rafmacalaba/datause-encoder-data huggingface dataset:
Figure 29: Irrigation Utilization for Agrarian Households (a) Irrigation Utilization for Agrarian Households by State, 2014/15 <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Nile Eastern - Blue and White Nile Kordufan Darfur -<br>(b) Irrigation Utilization for Agrarian Households by Region, 2009 and 2014/15<br>100%<br>2009 2014/15<br>80%<br>60%<br>40%<br>20%<br>0%<br>Nile Eastern Khartoum Blue & White Nile Kordufan Darfur Sudan<br>Northern River Nile Red Sea Kassala Al-Gadarif Khartoum Al-Gezira White Nile Sinnar Blue Nile North Kordufan South Kordufan West Kordufan North Darfur West Darfur South Darfur Central Darfur East Darfur Sudan<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

- (c) Lower Irrigation Uptake of the Poor compared to the Non-poor (by Region), 2014/15

<!-- Start of picture text --> Non-Poor Poor<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Nile Eastern Khartoum Blue & Kordufan Darfur Sudan<br>White Nile<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

Very little of the land cultivated for the four main crops—sorghum, millet, groundnut, and sesame—is irrigated (Figure 30). Of these four crops, sorghum has the highest rate of irrigation, at 21 percent, while millet has the lowest rate of irrigation, at only 5 percent. The low rate of irrigation for millet corresponds to where it is predominantly grown, in the regions of Kordofan and Darfur. Besides these four crops, cash crops are much more likely to be grown on irrigated lands. For example, wheat and cotton are grown on irrigated lands over 80 percent of the time, while dates are almost exclusively grown on irrigated land.

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# **4.2 Ad Valorem Equivalents (AVEs) of the Non-Discriminatory Barriers Against Suppliers of Business Services in Kenya and Tanzania**

Some regulatory barriers, such as complete bans on entry, are non-discriminatory in that sense that they impose costs on both domestic suppliers of services and foreign suppliers of services. In the cases of Kenya and Tanzania, we also estimate the AVEs of non-discriminatory barriers. We commissioned new updated surveys (using the World Bank survey instrument) and obtained supplemental information on the regulatory regimes.<sup>**16**</sup> Based on these surveys and supplementary information in both countries, Jafari (2014a; 2014b) scored the regulatory regimes according to the Australian methodology to develop services trade restrictiveness indices (STRIs) for the non-discriminatory barriers to services providers in the 11 sectors of the World Bank survey in Kenya and Tanzania. He then used these STRIs and the Australian regressions to estimate the ad valorem equivalents of the non-discriminatory barriers in Kenya and Tanzania. The results for the AVEs are in tables 4a and 4b.

# **4.3 Estimates of the Ad Valorem Equivalents of the Costs of Time in Exporting and Importing**

In order to estimate the impact of improved trade facilitation, in this paper we apply a new data set based on the path-breaking work of David Hummels and his co-authors (Hummels, 2007; Hummels and Schaur, 2013; Hummels _et al_ ., 2007). These authors estimate the time cost of trade by product and country in two steps: (i) they first estimate the cost of one day delay by product; (ii) they combine their estimates of the per day cost of trade by product with the Doing Business database on the number of days to export or import by country. This yields the costs Using the estimates of Hummels and his co-authors, Peter Minor (2013) provided estimates for the regions and products in the GTAP database on a bilateral basis. We use estimates from Peter Minor, which we aggregate to the sectors and regions of our model, yielding the cost of trade by

> 16 We thank Ms. Sonal Sejpal of the law firm of Anjarwalla & Khanna Advocates in Nairobi and Cyril Pesha and his law firm associates in Dare es Salaam for leading this research effort in Kenya and Tanzania, respectively.

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# **A Historical Lens**

Using a recently available data set from the Groeningen Growth and Development Centre’s 10-sector database (hereafter “GGDC data”) that provides employment and value added for ten disaggregated product sectors for a large set of countries historically, we begin the paper with high-level analyses investigating cross-country trends in sectoral productivity convergence and how the relationship between levels of development and economic structure has changed over time.

