rafmacalaba/datause-encoder-data huggingface dataset:
FIGURE 3: DATA COLLECTED FROM PROCURING ENTITIES AND E-PROCUREMENT SYSTEM19 <!-- Start of picture text --> 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18<br>Number of Manual<br>3953<br>Tenders in Dataset 3737<br>1952<br>424<br>248<br>5<br>Number of e- 87,912<br>procurement<br>Tenders in Dataset<br>45,488<br>32,024<br>17,810<br>8,045<br>15 491<br>Number of firms 56058<br>registered in the e-<br>GP system 40472<br>25996<br>17271<br>9698<br>285 1078<br><!-- End of picture text --> Source : Data collected from e-procurement system and procuring entities 39. The survey teams for manual tenders also conducted in-person interviews with about 600 procuring entity officials from February to June 2018. This additional survey served to capture district officials’ perceptions of the e-procurement system and to obtain measures of the costs associated with administering the procurement process manually vis-à-vis electronically. Each procuring entity office at the district level is headed by an executive engineer (XEN). In addition to the executive engineer, the procuring entity offices also have Sub-divisional Engineers (SDE), Assistant Engineers (AE) and Sub-Assistant Engineers (SAE), who work in conjunction with the XEN to dispose of the duties of the procuring entity office.

40. Furthermore, a survey of about 600 firms participating in public works contracts of the 3 agencies was done from June to August 2019, to capture firm transaction costs. To sample firms for the survey, the population of firms were stratified into 4 strata: (i) eGP winners (firms with revenue from eGP tenders at least 3 times more as compared to manual) – 175 such firms were selected; (ii) Manual winners (firms with revenue from manual tenders at least 3 times more as compared to eGP) – 175 such firms were selected; (iii) Always winners (firms that are not in the eGP or manual winners and had revenue greater than median

> 19 We did not apply any weighting to the survey data in the subsequent calculations.

14
<u>Table 3: Regression results.</u> OLS TSLS TSLS First Stage ~~In (A) In (A) Ln(Stds) ISO/Stds_ — Ln(1+t)~~ Ln(Stds) -0.763*** — -0.587** [0.152] [0.262] ISO/Stds 1.535*** — 1.816*** [0.334] [0.420] Ln(GDP) 0.056 0.885 -0.022 -0.006 1.384*** [0.500] [1.848] [0.013] [0.004] [0.378] Ln(GDPPC) 0.379 -0.394 0.036* 0.011 -1.278*** [0.470] [1.665] [0.021] [0.007] [0.334] Ln(1+t) -0.019 -0.617 [0.039] [1.156] ATC2 -0.112 0.028 -1.082*** — -0.04 0.024 [0.115] [0.214] [0.194] [0.079] [0.065] ATC3 -0.095 0.052 -0.204 -0.091* 0.052 [0.087] [0.130] [0.124] [0.046] [0.040] Quota -0.347*** = -0.349*** — -0.001 0 -0.005 [0.060] [0.082] [0.001] [0.001] [0.124] Quota*ATC2 0.006 -0.04 -0.001 -0.001 -0.079 [0.096] [0.150] [0.002] [0.001] [0.138] Quota*ATC3 0.017 0.061 0.002 0.001* 0.073 [0.176] [0.172] [0.002] [0.001] [0.134] Constant -3.501 -14.397 -33.228* 10.036 -27.295** [10.690] [20.457] [18.325] [7.226] [10.580] Ln(VA) -1.837** — -0.189 0.360** [0.697] [0.277] [0.168] Ln(Emp) 4.329*** — -0.553 -0.064 [1.267] [0.520] [0.351] Ln(Herf VA) -4.653*** — -0.341 0.820** [0.825] [0.345] [0.311] Ln(Herf Emp) 6.710*** = 1.051 -0.109 <u>[2.051] [0.770] [0.420]</u> Obs. 3607 3607 3607 3607 3607 R2 0.68 0.65 0.99 0.97 0.77 Exogeneity (F’) 14.59*** Overidentification (x?) 0.72 HO: Instr. = 0 (F’) 13.78*** = 11.63*** 6,23 ***

1. All models include fixed effects by exporter, sector, and year. Robust standard errors adjusted for clustering by sector-year appear in square brackets under the coefficient estimates. Statistical significance is indicated using * (10%), ** (5%), and *** (1%).

2. Exogeneity and overidentification tests are the regression-based tests outlined in Wooldridge (2002, pp. 118-124). The F-test is of the null hypothesis that all instruments are jointly zero.

