rafmacalaba/datause-encoder-data huggingface dataset:
# **3.1 Data on Standards**

Our policy variable measures the restrictiveness of mandatory standards imposed on agricultural products based on MRLs of pesticides allowed for agricultural products in several importing countries.<sup>14</sup> The source for our data is the Homologa database obtained from Agrobase-Logigram, a French company that collects information on monthly changes in allowable pesticides for 63 importing countries from each country’s relevant ministry and standardizes the information in terms of language, unit, and format for the period 2006-2012.

The Homologa dataset reports only the importing countries’ list of regulated pesticides. However, many countries use a ‘deferral policy’ for pesticides for which it has not set a specific limit: e,g., in the case of the EU the default MRL is 0.01 parts per million (ppm). Many countries defer to Codex Alimentarius standards, the set of international standards for food safety and consumer protection developed by the Food and Agriculture Organization and the World Health Organization. In fact, many countries, particularly developing countries, do not have a list of regulated pesticides but instead directly defer to Codex standards. For the empirical analysis we will use all the information available for each importing country, including the countries’ deferral policies as well as their default MRLs. Appendix Table A.1 provides the list of importing countries for which we have data on pesticide standards and indicates whether each country has its own set of regulations and what is each country’s deferral policy.

Using Agrobase-Logigram’s Homologa data we matched 243 agricultural products to their corresponding harmonized system (HS) codes at the 6-digit level of disaggregation.<sup>15</sup> The products covered are agricultural products belonging to HS Chapters 06-24 with the exception of HS Chapters 15 and 16 (oils and edible preparations of meat and fish).<sup>16</sup> Table 1 lists for each importing country the number of HS 6-digit products covered by MRLs as well as the number of HS 6-digit products regulated including deferral and default policies. The number of different products regulated in total by all importing countries range from 214 in 2006 to 241 in 2012. The product coverage is very heterogeneous across countries: e.g., in 2012 pesticide limits were set by Brazil on 73 products and by the EU on 136 products. However, the product coverage of each

> 14 Note that these standards apply both to domestic production and to imports of the products.

> 15 While Homologa’s product coverage is greater than 243 products, we were unable to match all products directly to an HS code. 16 We omit HS Chapters 15 and 16 from the analysis because they include animal products for which importing countries also regulate veterinary drug MRLs which are not covered by our standards dataset.

8
_Table 5: Impact of schooling on fertility choices_

||**Probability**<br>**had a child**|**of having**<br> **by age 21**|**Age at fi**|**rst birth**|
|---|---|---|---|---|
||**IV-**<br>**PROBIT**|**PROBIT**|**IV**|**OLS**|
|Years of schooling|-0.0777<br>|-0.0583<br>|0.0794<br>|0.0735<br>|
||(0.0650)|(0.0073)**|(0.0863)|(0.0149)**|
|Log of household consumption per<br>capita (Rp.'000000)|-0.0001|-0.0003|0.0008|0.0008|
||(0.0007)|(0.0003)|(0.0009)|(0.0004)*|
|Age at first marriage|<br>-0.2237|<br>-0.2295|<br>0.7149|<br>0.7164|
||(0.0243)**|(0.0140)**|(0.0291)**|(0.0184)**|
|Urban area of residence|0.0945|0.0669|-0.1457|-0.1370|
||(0.1080)|(0.0496)|(0.1505)|(0.0849)|
|Travel time to nearest hc/mw - hrs|-0.1271|-0.1108|0.1208|0.1157|
||(0.0802)|(0.0598)|(0.1199)|(0.0956)|
|**_Region fixed effects_**|||||
|Sumatera|0.2370|0.2231|-0.6556|-0.6512|
||(0.1216)|(0.1126)*|(0.1916)**|(0.1828)**|
|Java|<br>0.0990|<br>0.0968|<br>-0.4470|<br>-0.4464|
||(0.1053)|(0.1049)|(0.1731)**|(0.1725)**|
|Jakarta|0.3303|0.3243|-0.6455|-0.6432|
||(0.1286)**|(0.1272)*|(0.2011)**|(0.1985)**|
|Bali|-0.0787|-0.0594|-0.3609|-0.3665|
||(0.1594)|(0.1473)|(0.2513)|(0.2368)|
|Nusa|0.1399|0.1569|-0.7377|-0.7427|
||(0.1538)|(0.1425)|(0.2478)**|(0.2347)**|
|Kalimantan|0.2460|0.2533|-0.9400|-0.9420|
||(0.1457)|(0.1434)|(0.2451)**|(0.2434)**|
|**_Cohort of birth fixed effects_**|||||
|Cohort 1: 1-6yrs in 1974|1.3793|1.3511|-1.8993|-1.8899|
||(0.6099)*|(0.6021)*|(0.8558)*|(0.8429)*|
|Cohort 2: 7-12 yrs in 1974|0.0097|0.0084|-1.2085|-1.2060|
||(0.6407)|(0.6415)|(1.0362)|(1.0341)|
|**_Pre-programme share of children in the_**|**_population in re_**|**_gion of birth_**|**_x birth cohort_**||
|Cohort 1: 1-6yrs in 1974|-5.0066|-4.9542|6.0467|6.0288|
||(2.2500)*|(2.2417)*|(3.0928)*|(3.0814)|
|Cohort 2: 7-12 yrs in 1974|<br>0.0680|<br>0.1374|<br>3.2455|<br>3.2184|
||(2.4447)|(2.4281)|(3.9431)|(3.9188)|
|**_Pre-programme enrolment rate in regio_**|**_n of birth x birth_**|**_cohort_**|||
|Cohort 1: 1-6yrs in 1974|0.2301|0.0735|0.4098|0.4569|
||(0.7765)|(0.5958)|(1.0825)|(0.8975)|
|Cohort 2: 7-12 yrs in 1974|-0.1260|-0.3254|1.3141|1.3727|
||(1.0442)|(0.8573)|(1.5800)|(1.4404)|

