rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

COVID-19 is the largest aggregate shock to hit the global economy in recent years, with farreaching consequences for businesses, households, and individuals alike. Not only has the pandemic proven difficult to control, but the associated lockdowns and movement regulations have debilitated many micro and small businesses, especially in developing economies. At the same time, governments and international organizations are stretched thin in terms of their own resources and ability to respond. It is therefore crucial to know how to most effectively allocate these scarce resources, with implications for both precautionary and reactive policies.

Targeted support for entrepreneurs and small businesses can offer a crucial lifeline in times of crisis, with the potential for greater business viability, flexibility, and financial slack. In addition, such support can moderate the negative impacts on confidence, life satisfaction, and entrepreneurial aspirations that are undoubtedly at risk. Identifying which kinds of support are most useful and effective is therefore of great value to researchers and policy makers alike.

This paper addresses these pressing questions among a sample of youth entrepreneurs in Kenya and a repurposed field study on the effectiveness of mitigating policies. The original and ongoing study was designed prior to the pandemic with an aim to evaluate the impact of business support measures for self-employment among youth entrepreneurs in the form of grants and business development services (BDS). At the time the pandemic hit in March 2020, the baseline survey for the study had just been completed in January/February, with the intervention rollout of grants and BDS then coinciding with the onset of COVID-19 and its associated lockdowns.

The pandemic made landfall in Kenya in early March 2020, and the government reacted quickly and decisively with school closures, limits on social gathering, suspension of retail, restaurants, as well as international flights put in place before the end of the month. Early April also saw movement cessations and overnight curfews put in place in populous counties, which remained in effect for several months.

Given the sudden onset, this repurposed study assesses the immediate impact of the business support interventions of our original study in ameliorating the crisis. The analysis is based on three rounds of phone surveys among a sample of 1,054 youth who were part of the first rollout wave of the original study.<sup>1</sup> These surveys were conducted at three months (June), five months (August), and seven months (October) from the start of the pandemic, and

> 1 The original study comprises a sample of 9,380 youth across 15 counties in Kenya. Owing to the large scale of the program, the intervention rollout was organized by groups of 5 counties, and the sample in this repurposed study represents all youth who were part of the first wave of implementation. Please see section 2.1. for more details on study design and intervention timing.

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from Turkish Employers' Association. It shows approximately the same increase as the SMI series. This tends to reverse the development 1960-80 when SMI averages increased at almost double the rate of the SII averages. Both the wage arbitration board and the upper limits for social security contributions and the adjustment to inflation may here have played a role, speeding up the SII rate of increase. The SPO estimates are shown in Table 3.7 with SMI and SII and the Turkish Employers' Asssociation series for the sake of comparison.

Conclusions: My conclusions can only be that the SII data should not be used in serious wage studies covering the years 1960-1985. The same is the case for the SPO estimates. The census and survey data for manufacturing with the SIS data for mining should be used for establishing annually chained indexes (Laspeyres, Paasche, Fisher, Tornqvist, etc. and possibly others with superlative index qualities) for nominal wages in manufacturing and mining with weights by sub-industry, ownership, and, possibly size. Simple averages may not be reliable. To establish such indexes would be a major research effort. It would still leave us without wage indicators for trade, transportation, construction, agriculture, and personal services - and that is how it is!

Notes to Section 3:

1. Absolutely, in mining & quarrying almost nine-tenths are

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**Figure 21: Difference between historical and RCP 4.5 climate shock simulations with macro shocks**

<!-- Start of picture text --> % Real GDP % % GVA - Agriculture %<br>0.0 0.0 1.0 1.0<br>0.0 0.0<br>-0.1 -0.1<br>-1.0 -1.0<br>-0.2 -0.2<br>-2.0 -2.0<br>-0.3 -0.3<br>-3.0 -3.0<br>-0.4 -0.4<br>-4.0 -4.0<br>-0.5 -0.5 -5.0 -5.0<br>2020 2040 2060 2080 2100 2020 2040 2060 2080 2100<br>Mean Lower 95% C.I. Mean Lower 95% C.I.<br><!-- End of picture text --> Note: Change in the mean outcome (light blue) and the lower bound of the 95 percent confidence interval (dark blue) are shown.

# 7. Afterword

**This work contributes to understanding the macro-critical effects of climate shocks in Uruguay** . In doing so, it brings in elements that can be overlooked in standard macroeconomic assessments, which could be replicated in other emerging economies where climate shocks have relevant aggregate implications. It shows that climate shocks in Uruguay matter from a macroeconomic and fiscal perspective.

