rafmacalaba/datause-encoder-data huggingface dataset:
food and beverage manufacturing, fertilizer, pharmaceutical, wholesale and retail, and food services sectors. Table A.4 details the list of NAICS 6-digit codes. We defined the industry broadly given the fact that pharmaceutical companies source many of their ingredients from the agriculture sector, the growing size of the nutraceutical market, and the role of drug stores and general merchandise stores as retailers of processed food.<sup>9</sup> We eliminate a small number of links that are to be formed between the same en- reported tity and drop linkages that last for less than a day. From this sample, we create a yearly panel of active firm-to-firm connections categorized by relationship type. For the sample period from 2014 to 2022, the dataset contains about 160,000 supplier and customer links, involving more than 17,500 agriculture-related companies and approximately 26,000 nonagri partner firms. More than 4,500 of the agribusinesses are “actively covered”, that is they appear as reporting firms in the raw FactSet data.

In general, the number of firm nodes and links in the sample is increasing over time (see Figure A.9, left panel, and Figure A.10). The share of actively covered agribusinesses, non-reporting firm agribusinesses and non-agribusinesses among the set of nodes in a given year is fairly stable (see Figure A.9, right panel). More than 15,000 of the 17,500 are observed in the data. About 4000 nodes are agribusinesses during multiple years observed in all of the nine sample years, approximately 1700 of which are actively covered agribusinesses. Acknowledging the generally increasing size of our network sample, we report some results for the balanced set of firm nodes to study dynamics at the intensive margin.<sup>10</sup> While the FactSet database offers extensive coverage, it exhibits a potential sample bias towards larger, publicly listed companies due to its reliance on publicly available disclosures. Figure A.11 shows that public companies make up about 25% of all agribusi-

> 9The nutraceutical market refers to the industry centered around foods or food products that provide both nutritional and medicinal benefits. These products, known as nutraceuticals, combine aspects of nutrition and pharmaceuticals.

> 10We are aware that intensive margin link creation might still be influenced by extensive margins entrants and exits, and will keep this possible bias in mind in discussing results.

9
the 2010–11 survey increased from 26 percent during the period before the voucher program to 52 percent after the program launched (table 4). Although an increase in private facility births also occurred among women who had never used the voucher, the increase was modest—from 18 percent before the program began to 28 percent after the program began—representing an increase of 10 percentage points. This difference-in-differences estimate of 16 percentage points between voucher and nonvoucher users is statistically significant, with an odds ratio of 2.2 (1.3–3.8). Further, results from the multilevel logit model show that reductions in public health facility delivery and home-based births were significantly higher among voucher respondents compared with nonvoucher respondents. The significant increase in private facility births and similar reductions in home-based births among voucher respondents compared with nonvoucher respondents are further indications that the program improved access to facility delivery. However, no significant difference was seen in changes in the use of PNC, or attending four or more ANC visits between voucher and nonvoucher respondents.

Table 4 Percentage of Births to Voucher and Nonvoucher Respondents by Use of Maternal Health Services and Difference-in-Differences Estimates in Changes over Time between Voucher and Nonvoucher Clients 2010–11

|Indicator of<br>health service<br>utilization|Vou<br>respon<br>(%|cher<br>dents<br>)|Nonv<br>respo<br>(|oucher<br>ndents<br>%)|Differenc<br>differen|e-in-<br>ces|
|---|---|---|---|---|---|---|
||Before<br>program|After<br>program|Before<br>program|After<br>program|Percentage<br>points<sup>a</sup>|Odds<br>ratios<sup>b</sup>|
|Four or more|55|70|49|56|8|1.4|
|ANC visits|(_N_=|(_N_=|(_N_=|(_N_=||(0.9–|
||183)|459)|779)|1,281)||2.2)|
|Place of|(_N_=|(_N_=|(_N_=|(_N_=|||
|delivery|175)|434)|708)|1,184)|||
|Home|30|17|38|31|−6|0.6*<br>(0.3–<br>0.9)|

