rafmacalaba/datause-encoder-data huggingface dataset:
Whether or not wage information is available may not be random but may depend on the timing of events. Due to the set-up of the workers’ earnings dataset, unemployed that have searched for a long time or have entered their job recently have a smaller probability to be in the wage sample. To investigate this we assume that the availability of wage information can be specified in terms of the latent variable _y_<sup>_∗_</sup> which is assumed to depend on explanatory variables _r_ of individual _i_ : where _yi_ =1 if _yi_<sup>_∗≥_0 and</sup><sup>_yi_=0 otherwise.Furthermore,</sup><sup>_βy_is a vector of parameters</sup> of interest (including a constant) and _ui_ is an error term that is assumed to follow a logistic distribution so we can estimate the _βy_ -parameters with a logit model. As explanatory variables we use the duration of unemployment, whether or not the wage was measured after the change of the benefit law and whether or not the postunemployment job was temporary. The top part of the first two columns of Table 4 shows the parameter estimates. Indeed, the longer the unemployment duration the smaller the probability to have wage information. Furthermore, after the change of law the probability to have wage information is smaller. For temporary jobs the probability that wage information is present is also higher.

Table 1 provides some stylized facts about the evolution of wages. The wage increases comparing wages in post-unemployment jobs with wages in pre-unemployment jobs are smaller after the benefit reform than before. Of course this may have to do with a change in inflation. What is important is that the differences in wages changes do not seem to be related to the size in the reduction of the PBD.

Concerning the wage we assume that this depends on depends on personal characteristics _z_ of individual _i_ : where _βw_ is a vector of parameters of interest (including a constant) and _εi_ is the error term which is assumed to follow a normal distribution. The _βw_ -parameters are estimated using Maximum Likelihood. The parameter estimates are presented in the middle and bottom part of the first two columns of Table 4. As shown experience has a positive effect on wages but the reduction of the PBD did not lead to lower wages. On the contrary, females with a PBD reduction from 18 to 9 months have a wage that is about 5% higher. Although there is no effect of benefit expiration, in the month of benefit expiration there is an average wage drop of about 19
labor between food crops and sugar cane. All of the members had sugar cane on their family holdings because the Mumias Sugar Factory enticed the families in the area with inputs and services, Lncluding bush clearing, seeds and fertilizer. The farmers contribute only land and labor. The women complained that family involvement in sugar cane production was hurting more than it was helping. They pointed out .hat, although the family as a whole contributed labor on the sugar plots, the money earnings were controlled :-y the husband. They expressed a great preference for food crops, like maize and sorghum, saying that these the family could consume directly, some of which the housewife could sell in small installments to meet the day-to-day needs of the family" (Muzaale and Leonard, 1982, quoted in UNICEF, 1984).

The analysis of the Mumias scheme, while not able to make a firm connection between sugar growing and malnutrition, states nonetheless "that in an area with malnutrition problems, the reallocation of land from food to cash crops will not improve the nutritional situation" (emphasis added) (Lemmens, 1987).

Other studies in Kakamega District also suggest that farmers are not leaving sufficient land for food crops. Income derived from cash crops is not sufficient for necessary food purchasez and/or is spent on non-food items. While the Ministry of Agriculture is undertaking a campaign to encourage farmers to grow food crops alongside cash crops, it is not yet clear what impact this will have on farmers' planting decisi4ans.

A study in Eastern Province showed that the prevalence of stunting among children was significantly higher among coffee-growing households than among those producing the traditional drought-resistant millet/sorghum crop mixes. Although intra-household income and control and expenditure patterns were not analyzed in this study, it is possible that the increased prevalence of stunting among coffee-growing households was in part associated with women's increased labor input on coffee to the detriment of food crops and/or their loss of control over income :nd therefore over food purchases (Cornell, 1982).

One of the few studies showing the positive effects of cash cropping on nutrition was conducted in Taita. The study, which compared nutritional status in four communities, found that children in households where adult males were providing the bulk of income from the sale of cash crops were better off that children from other more subsistence-oriented communities. An interesting corollary finding was that children of landless laborers working on sisal estates were also better-off nutritionally that the children from subsistence farming households. This result appeared to be related to the fact that minimum wage rates for estate workers were well enforced by the Goverment as .;ell as to the fact that food prices were quite low due to market inefficiencit. (Fleuret and Fleuret, 1983).