30
_Notes:_ Means calculated using sample-weighted individual-level data from the 2014-17 Gallup World Poll. Standard errors are in parentheses. Asterisks indicate whether the difference in means is statistically significantly different from low-income countries at the 0.01 level. Low-income countries are defined as those with a gross national income (GNI) per capita of $995 or less, lower-middle-income countries are those with a GNI per capita of more than $996 but less than $3,895, and upper-middle-income countries are those with a GNI per capita of more than $3,895 but less than $12,055, and high-income countries are those with a GNI per capita of more than $12,055.

The remaining results are from the regression model discussed in section 2. Table 2 examines how food insecurity affects trust in national institutions (vertical trust, column 1) and trust in friends and family (horizontal trust, column 2). The estimates presented are the average marginal effects estimated by the linear model with country and year fixed effects.

Table 2: The effect of food insecurity on vertical and horizontal trust.

||Tru|st|
|---|---|---|
|Variables|Trust in national institutions|Trust in friends and family|
|Food insecurity|-0.027***|-0.117***|
||(0.004)|(0.005)|
|Female|-0.010***|0.013***|
||(0.003)|(0.002)|
|Age|-0.004***|-0.008***|
||(0.000)|(0.000)|
|Age squared|0.000***|0.000***|
||(0.000)|(0.000)|
|Number of adults|0.001|0.008***|
||(0.001)|(0.001)|
|Number of children|0.004***|-0.001*|
||(0.001)|(0.001)|
|Married or Domestic partner|0.016***|-0.001|
||(0.002)|(0.003)|
|Separated Widowed or Divorced|0.001|-0.012***|
||(0.003)|(0.003)|
|Secondary education|-0.046***|0.056***|
||(0.004)|(0.003)|
|Post-secondary education|-0.039***|0.082***|

10
Table 4: Descriptive Statistics by Subsample

|||||**Holdout **|**Samp**|**les**|
|---|---|---|---|---|---|---|
||**Estim**|**ation**|**Anan**|**tapur**|**E. G**|**odavari**|
|Choice _j_|_N_|_lj_|_N_|_lj_|_N_|_lj_|
|_U_ =unconstrained|436|5.10|184|3.20|31|10.23|
||[0.26]|(3.89)|[0.51]|(2.55)|[0.08]|(7.21)|
|_A_=autarky|381|6.29|170|3.82|104|13.00|
||[0.23]|(4.11)|[0.47]|(2.40)|[0.26]|(26.94)|
|_L_=leasing|91|2.45|5|0.90|12|6.78|
||[0.06]|(2.43)|[0.01]|(0.60)|[0.03]|(7.56)|
|_C_ =seasonal contract|400|2.80|1|1|238|3.61|
||[0.24]|(2.45)|[0.00]|_−_|[0.59]|(3.07)|
|_P_ =per-irrigation sale|338|1.77|2|1.87|21|3.42|
||[0.21]|(1.48)|[0.01]|(1.23)|[0.05]|(2.29)|
|Total|1646|_−_|362|_−_|406|_−_|

_Notes:_ Sample means (standard deviations). Proportions in square brackets.

Figure 4: Prevalence of spot contracts and uncertainty <!-- Start of picture text --> 1st 99th<br>pctile pctile<br>0 .1 .2 .3 .4<br>CV end-of-season flow<br> 95% CI local fit<br>.8<br>.6<br>.4<br>Pr(P | P + C = 1)<br>.2<br>0<br><!-- End of picture text -->

_Notes:_ Nonparametric regression of per-irrigation arrangement indicator ( _P_ ) on _CV_ using sample of 1000 borewells engaged in any groundwater sale (either _P_ = 1 or _C_ = 1). Endpoints of confidence interval approximated by logit transformation.

21
Table D1: Vessel characteristics in the six fishing seasons of 2017, 2018, and 2019

||All vessels<br>(1)|Large-frm vessels<br>(2)|Medium-frm vessels<br>(3)|Singleton vessels<br>(4)|
|---|---|---|---|---|
|A. Average|tons landed|per season|||
|Minimum|3.11|105.45|14.64|3.11|
|Mean|2607.41|6311.44|1842.81|903.56|
|Median|1324.49|6004.34|1303.62|722.86|
|Max|22261.64|22261.64|10852.99|9389.83|
|B. Average|number of a|ctive vessels per sea|son||
|Minimum|708|178|263|256|
|Mean|730.17|182|276.67|271.5|
|Median|731|182|278.5|269|
|Maximum|750|185|283|288|
|C. Vessel le|ngth (m)||||
|Minimum|11.23|15.85|11.3|11.23|
|Mean|26.05|41.64|24.06|17.62|
|Median|20.9|40.48|21.72|17.05|
|Max|77|77|53.75|42.57|