**The magnitude of the effects are consistent with estimates of the direct impact of droughts in Uruguay, but larger than what is found in cross-country studies** .  While cross-country studies do find a link between water deficits and GDP growth (see for example Zaveri et al. (2023) or Kalkuhl and Wenz (2020)), their estimated impacts are milder. There are a few reasons behind these differences. First, the temporal definition of a drought. Cross-country studies typically identify droughts at an annual frequency: there is a drought if the average annual rainfall is below a certain threshold. This hides the fact that intra-annual patterns are of first-order importance to determine agricultural yields. In Uruguay, low summer precipitations ruin soybean and maize yields, no matter how much it rains over the winter or spring. This study, building on IDB (2019), uses a three-month drought index instead. Second, this study complements precipitation data with temperature data to account for evapotranspiration, which jointly correlates with agricultural yields. Third, this study uses a bottom-up approach following IDB (2019) and MEF (2023). That is, it builds from estimated impacts at the micro level, to then account for indirect impacts and general equilibrium effects. Finally, cross-country studies find average effects across countries that have different structural characteristics and, as such, for which the effect of droughts may vary significantly. This study is instead intended to capture Uruguay’s specificities.

**The analysis in this paper supports broad policy messages around Uruguay’s development agenda** . Climate shocks on average lower GDP, but most importantly add significant uncertainty to economic outcomes, compounded by other macroeconomic shocks, increasing the likelihood of bad years from a macro and fiscal perspective. This stresses the importance of institutional arrangements that help

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11. "Unpredictability of the judiciary s. presents a major problem for my business operations." 70 To what degree do you agree with this **60** statement? **50** Now 1 0 years **40** (1) fully agree **3** El **3** (2) agree inmost cases **3j** El (3) tend to agree O - (4) tend to disagree 3 3 (5) disagree in most cases O Q li (6) strongly disagree E O* World _lDC_ DC SSEA MNA CEE **IAC** SSA **Cis** I **10(5)years ago**

**Percentage of firms who ticked (1), (2), or (3) for question 11**

Unreliable judiciaries are perceived as a major problem all over the world. In the less developed countries over 70 percent of entrepreneurs feel that judicial unpredictability presents a major problem for their business operations. Moreover, in most regions of the world entrepreneurs think that their problems with the judiciary have increased over the last 10 years On average in all regions entrepreneurs thought, that the reliability of judiciary systems today presents a larger problem for their business than 10 years ago (5 years in the case of the transition economies.

# _Question 12: Ranking of obstacles for doing business_

This question does not relate primarily to problems of uncertainty but asks businessmen to rank all kinds of obstacles comparatively. Some parts of this question have been used before in private sector assessments for individual countries by the World Bank and the question was introduced this survey to gain a comparative picture across countries.

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91 ESTIMATES OF COSTS AND BENEFITS Conversely, poorer households receive larger benefits, both absolutely and as a percent of total consumption.

Table **_5.9:_** Sumnary Cost-Benefit Measures By Consumption

|Household<br>Consumption<br>Quartile<br>Co|**Median**<br>**House-**<br>hold<br>nsumption|Controlled<br>Rent<br>PcQc|**Mkt**<br>Rent<br>Curr<br>Unit<br>**PMQc**|Uncon-<br>trolled<br>PmQm|Cost<br>**of**the<br>**Subsidy**<br>**(PmQm-**<br>PcQc)|Benef<br>1<br>(Ep--1)(Ep|its<br>2<br>--0.5)|Benefit/<br>Consump.<br>**(Ep--l)**|
|---|---|---|---|---|---|---|---|---|
|Median<br>Within<br>Top Quartile|21,160|300|573|1127|275|82|-214|.003|
|Median<br>Within<br>lst<br>Quartile|6,800|**00**|574|944|282|187|45|.030|
|Median<br>Within<br>2nd Quartile|10,799|300|574|1053|278|112|-140|.011|
|Median<br>Within<br>3rd<br>Quartile|14,250|300|574|1069|278|117|-129|.007|

