rafmacalaba/datause-encoder-data huggingface dataset:
# **Parameters:**

# **(A) Scenario-based**

**_greenodat_** ODA for Green Revolution Package Scenario 1, 2: _greenodat_ = 0 Scenario 3: {t=1, 0; t=2, 0; t=3, 0.3; t=4, 0.25; t=5, 0.2; t=6, 0.15; t=7, 0.1; t=8, 0; t=9, 0; t=10, 0}

# **(B) General**

|**_impcontcp _**|Import content share in commodities for public sector p<br>_impcontcp _= 1|
|---|---|
|**_impcontkp_**|Import content share in capital for public sector p<br>_impcontkp _= 1|
|**_ltott_**|Total labor<br>_ltott _= 19.6|
|_pimprt_|Price of imported goods in rural area<br>_pimprt =_1|
|_pimput_<br>**(C) Regional**|Price of imported goods in urban area<br>_pimput =_1|
|**_commodp,reg,t_**|Commodity expenditures in sector_p_in period_t_<br>_commodp,reg,t_ = 0.01|
|**_invroadi,t_**|Investment in rural road_i_in period_t_<br>_invroadi,t_ = 0.2|
|**_invrp,i,t_**|Rural public investment in sector_p_in period_t_<br>_invrp,i,t_= 0.04|
|**_invup,t_**|Urban public investment in sector_p_in period_t_<br>_invup,t_= 0.04|
|**_kcc0i_**|Initial capital in cash crops<br>{east 5, west 5, north 4, central 6}|
|**_kfi _**|Capital in food production<br>{east 5, west 6, north 5, central 4}|
|**_ksr0i_ **|Initial capital in rural services<br>{east 5, west 5, north 5, central 5}|
|**_klroadi _**|K-L ratio for road building<br>{east 0.02, west 0.02, north 0.02, central 0.02}|
|**_kpu0p _**|Initial urban government capital stock in sector p<br>{health 1, educ 1, infr 1, padmin 1}|
|**_kpr0i,p_ **|Initial rural capital stock in region i and sector p<br>_kpr0i,p_= 0.1|

55
Equation (12) still holds, so we must either have _q_ ≥ _c_ and _k_ = 0, or _q_ < _c_ and _k_ = 1.

Reasonably, the value of free allocations is likely related to the costs of leakage exposure; this may be because the authorities are inclined to compensate firms, in terms of reduced quota costs, for their loss of competitive position due to being subject to climate policy. Such effects are captured by the two terms inside the parenthesis of the last term on the RHS: The first is the expected value of future allocations per unit output today (where _b_ = _γβδ_ ), i.e., the implicit output subsidy. The second is the environmental cost of leakage due to a marginal reduction in domestic output. Assume first that this parenthesis is zero. Then we are back to equation (11) (remember that _R_ 1E _′_ = _q_ ). From the discussion in Section 3 with _a_ =0 we know that the optimal solution is characterized by _q_ < _c_ and _k_ = 1. This further implies that for the parenthesis to be zero, we must ˆ have _b_ > −∂ _E_ 2 / ∂ _X_ 1 . Let _q_<sup>_*_</sup> denote the optimal quota price in this case.

ˆ If the last term in (15) is negative, e.g., because ( −∂ _E_ 2 / ∂ _X_ 1 ) is large, it is optimal to reduce the quota price below _q_<sup>_*_</sup> . The reason is that leakage reduces the environmental effectiveness of climate policy in the policy bloc, and hence the optimal quota price falls.

If the last term is positive, e.g., because firms for some reason are given free quotas even though leakage exposure is negligible, the optimal quota price is higher than _q_<sup>_*_</sup> . The intuition is that the free quota allocation stimulates output too much, and so the optimal (second-best) response is to increase the price of emissions to moderate output.<sup>16</sup> If this effect, represented by the last term of (15), is big compared to the offset potential, represented by the third term of (15), then it is optimal to increase _q_ at least up to _q_ = _c_ (the two first terms in (15) are both positive for _q_ < _c_ ). But then we know from above that _k_ = 0, i.e., banning offsets is optimal.