The GGDC data “provides a long-run, internationally comparable dataset on sectoral productivity performance in Africa, Asia, and Latin America. Variables covered in the data set are annual series of value added, output deflators, and persons employed for 10 broad sectors” (Timmer, de Vries and de Vries 2014). These ten sectors comprise agriculture, mining, utilities, construction, manufacturing, wholesale and retail trade, FIRE industries, transport, government services, and other services (restaurants, hospitality, etc.). Among Sub-Saharan African countries, it contains data from Ethiopia, Botswana, Ghana, Kenya, Malawi, Mauritius, Nigeria, Senegal, South Africa, Tanzania and Zambia.

These data allow a more nuanced look at the role of specific sectors in structural change, particularly separating construction and mining from manufacturing aggregates, and FIRE industries, government services, transport, and utilities from services aggregates, allowing greater specificity in understanding which sectors are drivers of structural change. Many of the countries in the recent release have reliable data going back to the 1970s or earlier, so we are able to build a long historical quantitative view of structural change. This, in turn, allows us to place economies’ current and recent trends in comparative historical context.

# **Changing Relationship between Income and Economic Structure**

Figures 1 and 2 use the GGDC data to investigate the cross-sectional relationship between country income levels and economic structure (as embodied by the share of employment in the focal sector). As can be seen clearly, there is a clear trend in the changing nature of the relationship between productivity levels and economic structure for both manufacturing and the largest services subsector, wholesale & retail trade. At the beginning of the study period in the early 1970s, the relationship between the manufacturing labor share and income was far steeper than it is today, having followed a progressive erosion of the initial strength of this correlation over the past four decades.

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For example, this measure understates the administrative cost of highcontribution-rate PAYG-financed pension systems like those in continental Europe and overstate administrative costs in low-rate countries in Africa. This ratio also overstate costs of funded versus unfunded pensions, because the former earn hig'lier rates of return in the steady state and therefore need a smaller contribution rate to pay the same benefits.

Reporting administrative charges as a share of covered wages is suspect because of two reasons: (a) Administrative costs tend to be flat per person. Managing accounts for a worker is not proportional to the number of zeros in her paycheck. Individual country time series show that a substantial share of administrative costs are proportional to the number of effective covered workers served, not to the volume of funds or the level of covered wages. It is possible that the quality of labor needed to operate pension systems varies much less across countries than per-capita income, for a given, quality of pensions offered; and (b) Covered wages are subject to significant under reporting in many countries. In most cases independent workers are free to declare any wage they wish to cover. These workers are dominant -in labor markets in developing countries. Covered wages are a notoriously slippery concept in countries with large informal labor markets. Both problems imply that measures of cost that divide total cost by the volume of covered wages, or by per-capita income, are misleading. They exaggerate the degree to which developing country pension systems are more costly.

Because of these problems, we report administrative charges per covered worker. However, this also has defects, which the reader should keep in mind: a) This is a money measure, so an international comparison requires the use of exchange rates. The difference between PPP exchange rates and market exchange rates can be substantial, as reported in the Economics literature. In this case the bias is in favor of developing countres because in most of them the market exchange rate is more depreciated than the PPP rate.

b) The number of effectively covered workers is difficult to pin down when the degree of coverage is limited. In developing countries there is a large difference between the number of persons who contribute in one month, the number of persons that are covered by disability insurance and the number of persons that have contributed at least once in their lifetime. We introduce the concept of "effective covered worker"- an intermediate - number explained below to approach this problem.
tion: where _yhv_ is the outcome for household _h_ in village _v_ , _Mhv_ is a dummy of whether the has a migrant in Gulf-Malaysia, _Mv,_ 2001 is the share of households with Gulf-Malaysia migrants in 2001, _Xhv_ are controls, and _νv_ and _εhv_ are uncorrelated errors assumed to be clustered at the village level. The controls in these specification include household size, ethnicity of the household head, fixed effects for survey strata, and the logarithm of distance from Kathmandu. I estimate the system of equations in (1) with 2-SLS with the predicted migration rate of the household from the first-stage as an instrument for the actual migration status of the household. To estimate the marginal propensity to consume, I estimate a similar specification with the migration status of the household replaced by the logarithm or remittances the household received.