Notes: Large-firm vessels are vessels that belong to one of the seven largest firms, which each own at least 19 vessels. Medium-firm vessels belong to firms that own 2 to 10 vessels. Singleton vessels belong to a firm that owns only one vessel. Data are for the North-Central zone only. Landings data are used to calculate the number of active vessels each season. Landings and vessel length data are from PRODUCE (Englander, 2022).

Large-firm vessels account for 78% of the closures policy treatment effect, which is greater than their share of total juvenile catch in the fishery (70%). Large-firm vessels also catch a higher fraction of juveniles and underreport percentage juvenile by a greater amount than medium-firm or singleton vessels (Table D2).

Second, I find that above-median-length vessels have larger treatment effects than belowmedian-length vessels. The increase in juvenile catch because of the closures policy is 59% for above-median vessels, compared to 23% for below-median vessels. Above-median vessels account for 91% of the closures policy treatment effect, which is greater than their share of total juvenile catch in the fishery (83%).

It is difficult to determine whether above-median-length vessels respond more to closures because they are large, so have more flexibility in the length of their fishing trips, or because 43
The rest of the paper is structured as follows. Section 2 looks at the dynamics in the distribution of ownership of durable goods over time and across wealth classes. Section 3 analyzes whether the quantity of durable goods across wealth classes converged or diverged during 1995-2003. Section 4 investigates the differences in the quantity of durable goods across ethnic groups and regions (capital city/other urban/rural areas) in Bulgaria, and analyzes the respective convergence/divergence patterns. Section 5 develops the model which allows one to decompose the observed changes in the quantity of assets over time into the impacts of the “growth” vs. “convergence” components of changes in consumption. Section 6 presents the empirical results. Section 7 concludes by highlighting the main findings of the paper.

# **2. TRENDS OVER TIME AND ACROSS WEALTH CLASSES**

We first analyze the trends in the stock of durable goods owned by Bulgarian households by looking at the major durable goods comparable across surveys (1995, 1997, 2001, and 2003). The variable of interest is the average (across all households in the survey) quantity of a given durable good per household. The rationale for preferring this variable to a simple indicator whether a household has at least one asset of certain type is that many households possess more than one given durable good, and that rising prosperity may translate into ownership of more than one of the same durable good. For instance, a household whose income increased appreciably may purchase an extra TV set while still keeping the existing old one.

The analysis of the trends in average quantities per household of major assets over time (Table 1) suggest that durable goods can be divided into three broad categories: (i) “modernization” goods that registered a statistically significant increase over time, such as a freezer, an automatic washing machine, a dishwasher, a color TV, a satellite antenna, a personal computer, and a car;<sup>7</sup> (ii) “obsolete” goods that show a tendency to get out of use over time, such as a manual/electric

> 7 For cars the statistically significant increase only happened between 2001 and 2003. The average per household quantity of cars in 2001 was not significantly different from that in 1995.

- 5 -
during the same period.<sup>20</sup> We conjecture that such rapid growth in young workers’ wages could have led to an increased participation of these workers in Paraguayan labor market, thus increasing the labor wedge. Capital wedge summarized in equation (2) can be interpreted in a similar manner. Higher would imply a lower effective tax on capital, , resulting in higher investment and thus kk,tt benefiting GDP growth in Paraguay. This wedge, therefore, captures financing constraints 1 −ττ kk,tt affecting firms’ investment decisions. In our benchmark economy capital is the only asset available ττ for saving. Therefore, equation (2) also characterizes the optimal consumers’ intertemporal consumption-savings decision. Thus, the capital wedge also captures the liquidity constraints affecting these decisions. In effect, we can think of the capital wedge as reflecting the kk,tt relative importance of the two types of constraints. 1 −ττ As we discussed above, an increase in , interpreted as a lower effective tax on capital, ), will be associated with a fall in financing frictions facing firms. On the other hand, an kk,tt increase in can be interpreted as a lower effective return on savings to households and 1 −ττ kk,tt thus will be associated with an increase in liquidity constraints facing them. (ττ kk,tt 1 −ττ We find that the capital wedge shows some secular increase over time, although this increase is accompanied by a significant volatility around the trend. The fact that the wedge is secularly increasing is indicative of the improved access to financing faced by firms, relative to consumers. Furthermore, we find that a substantial part of volatility in the capital wedge is driven by the consumer (consumption growth) side, suggesting that financing constraints facing households remain pronounced and time-varying.

