rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 5. Contributors to increases in government debt in small states**

_The rise in government debt, relative to GDP, in small states since 2011 primarily reflects large and persistent fiscal deficits alongside interest costs. Although economic growth provided significant offsets to primary deficits during the pre-pandemic period, widening fiscal deficits between 2019 and 2023 overwhelmed the effects of growth, weakened by the COVID-19 pandemic. Debt is expected to fall modestly relative to GDP in the near term, with economic growth partially offsetting continued fiscal deficits._

## **A. Contributions to change in average debt-toGDP ratio, 2011-23**

<!-- Start of picture text --> Percentage points of GDP<br>80<br>60<br>40<br>20<br>0<br>Debt in Growth Primary Interest Other<br>2011 deficits cost<br><!-- End of picture text -->

## **C. Contributions to change in average debt-toGDP ratio, 2019-23**

<!-- Start of picture text --> Percentage points of GDP<br>80<br>60<br>40<br>20<br>0<br>Debt in Growth Primary Interest Other<br>2019 deficits cost<br><!-- End of picture text -->

## **B. Contributions to change in average debt-toGDP ratio, 2011-19**

<!-- Start of picture text --> Percentage points of GDP<br>80<br>60<br>40<br>20<br>0<br>Debt in Growth Primary Interest Other<br>2011 deficits cost<br><!-- End of picture text -->

**D. Contributions to change in average debt-toGDP ratio, 2023-26**

<!-- Start of picture text --> Percentage points of GDP<br>80<br>60<br>40<br>20<br>0<br>Debt in Growth Primary Interest Other<br>2023 deficits cost<br><!-- End of picture text --> _Sources:_ WEO (database); World Bank.

_Note:_ Contributions to changes in debt-to-GDP ratios. See annex 1 for further details. “Other” factors include exchange rate depreciation, inflation, privatization proceeds, the materialization of contingent liabilities and other ad-hoc changes to debt stocks.

A.-C. Orange bars indicate debt-decreasing contributions and red bars indicate debt-increasing contributions. Sample includes 32 small states.

D. Orange bars indicate debt-decreasing contribution and red bars indicate debt-increasing contribution. The decomposition is based on forecasts from the April 2024 _World Economic Outlook_ . Sample includes 32 small states.

33
Gregory et al find that the negative impact on labor rises, the more the substitutability between tasks, and the positive impact rises, the more the substitutability in demand.<sup>24</sup> Both effects are stronger in regions with a stronger initial share of routine tasks. To the extent that additional income from wages and profits is spent locally, demand for non‐tradables rises too. If firm owners are not located in the region or do not spend in the region, the total compensation effect is smaller. The authors caution that even if the net effect on employment is positive in the long run, unemployment can rise in the short run due to factors that constrain overall demand and factor redeployment. For example, there may not be mobility across regions, or across jobs, income earned may not be spent, expectations may hold back production and/or demand, or firms may have more market power, among other things.

In a very recent paper, Autor and Salomons (2017) examine the relationship between productivity growth and employment using country and industry level data for 19 countries<sup>25</sup> over a period spanning more than 35 years.<sup>26</sup> They find that industry‐level employment robustly falls as industry productivity rises, but country‐level employment rises with country‐level productivity. Both the employment‐reducing (in the advancing sectors) and employment‐increasing effects of productivity growth (in the spillover sectors) are sizable and the net effect on employment is modest. The size of the employment effects depend on the relative weight of the sectors in the economy, product market competition, demand saturation, and integration in international production chains. They conclude that increases in final demand and inter‐industry linkages play an important role in “compensation”.<sup>27</sup> They

> 24If the price of one good falls, there is more demand for it and production rises.

> 25 The countries are the developed‐ EU countries minus those in Eastern Europe, the Republic of Korea, among others.

> 26 Their basic measure is labor productivity but using total factor productivity gives comparable results.

> 27 They note that the results support the neoclassical story in that both labor supply and final demand jointly determine output and employment. The key driver of employment is the population of consumer‐workers.

