suggests that household over-indebtedness in Croatia is unlikely to become a drag on aggregate economic activity and that financial stability risks remain manageable. One caveat should be noted. Some 27–31 percent of households with debt, representing 8–9 of banking system assets, are vulnerable even before being subjected to an economic shock. Since NPLs were low before the global financial crisis, it can be argued that banks knew something about some of these households that is not captured by household budget surveys. It follows that the calculations in this paper should primarily focus on the increased vulnerability of households as a result of shocks and are likely to represent an upper bound to the financial stability risks faced by Croatia on account of household indebtedness.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at nsugawara@worldbank.org and jzalduendo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 15 -

An important perspective of the public debt problem of these six EMENA countries can be provided by comparing their experience with that of the more developed OECD countries.<sup>8</sup> First is the composition of debt. While these countries' total public debt consisted mainly of external debt, developed countriesI public sector debt consisted mostly of domestic debt. This difference made the debt problem of these six countries more prone to external shocks as that of the early-1980s. Second is the use of debt. While more developed countries increased their level of indebtedness to finance their capital accumulation (the only exceptions being the finance of wars), this is not so clear in the case of these six countries. If that was the case, the productivity of capital would have increased in the 1980s. And there is no indication that that happened. On the contrary, there is indication that the external public debt substituted partly for current revenues as evidenced by the weakening of their tax systems and also that it was invested in not very efficient investment projects. These differences made the public debt problem of these countries more difficult and countries more prone to external and domestic shocks. Third is the method used for reducing the debt to GDP ratios. In the case of the developed countries this resulted from an increase in capital productivity (higher growth rates) and an increase in tax revenues. The six EMENA countries showed a different pattern. x first group while reducing their total public debt, increased their domestic public debt; and the second group increased their total public debt by increasing central bank public debt.

8/ See Buiter (1985); Chouraqui, Jones and Montador (1986) and OECD (1990).
<!-- Start of picture text --> 5<br>Be<br>8<br>2<br>-<br>g<br>E<br>Bot<br>=<br>7<br>3<br>a<br>%<br>‘Ape2020 May-2020 Jun-2020 Jul-2020 Aug-2020 Sep-2020 Oct-2020 Nov-2020 Dec-2020 Jan-2021 Feb-2021 Mar-2021 Apr-2021<br>Months from March 2020<br>© Young <25 years old © Adult >=25 years old<br>‘Note: Standard errors clustered on survey wave<br><!-- End of picture text --> <!-- Start of picture text --> gCS<br>a<br>a<br>5<br>an<br>E<br>es<br>&<br>Ey<br>Bo<br>§<br>uy<br>%<br>é2 4<br>‘Ape-2020 May-2020 Jum-2020 Jul2020 Aug-2020MonthsSep-2020 fromOct-2020 MarchNov-2020 2020Dec-2020 Jan-2021 Feb-2021 Mar-2021 Apr-2021<br>—®— Young <25 yearsold ——®— Adult >=25 years old<br>‘Note Standard eors clustered on suvey nave<br><!-- End of picture text --> <!-- Start of picture text --> a<br>g*<br>[EP<br>:<br>an<br>f<br>os<br>2<br>5<br>aa<br>=<br>‘Ape2020 May-2020 Jun-2020 Jul-2020 Aug-2020MonthsSep-2020 fromOct-2020MarchNov-20202020Dec-2020 Jan-2021 Feb-2021 Mar-2021 Apr-2021<br>—®— Young <25 yearsold ——®— Adult >=25 years old<br>‘ote Standard enorschsteredon survey wave<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>go7<br>E<br>=.gBo2<br>a32<br>*<br>E<br>B a<br>%<br>a g °<br>Apr-2020 May-2020 Jun-2020 Jul2020 Aug-2020 Sep-2020 Oct-2020 _Nov-2020 Jan-2021 Feb-2021 Mar-2021<br>Months from March 2020<br>—®— Young <25 yearsold = ——®— Adult >=25 years old<br>pomp<br><!-- End of picture text --> <!-- Start of picture text --> 2i:<br>a=E|<br>&<br>:fe<br>aB=3°ro)<br>3<br>aZ<br>5 +<br>e<br>i<br>ona°<br>2 a<br>4 Apr-2020 May-2020 Jum-2020 Jul-2020 Aug-2020 Sep-2020 Oct-2020 Nov-2020 Jan-2021 Feb-2021 Mar-2021<br>Months from March 2020<br>—®— Young <25 yearsold —®— Adult >=25 years old<br>‘Note: Standard errors clustered on survey wave<br><!-- End of picture text -->
9 As the ban only applied to agrochemicals, one may ask if the decreased use of inorganic fertilizer was offset by an increased use of organic fertilizer. We show this offset was very limited by examining the use of manure, a typical organic fertilizer. The quantity of manure applied to the soil had been increasing steadily over the twenty years prior to the ban and, importantly, its increase in 2021 merely followed this long-run trend, without any visible “jump” that would occur if farmers resorted to increased organic fertilizer use as a key coping strategy to deal with the ban (Appendix Figure A3a). Moreover, manure’s share of total fertilizer use was not atypically high in 2021 compared to the previous six years and this share was never higher than 2.5% (Appendix Figure A3b). Trade data also confirms that the collapse of chemical fertilizer imports, which decreased to less than 40 million USD in 2021 from a pre-ban baseline of 100-250 million USD, was hardly compensated by organic fertilizer: while the organic share of fertilizer imports did increase after the ban, it was never higher than 4% (Appendix Table A3).