Figure **5.9:** Cost Benefit by Consumption <!-- Start of picture text --> Benefits are negatively related to<br>household income, suggesting that rent 35% 3 e3O<br>control may be somewhat progressive; but 250 e<br>the benefits are, again, small. When c 200 25% f<br>the price elasticity of -. 5 is used, only a 1S0 215<br>the lowest consumption quartile receive s 100 X o<br>s ioo .~~~~~~~~~~1% c<br>positive net benefits. Smaller benefits- .% - 0 05 c<br>-or more negative benefits for richer 50<br>tenants comes as no surprise since these 0 09000 900112375 12376 1200 GE 7201 0%<br>are exactly those households with the Consumption Quartile<br>largest welfare loss from low consumption m ueeian Cost EB Mjedia Benefit i<br>of housing. - Beneliliconsumption l<br><!-- End of picture text --> While benefits may be larger for poor tenants, again, overall they are small relative to the static costs imposed on landlords. Further, the distribution of benefits within the renter class is not the most important redistribution; more important is redistribution from landlords to tenants, discussed below.

# Benefits By Length of Tenure

Sulmmary cost-benefit measures were also tabulated by length of tenure (Table 5.10 and Figure 5.9). Here the picture is also clear. Median controlled rents do not fall with length of tenure; market rents for units occupied by long term tenants fall by a little. But we estimate that market rents for long term tenants would be considerably lower than for recent movers.LQY Therefore, long term tenants have _201_ As discussed above, and in Malpezzi, Ozanne and Thibodeau (1980), pp. 7882.
sample, we find that bribes to public officials (excluding those to the tax inspectorate) are significantly associated with increased tax evasion.<sup>28</sup> In Col. 4 we examine if there is a causal link between bribes and tax evasion by instrumenting bribe payments with the interactions with government officials. Specifically, the instrument is the percentage of senior management’s time in a typical week that is spent dealing with requirements imposed by government regulations including dealing with officials, completing forms, etc. In estimating the regression, we explicitly drop firms who report spending any days in inspections and mandatory meetings with officials of the tax inspectorate. Thus we expect the instrument to be correlated with bribe payments (the endogenous regressor), but uncorrelated with tax evasion (the outcome variable) for reasons beyond its effect on the endogenous regressor. It could be that firms that spend more time with government officials and bribe them are doing so for personal gain and hence also evade taxes since they are particularly prone to illegal behavior. However we find our results to hold even when we drop firms that report bribing the tax authorities. The instrumental variable regressions results in col. 4 show that for the sub-population of firms whose bribe paying behavior is influenced by their interactions with government officials other than those in the tax inspectorate, we find bribes to be significantly causally associated with tax evasion. The first stage F-stats reported in the table reject the hypothesis that the time spent with government officials is a weak instrument for bribe payments. However, just in case, we use the LIML estimator in the IV regression which is most robust to the weak instrument case. In addition our instrument pass a battery of tests of instruments. The Anderson-Rubin statistic tests the null hypothesis that the coefficient of the endogenous regressor in the main equation is 0 and is rejected as shown in col. 4.

# _5.2.1 Innovation and Tax Evasion_

In section 2, while we predict innovating firms to be particular targets for bribery and indeed find an empirical association between innovation and bribes above, we don’t expect there to be an

> 28 All our results are robust to choice of estimator. We get a significant and strong association between bribes and tax evasion using Logit regressions where the dependent variable is a dummy variable that takes value 1 if firm reported taxes and 0 otherwise as well as two-limit Tobit regressions to account for Tax Evasion taking on zero values with positive probability and being a continuous random variable over strictly positive values. As an alternative to tobit, we use a two stage hurdle model (Cameron and Trivedi (1998), Mullahy (1986)) and find that the level of bribe payments is associated with both the probability and extent of tax evasion.

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_Table 5. Effect of the treatment on mathematics scores_

