rafmacalaba/datause-encoder-data huggingface dataset:
IV. **Empirical Findings** We can now examine our findings. Table 1 presents the results of Ordinary Least Squares (OLS) estimates of the model. The observations for the twenty-five countries over the years 1992-97 have been pooled together.1<sup>8</sup> The lagged dependent variable is introduced among the regressors for two reasons: to eliminate serial correlation and, second, because the values of the variable _LIBERALIZATION,_ at each moment, are expected to depend on those from the previous period, since the liberalization index assesses the progress of the economic reform process, not its pace of change.

Our estimates show that _FREEDOM_ has a significant, positive impact on the liberalization process. However, the coefficients of the dummies _FREE_ and _NOTFREE,_ although they have the expected sign, are not significant, suggesting that it is not the absence (or presence) of freedom that has prevented (or helped) the transition to the market. It indicates on the contrary that the two processes of political and economic liberalization in the CEE and FSU countries have been strongly complementary. Note also that initial freedom **_(INITFREEDOM)_** always has a significant coefficient, confirming our hypothesis that democracy has been an important precondition for reformn implementation.

The coefficients of the government instability variables are not significant. _COHESION_ has a negative, slightly significant coefficient. Instead, **_COHESJDEOL_** has a positive, significant coefficient, suggesting that govemment cohesion has favored the transition, but only when the executive was not ran by a former communist party. The coefficients of the dummy for war tom countries always have the expected sign and are significant.

The significant, positive coefficient of the variable _AID_ indicates that external help has played an important role in the liberalization process, possibly because of the conditionality attached to programs involving foreign financial assistance.<sup>19</sup> However,

> 18 The choice of the 1992-97 time period has been dictated by data availability and reliability and by the disappearance of the Soviet Union by 1992, leading to the creation of independent and sovereign countries. Data on external aid are available only until 1996 so, in the regressions which include this variable, observations are for the period 1992-96.

> 19 The World Development Indicators data on aid do not distinguish among different types of assistance (programs, food aid, emergency assistance, etc.), each of which has diverse characteristics and different effects on the economy.

11
21 legislatures when legislatures become more fragmented, the relevant issue is the fragmentation of government-allied parties, on which the DPI has information.

# 5. Electoral Rules

The incentives of political decision makers, and their ability to act independently of each other, depends in large measure on the electoral rules under which they operate. Among other effects, electoral rules influence whether candidates have an incentive to pursue individual or party interests; whether small and single-interest parties have an incentive to coalesce or to remain independent; and whether party leaders exercise significant or weak influence on party candidates. Each of these and other, similar electoral characteristics have a bearing on whether legislators are more or less permeable to special interests.

One electoral rule that affects responsiveness to narrow interests is the Mean District Magnitude (MDM). The MDM is defined in the database as the average number of representatives (rounded to the nearest whole number) elected by each electoral district in a country.! Higher values of MDM (combined with proportional voting rules) reduce the incentives of parties to coalesce and allow parties representing exclusively narrow interests the chance to gain a voice in a government coalition. MDM is coded for up to two legislative chambers (House and Senate), if they have elected members. The MDM varies from one in the United States or the United Kingdom to as many as 153 for the Congo.

|

A second electoral characteristic that the data set captures is whether the districts of the upper house are states or provinces. This is particularly useful in studies of federalism: the

> 1 Cox (1997, p. 208), argues that the median district magnitude is superior, conceptually, but data on distribution of seats across constituencies is not available from our sources and Cox also acknowledges that empirically, the distinction has not proven important.
Berger and Udell (2006) suggest that financing forms such as leasing or factoring could ease financing constraints of SMEs, as they are based on the underlying assets and cash flows rather than borrowers’ financial history. But Klapper (2006) argues that these alternate financing forms also require an appropriate legal framework that supports its use. Brown, Chavis, and Klapper (2010) show that a strong institutional environment is associated with the greater use of leasing and only 6% of firms in low income countries use leasing compared with 34% of firms in high income countries. Overall, as suggested by Beck, DemirgüçKunt, and Maksimovic (2008), factoring, leasing and other sources of external finance do not fill the financing gap of small firms.

