rafmacalaba/datause-encoder-data huggingface dataset:
-4-

# 2. Some Basic Concepts and Magnitudes

Some simple distinctions among the various stocks and flows associated with debt should be made. Starting with stocks, one distinction is between disbursed and undisbursed debt, the second consisting of commitments made by lenders that have not been drawn upon, and therefore are not yet accumulating interest. Part of disbursed debt is interest arrears, accumulated unpaid interest obligations. Henceforth, by "debt" (or D t) I mean "disbursed debt." Turning to flows, debt service payments in period t (DS t) consist of interest payments (IPt) and principal repayments (PRt) associated with disbursed debt. Thus DSt = IPt + PR . The net flow in period t (Bt) is new borrowing (loan disbursements plus accumulation of arrears' in period t (NB t) less repayments of principal, i.e., Bt - NBt -PRt. This is the amount by which the nominal stock of disbursed debt changes in period t. Hence (denoting the change in variable x as x), Bt - Dt.

Finally, the net transfer in period t (NT t) equals the net flow less interest payments or, 3quivalently, new borrowing less debt service, i.e., NTt Nt = = Bt t - - IPt t = NB = t - DS t' . This concept is especially critical in that it represents the net flow of real resources from creditors to the debtor. A positive value means that creditors are contributing resources to the debtor country while a negative value means that creditors are taking resources away. A hypothetical observer privy to a nation's complete set of international transactions concerning goods and services (other than interest payments) and its foreign aid receipts could ascertain the net resource transfer even if it could not observe any balance sheets or financial transactions.

The appendix presents annual data on disbursements, principal and
all firms in sector _i_ in village _v_ . To ensure that outliers do not drive our results, we restrict our sample in regressions using outcomes from the Economic Census to villages where total employment is less than total inhabitants in the village.

We use data on demographics and village-level public goods (roads, electricity, schools, etc.) from the Population Censuses of 2001 and 2011. The 2001 data provides control variables for the main regressions and is used to establish baseline balance for the regression discontinuity, while 2011 data is used to measure endline outcomes such as total population and availability of transportation.

We also test for outcomes from two new measures of agricultural inputs from the 2011 Population Census. The first is crop choice. The census records the three major crops for each village—from this we generate an indicator variable for whether the village grows any non-subsistence crops, which we define as anything other than cereals (rice, wheat, etc) and pulses (lentils, chickpeas, etc). The second is total agricultural land, which we transform into logs.

These censuses also provide the basis for linking the various other datasets. We use a key provided by the 2011 Population Census to link data from 2011 to 2001. GIS data of village boundaries in 2011, procured from ML Infomap and based on official census maps, is used for the aggregation of gridded remote sensing to the village level. Additionally, these data are used to calculate distance to the nearest town.

# **A4 Agricultural production**

As no comprehensive village-level data is collected on agricultural production in India, we use the normalized difference in vegetation index (NDVI) to proxy for agricultural production in baseline and endline survey periods. NDVI is a chlorophyll-sensitive measure of plant matter, generated at global coverage and 250 m resolution by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Earth Observing System-Terra satellite. Each image represents a 16-day composite where each pixel value is optimized considering cloud cover obstruction, image quality, and viewing geometry via the MODIS VI algorithm (Huete et al., 2002). Composite images were downloaded from the Columbia University IRI Data Library for the years 2000-2014 for nine 16-day periods from late May through mid-October, covering the major (kharif) cropping season in India (Selvaraju, 2003).

For each composite image, NDVI pixels were spatially averaged to village polygons. After village aggregation within each 16-day composite, three proxies for agricultural production were calculated for each year’s growing season: the difference between early-season NDVI (the mean of the first three 16-day composites) and the max NDVI value observed at the village level (Labus et al., 2002; Rasmussen, 1997), mean NDVI (Mkhabela et al., 2005), and cumulative NDVI (Rojas, 2007) (the sum of NDVI from each of the nine composites during 61
during 1995-2013 in a difference-in-differences strategy, we examined if and how the evolution German acquisitions and firm performance in Poland following EU accession was mediated by pre-determined measures of industrial specialization in Germany. If the scope for capital movements and technology transfers was higher in sectors in which Germany was relatively more specialized at the moment of accession, improvements in performance would be stronger among Polish firms operating in those sectors.