One way to evaluate this result is to look at the dynamics of private credit to businesses and households. Unfortunately, we only have data available for aggregate credit to the private sector. We find that during 1994-2011 its stock (as a ratio of GDP) has expanded by 35 percent, suggesting an improvement in credit market conditions in Paraguay during this period.

Table 7 provides additional evidence on the improvements in credit conditions in Paraguay during 2004-2012. It reports the number of individuals and firms listed in a public credit registry or private credit bureau with information on their borrowing history from the past 5 years. The number is expressed as a percentage of the adult population (the population age 15 and above according to the World Bank’s World Development Indicators). We also include the recovery rate measured as cents on the dollar recouped by creditors through reorganization, liquidation or debt enforcement (foreclosure) proceedings.

20 These numbers are for wages measured in US dollars.
# **ANNEX 1: FIGURES AND TABLES**

Figure A1: Rainfall and prices, Burkina Faso and Ghana <!-- Start of picture text --> Burkina Faso Ghana<br>Rainy season Dry season<br>Rainy season Dry season Cassava Maize<br>Maize Millet Plantain_Apentu Rice_Local<br>Sorghum Yam<br>Source: Authors’ calculation using VAM and TRMM data<br>Figure A2: Rainfall and prices, Mali and Niger<br>Mali Niger<br>Rainy season Dry season Rainy season Dry season<br>Maize Millet Beans Maize<br>Rice_Imported Rice_Local Millet Rice_Imported<br>Sorghum Sorghum<br>2008m7 2009m7 2010m7 2011m7 2012m7<br>2000m72001m72002m72003m72004m72005m72006m72007m72008m72009m72010m72011m72012m7<br>2003m7 2004m7 2005m7 2006m7 2007m7 2008m7 2009m7 2010m7 2011m7 2012m7 2000m72001m72002m72003m72004m72005m72006m72007m72008m72009m72010m72011m72012m7<br>300 250 200<br>250<br>200<br>150<br>200 200<br>150<br>100<br>150 Rainfall(mm)<br>Rainfall(mm) 100<br>100<br>50<br>100 Average nominal price(XOF/KG) 50 Average nominal price(GHS/unit*)<br>0 0<br>0 50<br>150 500 100 600<br>400 80 500<br>100 60 400<br>300<br>Rainfall(mm) Rainfall(mm) 40 300<br>50 200<br>Average nominal price(XOF/KG) 20 200 Average nominal price(XOF/KG)<br>0 100 0 100<br><!-- End of picture text --> Source: Authors’ calculation using VAM and TRMM data Figure A2: Rainfall and prices, Mali and Niger 26
specifically targeted towards retirement planning, which often take the form of seminars, seem to indicate that they can be effective in increasing retirement savings, although there is little experimental evidence for this so far.

For instance, Bernheim and Garrett (2003) use a household survey to analyze the effect of employer-based financial education on the savings behavior of employees, both in general and for retirement. Their cross-sectional analysis finds positive correlation between education programs and savings, with sufficient controls to suggest that the relationship is causal. Lusardi (2003) likewise uses survey data to examine the impact of attending retirement seminars on savings behaviors, specifically among older households. The study finds positive effects on saving behavior, particularly for those with lower education and low initial savings. Clark et al. (2004) evaluate the impact of attending financial education seminars on desired retirement age and expected levels of retirement income, using a pre-test post-test survey. They find changes for both retirement goals and stated savings behavior, particularly for women.

Duflo and Saez (2003), one of the few randomized impact evaluations of any financial education programs in the U.S., demonstrates the importance of peer effects in retirement preparation, and perhaps in the take-up of financial products more generally. They study the effects of providing information to university employees about a benefits information fair on both fair attendance and their subsequent enrollment in a Tax Deferred Account (TDA) retirement plan. The fair attendance and TDA enrollment rates of both the treated individuals (who were also offered a small monetary reward) and their departmental colleagues significantly increased, suggesting not only treatment and motivational reward effects, but also social network effects. Their conclusion is that peer influence likely played an important role in individual’s decision to obtain information about TDAs, and in conjunction with this information, potentially also on their subsequent decision to enroll in one.