The firm-level evidence is complemented by data collected from banker surveys in Beck, Demirgüç-Kunt and Martinez-Peria (2007) who compile loan and deposit account data through surveys of bank regulators for a cross-section of countries. They show that there is a great variation in banking sector outreach across countries using measures of geographic and demographic branch indicators, geographic and demographic ATM indicators and loan and deposit per capita ratios. Importantly they also show that the share of small firms with bank loans is significantly correlated with branches, ATMs and loans per capita and with ATMs per sq.km While the evidence discussed above is focused on developing countries, research based on U.S. data also suggests that firm size is the most dominant variable explaining financial constraints (Hadlock and Pierce (2010), Hoberg and Maksimovic (2015)). Brown and Earle (2017) link administrative data on all loans 10
The historical trends indicate that the fraction of workers remunerated at the minimum wage level is significant. Between 2011 and 2020, for example, the share of formal employees earning the minimum wage fluctuated between 35-44 percent.<sup>15</sup> The overwhelming majority of them belong to the bottom half of the distribution. Table 2 presents nominal values of the minimum wage and three different calculations to express them in real terms: 1) overall CPI, 2) 1st decile inflation rate and 3) Bottom 40 inflation rate. The three definitions yield similar values, although the level of the minimum wage is 2% lower under the second definition on account of the higher inflation borne by households from the lowest decile. In the years with price stability (2011-2016), the nominal increase in the minimum wage outpaced price inflation. Until 2015, the average annual nominal wage increase was around 10%, namely a real increase of 2%-3%. In 2016, the government enacted a major policy change to boost nominal minimum wage levels by 37%, resulting in a real wage increase of 27%. Yet, from 2017 onwards, fast and accelerating inflation (CPI and Bottom 40) outpaced nominal minimum wage growth.

Table 2: Nominal and real minimum wage value using based on difference price adjustments

||**Minimum Wage**<br>**(Nominal)**|**Real Minimum Wage**<br>**(Overall Inflation)**|**Real Minimum Wage**<br>**(1st Decile Inflation)**|**Real Minimum Wage**<br>**(Bottom 40 Inflation)**|
|---|---|---|---|---|
|2011|630|630|630|630|
|2012|701|644|643|643|
|2013|773|660|658|658|
|2014|846|664|660|661|
|2015|949|692|683|686|
|2016|1301|880|870|873|
|2017|1404|854|846|848|
|2018|1603|838|832|835|
|2019|2021|918|904|910|
|2020|2325|940|923|931|

_Source_ : World Bank calculations using data from TUIK and Ministry of Family, Labour and Social Services<sup>**16**</sup>

# **6. Conclusions**

This paper documents the differences in spending across households and the distributional impacts of heterogeneity in inflation rates at the household level. This is often an aspect overlooked

> 15 Data from Social Security Institution. The share of formal workers with minimum wage among all formal workers was 44.0 in 2011; 41.6 in 2012; 38.3 in 2013; 38.1 in 2014; 38.6 in 2015; 40.9 in 2016; 35.0 in 2017; 36.2 in 2018; 36.8 in 2019 and 42.0 in 2020.

> 16 Numbers from the Ministry of Family and Social Services and author calculations using HBS.

21
**Figure 4. Share of Sugar Export Quantities sold to EU and U.S., 1998-2002**

<!-- Start of picture text --> EU US<br>100<br>80<br>60<br>40<br>20<br>0<br>Barbados BelizeDominican Rep. Guyana JamaicaSt. Kitts & NevisTrinidad & Tobago<br>Percent<br><!-- End of picture text --> Source: Comtrade data.

# **_Risk of Preference Erosion_**

The EU and U.S. sugar programs are under substantial pressure for reform and these reforms will likely affect the quota holding countries of the Caribbean. These reforms are expected to reduce prices for exports, but not the quantities under quota because of international agreements. The EU-ACP quotas are bound by the Lomé Convention and its successors and are not expected to be reduced beyond the levels shown in Table 2 (except when individual countries do not meet their quotas which can lead to reallocation of quotas to other ACP countries). However, the EU’s Special Preference Sugar imports can be reduced and that program would be phased out under a reform proposal by the European Commission. The U.S.’s import quotas shown in Table 4 are bound by agreements made by the U.S. in the WTO Uruguay Round Agreement on Agriculture and are not expected to be reduced. The prices received for these quotas are not bound by agreement and could be changed.