We first examined effects of German acquisitions on firm performance. Results from a difference-in-differences estimator point to sizeable positive effects of German acquisitions on firm size. In a second step, we examined if German acquisitions following EU integration were stronger in sectors in which Germany was more specialized at the moment of accession. Event studies pointed to a sharp rise in German acquisitions of Polish firms right after accession, and at a considerably greater extent in sectors in which Germany had higher export shares. Albeit these acquisitions began in 2001 along with the conclusions of the accession negotiations, it was only after joining the EU that FDI observed a sharp rise. Acquisitions increased also from other EU countries, but to a significantly lower extent. We then examine if the evolution of firm performance in Poland following EU accession was also mediated by pre-determined measures of industrial specialization in Germany. Difference-in-differences estimates revealed that Polish manufacturing firms operating in sectors in which Germany was more specialized at the moment of accession experienced stronger output and productivity growth in the post-2004 period.

Taken together, our findings suggest that deeper integration in goods and factor markets stimulates capital flows to the less advanced economy, that these flows occur predominantly in sectors in which the advanced economy had a comparative advantage, and that this contributes to narrow the productivity gap within industries. These findings point to the benefits of deeper economic integration in goods and factor markets. Despite the fact that both Poland and the EU have eliminated all tariffs ahead of 2004, it was only with the full EU membership that Poland began to significantly enjoy the benefits of FDI-induced productivity gains.

# **References**

- Ackerberg, D. A., K. Caves, and G. Frazer (2015). Identification properties of recent production function estimators. _Econometrica 83_ (6), 2411–2451.

- Amiti, M. and J. Konings (2007). Trade liberalization, intermediate inputs, and productivity: Evidence from indonesia. _American Economic Review 97_ (5), 1611–1638.

18
TABLE A.8 Testing Treatment Effect Heterogeneity (Continued)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||||Pa|nel B: Student|test score data|measured at _Y_|3|||
||Fraction<br>target<br>teachers|Total target<br>teachers|Student<br>asset index|Number of<br>students|Size relative<br>to biggest<br>school|Log number<br>of students|Log size<br>relative to<br>biggest<br>school|School-level<br>_Y_0 score|Teacher’s<br>age|
|Treatment|-0.10|-0.05|-0.06|-0.00|-0.01|0.05|0.01|0.01|-0.03|
||(0.07)|(0.04)|(0.08)|(0.04)|(0.04)|(0.19)|(0.07)|(0.02)|(0.09)|
|Covariate|0.00|0.01<sup>_∗∗∗_</sup>|0.20<sup>_∗∗∗_</sup>|0.00<sup>_∗∗∗_</sup>|0.30<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.22<sup>_∗∗∗_</sup>|-0.00|
||(0.08)|(0.00)|(0.02)|(0.00)|(0.09)|(0.03)|(0.03)|(0.03)|(0.00)|
|Treatment - covariate|0.21|0.01|0.02|0.00|0.06|-0.01|-0.00|0.01|0.00|
|interaction|(0.13)|(0.00)|(0.02)|(0.00)|(0.14)|(0.04)|(0.04)|(0.04)|(0.00)|
|Observations|274,993|274,993|274,993|274,993|274,993|274,993|274,993|270,578|251,671|
|_R_<sup>2</sup>|0.24|0.24|0.25|0.24|0.24|0.24|0.24|0.23|0.23|

_Notes:_<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. The table examines the heterogeneity in treatment effects. Test score data are constructed by first standardizing by subject-grade-year (so that mean and variance in the control group are 0 and 1 respectively), then stacked so that the unit of observation is student-subjectyear. The test score is then regressed on a dummy variable indicating a treatment school, a covariate term (mostly school-level (columns 1-8) and one teacher level covariate (column 9), and the interaction between the treatment indicator and the covariate). The estimated parameters on these three regressors are reported in the table. The rest of the model specification is the same as used for the Table V results presented in the main text. Panel A reports results based on _Y_ 2 test score data and panel B reports results based on _Y_ 3 test score data. School-level clustered standard errors reported in parentheses. The interaction variables used in analysis are the fraction of target teachers in the school at baseline, the total number of target teachers in the school at baseline, a student asset index constructed as the school mean of 7 different asset availability dummies constructed from baseline data, the number of students per school at baseline, the total number of students per school in proportion to the largest primary school (for primary schools) or secondary school (for secondary schools), the natural log of the number of students per school, the natural log of the relative measure of size, the school averaged student-level test score obtained at baseline, and teacher’s age.
Improved technology is critical for economic growth and assumes even higher importance in smallholder settings where new technologies can enhance yields, reduce risk, and contribute to better outcomes in a range of other ways. Obstacles to smallholder technology adoption traditionally identified in the literature include human capital, credit, wealth, information and transport constraints, risk aversion, poor tenure security, lack of economies of scale and lack of complementary inputs (Feder _et al._ 1985). Recent literature has focused on models of learning and risk aversion in the presence of credit and insurance market failures (Foster and Rosenzweig 2010) that highlight the importance of prior adoption decisions and outcomes of farmers within an agent’s network.