19
Table 1.8

|EstimatedAnnual ExportsofPreciousM<br>(inmillionsofrix-|etalsfromEuro<br>dollars)|petotheEast||
|---|---|---|---|
|Year<br>1600|1650|1700|1750|
|TheLevant(EasternMediterranean)<br>1.0|2.0|2.0|2.0|
|TheBalticregion<br>1.7-2.0|2.3-3.0|2.3-3.0|2.3-3.0|
|TherouteroundtheCape(Asia)<br>1.0|**'1.7**|3.3|5.7|
|Total<br>3.7-4.0|6.0-6.7|7.6-8.3|10.0-10.7|
|*****Includes**silver**fromJapan usedbyVOCtomeet demand<br>Source: Attman(1991:Table4,17)|from1635to166|8.||

> The need for precious metals increased markedly from the mid- 16th century when Portuguese trade around the Cape increased, and even more so in the 1 7 th century when

> the Dutch (VOC) and English (EIC) began their Euro-Asian trade. Attman's estimates reflect this. While exports to the Levant and Baltic regions remained unchanged from

> 1650 to 1750, those to Asia increased by 5 percent between 1600 and 1750 at which time they reached 5.7 million rix-dollars.

Table 1.9

||ExportsofSilv<br>|erandGoldfromW<br>(tonnesof"silvere|esternEurope,1<br>quivalent")|601-1780||
|---|---|---|---|---|---|
||Tothe<br>Baltic|ToEastem<br>Mediterranean|Dutch(VOC)to<br>Asia|British(EIC)to<br>Asia|Total|
|16014-1650|2,475|2,500|425|250|5,650|
|1651-1700|2.800|2,500|775|1,050|7,125|
|1701-1750|2,800|2,500|2,200|2,450|9,950|
|1751-1780|1,980|1,500|1,445|1,450|6,375|
|Total1601-1780|10,055|9,000|4,845|5,200|29,100|

Source: Maddison (2001: Table 2-10, 65); Barrett, in Tracy (1990: 251) (he does not show his equivalence conversion ratio for gold.)

> As further evidence of the significant growth in precious metal exports from Europe to the East over the 17th and 18'h centuries, Angus Maddison (2001) estimates export levels in tonnes of "silver equivalent". (See Table 1.9.) Although Tables 1.8 and 1.9 are not directly comparable,<sup>89</sup> a similar pattern can be observed in both tables. Table 1.9 shows the amount exported to Asia (by the VOC and EIC) increased 6 percent between 1601 and 1750 when it reached a total of 4,650 tonnes before declining by nearly 40 percent over the next thirty years. The estimates for the total shipped to Asia are over 10,000 tonnes for the two centuries, but the amount was almost certainly more than this since only the Dutch and British exports are shown here while other European

> countries (including Portugal, France, Denmark and Sweden) were also engaged in this

> activity, albeit to a lesser degree.

18
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negative.

3. In contrast, a child-labor ban that takes the form of a fine imposed on employers caught with under-age workers (a demand-side ban) will tend to _lower_ unskilled wages, lowering them more for underage workers than for other low-skill workers, and unambiguously increase the labor supply of the rest of the household, including children aged 14-17. In addition, it will not stop under-age child labor and can even increase it.

4. A labor clause that prohibits households from sending children below the age of 14 into the labor market is likely to raise low-skilled wages, lowering wage inequality. However, the direct effect of the lost labor on income of low-income households may tend to push inequality upward. For example, if high-skilled households—that never use child labor—are in the upper household income quantiles and low-skilled households—that would use child labor when it is allowed—are in the lower quantiles, income inequality will rise if the drop in<sup>_<u>w</u>_</sup> _w_<sup>_HL_dominates</sup> the direct loss of labor income to low-skill households from loss of child labor earnings.

# **3 Data and Empirical Strategy**

Our labor market data comes from the World Bank’s International Income Distribution Database (I2D2) which harmonizes income survey microdata from 170 countries covering response years (nonuniformly) between 1960 and 2019. The data include 1,777 unique nationally representative survey years covering 180,027,049 respondents. The underlying survey data comes from a variety of survey modules that cover income-related questions, including respondent age, employment status, wages, household income, etc. Because the incidence of and concerns over child labor are concentrated on developing countries, we likewise restrict our analysis to developing countries in the I2D2 data, resulting in a sample of 101 countries.