40
_Brazilian spatial labor dynamics Page 30_

# **Population distribution by region and labor market outcome**

<!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>N NE SE S CW<br>E+, W+ E-, W+ E-, W- E+, W-<br><!-- End of picture text -->

**Figure 3 Population distribution by region and labor market outcome**
The contribution of women to ar economy is in principle no different from that of men. But in practice, the problems of valuation, measurement, and policy inference are more complexand the implications for policy and programs may be more controversial and culturally sensitive.

# childbearing and childcare.

* For similar reasons, extension or similar outreach. activities can increase the productivity of women in home-based production, including agriculture and the provision and use of housenold water.

Reviewing and integrating several lines of economic research on how women affect economic and social development, Shultz concludes that:

* Private retums to investmen **t** hat enance women's market and productivity are high, notably in primary and secondary education, especially in regions where women now receive less education than men.

> * Extension or outreach programs can correct factor market distortions, provide women with snore equal agricultural and household technologies, and expand the effective supply of credit where women produce for maiket.

To give us more firm evidence of how public policies affect women's productivity and family consumption pattems, Schultz recommends pilot programs to:

* These investments shift the aBocation of women's time toward market work and away from home-based work.

* rhese investements benefit the health and nutrition of the women's children and immediate family members.

* Increase school enrollment and graduation rate for girls.

A assess the effect of equalizing access to agricultural extension services by male and female farmers in Africa, Southeast Asia, and Lain America.

* TMese investments reduce desired ferfility and increase women's ability to obtain and use family planning services more effectively.

* Explore altemative configurations of child and maternal health programs, varying the mix of staff by level and gender.

* These investments make women more efficient managers of family resources of land, fuelwood, water, and food.

* Evaluate program innovations in the delivery of credit, such as the Grameen Bank in Bangladesh.

* Family planning and family health "outreach" progrgn-s especially help women, because women's mobilty is particularly constrained by culture and the practical realities of

All these pilot programs should be designed to monitor the families' choices that affect whether they produce healthy and educated sons and daughters.

This paper is a product of the Women in Development Division, Population and HumanResources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Johanna Klous, room S9-121, extension 33745.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Researcn Complex. An objective of the series is to get these fndings out quickly, even if presentations are less than fully polished. The findings, interpretaions, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
second block models the determination of the price level and the wage rate. Finally, the interest rate is determined in the financial block of the model. The basic transmission mechanism between the real and the financial sides of the economy relies on the specification of real private consumption and investment as functions of the interest rate. Private consumption is also a function of disposable income and the general price index. The model includes a disaggregated representation of the balance of payments.

In order to improve the ability of the model to handle the agent heterogeneity observed in the household survey, supply is divided into six sectors: (1) urban tradable formal, (2) urban nontradable formal, (3) urban nontradable informal, (4) rural tradable formal, (5) rural nontradable formal, and (6) rural nontradable informal. Production in each of these sectors is assumed to be an elasticity of substitution function of capital and a composite labor input with skilled and unskilled components. Labor demand by skill level (low, intermediate, and high) is determined by profit maximization.