Except for laborers who did not own land, however, all other farmers continued to grow staple food crops for home consumption in addition to incomeproducing crops. While the overall findings point to positive effects of cash 16
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<!-- Start of picture text --> Policy, Research, and External Affairs<br>WORKING PAPERS .<br>Couniry Economics Gepartment<br>The World Bank<br>August 1991<br>WPS 753<br><!-- End of picture text -->

# The Legacy and the Aftermath

## Martin Schrenk

A brief history and critique of the Council for Mutual Economic Assistance and conjectures about the consequences of its demise.

The Policy, Research, end External Affairs Complex distributes PRE Working Papers todisseminate the findings of work in progress and to encourage the exchange of ideas arnong Bank staff and all others interested in development issucs. These papers carry the names of the authors, reflect only their views, and should be used and cited accordingly. The findings, interpretations, and conclusions are the authors’ own. They should not be attributed to the World Bank, its Board of Directors, its management, or any of its member countries.
12 We can test this assumption for 2018, when the last population census took place in Colombia. Figures 2 **B.** and 2 **D.** show the municipal distribution of, respectively, the observed number of the Venezuelans and the predicted cumulative figure for 2018 (the predicted 2018 inflow is reported in Figure 2 **C.** ). Reassuringly, not only the distribution looks very similar, but also their correlation is 0.67. We are thus confident that our predicted migration shock has predictive power.<sup>11</sup> Secondly, the 1993-based predicted inflow measure needs not to be correlated with contemporaneous schooling outcomes through any channel different than actual Venezuelan migration. Regarding this assumption, it is worth noting that, because our estimates include fixed effects by municipality as well as by department _×_ year, they are confounded neither by time-invariant differences across municipalities nor by annual aggregate department-level shocks.<sup>12</sup> This is, however, not enough to achieve identification. It may well be the case that pre-shock migrants disproportionally settle in places with characteristics that explain future educational outcomes. Indeed, as noted by Goldsmith-Pinkham et al. (2019), identification in the Bartik/Shift-Share-type instruments comes mainly from the cross-sectional (“share”) variation, so it is important to check the extent to which the initial shares (of migrants) are correlated with potential confounders prior to the current migration wave. To this end, following Belloni et al. (2014), we use machine learning to select the most robust determinants of Venezuelan settlements according to the 1993 census and include in our main specification the interaction between each of these and the year fixed effects. By doing so, we flexibly control for municipal-specific trends, parametrized by a large set of pre-determined characteristics that predict early settlements.<sup>13</sup>

> 11In other words if we were to rely on a 2SLS strategy, the first stage would most likely be strong and the instrument relevant.

> 12The just over 1,100 Colombian municipalities are distributed across 32 departments.

> 13Even when the share of early migrants is not exogenous, Borusyak et al. (2018) show that identification can be achieved if the aggregate shocks are as good as random, a condition that is satisfied when: i) one controls by observable municipal characteristics weighted by shock exposure and ii) there
# **Figure 6 Fiscal policy volatility**

# **A. Fiscal volatility: Advanced economies versus EMDEs**

<!-- Start of picture text --> Percent<br>Advanced economies<br>10<br>EMDEs<br>8<br>6<br>4<br>2<br>0<br>C. Evolution of fiscal volatility in EMDEs<br>Percent 1980-2006 2007-2020<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>E. Fiscal volatility: LICs versus non-LICs<br>Percent LICs Non-LICs<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Primary Revenues Primary balance<br>expenditures Government consumption<br>Primary balance<br>revenue<br>Government expenditure Government consumption Government<br>Primary balance<br>revenue<br>Government expenditure Government consumption Government<br><!-- End of picture text -->

# **B. Fiscal volatility: EMDE commodity exporters versus importers**

<!-- Start of picture text --> Percent Commodity importers<br>16 Commodity exporters<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Primary Revenues Primary balance<br>expenditures Government consumption<br><!-- End of picture text -->

# **D.  Evolution of fiscal volatility in advanced economies**

<!-- Start of picture text --> Percent 1980-2006 2007-2020<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Primary balance<br>revenue<br>Government expenditure Government consumption Government<br><!-- End of picture text -->

# **F. Expenditure volatility in commodityexporters, by regions**

<!-- Start of picture text --> Percent<br>16<br>12<br>8<br>4<br>0<br>SSA MNA EAP LAC SAR ECA AEs<br><!-- End of picture text --> _Sources_ : Arroyo Marioli, Fatás, and Vasishtha (2023); IMF WEO database; World Bank.