The temporal resolution varies by country and surveys are repeated cross-sections—respondent identification is unique only within survey-country-year, which precludes linking any repeatedly sampled individuals across time. To circumvent this limitation, we aggregate the individual microdata to the country-year. Because the surveys are nationally representative, this aggregation 14
_Gender Equality and Economic Development: The Role for ICTs_ 31

# **Figure 7**

<!-- Start of picture text --> Selection-Distortion and Environment Effects<br>Gender Inequality in Education<br>Boys with lower Less Educated Mothers - Less life-long<br>innate abilities get Less intellectually learning at the<br>educated stimulating environment household level<br>Lower quality Lower Quality<br>of students Labor Force<br>Gender Inequality<br>in Employment Lower TFP Marginal Product of<br>Growth Capital Decreases<br>Less Investment<br>Lower Economic Growth<br>Figure 8<br>Demographic Transition Effect<br>Gender Equality in Education<br>Education attainment of women increases<br>Lower fertility – less children<br>Aggregate Savings Relative size of the working–<br>Increases age population increases<br>Marginal Product of<br>Capital Increases<br>More Investment<br>Higher per-capita Income<br><!-- End of picture text -->
**Table 6: NDVI, SAVI, and LSWI  -  Pixel-level Fixed-effect Estimates of SWC Impact by Length of Treatment**

||**SWC on cultivated land**|**SWC on degraded land**|**New area planted with trees**|
|---|---|---|---|
|**Dependent variable NDVI**||||
|Treated only one year|-0.01908***|0.01638***|0.03137***|
||(-114.53)|(62.91)|(40.81)|
|Treated ≥ 2 years|0.01232***<br>|0.03099***<br>|0.05392***<br>|
||(205.10)|(215.30)|(108.11)|
|R<sup>2</sup>|0.752|0.752|0.752|
|**Dependent variable SAVI**||||
|Treated only one year|-0.01175***<br>(-101.40)|0.00772***<br>(42.65)|0.01100***<br>(20.58)|
|Treated ≥ 2 years|0.00789***|0.01956***|0.03449***|
||(188.79)|(195.34)|(99.41)|
|R<sup>2</sup>|0.723|0.723|0.723|
|**Dependent variable LSWI**||||
|Treated only one year|-0.00628***|0.01135***|0.06344***|
||(-40.54)|(46.87)|(88.73)|
|Treated ≥ 2 years|0.00956***|0.02354***|0.05504***|
||(170.96)|(175.76)|(118.63)|
|R<sup>2</sup>|0.744|0.744|0.744|

_Notes:_ Number of observations: 75,018,149; marginal effects are reported. Mean seasonal rainfall, time-season, and pixel fixedeffects included throughout but not reported. t-statistics are in parentheses: *** significant at 1%; ** significant at 5%; * significant at 10%.

21
Figure 7. Inequality Decomposition by Fiscal Categories <!-- Start of picture text --> 0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Market Disposable Consumable Final<br>income income income income<br>Within Between Total<br><!-- End of picture text --> _Source:_ Rodriguez, Wai-Poi, and Woodham (2022).

Figure 8. Inequality Decomposition by Care Categories <!-- Start of picture text --> 0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Market Disposable Consumable Final<br>income income income income<br>Within Between Total<br><!-- End of picture text --> _Source:_ Rodriguez, Wai-Poi, and Woodham (2022).

28
- Clarke, G., C. Z. Qiang, L.C. Xu. 2015. “The Internet as a General-Purpose Technology.” _Economics Letters_ 135, 24-27.

- Clarke, G., Y. Li, and L.C. Xu. 2016. “Business Environment, Economic Agglomeration, and Job Creation around the World.” _Applied Economics_ , 48(33), 3088-3103.

- Claessens, S., E. Feijen, and L. Laeven, 2008. “Political Connections and Preferential Access to Finance: The Role of Campaign Contributions.” _Journal of Financial Economics_ 88(3): 554-580.

- Cull, R., L. Gan, N. Gao, L.C. Xu. 2019. “Dual credit markets and household access to finance: Evidence from a representative Chinese household survey.” _Oxford Bulletin of Economics and Finance_ 81(6), 1280-1317.

- Cull, R., W. Li, B. Sun, and L.C. Xu. 2015. “Government Connections and Financial Constraints: Evidence from a Large Representative Sample of Chinese Firms.” _Journal of Corporate Finance._ 32: 271-294.