Cognitive skills are essential to quickly acquiring and processing information not only on the availability of new technologies but also on how to apply them in a given setting. It is thus not surprising that education, as the most commonly available proxy for such skills, emerged as a key determinant of adoption decisions. Skinner and Staiger (2005) show that education and measures of individuals’ social network were the only predictors of adoption significant for all of four key technologies adopted by US farmers over the 20<sup>th</sup> century. Recent literature found that education improves learning (information processing), and increases the adoption of new technologies and the efficiency of their application (Foster and Rosenzweig 2010).

But education itself may be endogenous if it is particularly sought in settings where returns to such skills are high. Indeed, Foster and Rosenzweig (1996) use Indian panel data to show that returns to education are higher in areas where agricultural technologies are available for adoption and that demand for education increased where, due to availability of new agricultural technology, returns to education had increased (an effect limited to land-owning households). This is consistent with the finding of many studies that, beyond basic numeracy, education is a statistically insignificant determinant of adoption decisions (Huffman 2001). For example high levels of unobserved heterogeneity are found for GM seed adoption in the US (Barham _et al._ 2015) and it would be of interest to explore if this variation could be explained by personality or noncognitive traits.

One non-traditional factor recently highlighted as important to smallholder adoption decisions is social learning (Conley and Udry 2010). Farmers observe the effect of adopting a new technology on individuals in their network and then update their beliefs and decisions in response. At least in the long term, learning from neighbors seems to be more important to promoting the adoption of fertilizer and improved seeds than learning from extension agents (Krishnan and Patnam 2014) suggesting that different information sources are weighted differently by farmers. For instance, networks which individuals choose to join have been found to be more effective at facilitating technology adoption than those based on mere proximity (Liverpool-Tasie and Winter-Nelson 2012).

4
# **Table 1: Pre-vignette and post-vignette subjective welfare rankings**

|**Tajikistan**|||Post-vi<br>|gnette<br>||||
|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6||
|Pre-vignette|poorest|||||richest|Total|
|1 poorest|247|64|23|3|4|0|341|
|2|72|933|232|53|7|5|1,302|
|3|34|242|1,735|223|37|4|2,275|
|4|4|33|112|592|34|1|776|
|5|2|0|2|11|51|1|67|
|6richest|0|0|1|1|4|4|10|
|Total|359|1,272|2,105|883|137|15|4,771|

_Note:_ Pearson chi2(25) = 8.0e+03 Pr = 0.000; likelihood-ratio chi2(25) = 4.9e+03 Pr = 0.000; Cramér's V = 0.5806; gamma = 0.8532 ASE = 0.009; Kendall's tau-b = 0.6991 ASE = 0.009.

|**Guatemala**|1|2|Post-vi<br>3|gnette<br>4|5|6||
|---|---|---|---|---|---|---|---|
|Pre-vignette|poorest|||||richest|Total|
|1poorest|265|118|17|7|4|1|412|
|2|37|422|103|7|2|0|571|
|3|7|50|128|22|3|1|211|
|4|1|5|4|9|1|0|20|
|5|0|0|0|2|3|0|5|
|6 richest|0|0|0|0|1|0|1|
|Total|310|595|252|47|14|2|1,220|

_Note:_ Pearson chi2(25) = 1.1e+03 Pr = 0.000; likelihood-ratio chi2(25) = 802.2182 Pr = 0.000; Cramér's V = 0.4287; gamma = 0.8000 ASE = 0.022; Kendall's tau-b = 0.6049 ASE = 0.021

|**Tanzania**|||Post-vi<br>|gnette<br>||||
|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6||
|Pre-vignette|poorest|||||richest|Total|
|1poorest|59|31|12|3|1|1|107|
|2|12|91|52|12|1|0|168|
|3|6|26|87|26|3|0|138|
|4|0|1|7|17|4|0|29|
|5|0|0|2|0|1|2|5|
|6 richest|0|0|0|0|0|2|2|
|Total|77|149|150|58|10|5|449|

_Note:_ Half-steps included in the survey question have been aggregated into one step (the one below) to facilitate comparisons. Pearson chi2(45) = 551.2519 Pr = 0.000; likelihood-ratio chi2(45) = 301.5191 Pr = 0.000; Cramér's V = 0.4955; gamma = 0.6892 ASE = 0.036; Kendall's tau-b = 0.5446 ASE = 0.032.