||Dependen<br>|t variable is the<br>|score in mathemati<br>|cs. OLS estima<br>|tion<br>||
|---|---|---|---|---|---|---|
||(1)|(2)<br>All children|(3)<br>Li|(4)<br>Ch|(5)<br>ildren aged 8|(6)<br>to 12<br>Li|
||||eavng out|||eavng out|
||All sample|All Sample|untreated HH in<br>treated villages|All sample|All Sample|untreated HH in<br>treated villages|
|Treated*Endline|-0.149|0.119|0.117|-0.155|0.129|0.0862|
||(0.333)|(0.349)|(0.373)|(0.369)|(0.385)|(0.419)|
|Endline|1.652<br>(0.256)***|1.652<br>(0.256)***|1.652<br>(0.257)***|1.692<br>(0.272)***|1.690<br>(0.273)***|1.695<br>(0.273)***|
|Girl|-0.571<br>(0.0671)***|-0.469<br>(0.0740)***|-0.492<br>(0.0777)***|-0.601<br>(0.0892)***|-0.482<br>(0.101)***|-0.507<br>(0.107)***|
|Treated*Girl||-0.544|-0.582||-0.583|-0.592|
|||(0.148)***|(0.177)***||(0.197)***|(0.236)**|
|Constant|-3.998|-4.045|-4.141|-5.254|-5.281|-4.439|
||(0.547)***|(0.546)***|(0.578)***|(2.355)**|(2.343)**|(2.447)*|
|Observations|16,982|16,982|14,917|8,025|8,025|7,059|
|R-squared|0.300|0.301|0.305|0.182|0.183|0.187|
|Individual controls|yes|yes|yes|yes|yes|yes|
|HH controls|yes|yes|yes|yes|yes|yes|
|Fixed-Effects|Villages|Villages|Villages|Villages|Villages|Villages|
|Clustered-SE|Villages|Villages|Villages|Villages|Villages|Villages|
|Robust standard erro<br>Individual controls in<br>include household siz|rs in parenthe<br>clude: age, age<br>e|ses. *** p<0.01,<br>squared, ethni|** p<0.05, * p<0.1<br>city dummies (souso|u, malinke, pe|ul, other). Ho|usehold controls|
|Standard errors are c|lustered at the|village level, fo|r 148 villages.||||
_Intertemporal Adjustment and Fiscal Policy in a Fixed Exchange Rate Regime_ Table 3.1 Estimation of equations 41–43 Using nonlinear least squares system

|Coefficients|Estimates<br>|Standard error<br>|T-stat|
|---|---|---|---|
|x1|2.18464|0.28178|8.5915|
|x2|1.64126|0.00718|242.3771|
|a|0.33055|0.03944|8.2536|
|Θ|–1.42972|0.102373|–13.6523|
|x0|0.489195|0.687573|0.71148|
|c0|–0.07861|0.000872|– 89.9925|

# **Nonlinear system estimation**

Robust standard error calculations with flat window and 4 lags

# **Observations**

Quarterly data from 1991:04 to 2001:03 Usable Observations 40

# **Dependent variable LREER**

Centered R2 0.5288 R Bar2 0.5406 Uncentered R2 0.5821 T x R2 23.286

# **Dependent variable XR**

Centered R2 0.8086 R Bar2 0.8134 Uncentered R2 0.9998 T x R2 39.992

# **Dependent variable TVK**

Centered R2 0.7099 R Bar2 0.709942 Uncentered R2 0.8379 T x R2 33.519 Using _in-sample_ historical values for world demand, productivity, and the U.S. real interest rate, and _out of sample_ observations (from the third quarter of 2001) of the same series, a dynamic simulation of the model demonstrates that the nonlinear procedure we adopted is appropriate (figure 3.1). Correlation coefficients between observed and simulated time series total 0.992 for GDP, 0.756 for REER, 0.997 for capital stock, and 0.797 for exports.

To learn the dynamics of the model we simulated a permanent 1 percent shock to the tax rate and another shock, also 1 percent but lasting for just one period, to the foreign real interest rate (figure 3.2). The panels in the figure show that an increase in the tax rate of one percentage point exerts a slight stimulus on capital stock (0.6 percent), at least in the short term, while causing a maximum depreciation of 0.7 percent in the REER. In the long run, these variables converge toward their steady-state values, with capital unaffected since the REER remains depreciated. Given the elasticity of exports with respect to the real exchange rate, we infer that the dynamic path of exports is the same as that of the exchange rate, though at a larger order of 25
We run the analysis at several cut-off levels for both methods to discover whether there is an embedded tipping point in the data that minimizes cutting data and maximizes information (Figure C.1). Figure C.1 suggests the higher the cut-off, the more variables we keep, which is expected. However, there does not seem to be a unilaterally optimal cut-off point at the aggregate level. This would have occurred if each increasing the cut-off point did not significantly alter the aggregate number of variables retained by the analysis.

|50<br>60<br>70||Cut-off<br>**0.5**|Survival<br>method<br>28|Average<br>method<br>23|
|---|---|---|---|---|
|20<br>30<br>40||**0.6**<br>**0.7**|46<br>55|29<br>29|
|10||**0.8**|63|30|
|0<br>0.45|0.5<br>0.55<br>0.6<br>0.65<br>0.7<br>0.75<br>0.8<br>0.85<br>0.9|**0.9**|66|30|

Figure C.1: Number of variables/groups/headings remaining (y-axis), by cut-off point (xaxis), survival method (dashed), average method (solid) _Source: ESMAP RISE dataset and authors' elaboration based on dataset described in this paper._ The survival method retains fewer independent variables than the average method. While averaging retains more information than the survival method, it may obscure underlying disparities in the averaged variables. Specifically, using group averages fails to identify policies that are different from others in their group.