- Cull, R., L.C. Xu, and T. Zhu. 2009. "Formal Finance and Trade Credit During China's Transition," _Journal of Financial Intermediation_ 18(2): 173-192.

- Croqvist, H., F. Yu. 2017. “Shaped by their daughters: Executives, female socialization, and corporate social responsibility.” _Journal of Financial Economics_ 126, 543-562.

- Croson, R., U. Gneezy. 2009. “Gender differences in preferences.” _Journal of Economic Literature_ 47(2), 448-474.

- Croson, R., and N. Buchan. 1999. “Gender and culture: International experiment evidence from trust games.” _American Economic Review_ 89(2), 386-91.

- Cull, R., and L.C. Xu. 2005. “Institutions, Ownership, and Finance: The Determinants of Investment among Chinese Firms.” _Journal of Financial Economics_ 77, 117–46.

- Devine, T. 1994. “Changes in wage-and-salary returns to skill and the recent rise in female self-employment.” _American Economic Review_ 84(2): 108-13.

- Dollar, D., R. Fisman, R. Gatti. 2001. “Are women really the `fairer’ sex? Corruption and women in government.” _Journal of Economic Behavior and Organization_ 46(4), 423-429.

- Dollar, David, Mary Hallward-Driemeier, Taye Mengistae. 2005. “Investment Climate and Firm Performance in Developing Economies.” _Economic Development and Cultural Change_ 54(1), 1-31.

- ———. 2006. “Investment Climate and International Integration.” _World Development_ 34(9), 1498–516.

- Faccio, M., M.T. Marchica, R. Mura. 2016. “CEO gender, corporate risk-taking, and the efficiency of capital allocation.” _Journal Corporate Finance_ 39, 193-209.

- Faccio, M. 2006. “Politically connected firms.” _American Economic Review_ 96(1), 369-386.

- Ferreira, Fernando, Joseph Gyourko. 2014. “Does gender matter for political leadership? The case of U.S. mayors.” _Journal of Public Economics_ 112, 24-39.

- Fiske, A. P.,1992. “The four elementary forms of sociality: Framework for a unified theory of social relations.” _Psychological Review_ , 99(4): 689-723.

- Fisman, R., I. Love. 2003. “Trade credit, financial intermediary development, and industry growth.” _Journal of Finance_ 58(1), 353-374.

26
<!-- Start of picture text --> Modelling Insurance<br>Rainfall amount Scheme<br>Climate Change Pool<br>Impacts Risk management<br>Strategies<br>Dynamic financial analysis<br>Risk measures of<br>Financial robustness<br>Ruin probability,<br>Initial capital requirements<br><!-- End of picture text --> Fig. 2: Modeling approach from the insurance perspective.

The input variables for modeling intensity and frequency of rainfall include the current rainfall patterns in Chitedze. To assess climate change impacts, we used the scenario based future rainfall patterns relying on the analysis of Tadross et al. (2005) and compared those to the current scenario. Furthermore, we implemented insurance conditions, such as contract and trigger events as currently employed in Malawi, in combination with the simulated rainfall amount to determine the claim payments for each year. A dynamic financial analysis for a 10-year time period is performed to analyze the financial robustness of the contract under the different climate change settings. Output variables include (i) the probability of ruin of the insurance pool as a measure of its robustness and, (ii) initial capital necessary to reduce the probability of ruin to 5% resp. 1% over 10 years.

By considering a 10-year time horizon, this analysis assesses the financial robustness (risk of insolvency) of the Malawi scheme by estimating the scheme’s capital accumulation and depletion accounting for stochastic shocks under dynamic climatic conditions. The assumptions of the analysis are detailed below:

- All four regions in the Malawi pilot study are assumed to be identical to the Chitedze region for which more complete data exists;

- Insurance premiums and triggers are held constant;

- Insurance is stand-alone, i.e., the bundled credit-insurance structure and links between these financial instruments are not accounted for in this analysis;

- There is no accumulated back-up capital at the outset of the 10-year period;

- There is no opportunity for the insurer to diversify, and no reinsurance is purchased.