34
World Bank. 2011a. Carbon Finance Unit Website, available on the internet at: wbcarbonfinance.org. World Bank. 2011b. Enterprise Surveys. Available on the Internet at: http://www.enterprisesurveys.org. WRI. 2011. Climate Analysis Indicators Tool (CAIT) version 8.0. Washington: World Resources Institute.

Wunder, Sven, Stefanie Engel and Stefano Pagiola. 2008. Taking stock: A comparative analysis of payments for environmental services programs in developed and developing countries, Ecological Economics 65(4), 834-852.

39
28 was Russia – it was its own disclosure that informed the creditors – and the language is clear: “beyond [Russia’s] control.”<sup>93</sup> Having another barge means it can still perform and thus should not be paying in rubles into blocked accounts in Russia.<sup>94</sup> How courts ultimately interpret these novel clauses is uncertain and any decision would be highly consequential for sovereign debt markets. But the burden to show that their use was appropriate on this occasion is heavy.<sup>95</sup> ii. Fiscal laws applicability clauses The bonds are governed by the laws of England. Yet, they all contain provisions subjecting “[a]ll payments of principal and interest cases to any applicable fiscal or other laws and regulations.” Writing almost ten years ago, a group of experts remarked on similar language in English law Cypriot bonds, asking the question “Does this include Cypriot laws, regulations, and directives?”<sup>96</sup> The authors highlighted that this language is common among English law sovereign bonds; but in about three-quarters of the bonds containing it, the language is modified to state “in the place of payment.”<sup>97</sup> That all Russian issuances contain it, from 1998 to 2021, suggests that it has been copied-and-pasted for generations and carried on without a serious legal challenge. Therefore, this is probably a boilerplate clause.<sup>98</sup>

> performance had already begun using the contracted structures in the case of the bonds. Additionally, since the payment structures are laid in contract, Russia could argue that there is no other barge it can legally use. However, like in the _Super Servant_ case, that Russia would have to disregard another contractual obligation may not be sufficient to show frustration.

> 93 Prospectus, note 83 _supra_ , at 11-12.

> 94 _See_ Morgan Lewis Lawflash, note 45 _supra_ .

> 95 Lastly, the reader should be reminded that the bonds currently in their grace period do not contain ACCs. Therefore, in the default scenario laid out above the ACC bonds would be cross-accelerated to which Russia would respond by making all accelerated payments in rubles.

> 96 Lee Buchheit, Mitu Gulati, and Ignacio Tirado, _The problem of holdout creditors in Eurozone Sovereign Debt restructurings_ , Butterworths J. of Int’l Banking and Financial L., 191 (April 2013) at 193.

> 97 _Id_ .

> 98 Boilerplate clauses are broadly used clauses that appear in most contracts in a market. _See_ Lam, note 52 _supra_ , at 94.
countries, this association weakens to only 1.8 percent higher prices during a period of export ban (column 3, Table 3). Besides, the association is not statistically significant.

We further examine the relationship between export bans and harvest levels. Observing the trends in harvest levels and the timing of export bans as well as anecdotal evidence from discussions with local experts suggest that, in general, these bans tend to be imposed when the harvest levels are expected to be below average. The analysis presented in Table 3 indicates that once we fully account for harvest conditions, the association of export bans with price is quite different. To identify the difference in the association of prices and harvest during a period with an export ban against a period with no export ban, we include an interaction term between maize harvest and an indicator of the export ban in the above regression specification. Table 4 presents the results of estimating the regression specifications from equations 4 and 5.

**Table 3: Association between export bans and domestic prices**

|_(logarithm of_|_price in kwac_<br>|_ha/kg)_<br>||
|---|---|---|---|
||**(1)**|**(2)**|**(3)**|
|Malawi's export ban|0.207<br>***<br>(0.01)|0.046<br>***<br>(0.07)|0.018<br>(0.02)|
|Malawi's maize harvest||−0.006<br>***<br>(0.00)|−0.005<br>***<br>(0.00)|
|Tanzania's export ban|||−0.219<br>***<br>(0.02)|
|Zambia's export ban|||0.021<br>***<br>(0.00)|
|Control for market-year fixed effects|Yes|Yes|Yes|
|Observations|10,087|10,087|10,087|

_Note:_ Column (1) presents the regression results from estimating equation (1), Column (2) from equation (2), and Column (3) from equation (3). Standard errors are clustered by region pairs. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01. Maize harvest is the national maize production per capita in each year.