23
**Table 15: Teach: Student test scores regressed on classroom observation variables (level 1) and teacher test scores**

|||All te|achers||Tea|chers with|test score <|=0|T|eachers wit|h test score|>0|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Share of time teaching|0.7390**|0.343|0.5026|0.137|1.7094***|1.226***|1.1430**|0.656|-0.3998|-0.307|-0.4247|-0.285|
||(0.3668)|(0.343)|(0.3759)|(0.321)|(0.3174)|(0.366)|(0.4249)|(0.416)|(0.4583)|(0.516)|(0.4532)|(0.490)|
|Share of time lost|-0.4146|-0.486|-0.5124|-0.485|0.8190|1.140|-0.3081|-0.0645|-0.3392|-0.475|-0.3212|-0.451|
||(0.4780)|(0.460)|(0.3647)|(0.391)|(1.8768)|(1.574)|(1.9505)|(1.600)|(0.3451)|(0.312)|(0.3587)|(0.366)|
|Classroom culture|0.0837|0.0407|||-0.0383|-0.407|||0.2768|0.263|||
||(0.1924)|(0.184)|||(0.2901)|(0.268)|||(0.2644)|(0.209)|||
|Instruction|-0.3458|-0.0894|||-0.9166***|-0.498**|||0.3884|0.472*|||
||(0.2135)|(0.198)|||(0.1912)|(0.192)|||(0.3346)|(0.250)|||
|Socioemotional Skills|0.6695***|0.483***|||1.1166***|1.239***|||0.4068*|0.134|||
||(0.1911)|(0.179)|||(0.2669)|(0.223)|||(0.2197)|(0.170)|||
|Teach PC1|||0.0880*|0.0795*|||-0.0064|0.0353|||0.1810***|0.141**|
||||(0.0497)|(0.0416)|||(0.0798)|(0.0694)|||(0.0664)|(0.0586)|
|Teacher Test Score|-0.0073|0.0189|-0.0198|0.0275|0.1585**|0.138***|0.2264***|0.177***|-0.1810|-0.328|-0.1812|-0.249|
||(0.0913)|(0.0777)|(0.1055)|(0.0782)|(0.0661)|(0.0478)|(0.0741)|(0.0620)|(0.3231)|(0.323)|(0.3058)|(0.320)|
|Observations|4,619|4,586|4,619|4,586|2,333|2,300|2,333|2,300|2,286|2,286|2,286|2,286|
|R-squared|0.0618|0.237|0.0307|0.229|0.1699|0.326|0.0691|0.282|0.0678|0.275|0.0655|0.270|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Teacher Test|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|F-Test: Observation vars.|0.0173|0.138|0.101|0.111|0.000|0.000|0.0758|0.425|0.0566|0.0159|0.0176|0.00330|
|F-Test: Student vars.||0.0382||0.0479||0.000||0.000||0.000594||0.000492|
|F-Test: Household vars.||0||0||0.000||0.000||0||0|
|F-Test: Teacher vars.||0.179||0.195||0.132||0.463||0.0224||0.0108|
|F-Test: School vars.||0.00310||0.000956||0.00374||0.00577||0.000148||0.000|

Note: Standard errors clustered at the teacher level in parentheses. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

55
borrowers, by effectively stopping access to capital, affects policy choices and the pace of adjustment in each country. However, even when reforms are adopted, economic outcomes take time to change in the “real” sectors and market perceptions can continue to move in ways that worsen outcomes on the ground, despite reform initiatives having been begun.

**Figure 1: Sovereign credit default swap (CDS) spreads (basis points)**

<!-- Start of picture text --> 2,000<br>25,000<br>1,600<br>20,000<br>1,200 15,000<br>800 10,000<br>400 5,000<br>0 0<br>Ireland Portugal Spain Greece<br>2007‐Jan 2008‐Jan 2009‐Jan 2010‐Jan MoU‐GRC (1st) MoU‐IRL2011‐Jan MoU‐PRT 2012‐Jan MoU‐GRC (2nd) MoU‐ESP 2013‐Jan 2014‐Jan 2015‐Jan MoU‐GRC (3rd) 2016‐Jan<br><!-- End of picture text --> Monthly data for 5‐year tenor. Values for Greece are on the right axis. Source: Bloomberg, CMA.

The interest differential with the German 10 year bund shows that spreads for the GIPS decreased dramatically until 2007 so that by that year, the interest rate differential was .28, .09, .21 and .09 respectively. Following the 2008 crisis, during the first part of 2009, the VIX index, reflecting global risk aversion and CDS spreads for GIPS had both fallen. Surprisingly perhaps, Greece’s risk premium, as measured by CDS spreads, was lower than Ireland’s until the end of 2009. Yet suddenly, from the end of 2009 until the middle of 2011, global risk aversion as measured by the VIX and CDS spreads for GIPS were moving in opposite directions, the latter having risen dramatically. These differentials were to rise to 21, 6.99, 9.05 and 4.35 during 2011‐2012. At the time that each of these countries faced rising risk premia, all four had something in common: debt of various types was high (relative to others in the EU/Eurozone) and increasing fast.