An important consideration is uncertainty, which is pervasive throughout the analysis. While input uncertainty, such as associated with rainfall projections, should ideally be incorporated in the analysis, because of the lack of information and data on the variability of rainfall, it is not dealt with quantitatively. Uncertainty in terms of natural variability of 7
interventions includes smaller loans or grants covering several areas (such as a $2m investment grant covering 8 areas), large and focused ($100m investment loan addressing 2 areas), as well as medium-large and diverse ($49m for an investment loan addressing 14 areas). Some of these differences are related to the countries‘ size, but they also represent considerable diversity as to the financial ̳firepower‘ brought to bear relative to the number of issues covered and the degree of challenge presented by these.

Overall, current practice of Bank support for public sector reforms is spread widely across countries and across issues. Most operations tend to be moderately or significantly complex and virtually all touch on some sensitive aspects which require substantial political commitment to succeed. These indications of limited selectivity imply that an important share of public sector operations would be expected to encounter political economy challenges in implementation. The following section explores the actual success rate of operations according to IEG outcome ratings, the patterns in the distribution of more and less successful operations across countries, and the type of challenges encountered.

# **III.2 Frequency and Types of Political Economy Problems: Patterns across Countries and Operations**

To explore the frequency of political economy problems in public sector reform support provided by the World Bank, this section examines what share of recently closed World Bank operations was rated less well, whether poor performance has been related to political economy challenges, and if so to what type of political economy problems.

**Graph 1: Distribution of outcome ratings – all public sector operations, ILs and DPLs**

<!-- Start of picture text --> Outcome rating distribution in Outcome rating distribution Outcome rating distribution<br>%, all public sector operations in % for DPLs only in % for ILs only<br>45.0 41.0 50.0 46.1 40.0 36.2<br>45.0 34.5<br>40.0 35.0<br>33.6 40.0<br>35.0<br>35.0 32.9 30.0 n = 58<br>30.0<br>30.0 25.0<br>25.0 n = 76<br>20.0 25.0 20.0<br>20.0<br>15.0 11.2 11.2 15.0 12.1 13.8<br>15.0<br>10.0 10.5<br>9.2 10.0<br>10.0<br>5.0 0.7 1.5 3.4<br>5.0 1.3 5.0<br>0.0 0.0<br>0.0<br>0.0<br>HS S MS MUS US HUS 0.0<br>HS S MS MUS US HUS<br>HS S MS MUS US HUS<br><!-- End of picture text --> †Closed PSGB mapped projects with approval dates between FY2000 and 08 for which IEG outcome ratings are available. Ratings are as follows: HS = Highly Satisfactory; S = Satisfactory; MS = Moderately Satisfactory; MUS = Moderately Unsatisfactory; US = Unsatisfactory; and HUS = Highly Unsatisfactory.

26
questions and time diary data for the same individual. We then discuss methods of data collection aimed at measuring quality of time (e.g., enjoyment, energy expenditure), and conclude with a broad discussion of the advantages and disadvantages of each of the methods considered in the paper and suggest ways in which elements of these methods might be combined to provide richer insights into gendered time use patterns and trends.

# **2. OVERVIEW OF TIME USE METHODS**

Survey methods commonly used to collect time use data fall into two general categories: methods that focus only on specific activities ( _stylized questions_ ) and methods that ask about all the activities undertaken within a specific period ( _time diaries_ ).

Which method is best suited to collect time use data in developing countries? The answer largely depends on the research question of interest. In general, the types of research questions that can be answered by time use studies fall into two broad categories: questions that only require information on the absolute quantity of time spent on particular activities (unidimensional), and questions that require information not only on _quantity_ but also on the _qualit_ y of time spent on these activities (multidimensional). We consider two additional criteria when evaluating time use methods: to what degree does it provide estimates that match how people actually spent their time (accuracy) and how difficult is it to implement in a developing country setting, including cognitive concerns as well as time and financial costs (feasibility)?

# **2.1. STYLIZED QUESTIONS**

_Stylized questions_ typically ask respondents to estimate the actual or, in some cases, the “usual” or “typical” amount of time they devoted to a particular activity during a specific time interval (e.g., the previous day, week, month, or year).<sup>4</sup> They are commonly used in comprehensive household surveys

> 4 The questions are “stylized” in the sense that they refer to a hypothetical construct (a “typical” day) rather than a specific time period (yesterday).

3