19
**Table 9: Investment in Islamic mutual fund Y with increasing risk – Alternate fund is traditional (control) or socially responsible**

||||**(treatment)**|||||
|---|---|---|---|---|---|---|---|
|Depen|dent Variable:|Investment i|n Islamic fund|- Alternate: Soci|al vs Traditiona|l)||
||I<br>Baseline|II<br>Controls|III<br>Decision<br>interaction|IV<br>Risk<br>preference<br>interaction|V<br>Religious<br>preference<br>interaction|VI<br>All<br>interactions|VII<br>Triple<br>interaction|
|Treatment (Alternate is Social fund)|-8.46|-49.51|-310.8*|-120.70|96.40|-291.80|-110.50|
||(124.5)|(132.6)|(160.2)|(468.2)|(185.7)|(482.6)|(491.3)|
|Decision number (1 - 12)|<br>-143.0***|<br>-143.0***|<br>-163.1***|<br>-143.0***|<br>-143.0***|<br>-163.1***|<br>-190.7***|
||(6.9)|(6.9)|(9.8)|(6.9)|(6.9)|(9.8)|(13.3)|
|Risk preferences||-57.55|-57.55|-69.02|-44.99|-65.79|-65.79|
|<br>7 = Risk-seeking||(74.03)|(74.03)|(103.70)|(74.79)|(103.60)|(103.60)|
|<br>Religious preferences||<br>1.774**|<br>1.774**|<br>1.758**|<br>2.655**|<br>2.652**|<br>0.446|
|<br>Donation to mosque||(0.69)|(0.69)|(0.70)|(1.05)|(1.05)|(1.28)|
|Treatment X|||40.19***|||40.19***|12.300|
|Decision number|||(13.83)|||(13.83)|(19.82)|
|Treatment X||||23.96||<br>44.3|<br>44.3|
|Risk preference||||(151.1)||(151.9)|(151.9)|
|Treatment X|||||-1.560|-1.608|-2.516|
|Religiouspreference|||||(1.39)|(1.41)|(1.70)|
|Religious preference X|||||||0.339***|
|<br>Decision number|||||||(0.11)|
|Treatment X|||||||0.140|
|Religiouspreference X Decision|number||||||(0.15)|
|Constant|2930.1***|2962.2**|3092.8***|2992.3**|2946.1**|3131.9***|3311.7***|
||(98.90)|(1195.20)|(1196.00)|(1215.40)|(1193.90)|(1214.60)|(1216.00)|
|Additional Controls|NO|YES|YES|YES|YES|YES|YES|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. The baseline is where the fund with fixed returns and risk is the traditional fund, the treatment is where the fund with fixed returns and risk is the socially responsible fund. * 10%, ** 5%, *** 1% significance level. Additional controls include gender, age, financial literacy, investing experience, confidence that the charity was paid in line with instructions, and clarity of instructions (see table A.6 in the appendix B). Appendix A defines the main variables. Appendix B contains the full set of results.
**_Table 9. Relationship between ESW/ASA and IEG outcome rating by project group_**

|||**Dependent varia**<br>**By lendi**|**ble: IEG 1-6 outco**<br>**ng source**|**me rating**<br>**By count**|**ry group**<sup>**2**</sup>|
|---|---|---|---|---|---|
||**Overall**|**IBRD**|**IDA**|**FCV**|**UMIC**|
|Dummy for any ESW/ASA in<br>|0.120**|0.065|0.145**|0.182|-0.012|
|the corresponding GP<br>within 3 years before|(0.051)|(0.095)|(0.059)|(0.136)|(0.105)|
|<br>project approval<sup>1</sup><br>|2.346***|2.197***|2.453***|2.184***|3.555***|
|Constant|(0.278)|(0.462)|(0.369)|(0.743)|(0.708)|
|Observations|2,374|1,031|1,343|301|324|
|Adjusted R-squared|0.081|0.093|0.077|0.035|0.034|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS and controlling for region, sector, CPIA score of project country at approval, change in the CPIA score over project lifetime, original commitment amount of project and the exit year. Sub-sample of projects within exit fiscal year 2004-2016. Specific project groups displayed in the first row consisting of: International Bank for Reconstruction and Development (IBRD); International Development Association (IDA); fragile, conflict and violence settings (FCV); upper-middle income countries (UMIC); Equitable Growth, Finance and Institutions (EFI); Human Development (HD); Sustainable Development (SD). (1) Considers only Economic and Sector Work/Analytical and Advisory Services (ESW/ASA) work executed by the same Global Practice (GP) for a given country. (2) Country Groups are not mutually exclusive nor collectively exhaustive, with FCV and UMIC selected only for reference. Designation as FCV is determined by project country being on the WB Harmonized List of Fragile Situations in Fiscal Year of Approval.