# **_Linkage Variables_**

The solution of the model affords the linkage variables needed for impact analysis with the microsimulation module. In particular, it produces 18 wage rates (for three skill levels in six sectors), and 21 occupation rates (six employment levels and one value for unemployment for six sectors). Another set of linkage variables includes changes in output prices in the six sectors. The authors use an algorithm to ensure consistency between the macroeconomic solution and the predictions of the microsimulation model.

# **_4.4. The IMMPA Framework_**

The IMMPA is a dynamic framework designed around some key characteristics of low-income countries. It focuses on issues relating to labor-market segmentation, informal activities, credit market imperfections, and the composition of public expenditure. The macro- and meso-parts of the economy are represented by a financial CGE model with seven distinct blocks: (1) production and employment, (2) the demand side of the economy, (3) the price system, (4) external trade, (5) income formation, (6) the financial sector, and (7) the public sector. The specification of each of these blocks follows well-known principles in general equilibrium modeling. We will therefore 50
- 23 -

- (ii) debts from creditors taking part in distribution (the budget, banks, other); and

# (iii) "material benefit" for employees (under Decree 176-HDBT)."A

If the funds were insufficient to cover item 3, the provincial or city budget would be expected to fund "reasonable amounts"; if not all debts were paid off after item (1), then the debts were not to be considered settled. After one year of implementation of the Regulations, they were amended to provide for the following more detailed order of priority, giving support for displaced workers a higher priority:

- (i) unpaid sa±ary and social insurance;

- (ii) financial support for displaced state enterprise employees (under Decree 176);

- (iii) secured creditors (economic institutions and individuals) **;52**

- (iv) loans from other economic institutions;

- (v) principal to banks;

5'Decree 176-HDBT on the Reallocation of Labor Force of State Economic Units, Council of Ministers, October 9, 1989. This decree provides for a severance payment to workers laid off in state enterprises of one month salary (including any subsidies) for every year of service, with a minimum of three months' salary. The state enterprises are required to pay this severance allowance, but the enterprise if in difficulty can apply for up to 50% to be paid by the State. The allowance is intended to be paid as a lump-sum, but may be paid in installments if there is a funding shortfall, after consultation with the beneficiary.

> **S2It** is not clear from the decrees whether this payment to secured creditors is before or after the sale of security.
# **1. Introduction**

Although there is an extensive literature illustrating the potential consequences of resource-intensity on economic growth and mechanisms through which growth in the resource sector might affect poverty and household-level well-being,<sup>1</sup> there is relatively little evidence of how much of resource-led growth might lead to household-level benefits in countries highly dependent on natural resources (e.g., Savoia and Sen 2021). Importantly, the quasi-experimental studies that have estimated the impacts of resource growth on poverty have found ambiguous results (e.g., Loayza and Rigolin 2016; Zabsonre et al. 2018; Bazillier and Girard 2020; etc.).

To investigate the degree to which growth in the resource sector might support the population, we analyze how well-being changed in Papua New Guinea following the end of its resource boom in 2014 (e.g., Fox and Shroder 2018). This is a period during which there was growth in the resource sector (e.g., World Bank 2023), an associated increase in the collection of government revenue (e.g., Howes et al. 2022), and also a substantial increase in foreign assistance as the country became increasingly important geopolitically (e.g., Hayward-Jones 2017).

This is also a critical public policy issue in PNG where there is significant debate regarding the extent of monetary poverty in the country (e.g., Pandey and Howes 2022). There has not been an official estimate of monetary poverty since 2009 given substantial challenges in collecting data in the country (e.g., DHS 2019). It is possible that the economic growth and other significant changes in the country noted above have substantially improved well-being since 39.7 percent of the population was estimated to be living below the World Bank’s International Poverty Line of $ 2.15 (2017 PPP) in 2009 (World Bank 2013).

But even aside from the significant changes that potentially improved well-being since the last official monetary poverty estimate, many viewed the high estimates in 2009 with skepticism. In particular, many found it difficult to reconcile the high level of economic activity in the country with such a high estimate of monetary poverty. For example,

> 1See Ross (2015) for a survey of the historical context and of the many strands of the literature.