The pressure for reform of the EU sugar program intensified when the WTO released its ruling in August 2004 and upheld the ruling on appeal in April 2005 in the dispute case brought by Australia, Brazil and Thailand in 2003. The ruling concluded that the EU’s sugar program subsidized exports in excess of the levels agreed in the Uruguay Round Agreement on Agriculture. The European Commission released a proposal for reform of the EU’s sugar regime in July 2004 which was not acted upon and a subsequent proposal on June 22, 2005 (Box 1). The subsequent proposal was in response to the WTO initial and appeal ruling and is intended to bring the EU sugar policy into WTO compliance. The proposal calls for a 39 percent cut in domestic support prices to be phased in over two years. This reduction would apply to both EU producers and ACP quota holders. The proposal also called for the phase out of the EU’s Special Preference Sugar imports which would affect some Caribbean exporters (see Table 3). While the 10
level, with the latter managed by Ministries of Education. Many of the national training systems employ a significant degree of decentralization in order to strengthen training center responsiveness to local industry needs. Decentralization strategies, however, have encountered implementation difficulties where adequate preparation of decentralized units has not been undertaken.

xxii. Such systems may be under development in current projects in a limited number of countries in Sub-Saharan Africa. However, most of the early investments in these countries have been small projects supporting a few vocational schools; in later years these have supported nonformal training centers. Sustained investment in systems development has so far been comparatively rare.

# Performance

xxiii. Data on economic outcomes of training are absent from project evaluations. Reliable unit recurrent cost data is almost entirely lacking, although the data that are available confirm that secondary vocational schools are more expensive than general secondary schools. Thus assessment of investment performance is largely limited to criteria of implementation success.

xxiv. The exception is found for the investments in Asia. There information on graduation rates, placement rates and employer satisfaction was available for a sufficient proportion of the components to justify inferences. On all three criteria, the institutions supported in these components performed very well. These included all modes of training.

-xv. On the criterion of proportion of enrollment targets achieved at completion, performance varied directly with the income level of the country. About 40% of targets were achieved in low income countries; 90% in lower-middle income countries, and 109% in upper-middle income countries. Overall, there was little varlition across modes. However, secondary vocational schools performed best overall, and slightly better across income levels than the more complex nonformal institutions. The data indicate that it is difficult to establish any institutional mode in low income countries (including secondary vocational schools), with weak implementation capacity and financial constraints. The opposite is true for middle income countries.

xxvi. This criterion, of course, does not capture the eventual enrollment performance of project institutions, many which suffered from significant delays in completion. But it can be taken as an indicator of the difficulties encountered in establishing a given mode of training.

xxvii. These findings are mirrored in ratings of institutional performance of VET components. On a regional basis, components in Africa performed at a significantly lower level than those in the other three regions.
# **Table 11:** WTP for fertilizers

||(1)<br>Reported WTP for<br>YARA fertilizers|(2)<br>WTP f<br>fertiliz|(3)<br>or a bag of<br>er in 2017(|(4)<br>YARA<br>Mex$)|
|---|---|---|---|---|
||(1=Yes)|Urea|DAP|KCl|
|Extension (1=Yes)|0.20***|71.40***|64.91**|53.11**|
||(0.06)|(19.58)|(24.52)|(20.58)|
|Individual recommendation (1=Yes)|-0.05|-15.16|-11.49|-10.82|
||(0.04)|(14.92)|(19.60)|(18.62)|
|Grant (1=Yes)|0.36***|111.20***|139.15***|110.29***|
||(0.05)|(17.68)|(22.54)|(20.23)|
|Flexible (1=Yes)|0.04|13.75|24.86|46.21**|
||(0.03)|(13.71)|(18.49)|(17.47)|
|Observations|678|678|678|678|
|R-squared|0.33|0.28|0.24|0.25|
|Mean dep. var. control (or T4)|0.33|100.38|121.73|98.46|
|SD dep. var. control (or T4)|0.47|151.12|185.92|157.18|
|T1: _βE_+_βI_+_βG_ =0|0.00|0.00|0.00|0.00|
|T2: _βE_+_βG_ =0|0.00|0.00|0.00|0.00|
|T3: _βE_ + _βG_ + _βF_ =0|0.00|0.00|0.00|0.00|