# **III Data**

Our reduced-form and model-driven analyses rely on a rich assortment of mostly novel data. Table I presents our main data sources and variables, grouping them into several categories: trade policy; trade flows; agricultural production, cultivated area, and yields; agricultural prices; fertilizer use and subsidies; potential agricultural yields; labor share in value added; wages, land ownership, occupation, and household characteristics; and night lights. Appendix A provides additional details on all data sources and variables used. Below, we briefly highlight a few important features of our data.

**Trade-related variables** . The list of products targeted by import bans and their time coverage are hand-coded from Sri Lanka’s Extraordinary Gazettes on Imports and Exports. The fertilizer import bans started in May 2021, with most bans lifted by July 2021 or by November 2021 at the latest. We also use novel high-frequency granular data covering the universe of Sri Lanka’s export and import transactions, relying on machine learning and text analysis to clean firm names and assign unique firm identifiers to create a panel of firms involved in exports.

**Agricultural production** . We complement administrative data on crop-district-level yields with remote sensing estimates of rice and tea yields at a highly granular level. Remote sensing provides a spatially granular view of crop cultivation, allowing a detailed assessment of the heterogeneous effects of the fertilizer import ban within Sri Lanka. For rice, we apply an expert-based image classification algorithm to satellite data to identify rice-growing areas using the specific growing conditions for paddy rice in Sri Lanka (Ozdogan et al., 2024).<sup>22</sup> To estimate rice yields, a statistical

> 22The presence of rice-growing areas is determined by factors related to climate, water availability, and topographic position, along with farmer decision-making and technical expertise. The methodology monitors rice-related vegetation
29 of the package became known. W At the same time, the maturity structure of public debt has widered considerably, another indication of private confidence.

<!-- Start of picture text --> Impact of Debt Relief on Growth<br>one plus growth rate<br>1.00<br>1.06<br>___-___<br>* 04/=_L<br>19Se 1989 1990 1W1 192 18 1e"<br>Year<br>Wlth Det RIlf -4- No DRdot e. - DRUtndi c n<br><!-- End of picture text --> Figure 3 shows the impact on economic growth of the direct transfer effect and of the secondary effect through restoration of confidence and reduction of uncertainty. This is done by using an econometric model for Mexico to simulate what would happen without a debt package. I The difference between this simulation and the base scenario based on the availability of the subscribed debt package, indicates the macroeconomic impact of Mexico's debt package. In the absence of the debt package, Mexico would have to service fully interest and amortization on all public commercial debt. This would need to be met out of an already severely cut back budget. Therefore, the Government would be forced to cut back public investment in line with the decline of "allowable" external net borrowing. The drop from the top line to the middle line in Figure 3 indicates the direct impact on growth of this cut back in public sector investment. Moreover, it is assumed that the Government would, even under such stressful circumstances, attempt to _li/_ Nominal interest rates have since then rebounded to 40%.