Let us now discuss the sign of this last term in (15), based on the allocation rules of the EU-ETS. The sign depends on i) the ratio between the quota price _q_ and the marginal environmental costs _c_ , and ii) how generous the free allocation is (the size of _b_ ) compared to the leakage exposure ˆ ( −∂ _E_ 2 / ∂ _X_ 1 ). We have just concluded that _q_ < _c_ if the term is non-positive, but cannot conclude if the term is positive.

What about the magnitude of _b_ ? For the most exposed sectors in the EU ETS, which jointly account for a majority of industry emissions in the EU, _γ_ = _b_ /( _βδ_ ) is set close to _E_ 1/ _X_ 1 (almost

> 16 Obviously, the first-best response would be to lower _b_ , but this might be difficult for political reasons.

18
defense spending from the _a priori_ definition of productive public spending and, instead, includes public expenditures on fuel and energy, which are proven to be critical for growth in most developing countries, and often influence decision making by private investors.

“Non-core” spending includes social security and welfare; recreational, cultural and religious affairs; agriculture, forestry, fishing and hunting; mining and mineral resources, manufacturing and construction; and other economic affairs and services.

Defense spending is added to “other” public spending and therefore excluded from the regressions, given our lack of knowledge of the sector.

Table 11 shows the empirical results with this alternative definition of a _priori_ “core” public spending. When we compare the results in Table 11 with the ones in Table 5, we can see that the original results are robust. The “core” spending is again a statistically and economically significant determinant of growth only for the fast-growing group. It is not significant for the comparison group, or when we pool all countries together. Moreover, this robustness test leads us to conclude _a posteriori_ that “core” public spending is a positive determinant of growth in the group of fast-growing countries.

Table 12 presents the estimated coefficients calculated, this time using the GMM methodology in the previous sections. The results are consistent with the ones presented in Table 11. The “core” component of public spending is significant only for the fastgrowing group. Similarly, openness is statistically significant only in this group. The inflation rate consistently produces a negative and highly significant coefficient in each specification.

The nonlinear results presented in the previous section are also robust to the new definition of “core” spending (Table 13). Again, the nonlinear term, the squared term of “core” spending, does not have a statistically significant coefficient in any specification and the linear term of “core” spending is statistically significant only for the fast-growing countries. This indicates the absence of the nonlinear relationship between growth and public spending in our dataset.

# **VI.** **<u>CONCLUSIONS</u>**

The main purpose of this paper is to empirically investigate the link between GDP per capita growth and public spending components with the help of a panel dataset where developing countries are classified in two groups according to their GDP per capita growth rates for over three decades: fast-growing countries and a comparison group including a mix of countries with different growth patterns (a lower growth rate on average and more volatile growth paths).

The main result of the study is that the influence of public spending on economic growth is clearly different between the two groups, even though the size of government, measured as total public expenditures in percent of GDP, is similar. _A priori_ , a possible 19
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _20_ <!-- Start of picture text --> Severe Food Insecurity in IDA + IBRD<br>(U.S.$ Needs, billions) 2005−01−01 / 2027−01−01<br>140 140<br>120 120<br>100 100<br> 80 80<br> 60 60<br> 40 40<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan<br>2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027<br><!-- End of picture text --> Figure 2. Predictions made with final model, aggregated across all 144 IDA, Blend and IBRD countries.

_Note:_ The total food insecure population is based on the predicted prevalence rates, combined with the country population totals taken from the WDI database, and the population growth rates projected by the WEO. The black solid line corresponds to the period with observed data (2015-2019), the historical part of the black dashed line are predictions generated based on historical covariate values from the World Bank WDI database and the future values are generated using the IMF’s WEO outlook of April 2022. The red dash line is a downside projection that considers the slowed growth rates and higher inflation rates identified in the WEO’s downside analysis. Projected population totals are converted to financing needs based on the assumption of a 25% replacement cost, roughly sufficient to offset a 30% price hike, and assumes a US $ 0.75 daily cost of a minimum calorie sufficient diet (2020), adjusted annually for food price inflation using the FAO FPI. _Source:_ Figure prepared by the author for this paper.

by 2027 compared to below US $ 60 billion prior to the pandemic. In the downside scenario, the cash value of feeding the entire severely food insecure population a full year (4 times the plotted values, a 100% replacement cost) increases by US $ 387 billion from 2018 to 2027. This top up alone is 172% of the total cash value of feeding the entire 2017-2019 average size of the severely food insecure population. The basic arithmetic suggests that global food prices must come down in order for the global food security situation to become more manageable.