To interpret the estimate as a causal impact of migration on the outcomes, the 2001 migration rates must be uncorrelated with the outcomes in 2011, except through its influence on current migration. This assumption is plausible if current migration is influenced by the social network of the previous migrants. Even in this case, the estimate will be the causal impact of migration on the migrants induced by the prior social networks to migrate. That is, this identifies the localaverage-treatment-effect (LATE) in the language of Imbens and Angrist (1994).

However, a frequent criticism of this approach is that the village level characteristics could be correlated with migration rates in 2001, migration rates in 2011, as well as the outcomes in 2011. For instances, villages with higher degree of entrepreneurial individuals may have migrated early (in 2001), and also have better outcomes in 2011 (not necessarily due to the impact of migration). Or, alternatively, villages that consistently have low labor demand (due to the rough geography, or local political institutions) could have resulted in higher early migration as well as the labor outcomes in 2011. To address these concerns, I use a second empirical strategy, a difference-in-difference method, that is immune to the village specific characteristics that influences the outcomes. For the outcomes that I can use both empirical strategies, the estimates from both specifications yield similar results, which is very comforting.

# **estimates**

As discussed earlier, villages that have high migration rates could be very different from villages which have lower migration rates. These differences might reflect several observable and unobservable characteristics that also affect the outcomes. Similarly, there could be national trends in certain outcomes. The most relevant in this context is the demand for Nepali workers from Gulf-Malaysia, which has drastically increased in recent years. To the extent that village specific characteristics are time-invariant, and the trends in outcomes do not differ by villages, a difference-in-difference model produces the causal estimate of migration on the outcomes.

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Table 3: Estimation results: alternative spatial weight matrices

||||All countries||
|---|---|---|---|---|
||Weight matrix|Trade|FDI|Distance|
|_gt−_1||0.260<br>|0.251<br>|0.259<br>|
|||(15.776)|(15.221)|(15.722)|
|GDP growth||0.301|0.298|0.338|
|||(2.892)|(2.831)|(3.235)|
|∆Change in real interest rate||-0.663|-0.681|-0.685|
|||(-11.032)|(-11.281)|(-11.393)|
|Real credit growth||0.169|0.168|0.163|
|||(4.766)|(4.688)|(4.535)|
|_Wgt_||0.442|0.234|0.230|
|||(15.283)|(8.111)|(9.493)|
|_Wgt−_1||-0.178|-0.028|-0.112|
|||(-5.784)|(-1.081)|(-4.317)|
|Pesaran CD statistic||0.472|0.776|-0.521|
|(p-value)||(0.319)|(0.219)|(0.301)|
|Exponent of CSD<br>||0.441|0.405|0.386|
|_R_<sup>2</sup>||0.590|0.585|0.588|
||||Advanced countries||
|_gt−_1||0.254|0.283|0.280|
|||(12.009)|(13.577)|(13.360)|
|GDP growth||0.997|0.898|0.884|
|||(5.752)|(4.885)|(4.822)|
|∆Change in real interest rate||-0.791|-0.503|-0.552|
|||(-3.611)|(-2.105)|(-2.320)|
|Real credit growth||-0.039|-0.035|-0.016|
|||(-0.696)|(-0.547)|(-0.251)|
|_Wgt_||0.799|0.366|0.310|
|||(62.788)|(10.607)|(10.714)|
|_Wgt−_1||-0.254|-0.251|-0.171|
|||(-8.937)|(-5.666)|(-4.612)|
|Pesaran CD statistic||1.694|1.857|1.13|
|(p-value)||(0.045)|0.032|(0.129)|
|Exponent of CSD||0.757|0.717|0.680|
|_R_<sup>2</sup>||0.721|0.687|0.689|