The results in Table 4 suggest that export bans are associated with lower domestic prices after we account for harvest levels. Table 4 (column 1) presents the difference in (logarithm of) price responses to a 1 kg per capita increase in harvest during periods of export ban minus the responsiveness of prices to similar harvest increases in periods with no export ban. The coefficient on the interaction term in the first row suggests that a 10 kg per capita increase in harvest, for example, is associated with a 6 percent decrease in prices during periods with an export ban, compared to the response of prices for a comparable increase in harvest during periods without an export ban. The relationship between harvest and domestic prices is different when there is an export ban in place and when it is not (column 2 of Table 4). Specifically, with an export ban in place, domestic prices tend to decrease with an increase in harvest.

On the other hand, with no export ban, prices tend to increase even when harvest levels are high. When Malawi’s markets are open to global markets, price levels are likely to be driven by the global market and 18
Figure 3: Fragile Lands: Population at Risk <!-- Start of picture text -->
-<br>ss-  - - - ,  Iz<br>r  - r~~~~~~~~ i  -<br>- v~~~~~~~~~~~~~~~~I<br>Rural  Population on  Fragile Lands<br>0-  1001693<br>1029588  - 5323851<br>5352795-  10416502<br>10686560  -40585050<br>_  43819400-  349975712<br>- n.a.<br><!-- End of picture text -->

> Source: World Development Report (2003)

34
|Table 2. Descriptive Statistics, regression sa|mples|**Armenia**|||**Bolivia**|||**Colombia**||
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>Male|(2)<br>Female|(3)<br>All|(1)<br>Male|(2)<br>Female|(3)<br>All|(1)<br>Male|(2)<br>Female|(3)<br>All|
|Log of hourly earnings in USD (trimmed)|1.229|0.828|0.979|1.392|1.033|1.195|1.283|1.007|1.144|
||(0.631)|(0.611)|(0.648)|(0.905)|(0.957)|(0.950)|(0.798)|(0.845)|(0.834)|
|Female|--|--|0.625|--|--|0.550|--|--|0.504|
||--|--|(0.484)|--|--|(0.498)|--|--|(0.500)|
|Age, years|39.028|40.083|39.687|37.067|36.927|36.990|37.547|38.486|38.021|
||(9.149)|(9.010)|(9.070)|(8.069)|(8.015)|(8.036)|(8.563)|(8.754)|(8.668)|
|# of years of education <=9 years|8.919|8.949|8.938|8.314|7.818|8.041|8.009|7.842|7.925|
||(0.443)|(0.473)|(0.462)|(1.614)|(2.181)|(1.961)|(1.883)|(2.065)|(1.978)|
|# of years of education 10-13 years|3.142|3.337|3.264|2.621|2.259|2.422|2.043|1.980|2.012|
||(1.394)|(1.256)|(1.312)|(1.613)|(1.702)|(1.672)|(1.598)|(1.643)|(1.621)|
|# of years of education > 13 years|1.988|1.930|1.951|1.617|1.305|1.445|0.536|0.467|0.501|
||(2.025)|(2.030)|(2.027)|(2.099)|(1.969)|(2.033)|(1.210)|(1.109)|(1.160)|
|Literacy assessment (plausible values Z score)|0.026|0.100|0.072|0.107|-0.187|-0.054|0.103|-0.002|0.050|
||(0.636)|(0.606)|(0.618)|(0.850)|(1.001)|(0.947)|(0.763)|(0.794)|(0.780)|
|Extraversion (Z score)|-0.072|0.105|0.038|0.028|-0.019|0.002|0.115|-0.017|0.049|
||(0.948)|(1.053)|(1.018)|(0.963)|(0.994)|(0.980)|(0.910)|(1.018)|(0.968)|
|Conscientiousness (Z score)|0.082|0.215|0.165|0.111|0.113|0.112|0.171|0.180|0.175|
||(1.025)|(0.914)|(0.959)|(0.945)|(0.971)|(0.959)|(0.952)|(0.970)|(0.961)|
|Openness (Z score)|0.070|0.188|0.144|0.100|0.016|0.054|0.120|-0.027|0.046|
||(0.990)|(0.970)|(0.979)|(0.978)|(0.996)|(0.988)|(0.971)|(1.038)|(1.007)|
|Emotional stability (Z score)|0.231|0.004|0.089|0.335|-0.211|0.035|0.330|-0.234|0.046|
||(0.921)|(0.969)|(0.957)|(0.932)|(0.996)|(1.005)|(0.929)|(0.952)|(0.982)|
|Agreeableness, cooperation (Z score)|-0.100|0.136|0.047|0.095|0.025|0.057|-0.000|-0.005|-0.003|
||(0.952)|(0.962)|(0.964)|(0.955)|(1.030)|(0.997)|(0.962)|(1.021)|(0.992)|
|Grit (Z score)|-0.013|0.143|0.085|0.139|0.133|0.136|0.159|0.079|0.119|
||(0.957)|(0.958)|(0.960)|(0.950)|(0.996)|(0.975)|(0.942)|(0.959)|(0.951)|
|Decision making (Z score)|<br>0.059|<br>0.084|<br>0.075|<br>-0.037|<br>0.110|<br>0.044|<br>-0.048|<br>0.080|<br>0.016|
||(0.961)|(0.946)|(0.951)|(1.010)|(0.980)|(0.996)|(1.015)|(1.014)|(1.016)|
|Hostile attribution bias (Z score)|0.038|-0.059|-0.022|0.025|0.220|0.133|-0.096|0.152|0.029|
||(1.038)|(0.970)|(0.996)|(0.996)|(0.994)|(0.999)|(0.927)|(1.027)|(0.986)|