# _D. Counter-factual estimates_

This section uses the model to further investigate whether a basic scenario of normalizing growth, poverty and inflation is sufficient to revert food insecurity back to pre-pandemic levels. Several counterfactual scenarios are considered and
Policy Research Working Paper 10109

# **Abstract**

Economic statistics are frequently produced at an administrative level such as the sub-national division. However, these measures may not adequately capture the local variation in the economic activities that is useful for analyzing local economic development patterns and the exposure to natural disasters. Agriculture GDP is a critical indicator for measurement of the primary sector, on which 60 percent of the world’s population depends for their livelihoods. Through a data fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of Agricultural GDP into a global gridded dataset at approximately 10 x 10 kilometers using satellite-derived indicators of the components that make up agricultural GDP, namely crop, livestock, fishery, hunting and timber production. The paper examines the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP, where nearly 1.2 billion people live. The findings show an estimated US$432 billion of agricultural GDP circa 2010.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bblankespoor@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **<mark>Box 4.4 Contracts Not Yet  Automatic Feature of Private Sector Employment</mark>**

<mark>In 1994, Ms. Tran Thi Huong accepted a modestly paid accounting with a branch office of Thang Cong Private Company, one of Vietnam’s best known domestically oriented private companies. Feeling fortunate to have gotten the job, Ms. Huong at first did not object to the lack of a contract, particularly since nobody else in the office had ever signed one. Her lack of a contract meant no social or health insurance payments were made for her, but the pay was enough and Ms. Huong held little regard for public health care.</mark> After adjusting to the work, however, Ms. Huong decided to ask for an increase in pay from her <mark>starting rate of VND one million (US$ 67) a month. Six years later, when a raise had still not come through, Ms. Huong decided to give notice of her plans to switch to a new job. In response, Thang Cong offered her a raise to VND 1.5 million (US$100) a month and the opportunity to sign a contract, but Ms. Huong’s patience was at an end and she refused. Unable to convince Ms. Huong to stay, the company decided not to pay her any severance pay—in Vietnam expected to be about 3-6 months pay for six years of service.</mark> When Ms. Huong specifically requested severance pay, the head of Thang Cong’s Ho chi Minh <mark>City branch responded, “If you don’t reveal any internal information, then at some later time we will look into this issue and resolve it”. Two months later, only when the company needed Ms. Huong to return to help with some tax accounting matters, Ms. Huong was paid VND three million (US$200), a lump sum not based on any particular legal or systematic principle under the Labor Law. Dissatisfied with the behavior of Thanh Cong, Ms. Huong finally began to read the Labor Law and sought out a legal consultant, but by then it was too late. With no official contract, Ms. Huong had no legal basis for her complaints.</mark> _39._ The majority of workers are recruited either within the same district or the same province. Consistent with their reputation for commonly hiring underemployed farm labor, surveyed construction firms reported the highest numbers of workers from outside the province. Workers are mainly recruited through informal channels, like word of mouth through existing employees and family and friends.

**Table 4.16 Origin of Company Workers, Shares (%)**

|**Industries**|**Same district**<br>**or village**|**Other districts in**<br>**sameprovince/city**|**Neighboring**<br>**province**<br>**Other provinces **|**Total**|
|---|---|---|---|---|
|Manufacturing|44|27|18<br>11|100|
|<br>Other industry|27|40|21<br>12|100|
|<br>Services|36|44|9<br>11|100|
|**_Total_**|**_39_**|**_37_**|**_13_**<br>**_11_**|**_100_**|