Both methods may mask and compound errors. At the p=0.05 level, five out of every 100 correlations fail to reject the null hypothesis. If these variables are carried into the subsequent rounds, then the possibility of rejecting the null hypothesis is carried with them.

The most glaring shortcoming to using only correlation methods is that the dimensionality is not considerably reduced. Using the minimal cut-off of 0.5, we are left with at least 23 variables. Therefore, the second step, summation, creates our final "Composite index" that is comparable to the default RISE and the Summation index described in the text.

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# **1. Introduction**

The primary feature of the Russian public pension system has been the relatively generous pension eligibility rules, the exceptionally low retirement age (age 60 for males and age 55 for females), and the privileged retirement plans for specific groups (which accounted for almost a third of the entirety of the retired Russian population in early 2000), such as those working in unfavorable conditions or territories (Gurvich, 2004). The declining birth rate and increasing mortality rates in the last two decades, along with early retirements due to privatization, have left Russia‘s population disproportionately middle-aged and older; e.g. the percentage of elderly people (aged 65+) in Russia reached 13.8% in 2005 (17.1% for 60+). The standard definition of an elderly society is when the fraction above age 60 exceeds 8%–10% of the total population (Gavrilova and Gavrilov, 2009). With 1.24 employees per pensioner today in Russia compared to 2.2 in 1991, the Russian population is aging faster than almost any other country in Europe, and the public pension fund deficit is also growing quickly (Terra Daily, 2007).

In 2005 the Russian Federation underwent a major systemic reform of its pension system in order to strengthen the security of long-term retirement savings<sup>1</sup> and decrease the role of the state. The system shifted from a publicly managed distributive system to one supplemented by a privately managed mandatory funded component (OECD, 2006). However, federal allocations still made up 53.3% of the pension fund budget in 2007 and total pension expenditures made up 6% of GDP (World Bank, 2007). In addition, there has been increasing demand for private employee benefit funds (Hauner, 2008).

Research conducted in recent years has shown that the likelihood of planning for retirement is highly correlated with financial literacy and education, and the relationship remains strong even after controlling for wealth and other demographic variables. ( _e.g._ Bernheim, 1995; Lusardi and Mitchell2007; 2011 _inter alia_ ). As Russia transitions to a market-based banking system, the fear is that financial education and basic financial

> 1 The average accrued pension was 3,084 rubles per month in April 2007, compared with a living wage of 3,713 rubles and an average monthly wage of 12,744 rubles. The income replacement ratio (average pension vs. average wage) is only 24.2% (Terra Daily, 2007).

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<!-- Start of picture text --> Marginal Effects of Land Size on Maize<br>Yield<br>Smallholders Commercial Farms<br>.2 .2<br>0 0<br>-.2 -.2<br>-.4 -.4<br>-3.95 -2.90 -2.06 -1.38 -0.50 0.47 2.10 3.18 4.21 5.31<br>Incidence of Fertilizer Use<br>Smallholders Commercial Farms<br>.15 .15<br>.1 .1<br>.05 .05<br>0 0<br>-.05 -.05<br>-.1 -.1<br>-3.95 -2.90 -2.06 -1.38 -0.50 0.47 2.10 3.18 4.21 5.31<br><!-- End of picture text -->

**Figure 2: Marginal effects of land size on maize**

<!-- Start of picture text --> Marginal Effects of Land Size on Sorghum<br>Yield<br>Smallholders Commercial Farms<br>.2 .2<br>0 0<br>-.2 -.2<br>-.4 -.4<br>-.6 -.6<br>-3.10 -2.07 -1.40 -0.79 0.01 1.65 2.56 3.11 3.74 4.67<br>Incidence of Fertilizer Use<br>Smallholders Commercial Farms<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>-.1 -.1<br>-3.10 -2.07 -1.40 -0.79 0.01 1.65 2.56 3.11 3.74 4.67<br><!-- End of picture text -->

**Figure 3: Marginal effects of land size on sorghum**

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