2
**Table 6. Debt, Sub-Saharan Africa, 1996 and 1997**

||**Total external**<br>**debt (percentage**<br>**of exports of**<br>**goods and**|**Debt service**<br>**(percentage of**<br>**exports of**<br>**goods and**|**Concessional**<br>**debt**<br>**(percentage**<br>**of total**<br>**external**|**Present value**<br>**of debt**<br>**(percentage of**<br>**exports of**<br>**goods and**|
|---|---|---|---|---|
|**Countries**|**services) 1997**|**services) 1997**|**debt) 1997**|**services) 1996**|
|**_West and CentralAfrica_**|||||
|Benin|268.9|9.1|77.9|135.4|
|Burkina Faso|295.5|11.8|83.0|152.3|
|Cameroon|368.7|20.4|42.6|358.1|
|Central African Republic|410.1|6.1|82.0|264.0|
|Chad|364.8|12.5|78.3|185.1|
|Congo, Rep. of|280.9|6.2|36.6|265.8|
|<br>Cote d'Ivoire|314.6|27.4|28.9|379.4|
|Equatorial Guinea|65.7|1.4|48.8|117.9|
|<br>Gabon|129.6|13.1|22.7|109.5|
|Guinea-Bissau|1,645.2|17.3|72.2|2,204.6|
|Mali|395.4|10.5|89.0|282.0|
|Niger|509.4|19.5|66.9|287.8|
|Senegal|226.6|15.3|65.2|139.9|
|Togo|198.0|8.1|71.3|134.6|
|Gambia, The|184.8|11.6|91.5|104.3|
|Ghana|349.4|29.5|66.5|226.3|
|Guinea|469.7|21.5|70.6|277.9|
|Liberia|..|..|29.1||
|Mauritania|553.8|25.6|69.2|287.2|
|Nigeria|156.6|7.8|4.6|187.6|
|Sierra Leone|1,235.2|21.1|63.8|590.4|
|**_East and Southern Africa_**|||||
|Angola|191.4|15.9|21.9|165.4|
|<br>Botswana|..|..|51.6|16.5|
|Burundi|1,062.3|29.0|92.8|939.3|
|Congo, Dem. Rep.|845.7|0.9|25.2|648.9|
|<br>Eritrea|19.1|0.1|96.1|5.2|
|Ethiopia|962.9|9.5|85.7|971.6|
|Kenya|214.9|21.5|57.5|168.3|
|Lesotho|93.5|6.4|69.1|155.8|
|Madagascar|522.2|27.0|65.3|391.6|
|Malawi|351.3|12.4|88.2|226.9|
|Mauritius|95.4|10.9|13.9|62.5|
|Mozambique|1,066.0|18.6|56.5|757.3|
|Namibia|..||||
|Rwanda|673.3|13.3|88.7|561.8|
|Somalia|..|..|58.7||
|South Africa|67.8|12.8|0.0|61.4|
|Sudan|1,459.8|_5.1_|28.4|1,608.7|
|Tanzania|576.5|13.0|70.9|390.9|
|<br>Uganda|<br>429.6|<br>22.1|<br>79.5|264.5|
|<br>Zambia|501.3|19.9|56.2|447.1|
|Zimbabwe|159.2|21.7|28.2|136.4|
|Sub-Saharan Africa 1996|**215.4**|**14.2**|**36.7**|**182.1**|
|Sub-Saharan Africa 1997|201.7|12.8|38.0|..|
|_Source:_World Bank 1998a|,b and 1999b.||||

assumed re-financed (short term or long term fixed-rate).

18
frequency. Under the CCC scenario, the net revenues of mixed rainfed farms increase, but the net revenues of livestock-only farms and crop-only irrigated farms fall. Consequently farmers move away from the specialized farm types in favor of mixed farms that have both crops and livestock.

We next examine the final impacts of climate change assuming that farmers do not change farm type. That is, we adopt the assumption of many existing studies of climate impacts that assume farmers continue to choose their current farm type as climate changes (Rosenzweig and Parry 1994, Schlenker et al. 2005, Deschenes and Greenstone 2007). We multiply the current probability of each farm type for each district times the predicted conditional income for each climate scenario. These “exogenous model” results are shown in Table 9. With the CCC scenario, farmers will lose up to 75 percent of their incomes. With the CCSR scenario, the expected losses climb to 100 percent of their income. Only with the PCM scenario is there a predicted beneficial effect with a 65 percent increase in expected income.