Note: this table reports results willingness to pay for YARA fertilizers. Using the full sample of 678 farmers in our study, we report the point estimates of the following specification: _Yit_ = _β_ 0 + _βE Extensioni_ + _β I IndRecommi_ + _βG Granti_ + _βF Flexiblei_ + _αc_ + _εit_ , where _i_ corresponds to a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard deviation of the outcome for the control group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coefficients that map into the original study design. We use data from the Final survey conducted in May 2017. In Column 1, the dependent variable is a dummy that takes value 1 if the farmer reported his or her willingnes to pay for any of the 3 YARA fertilizers (Urea, DAP and KCl). In Columns 2-4, the dependent variables are the self-reported willingness to pay for a bag of each of the 3 YARA fertilizers. We imput WTP equal to zero for those who did not report their WTPs. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

41
**Figure 2. Average Damages from Large Disasters**

<!-- Start of picture text --> 34.7<br>25.0<br>20.0<br>Developing countries<br>15.0<br>Small states<br>Low income<br>10.0<br>Other developing<br>5.0<br>0.0<br>1977-81 1982-86 1987-91 1992-96 1997-2001<br>period<br>percent of GDP<br><!-- End of picture text --> Source: IMF 2003.

Indeed, a quick analysis of recent catastrophes shows that funding for relief and reconstruction in developing countries generally comes from very different sources than is the case in industrialized countries. In more advanced economies, losses from natural disasters are typically funded through a combination of private risk financing arrangements and an efficient public revenue system relying on wide and deep taxation catchments. In middle and low-income countries, which have relatively low tax ratios and ongoing fiscal pressures, funding sources for post disaster reconstruction tend to be more varied, with strong reliance on ex-post borrowing and assistance from international donors. Assistance from multilateral financial agencies plays a particularly important role in middle-income countries, while support from bilateral donors is generally dominant in low-income countries. The World Bank alone has disbursed more than US$40 billion in emergency loans related to natural disasters, as shown in Figure 3. Much of this assistance is recent: about 43 percent of all disaster-related loans have yet to close.

**Figure 3.  World Bank Emergency Lending Related to Natural Disasters**

<!-- Start of picture text --> 50 3500<br>45 Number of Projects Loan Commitments<br>3000<br>40<br>35 2500<br>30<br>2000<br>25<br>1500<br>20<br>15 1000<br>10<br>500<br>5<br>0 0<br>1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Approval FY<br>millions of US$<br>Number of Projects<br><!-- End of picture text --> Sources: World Bank 2005 The economic theory suggests that countries should ignore uncertainty for public investment and behave as if indifferent to risk because they can pool risks to a much greater extent than private investors. In particular Arrow and Lind (1970) demonstrate that when the risks are publicly borne, the social cost of risk-bearing is insignificant and, therefore, the government should ignore uncertainty in evaluating public investment because it can distribute the risk associated with any investment among a large number of 3
# **5 Discussion**

The previous section has spent time rejecting a number of theories about why recent insurance payouts could be driving insurance purchases. The data does not show support for a number of explanations (learning, trust, wealth, liquidity) that would be consistent with a neoclassical framework. However, there are a number of behavioral explanations that are consistent with the results.

Although we established that salience or recency bias related to the weather is unlikely to be driving the results, it is possible that recency bias unrelated to the weather is playing a role. People may simply believe that since the insurance paid out in the previous year, it is more likely to pay out in the follow year. This may be especially important if people consider insurance as an investment, as in Karni and Safra (1987). One might think that this type of bias would have spillover effects (which we do not observe), but may also be present at the individual level if people believe recent insurance payouts are a sign of individual luck.