Source: World Bank Survey, 2001 20
# **Table S1.3: Impact of cyclical fluctuations in total (male plus female) unemployment rates on IPV**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|A: Physical Violence|||||
|ln(U)|-0.104|-0.107|0.061|0.063|
||(0.089)|(0.093)|(0.104)|(0.109)|
|Observations|381730|381730|381730|348165|
|B: Sexual Violence|||||
|ln(U)|-0.093*|-0.089*|0.003|0.002|
||(0.051)|(0.048)|(0.049)|(0.050)|
|Observations|275718|275718|275718|268173|
|C: Any Violence|||||
|ln(U)|-0.146|-0.177*|0.066|0.061|
||(0.100)|(0.087)|(0.121)|(0.123)|
|Observations|274002|274002|274002|266517|
|Year FE||Y|Y|Y|
|Country FE|||Y|Y|
|X controls||||Y|

## _Source:_

Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

## _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. The dependent variables, controls and standard error adjustments are as in Table 1. Relative to Table 1, the study now uses the total (male + female) unemployment rate rather than include the male and female rates separately.
# **Figure 2: Child Survival**

<!-- Start of picture text --> 1<br>0.99<br>0.98<br>0.97<br>0.96<br>0.95 SSA<br>0.94<br>OTHER<br>0.93<br>RURAL<br>0.92<br>0.91 URBAN<br>0.9<br>0.89<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58<br>Age in Months<br>Cumulative Survival Probability<br><!-- End of picture text --> The main explanatory variables of interest are the use of water and sanitation infrastructure. The classification of sanitation varies substantially across time and countries in the DHS surveys: some surveys focus on the distinction between private and public facilities, while others focus on the location (in or outside the house) or the exact type of the facility (e.g., ventilated vs. non-ventilated latrines). In total we found over 400 different sanitation codes in the DHS surveys. Water categories were even more heterogeneous, with over 500 different codes across all DHS surveys.<sup>4</sup> Similar to sanitation infrastructure some DHS surveys contain very detailed information, distinguishing between multiple technologies and between private versus public water access, whereas several surveys only report very broad categories of unimproved versus improved water sources, with no indication whether the water point is private or shared.

To abstract from these classification differences across countries and time we divide water and sanitation in three different groups following some relatively simple coding rule as illustrated in column 2 of Table 1. In our initial approach we divide water and sanitation in “technology classes”. We define all latrines as “basic” sanitation technology, and flush toilets as “advanced” sanitation technology. With respect to water, we regard boreholes and any kind of protected and unprotected wells and springs as “basic” water technology, while we classify piped

> 4 A detailed list of all water and sanitation infrastructure categories in the DHS, as well as a suggestion to recode them, and as done in this paper, is available from the authors by request.