We contrast the “exogenous model” results with the Structural Ricardian results that include the choice of farm type, the “endogenous model” results in Table 9. The endogenous model results combine the results from Table 7 and Table 8 to estimate the changes in the expected income for African farmers. Currently, African farmers earn about 550 USD per hectare of farmland. With the CCC scenario, farmers are expected to lose 40 percent of their incomes in contrast to the 70 percent predicted by the exogenous model. With the CCSR scenario, farmers in some countries get large gains which offset the large losses of farmers in other countries. In contrast, the exogenous model predicts that both sets of farmers lose because conditions change. By allowing farmers to shift to more profitable farm types, the endogenous model predicts far smaller impacts than the potential effects predicted by the exogenous model. Even in the case of the PCM scenario, the endogenous model predicts a smaller benefit because the exogenous model overestimates the potential gains from choices farmers will move away from.

# **6. Conclusion and Policy Discussion**

This paper provides a new econometric method to measure the impacts and adaptations to 12
16 A prominent example are the ANC data points from the WHS where a coding error caused the 2018 HEFPI version’s points to substantively understate the true rate of pregnancies with 4 or more ANC visits – in the case of Burkina Faso by over 14 percentage points.<sup>17</sup> In some cases, we discovered that the omission of a questionnaire’s filter questions or errors in the coding of missing values in raw survey data sets caused mistakes when we computed indicator values from micro-data. These errors were sometimes also committed by the survey reports – for instance, due to an erroneous coding of missings in the Côte d’Ivoire 2006 MICS immunization variables, the survey report and the 2018 HEFPI data point overstate the true full immunization rate by more than 15 percentage points. For the 2019 version of HEFPI, we have corrected such errors whenever we were confident that we could recover the true indicator values from the raw data.

Moreover, for the 2019 HEFPI database, we reviewed all MICS data points. We corrected coding errors, and, among other things, better aligned the definitions of “skilled birth attendants”, “modern contraceptives”, and “formal health care providers” (to treat child acute respiratory infections) with those used for points from DHS and other maternal and child health surveys.

In total, coding corrections led to changes of 10 percent or more relative to 2018 HEFPI indicator value levels for 152 data points for which sources remained unchanged. These data points

> 17 Another survey-family-wide correction in the 2019 HEFPI version is that we now follow the official DHS (StatCompiler) method to compute rates of stunting and underweight from DHS data by (a) using all children sampled in the anthropometry module and (b) applying anthropometry module sampling weights. For the 2018 database, we had limited our analysis to children whose mothers were present in the household and applied the ordinary DHS child sample weights. The changes make only small differences in practice, as all changes in rates of stunting and underweight are smaller than one percentage point.
# **5. Concluding discussion**

As our climate changes, preparing for – and responding to - disasters caused by natural hazards in the Caribbean is becoming an increasing priority. World Bank (2018) reported that “within the Caribbean, direct damages due to natural disasters have averaged almost US$1.6 billion per year over the last 20 years.” A cursory comparison suggests that these damages already exceeded the total combined value of net official development assistance and official aid revenue received by Caribbean small states (see World Development Indicators) by a factor of about 2.5. And the damages inflicted by natural disasters will only increase as weather events become more erratic, unpredictable, and extreme.

Governments and development partners frequently cite a need for a people-centered approach, and up-to-date information and statistics, to guide preparations and responses to natural disasters. The Caribbean Disaster Emergency Management Agency (2019), for instance, considers research and data management a foundation of its strategy and evidence-based approaches a core principle to enhance resilience. Household survey data are a central part of the information required to enhance to disaster risk management in the Caribbean. However, as described in detail by Rozenberg et al. (2021), such data are frequently lacking. This paper attempts to help address this gap, by presenting analysis of novel survey data collected in five countries in the Caribbean.

The paper shows that it is possible to integrate disaster risk management in planned household survey data collection at scale and at relatively low cost. The survey module used for the analysis presented in this paper can be adapted for future data collection efforts. Including it in a new household survey will only marginally increase interview time and cost of data collection. One advantage of including a disaster risk module in a broader household survey is that it allows for an analysis of the correlates of exposure to and ability to cope with risks. In the case of this paper, for instance, we look in detail at the correlation between household financial wellbeing and ability to deal with disasters.

In a review of the literature on the relationship between poverty and disasters, Hallegatte et al. (2020) discuss that poor people are more often affected by natural disasters (which they call exposure bias), lose more when hit by a disaster (vulnerability bias), and are less able to cope with disasters. The findings presented in our paper are in accordance with these patterns, as we observe an income gradient in ability to cope with shocks, underlining the conclusion of 16