30
Table 1: Frequency of shocks Proportion of households reporting shock:

||2005|2011|
|---|---|---|
|Food price|0.02|0.19|
|Drought|0.10|0.05|
|Crop damage|0.09|0.03|
|Job loss|0.01|0.00|
|% Crop loss (from LEAP)|23.49|13.82|
|Average monthly increase in CPI|0.2|1.7|
|Times Woreda experienced over 50% crop loss (past 15 years)||1.59|

Notes: Shocks are self-reported, except for LEAP and CPI data.

in this paper is on vulnerability to absolute poverty, rather than to downturns in the wellbeing indicator (Kamanou and Morduch, 2002; Dutta et al., 2011) or vulnerability as welfare loss due to increased variance (Ligon and Schechter, 2003). We consider our method to be complementary to qualitative studies, or other approaches. We focus on the expected Foster Greer Thorbecke (FGT) measures that are the vulnerability analogues of the static poverty measures used in national policy, combining these with the CalvoDercon measure which is more sensitive to the threat of extreme poverty. Our focus is deprivation in the space of household consumption, suitably spatially deflated and adjusted for household composition.<sup>9</sup> The first applied vulnerability measures that calculated expected poverty and probability of future poverty were developed around 15 years ago and are still most widely used (see Hoddinott and Quisumbing (2003) for a review). Empirically, household poverty is modeled as the realization of a stochastic process that generates consumption outcomes. If realized consumption falls below a poverty line _z_ , the household is classified ex-post as a poor household. Vulnerability measurement, being ex-ante by definition, thus needs a poverty measure, a poverty line, and a prediction of future consumption based on assumptions about the stochastic process generating consumption. Household poverty is defined in the above papers using the FGT family of poverty indices (Foster et al., 1984):<sup>10</sup> With _α_ = 0 being the headcount, 1 the poverty gap and 2 the squared gap. Chaudhuri et al. (2002) therefore defined vulnerability as the expectation (i.e. the predicted value) of poverty:

> 9Other perspectives have also been applied but are not used in this paper. For example, understanding the importance of assets for protecting livelihoods under stress, notably Moser (1998), and developed in economics through the work of Carter and Barrett (2006); Echevin<sup> ́</sup> (2013) and others. Dutta et al. (2011) and G ̈unther and Maier (2014) consider that a desirable property for a vulnerability measure is to reference it to the starting point of household welfare.

> 10For convenience in this section we omit the community and district subscripts.

8
skewed, with a large majority of caregivers perceiving their children to be more intelligent compared to other children in the community (Figure 1).

<u>Table 1. Cohort Characteristics, Madagascar, N=3,361</u>

||Mean(SD;range)|%(N)|
|---|---|---|
|Child Characteristics|||
|Age (months)|29.38 (5.15; 16-42)||
|Female||50.28 (1,690)|
|Birth Order|2.64 (1.25; 1-4)<br>||
|HAZ|-2.34 (1.05; -5.99-3.53)||
|WAZ|-1.56 (0.93; -4.94-2.45)||
|Stunted (HAZ < -2)||63.37 (2,130)|
|<br>Wasted (WHZ < -2)||4.08 (137)|
|Underweight (WAZ < -2)||30.17 (1,014)|
|ASQ-I age-standardized score|0.00 (1.23; -4.93-4.16)||
|ASQ-I ranked scores [Whole sample]|||
|≤ -1.42||14.31 (481)|
|-1.41 to -0.62||14.28 (480)|
|-0.61 to -0.10||14.04 (472)|
|-0.11 to 0.34||14.31 (481)|
|0.35 to 0.75||14.25 (479)|
|0.76 to 1.27||14.25 (479)|
|≥ 1.28||14.55 (489)|
|Perceived Child Intelligence Ladder|||
|1||0.92 (31)|
|2||3.01 (101)|
|3||5.98 (201)|
|4||13.86 (466)|
|5||22.97 (772)|
|6||25.59 (860)|
|7||27.67 (930)|
|Caregiver Characteristics|||
|Age (years)|28.57 (7.78; 15-83)||
|Education|||
|Did not attend school||25.02 (841)|
|Primary or less||52.93 (1,779)|
|Secondary or higher||22.05 (741)|
|CESD Depression score|17.15 (3.40; 10-29)||
|<br>Perceived influence on child's intelligence|||
|None||14.67 (493)|
|Some||25.59 (860)|
|A lot||59.74 (2,008)|
|Household Characteristics|||

11