**Figure 2.a: Real GDP per capita                            Figure 2.b: Real GDP growth (%)**

<!-- Start of picture text --> 50,000 15<br>40,000 10<br>30,000 5<br>20,000<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014<br>10,000<br>‐5<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 ‐10<br>Greece Ireland Portugal Spain Greece Ireland Portugal Spain<br><!-- End of picture text --> Chain linked volumes, 2010 Euros. Values for Greece 2011‐2014 and Spain 2012‐2014 are provisional and 2014 value for Portugal is estimated.

Source: Eurostat.

_<u>The private credit boom and rise in private debt.</u>_ Fueled by global liquidity, financial sectors grew 5
# **<u>Table A1. Overview of contractual insurance after the conversion to DC</u>**

||ITP-ITPK<sup>a</sup>|SAF-LO<sup>a</sup>|Civil servants,<br>national<br>government<br>employees|Local and<br>county council<br>employees|
|---|---|---|---|---|
|**Agreement became**<br>**effective**|2007|1996|2003|1998|
|**First birth cohort covered/**<br>**fully covered**|Employees<br>born 1979 and<br>later are fully<br>covered|Employees<br>born 1932 are<br>covered (with a<br>transition rule).<br>Persons born<br>1968 and later<br>are fully<br>|Employees<br>born 1943 are<br>covered (with a<br>transition rule).<br>Persons born<br>1973 and later<br>are fully<br>|Employees<br>born 1938 are<br>covered (with<br>a transition<br>rule). Persons<br>born 1971 and<br>later are fully<br>|
|||covered|covered.|covered|
|**Age at which coverage**<br>**begins**|Age 25|Age 21|Age 23<sup>d</sup>|Age 21<sup>e</sup>|
|**Form of coverage**|||||
|1. Earnings up to the ceiling<br>in the public system (7.5<br>basic amounts<sup>b</sup>)|FDC plan with<br>a contribution<br>rate of 4.5%|FDC plan with<br>a contribution<br>rate of 3.5 %<sup>c</sup>|FDC plan with<br>a contribution<br>rate of 2.3 % +<br>2.0 % from an<br>FDC plan<br>preceding this<br>agreement<br>(KÅPAN).|FDC plan with<br>a contribution<br>rate of 3.5-4.5<br>%. (4.5 % for<br>all from 2010<br>following a<br>stepwise<br>transition.)|
|2. Earnings equivalent to<br>7.5-20 basic amounts<sup>b</sup>|Contribution<br>rate of 30%|FDC plan with<br>a contribution<br>rate of 3.5 %<sup>c</sup>|<br>NDB that<br>replaces 60 %<br>of final<br>earnings +<br>Kåpan|<br>FDC as above<br>plus NDB that<br>replaces 55 %<br>of final<br>earnings|
|3. Earnings equivalent to 20-<br>30 basic amounts<sup>b</sup>|Contribution<br>rate of 30%|FDC plan with<br>a contribution<br>rate of 3.5 %<sup>c</sup>|NDB that<br>replaces 30 %<br>of final<br>earnings +<br>Kåpan|FDC as above<br>plus NDB that<br>replaces 55 %<br>of final<br>earnings|
|4. Earnings above 30 basic<br>||FDC plan with|||
|amounts<sup>b</sup>|No coverage|a contribution<br>rate of3.5 %<sup>c</sup>|No coverage|No coverage|

a Insurance premiums paid by employers are tax deductible on earnings up to 30 basic amounts. Employees covered by the SAF-LO agreement are unlikely to have earnings above 20 basic amounts. b The basic amount is a unit used in social and private insurance in Sweden. The basic amount applicable in the present case was 45 900 SEK or 6851 USD in 2007, with an exchange rate of 6.7 SEK per USD. The ceiling in the public system was, thus, 51 381 USD in 2006.<sup>c</sup> An increase in the contribution rate to 4.5 % is a likely result of discussions at the time of this writing.<sup>d</sup> KÅPAN provides coverage from age 28 and full coverage requires 30 years of participation.<sup>e</sup> A full NDB supplement requires 30 years of coverage from age 28.