10/36
Table 5 12

||||NMC|4aaFlaw'||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|______________|_________**_**__<br>19S3|______<br>194|t<br>168|<br>91188|188i|1 6|low<br>1W||2201|2002<br>.||
|crulsCowaslrbr'<br>~|~|_||||||||||
|P____C_-______roe_|308|354|566|743|-58|-316|-139|157|-162|_|-39|
|P____<br>d<br>hb<br>,<br>_not<br>_7_a_|__<br>ei|es|103|117|102<br>|115|148|143|83|103|10e|
|P<br>MaW<br>dinsbwt<br>r|250|133|186|289|B9|-90|118|70|32|51|15|
|Qdwpi<br>cmpna_fbvarot_|-o0e|156|2790|357|-247|-341|-A04|369|-277|219|.180|
|OflaMAwnr|32|07|a8|-47|137|170|22|68|0o|14|33|
|Chwiinkltn|-200|-a5|175|-49|406|-489|-382|-.24|_.117|113|121|
|C'rrlnaccxr|-135|-232|-39|-531|-25|097|627|471|32e|231|179|
|_OUCf_AsianS!igng<br>a_t_|_m|||||||||||
|A<br>.lvS<br>_mm"nee_|2ZS|349|407|460|196|-154|15.2|02|354|159|95|
|Privaedmuthvunlnot|264|352|441|439|473|483|473|401|452|492|so5|
|PrtcampaWgdionwrsfwL notn|0 9|75|2.1|33|21i|-92|24|-2.8|-176|-4 3|-59|
|Olt_wprvsC*W_flows.nd|-47|-108|-54|-12|-256|-544|-348|373|78|291|-347|
|_ofa!flowsnot_|82|25|-33|-73|-4e|31_|<br>s|-21|-20|40|48|
|Chmin<br>rara|-166|-e14|254|-417|-468|-16e|-386|-26.2|807|-42a|-344|
|Ctrrwmoowt|72|183|845|147|514|410|32.7|35a|547|421|359|
|ovopingAsiaTotal<br>_||-|||||.|..._|...|=||
|PthCwiteflw<br>as|1<br>53-3|703|975|1203|_140_|_-470_|13|_-155_|iz2|Is|se|
|Pctflcdtiflyimtnd|331|447|544|55s|875|598|el|**5** 44|535|55|807|
|PrtvpXoiftbnat<br>not|259|208|20 7|302|68|-182|142|44|144|08|-44|
|OVwprh<br>eqtrctdflows<br>no|-65|46|225|345|503|-85|760|-742|-199|-5410|507|
|0dd<br>_fal_<br>__<br>_|<br>14|32|55|-120|71|207|18|45|_-14_|_54_|81|
|COwgin**_rsSiU_**|-368|-57<br>9|-429<br>S|45|42|-435|768|-48|-924|-541|-485|
|CLewntmauzt<br>.|-207|**_449_**|413|-X34|259|1107|954|029|873|652|546|
|Hong**Kong**||-.||||||||||
|P<br>n<br>sCq.f<br>nram||73|-72|.94|117|4-5|10|42|-5|-99||
|1Notcwdt<br>anpria<br>rnaantwhnssno<br>2Rn.<br>cdabntda,<br>5wiwirslmay<br>3**A**<br>n*sign_v%WeanonIncre._<br>**4**ThesmnoflancunwsefLd**baa..**notpul<br>g<br>tuxn<br>d towctmd**_mctmi_**sins**mid**<br>5<br>1<br>ndxAsKam<br>Sappo<br>aidTiwr<br>Pru*f|tw-f<br>ht<br>sesctlWra<br>akisu<br>ofCine|vIflmtnam<br>**_mm_**_dl_1d<br>n.etdflc|Sir<br>-n<br>W<br>i_flows_<br>tFlowsaid|<br> <br>nd**_SmIt_**<br> **chdg.**inr|**_tuM_**natrm<br>wwn<br>equ|tanlbIo<br>asiwhim|hCatu<br> qppos.lziL|d8C1S mid|p1$obM|mw||
|6I1diAnluirdwi<br>KCr<br>MatiwiVWFtip|HsmWdald|Tid||||||||||
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The abbve table shows that pnvate portfolio investment recovered quite significantly in 1999 after the cnsis but not to anything near pre-crisis levels In fact, Investment fell again in both 2000 and 2001 This could be attributed to the world economic slowdown following the bursting of the dot-com bubble in the U.S. and the September 11 th terrorst attack in the U S A small recovery is expected in 2002 as recovery of the U.S. economy appears to be gaimng momentum.

The capitalization and trading volume of these equity markets **is** extremely small compared to that of some major stock markets, particularly those in the U.S. (See Table 5 13 for some comparisons) 8I
Our results for new investment are shown in Table 8. We find that age is positively and significantly related to the percentage of Bank Finance while significantly and negatively related to the percentage of Operations and Informal finance. Furthermore, the economic significance of age on the amount of bank financing is large, relative to its impact on operations and informal financing, which though statistically significant are small economically. Second, we confirm that rule of law is significantly and positively related to greater usage of formal financing – from bank and operational lenders – and significantly and negatively related to the size of informal finance. As before, we find that the interaction of rule of law and bank finance is negative and significant. In other words, we find that weak legal environments significantly magnify the disparities between usage of bank financing between young and old firms. However, the relationship with informal and operations finance is insignificant. We find similar results for the percentage of working capital financing and the interaction with credit information (not shown).

Second, our results are very robust to various sub-samples of countries and firms. As discussed earlier, an important caveat of enterprise surveys that is relevant for our analysis is the sampling design, which in some countries excluded firms under 2, 3 or 4 years old. Thus, the very youngest and hence most entrepreneurial firms are simply not sampled in some countries. To eliminate the possibility that this sampling feature will bias our results, we reproduced all our results using only surveys (i.e. countries) with firm minimum age of one year. Our baseline results reported in Table 6 are even stronger in this sample, even though the sample size is significantly reduced (not reported). Again, 18
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