Individuals planning for retirement may also be expected to be literate in investment strategies. However, the provision of investment information may or may not have impact on how they actually invest. Hung and Yoong (2010) conduct a randomized experiment to analyze the effect of the provision of financial advice on a hypothetical portfolio allocation task. They find that individuals who solicit advice improve their investment performance (despite negative 23
literature has had mixed results. The consensus, if there is one, is that leakage exists but for local pollutants such as SO2 and particulate matter it is probably small. However, these results may not be a useful guide to carbon leakage. Because of the importance of energy in the production process and the aggressive policies for carbon reduction needed to tackle climate change, abatement costs may be higher for a wider range of industry than has been the experience for other pollutants. Most estimates of carbon leakage across countries come from CGE models and these typically range from 5% to 20% (see for example Elliott et al. 2010).

From the perspective of newly industrializing countries that choose not to be constrained by an international agreement on climate change, leakage raises a number of concerns. First, the specter of carbon leakage is a powerful rallying point for those opposed to joining an agreement aimed at reducing carbon emissions. That is, without a set of policies aimed at mitigating the potential for carbon leakage, it is unlikely that many high-income countries will commit to a serious program aimed at reducing their own carbon emissions. Since estimates suggest that the expected costs of climate change will fall heavily on low-income countries, this is a cause for concern for developing countries.

Second, carbon leakage or pollution haven effects can make it more difficult for a country that wants to move to a greener growth path because of its effect on comparative advantage. One channel for leakage is via the effects on fossil fuel prices. A reduction in carbon emissions by a coalition of countries would lead to a fall in demand for fossil fuels, thus reducing their price. This would increase the relative cost differential between green and dirty technologies for countries outside the coalition, and thus make a green growth strategy less attractive. Another channel of leakage is via firm relocation: if pollution-intensive industry relocates from coalition to non-coalition countries, it could squeeze out cleaner industry via competition in input markets. The net effect of leakage may well be to increase growth rates in countries outside the coalition in the short run. Whether or not this would lead to long run benefits is difficult to determine. If lock-in 35
Despite the pre‐monsoon measures, when the flooding happens every year, the families have to undertake immediate responses in terms of shifting assets to higher elevation, protecting durable assets, documents, jewelry, furniture, etc. As seen in Table 8, the non‐poor households shift assets and protect durables and jewelry in more numbers than the poor families since their ownership would be more among the non‐poor.

Table 8: Immediate response to floods (% of households)

||BPL|Poor|Non‐poor|
|---|---|---|---|
|Shift assets to higher elevation|75.0|77.0|83.7|
|Protect durable assets|80.0|91.8|95.3|
|Protect documents|90.0|94.3|95.3|
|Protect jewelry|85.0|79.5|90.7|
|Protect vehicles|25.0|17.2|18.6|
|Protect small furniture and utensils|60.0|64.7|62.8|

Source: Calculations based on data obtained from 200 households

# 4.3.2 Structural responses

After the floods of July 2005, many households have also undertaken structural measures to protect their assets from floods. Some of the responses on structural measures obtained from the survey of 1,168 households are listed in Table 9 below. The poorest households have had to undertake major repairs and reconstruction work after the damage done by July 2005 floods. Further, the measures have been undertaken with the hope that they will provide effective long‐term solutions to protect against floods or reduce the impacts of floods. Most of these households have reported obtaining loans to undertake these measures, in the absence of insurance or other compensation mechanisms.

Table 9: Measures undertaken by households after July 2005 event

|**Measures after 2005 floods**|**BPL (< Rs.**|**LIG (Rs.**|**MIG (Rs.**|**Higher MIG**|
|---|---|---|---|---|
||**5000)**|**5000‐**|**15001‐**|**(> Rs.**|
|||**15000)**|**30000)**|**30000)**|
|Increasing height of surrounding plot|57.5|52.9|59.4|73.3|
|Reconstruction within the house/parking|14.5|12.2|12.1|6.7|
|Repairing & elevating electrical meters|45.1|30.1|34.7|26.7|
|Repairs inside house to elevate furniture|45.1|40.2|54.0|80.0|
|Repairs inside house to elevate electronic|44.6|32.6|37.1|26.7|
|gadgets|||||
|Repairing/ modifying toilets|8.3|5.3|11.3|13.3|

Source: Calculations based on survey of 1168 households Besides household efforts, cooperative housing societies have also undertaken structural measures as shown in Table 10. Many poor and most non‐poor households in Mumbai organize themselves into housing societies. A typical housing society has a number of families residing in apartments and 22