61
# **III. Poverty Trends**

III.1. Poverty headcount rates at a point in time

- <mark>The 2001 poverty line</mark>

In Rwanda, the 2001 poverty line was defined using data from EICV1, with a reference group including the bottom three deciles of the expenditure distribution. The COLI used to spatially and temporally deflate the consumption aggregate also used the consumption patterns of the bottom 60 percent. This generated a poverty threshold of RWF 64,000 in January 2001 prices, yielding a poverty headcount rate of 58.9 percent.

For two subsequent surveys, EICV2 (2005/06) and EICV3 (2010/11), poverty was estimated using the 2001 poverty line derived from EICV1, after deflating the nominal consumption expenditure in EICV2 and EICV3 to January 2001 prices and comparing them to the poverty threshold of RWF 64,000 per adult equivalent per year. Thus mathematically, poor households in EICV3 for instance were identified using the following inequality: where e����/�� denotes nominal consumption expenditure per adult equivalent of household _i_ in month _m_ ����/�� of EICV3, and π����� is the price index used to deflate nominal consumption expenditure from EICV3 survey months to January 2001 prices (see NISR, 2012). This method yielded a poverty rate of 44.9 percent in 2010/11. Going forward, the government decided to update the poverty line. This was done using EICV4 data.

<mark> The 2014 poverty line</mark> Typically, rapidly changing consumption patterns in a globalized world, combined with improvements in social and economic indicators of well-being warrant a periodic update of the monetary threshold established to evaluate changes in welfare. This is particularly true for developing countries, where the ‘relevance’ of a poverty threshold tends to erode with increasing access to markets, new product varieties and overall economic growth. This was also true in the case of Rwanda, where significant changes in the socio-economic structure of the country as measured by EICV4, necessitated an update of the poverty line.

In Rwanda, changes in consumption patterns between 2000-2014 were primarily reflected through a reduction in the share of food consumed by the poorest households (Engel’s law), decreases in household size (reflected in reduced fertility rates and falling dependency ratios), increases in asset ownership (ownership of more expensive assets like televisions and mobile phones increased in the bottom quintile), and improvements in child and maternal health (reduced stunting, wasting, infant and maternal mortality).<sup>6</sup> In addition, per capita GDP growth remained positive, and education and labor market outcomes also improved over time. All these changes that were reflected in the consumption patterns captured by EICV4 needed to be incorporated in a poverty measure intended for evidence-based policy making going forward.

> 6 See Rwanda Poverty Profile Report (2015).

8
<!-- Start of picture text --> norma50.210EmployedUnemployedDensityFigureGHQ12465HQ 1distributionGHQ12 by employment Figure 1 status<br>GHQ distribution by employment status<br>Employed Unemployed<br>0 5 10 15 0 5 10 15<br>GHQ12<br>Density<br>normal GHQ12<br>.6<br>.4<br>Density<br>.2<br>0<br><!-- End of picture text --> 8
rejected at 20 percent significance), and (2) the speed of adjustment to shocks was statistically significant. Markets were considered to be _weakly_ integrated with Juba if only (1) or (2) held.

For those markets that show some degree of integration, transmission of price shocks is incomplete and slow and highly heterogeneous by market and by product.

The pass-through coefficient, that shows the long run effect of a shock to prices in Juba, to prices in a given state capital, for a given product, varies substantially depending on the product and on the state capital. For example, a shock to groundnut prices in Juba, implying for example, a 1 percent increase, is associated with an increase in groundut prices in Rumbek of about one-third of a percentage point in the long run. However, if the same shock affects the prices of sorghum in Juba, transmission is almost complete: in the long run, sorghum prices in Rumbek will end up increasing by 0.84 percent. Figure 4 plots the distribution of the estimated pass-through coefficients across cities and products, reflecting heterogeneity in the extent of the transmission.

Similarly, the speed at which markets adjust prices after a shock in the capital city is heterogeneous, and often slow. Figure 5 plots the distribution of the estimated speed of adjustment to shocks that bring markets to temporary disequilibria, and also reveals substantial heterogeneity. For example, the aforementioned price adjustment in Rumbek after a 1 percent increase in sorghum prices in Juba will not be automatic. Our estimates of the speed of adjustment to equilibrium in that market for that product reveal that 45 percent of the adjustment will take place in the first month. However, the speed of adjustment to the same shock in Wau reveals that only 11 percent of the shock will be adjusted in the first period.