Another explanation could be that insurance purchasers behave as if they are “gambling with house money”, as in Thaler and Johnson (1990). If customers exhibit loss aversion, they may not view insurance premiums paid after receiving payouts as true losses, since they are still “in the red” in their relationship with the insurance company. If we assume that reference points adjust after receiving insurance payouts, the observed behavior is consistent with this theory.

A related explanation comes from an observational study on mutual insurance among fisherman in the Côte d’Ivoire by Platteau (1997). Platteau observes malfunctioning mutual insurance cooperatives and theorizes that they are failing because members view insurance as a system of balanced reciprocity, meaning that they expect to break even over the lifetime of the scheme. When members have not received the services (in this case sea rescue) of the mutual in a long time, they start to view the insurance as a bad deal and ask for their contributions back. The results in this paper are consistent with people viewing insurance as a system of balanced reciprocity, as they could see insurance purchases after receiving a payout as giving back to a system that has helped them previously.

The data used in this study does not allow me to distinguish between these competing explanations. They remain as areas for future research.

# **6 Conclusion**

After receiving an insurance payout, customers of rainfall insurance in India are 9-22% more likely to purchase insurance again the next year. This behavior seems to be driven by actually receiving the money from the insurance company, as the data does not support alternative theories of re-purchase being driven by direct weather effects, trust, learning, wealth, or liquidity.

This study brings to light a number of questions that would be ripe for future research. First of all, it would be interesting to understand whether insurance payouts have long-term effects on future 15
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<!-- Start of picture text --> 7.00<br>6.00 EU Average<br>* A<br>4.00<br>3.00<br>2.2<br>2.00oo | TW<br>PEEEEEEBEX BSERHRgeeryrowrwxgyeszeeEgrERE REE STSEE EERE<br>=2° ESE ESP SESS EFS2B 88<br>g*i“gua 2825 2 o< 25%<br>2 5<br><!-- End of picture text -->
survey data, MEAs capture enrollment figures from school enrollment registers, which are under the control of schools.<sup>19</sup> Children mainly join public primary schools in preschool and first grade; re-entrants or transfers into higher grades are rare and dropouts much more frequent. Hence, we expect that enrollment in grade _g_ in year _y_ is equal to or less than enrollment in grade _g_  1 in year _y_  1. Enrollment inflation in year _y_ can flatten or invert this gradient. We test for this by estimating an OLS regression of the form where _Eg_ ,3, _s_ denotes enrollment in grade _g_ in the third program year in school _s ,_<sup>_T_</sup> _s_<sup>the pooled</sup> treatment indicator, _Eg_  _t_ ,3  _t_ ,s<sup>enrollment in grade</sup><sup>_g_</sup>  _t_ in program year 3  _t_ in school _s_ , where _t_ denotes a one-year  _t_  1  or two-year split  _t_  2  , 1  _t_  2  an indicator for a two-year time split,  _g_ grade _g_ indicators, and<sup></sup> _s_<sup>school indicators. A positive</sup> 1<sup>is taken to indicate inflation</sup> in untreated schools and a positive<sup></sup> 2<sup>the differential inflation in treated schools. We find</sup> <sup>ˆ</sup> 1 .53  _p_  .000  and <sup>ˆ</sup> 2 .03  _p_  .703  . Thus, we do not find evidence consistent with potential enrollment inflation.

_Potential strategic manipulation of exam grade size_ Increased exam participation rates may be due to more fifth-grade students taking the exam (an increase in the fraction’s numerator) or to fewer fifth-grade students (a decrease in the fraction’s denominator), or to both. Exam grade size can decline if teachers hold back students in earlier grades or if teachers expel fifth-grade students (although the bonus’s link to school enrollment would deter such behavior).<sup>20</sup> Evidence of a negative mean impact on fifth-grade enrollment would be consistent with strategic manipulation of exam grade size by program school teachers.

> 19 The provincial education department reports that MEAs crosscheck school enrollment figures against the actual presence of students but specific written guidelines are not provided.

> 20 Holding back or expelling academically-weaker students can provide the added benefit of higher student exam scores.

17