Notes: Bias-corrected QML estimates. For both country samples, the corresponding columns report results with trade, FDI and distance weight matrices. The dependent variable is the real stock return computed as the first difference of the log real equity price. GDP growth is the first difference of the log of real GDP, ∆Real interest rate is the first difference of the real interest rate, and Real credit growth is the first difference of the log of the real credit stock. ’Exponent of CSD’ is the exponent of cross-sectional dependence. T-statistics in brackets and p-values in brackets for Pesaran CD statistic. The sample period covers 1995:1-2016:3.

# **4.2 Transmission of spatial impacts**

The fundamental implication of the dynamic spatial model is that a shock in a particular country affects not only the equity returns of that country itself, but also the equity 15
represented almost 50 percent of the outstanding amount of nonfinancial corporate bonds in emerging economies at the end of 2019. Most likely, a substantial share of this maturing debt will have to be rolled over, but it is unclear whether investors will still be interested in buying emerging market nonfinancial corporate bonds when the time to refinance comes. Willingness to rollover the debt will depend, among other factors, on whether global interest rates are low enough to maintain investors’ appetite for riskier assets in emerging economies as well as investors’ assessment on the overall vulnerability of firms.<sup>5</sup> In addition to the declining bond maturities, the increasing reliance on bond financing might have exposed emerging economy firms to higher rollover risks per se because bonds are more difficult to restructure than bank loans. The idea is that the dispersion of bond investors reduces their incentives to renegotiate debt payments relative to banks, which can monitor firms more closely and offer greater flexibility for firms than bondholders in times of distress (Bolton and Scharfstein, 1996; Hackbarth et al., 2007; Crouzet, 2018). Another factor influencing the greater financial flexibility of bank loans with respect to bonds is that loan creditors are senior in the event of default (whereas bond holders usually are not), so lenders can recover the principal more easily (Schwert, 2019).

While some developments increased rollover risk, other forces might have helped to reduce it. Even if bond maturities declined, the compositional change from loan to bond financing could have a positive effect on aggregate (loan plus bond) debt maturities because bond debt is usually longer term than bank debt (Cortina et al., 2019). As a result, the overall effect on debt maturities is unclear. Analyzing whether overall debt maturities increased or decreased for emerging market firms since the GFC would require data on bank loan maturities, which are not typically available across economies.

> 5 The COVID-19 pandemic could have exacerbated problems with debt rollovers. Aggregate estimates for all emerging economies (excluding China) show that, assuming these economies lost market access in 2020, they might not be able to rollover as much as $1.6 trillion of their external public and private debt maturing in 2020. This value represents 28 percent of the stock of external debt in emerging economies (excluding China) in 2018 (Bolton et al., 2020).

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economies. By comparison, we find that those households which received formal credit are less likely to cut back on staple-food consumption in both high-income and low-income countries.