_I2_ The model is presented in S. van Wijnbergen (1989), "Growth, External Debt and the Real Exchange Rate in Mexico", PPR Working Paper no 257.
# b) Uruguay. 1995 – 2015

<!-- Start of picture text --> 0.3<br>0.2<br>0.1<br>0<br>1995 2003 2015<br>-0.1<br>-0.2<br>-0.3 Non-routine cognitive (analytical) Non-routine cognitive (interpersonal)<br>Routine cognitive Non-routine manual<br>Routine manual<br><!-- End of picture text --> Source: Our own estimation based on data from household surveys and O*NET In observing the variation between the extreme years in each country, one can appreciate the increment in the intensity of cognitive tasks, both routine and non-routine, and a fall in the relative importance of manual tasks. However, in the case of Argentina in particular, the tendency is not strictly upwards, as there is a relatively important fall in cognitive tasks and a rise in manual tasks between 1998 and 2003. This can be associated with the economic crisis which the country experienced in 2001-2002. Given the extent of the crisis in the labor market, many workers were forced to carry out subsistence occupations in a situation of economic recession, which are generally intensive in manual tasks. The improvement in employment in subsequent years allowed this process to be reverted.

With respect to the non-routine cognitive tasks, the increase in the relative importance of analytical tasks in the workplace was greater than that observed for tasks associated with personal relations in the period studied, in both countries. This gap seen in the growth of both types of non-routine cognitive tasks appears relatively early on. Effectively, in the years 1990 and 2003 the intensity of analytical tasks in the workplace is already increasing at a greater rate than those associated with the development of personal relationships.

In the case of manual tasks, either routine or non-routine, their importance reduced during the period being studied. In the case of Uruguay, this fall was almost two times greater for routine tasks. In Argentina, and inversely to that observed for non-routine cognitive tasks, an increase in the intensity of manual tasks is seen between 1998 and 2003, although after the economic crisis, these begin to lose relevance in the average workplace.

- 13 -
**Table 5: Three Comparator United States–based Studies**

|**Study**|**Number of**<br>**Teachers**|**Grades**|**Raters**|**Supporting**<br>**Organization**|**Subjects**|**Period**|
|---|---|---|---|---|---|---|
|Measures of Effective<br>Teaching (MET)|1,333|4–9|Graduate<br>Students at<br>University of<br>Virginia (UVA)|Bill & Melinda<br>Gates Foundation|Math and<br>English|2009|
|Understanding Teaching<br>Quality in Algebra Study<br>(UTQ-A)|82|6–12|Trained Raters at<br>Educational<br>Testing Service|UVA, W.T. Grant<br>and Spencer<br>Foundations|Math and<br>English|2009–11|
|Secondary<br>MyTeachingPartner Study<br>(S-MTP)|78|7–12|Graduate<br>Students at UVA|UVA|No subject<br>specified|2007–09|

Measuring agreement allows us to see where raters had an easier or harder time in agreeing on observed behaviors. It is also in line with CLASS protocols, as the same measure is used to evaluate whether a rater has passed CLASS training.

Table 6 shows the average percentage agreement between raters across the 12 CLASS dimensions. The raters in Guangdong had similar measures of agreement to those in the three other studies. Certain dimensions proved harder for the Chinese raters to assess similarly, such as Regard for Student Perspectives, Instructional Dialogue, Teacher Sensitivity, and Analysis and Inquiry, perhaps due to differing definitions of what comprises effective interactions for each of these dimensions in the context of a Chinese classroom. On other measurements, such as Productivity and Student Engagement, the Chinese raters agreed a higher percentage of the time than the raters in the other three studies.

**Table 6: Agreement, Exact + Adjacent (%)**

||**Guangdong**|**S-MTP**|**MET**|**UTQ-A**|
|---|---|---|---|---|
|**Positive Climate**|78.4|79.3|74.6|69.3|
|**Teacher Sensitivity**|58.4|73.8|72.6|65.0|
|**Regard for Student Perspectives**|64.8|78.6|67.9|70.2|
|**Behavior Management**|80.4|89.2|85.6|92.8|
|**Productivity**|91.5|84.3|82.4|87.2|
|**Negative Climate**|95.6|95.1|95.1|97.6|
|**Instructional Learning Formats**|72.0|80.3|78.2|73.9|
|**Content Understanding**|70.7|73.4|75.5|76.6|
|**Analysis and Inquiry**|66.4|73.7|71.5|84.3|
|**Quality of Feedback**|78.1|72.5|71.9|64.1|
|**Instructional Dialogue**|64.8|—|74.8|—|
|**Student Engagement**|83.1|81.2|78.4|82.0|

Source: Guangdong observations; Teachstone.

Note: — indicates that the dimension was not scored for that study. Adjacent indicates (±1) between the two raters. For example, if one rater scored a certain dimension a 4, while their partner rated it a 3, 4, or 5, that would count as being in agreement An alternate method of considering differences in coding is to examine the spread between scores of the raters for the same lesson segment. For example, two raters may give a score of 5 on the dimension of 9