25
_ROA_ + _CAR_ _SROA_

# **Table 14 Determinants of the Z-score: IV Estimation**

The dependent variable is the _Z score_ defined as where ROA is return on assets, CAR represents capital assets ratio and SROA stands for standard deviation of return on assets _Fee income_ is the share of non-interest income in total operating income. _Non-deposit funding_ is the share of non-deposit short-term funding in total deposits & short-term funding. _Assets_ is the natural logarithm of total assets in US $. _Equity_ is the capital to asset ratio, which is defined as equity as a percentage of total assets. We used one period lag of equity in the regressions. _Assets growth_ is the inflation-adjusted growth rate of bank assets. _Overhead_ is defined as overheads as a percentage of total assets. The data for these bank level variables are obtained from Bankscope. _Inflation_ is the inflation rate based on consumer prices. _GDP growth_ is the inflation adjusted growth rate of GDP per capita of the country. _GDP per capita_ is in thousands of 2000 constant US$. All these macroeconomic variables are from World Development Indicators (WDI) of World Bank. The regressions in Panel A use instruments for the _Fee income_ variable, and the regressions Panel B use instruments for the _Non-deposit funding_ variable. The regressions in column 1 use as instruments dummy variables indicating whether the bank is an investment bank, a non-bank credit institution, or a bank in the other category. The regressions in column 2 use as instruments dummy variables indicating in 2001 whether the bank is an investment bank, a nonbank credit institution, or a bank in the other category. Other data are for 2002-2007. We estimate all regressions with mean data and clustering at country level. We also report the coefficient estimates for the instrumental variables of the first-stage regression, the p-value of the F-test of joint significance of identifying instruments, the p-value of the Hausman specification error test which compares the difference between the IV and OLS estimators, and the p-value of the Hansen J-test of overidentifying restrictions. Standard errors are in parentheses. *, ** and *** <u>denote significance at 10%, 5% and 1%, respectively.</u>

||(1)|(2)|
|---|---|---|
||IV: Bank category|IV: Bank category 2001|
|Fee income share|-39.485***|-26.815***|
||(13.872)|(5.030)|
|Assets|-0.940|-1.258***|
||(0.661)|(0.487)|
|Equity|23.079**|19.417**|
||(11.093)|(9.233)|
|Assets growth|17.607**|16.190**|
||(7.161)|(6.691)|
|Overhead|-43.388|-62.585***|
||(29.177)|(20.772)|
|Inflation|-18.800|-22.612|
||(32.263)|(45.325)|
|GDP growth|-40.745|-53.638|
||(45.269)|(50.358)|
|GDP per capita|0.510***|0.529***|
||(0.072)|(0.073)|
|_First-stage_|||
|Instrumental variables|||
|Investment bank dummy|0.251***|0.274***|
||(0.023)|(0.023)|
|Non-bank credit institution dummy|-0.042|-0.034|
||(0.031)|(0.031)|
|Other dummy|0.035|0.042|

53