Table 8. Household-level stabilizers of income shocks on consumption

|Countries:|Crisis,Hi|gh-Income|Crisis,Mid|dle Income|
|---|---|---|---|---|
|||Other||Other|
|Dependent variable:|Staple foods|consumption|Staple foods|consumption|
|Rural|0.0308***|0.00|-0.01|-0.0287**|
||[0.00960]|[0.0140]|[0.0119]|[0.0118]|
|Low income|0.0795***|0.01|0.135***|0.00|
||[0.0166]|[0.0242]|[0.0170]|[0.0168]|
|Income shock|0.0937***|0.273***|0.191***|0.272***|
||[0.00892]|[0.0130]|[0.0119]|[0.0118]|
|Income diversified|-0.0408**|0.01|-0.03|0.02|
||[0.0176]|[0.0257]|[0.0222]|[0.0220]|
|Informal credit applied|0.150***|0.125**|0.192***|0.189***|
||[0.0409]|[0.0596]|[0.0383]|[0.0380]|
|Informal credit received|-0.01|0.06|-0.0747*|-0.116***|
||[0.0427]|[0.0623]|[0.0396]|[0.0393]|
|Formal credit applied|0.147***|0.158***|0.152***|0.05|
||[0.0381]|[0.0555]|[0.0490]|[0.0486]|
|Formal credit received|-0.115***|-0.10|-0.143***|-0.02|
||[0.0404]|[0.0588]|[0.0530]|[0.0526]|
|Assets|-0.0402***|0.0183**|-0.0966***|0.0788***|
||[0.00595]|[0.00867]|[0.00705]|[0.00700]|
|Bank account|-0.0913***|-0.01|-0.0681***|0.0854***|
||[0.0212]|[0.0309]|[0.0159]|[0.0158]|
|Mortgage|-0.01|0.0398***|0.0518*|0.0823***|
||[0.0103]|[0.0151]|[0.0283]|[0.0281]|
|Observations|5'351|5'351|6'822|6'822|
|Countries|5|5|6|6|
|CountryFE|yes|yes|yes|yes|
|Method|OLS|OLS|OLS|OLS|
|R2|0.10|0.24|0.16|0.18|

_Notes: The reported coefficients are estimates from a linear probability model. All regressions include country fixed effects. See the Appendix for a definition of all dependent and explanatory variables._ The Table 8 results suggest that the accumulation of pre-crisis debt is associated with a stronger reduction in (non-essential) consumption in middle-income countries and high-income countries.

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,ew deal with none of them, while the attention given to item (i) and to a lesser extent item (ii) generally exceeds that afforded to items (iii) and (iv). It is clear that there is no consistent or systematic approach to these issues. Some staff question whether the Bank has the expertise necessary to make recommendations in this area and the credibility to follow them up with appropriate lending instruments. Such self questioning is to be expected and is characteristic of most new or rapidly evolving activities which the Bank.undertakes.

8. In addition to public expenditure reviews, the Bank sometimes uses its regular reports on the economies of borrowing countries, or similar vehicles, to discuss public expenditure management issues. Again, the coverage give'.i to institutional and procedural problems varies greatly and does not follow any consistent or systematic format.

9. Exceptionally, CESW is used as a tool for detailed Bank collaboration with Government in implementing improved investment programing and budgetary systems and procedures. A leading example is that of Zambia where the CESW approach had zhe great advantage of direct involvement by Bank staff who had been engaged for some time in an intense policy dialogue with the authorities. However, this is very expensive in use of staff time, 3 and does not, unlike a lending operation, involve the Government in implementation deadlines. Although it may be appropriate to commence with such a process rather than policy oriented approach, in order to build longterm sustainability of the reform effort, this does not, as Zambia's subsequent experience has shown, eliminate the necessity of deep-seated policy level commitment to change.

# The Planning Concept

10. The planning experience4 is immensely varied among developing countries, where more than 300 plans have been formulated during the past 30 years. At the risk of over-simplifying, countries may be divided into four broad categories: (i) those where e-omprehensive, central medium-term planning has been practiced with the full backing of the political authorities (Eastern Europe plus some countries in South Asia, especially India, and North Africa) and seeks to directly influence private as well as public spending; (ii) those where planning is seen as a means of controlling and directing public expenditure and of setting a framework of active

- 3/ In fiscal year 1985 alone, 51 staff weeks were used in Zambia with a further 18 planned for the next two fiscal years. This does not include the substantial amount of time devoted to these issues in the context of economic missions, preparation of Consultative Group meetings, and the broader policy and sector dialogue.

- 4/ The discussion in this section draws upon work carried out by Ramgopal Agarwala in preparation of the 1983 World Development Report and subsequently. Much of this is also summarized in his "Planning in Developing Countries: Lessons of Experience" Staff Working Paper No. 576.