Figure 4: Distribution of estimated pass-through of shocks in Juba’s prices (all regions, all markets) Figure 5: Distribution of estimated speed of adjustment to equilibrium after a shock to prices (all regions, all markets) <!-- Start of picture text --> Pass-through Coefficient - Juba/All Products Speed of Adjustment Juba/All Products<br>0 .5 1 1.5 2 -1.5 -1 -.5 0<br>Pass through Speed of Adjustment<br>Source: Authors’ calculations based on WFP data. Source: Authors’ calculations based on WFP data.<br>2<br>1<br>1.5<br>1<br>Density .5 Density<br>.5<br>0 0<br><!-- End of picture text -->

# **3.5. Determinants of Price Differences**

As documented above, price differences for narrowly defined products are heterogeneous across the country, as well as the degree of integration of relevant regional markets. This stresses the importance of understanding the drivers of these differences. Understanding whether price differences, for example, are driven by distance, poor infrastructure, fuel costs, or other factors 14
_Brazilian spatial labor dynamics Page 36_

## Changes in transport costs to capital. 1995/1980

<!-- Start of picture text --> Changes in transport cost to capital - 95/80<br>-0.916 - -0.5<br>-0.5 - -0.259<br>-0.259 - -0.098<br>-0.098 - 0<br>N<br>W E<br>1000 0 1000 2000 Miles<br>S<br><!-- End of picture text -->

**Map 8**

# Change in transport cost to SP. 1995/1980

<!-- Start of picture text --> Changes in transport cost to SP - 95/80<br>-0.916 - -0.331<br>-0.331 - -0.197<br>-0.197 - -0.087<br>-0.087 - 0<br>N<br>W E<br>1000 0 1000 2000 Miles<br>S<br><!-- End of picture text -->

**Map 9**
one and nine years. Information on the rest of the population relevant for the analysis – those eligible but not participating and those with _Sisben_ scores above the threshold of eligibility— is obtained from a census of the poor that was collected between 1994 and 2003 used to compute the _Sisben_ proxy-means test. Both datasets are merged together to create the sample of analysis for the RDD.<sup>24</sup> In practice, the _Sisben_ poverty score predicts substantial but not perfect changes in the probability of receiving the treatment. In fact, the data show a significant discontinuity in the probability of assignment to treatment at the threshold of around 66 to 72 percentage points as presented in Figure 1 and Table 3.<sup>25</sup> Therefore, we perform a _fuzzy_ instead of a _sharp_ RDD. The average causal effect of this design is given by the ratio of the jump in the outcome variable at the threshold to the jump in the probability of participation in FA also at the threshold. This is equivalent to an instrumental variable setting in which the average effect of the treatment is obtained from compliers -- individuals whose participation is affected by the cutoff. To check the sensitivity of the results to different specifications, estimates of program impacts based on the RDD are computed using different parametric functional forms and nonparametric procedures. Besides, to restrict the sample close to the cutoff, optimal bandwidths for the nonparametric analysis were also estimated using Imbens and Kalyanaraman (2010) methods.

Our first stage regression formally tests if the probability of treatment for individual _i_ , in municipality _m_ , and in year _t_ changes discontinuously at the cutoff point. We estimate different polynomial specifications of the model allowing the regression function to differ on both sides of the threshold as follows:

> 24 Given that the information system of the program does not  reports SISBEN scores for participant families, uniform scores for the whole sample are calculated by using  the proxy means testing  algorithm  and the information from the Census of the Poor 1994-2003. 25 This may happen for at least two reasons. First, take up rates were in the order of 65 percent so participation is not universal among the eligible population. Second, households above the threshold may lobby with local authorities to gain access to the program. Concerns of nonrandom sorting that could arise from this or other gaming behaviors to influence the _Sisben_ poverty index score are discussed in the robustness section.

19
**Figure 6  Labor Productivity by Firm Ownership and Size, 2001**

<!-- Start of picture text --> A. Productivity a) by Firm Ownership<br>Private<br>Privatized<br>Mixed<br>State<br>-80 -60 -40 B. Productivity -20 0 a) by Firm Size 20 40 60 80 100<br>Large<br>Medium<br>Small<br>Micro<br>-50 -40 -30 -20 -10 0 10 20 30<br><!-- End of picture text --> a) Percentage deviation from the mean. Source: Annual Survey of Enterprises, 2001; author’s calculations.

To sum up, there is a positive relationship in Moldova between labor productivity and employment growth. High productivity firms create jobs, while low-productivity firms eliminate jobs. High productivity firms tend to be privately owned and small. Publicly owned and privatized firms are lagging behind and will need to shed labor in order to improve their competitiveness. To counteract this negative employment effect of 33