rafmacalaba/data-use-mentions-extended huggingface dataset:
include (1) a demand-driven fund that finances women’s group activities, such as handicraft production, the rearing of animals and poultry, and the organization of a daycare center; (2) capacity building and training for women’s groups and other project stakeholders on organization, facilitation, project design, appraisal, and monitoring and evaluation; and (3) information, education, and communication activities that enhance gender awareness (World Bank 2000a).

# _Measuring Empowerment in WDIP_

This exercise has two objectives: (1) to examine the general empowerment status of poor women in Ethiopia, and (2) to assess the impact WDIP may have on women’s empowerment. Communities are selected from the Amhara and Addis Ababa regions. The study uses a mixed-methods approach, applying qualitative techniques such as semistructured individual and key-informant interviews, and focus group discussions. Results from this qualitative enquiry were used to develop testable hypotheses and indicators, which then framed a quantitative survey. Data collected using a household survey instrument are currently under analysis.

Qualitative data were collected in communities where WDIP is present from both beneficiaries and non-beneficiaries. During the quantitative stage as well, data were collected for both the treatment and a control (non-WDIP beneficiaries) group. The sample communities are at different stages of implementing WDIP components.

The qualitative and quantitative instruments gather data on women’s assets, their opportunity structure, and degrees of empowerment in all three domains. Asset and degree of empowerment indicators mainly correspond to the local and intermediary level. Opportunity structure indicators also encompass the macro level.

Table 1 in annex 2 provides examples of indicators used in this study. To illustrate, in the state domain/political sub-domain, the degree of empowerment indicators include the ratio of women versus men represented in village and district councils as well as the question of how women are affected by the country’s judicial environment. This includes both how women are treated within the national judicial system and by the laws upheld in the traditional courts still operating in the
demand for compliance verification (dependent on importing patterns) can generate delays in clearance times.

How do import tariffs and NTMs affect firms’ responses to export demand shocks? To fix ideas, let us assume a depreciation of the yuan versus the USD, which is the case for most of our sample period (Figure 1). For Indonesian exporters to Japan, where they invoice in USD, this exchange rate movement represents a negative demand shock, particularly for those goods where Chinese competition is stronger. We are interested in how import measures on firms’ intermediate inputs affect the heterogeneity of responses to this shock across Indonesian exporters. Once hit by the exchange rate shock, the exporter could counter the associated increase in competition by lowering its marginal costs and/or its mark-up. The extent to which the Indonesian exporter can alter its mark-up depends on a number of factors such as the degree of market power of the firm, but not directly on the intermediate input choice.

The firm can lower its marginal costs by adjusting its variable costs, particularly those associated to intermediate inputs.<sup>5</sup> That is the case as the other variable cost–labor–is typically sticky in the short-term especially if firing costs are high (Adhvaryu et al., 2013), as in the case of Indonesia.<sup>6</sup> Reducing input costs typically involves switching to cheaper intermediate inputs, whether from the same supplier or a different one. As exporters are especially dependent on imported inputs, including in Indonesia (Rahardja and Varela, 2015), then import measures can affect the exporter’s ability to switch inputs and, hence, to respond to the shock.

> Consider first the case of NTMs. As NTMs increase uncertainty and/or the fixed cost

> 5Lowering the marginal costs may entail switching to a lower quality segment or sourcing from a new supplier.

> 6According to World Bank Doing Business data, during the period of analysis, Indonesia had the most onerous employment termination
most recent DHS survey for each country. These numbers are, in general, very similar to the mortality numbers published by the WHO and the World Bank with the notable exception of Niger and Liberia, where the mortality estimates from the DHS appear significantly lower than the official WHO figures.<sup>17</sup> In column 3 of Table 6, we show the calculated child mortality rates for counterfactual scenario 1 (basic improved water and sanitation), while we show imputed child mortality rates under counterfactual scenario 2 (privately piped water and flush toilets) in column 4 of the table.

The average expected decrease in child mortality obtained from upgrading households to at least basic improved water and sanitation (counterfactual scenario 1) is 11 deaths per 1000 in Sub-Saharan African countries, and approximately 4 deaths per 1000 children born in the other developing countries in our sample (Table 6, column 5). Under scenario 2 – upgrading all households to private water connections and flush toilets – the effects are substantially larger. On average, we get a reduction in under-5 mortality of 31 deaths per 1000 in SubSaharan Africa, and a reduction of 14 deaths per 1000 children born for other countries in our sample (Table 6, column 6).

Table 6 also highlights the heterogeneity in the mortality effect of water and sanitation upgrades across regions and countries. The potential of reducing child mortality through improved water and sanitation technologies does not only depend on the existing stock of water and sanitation infrastructure, but also on the child mortality levels in the country of interest. As shown in columns (5) and (6) of Table 6, the highest absolute decreases in mortality are predicted for Nigeria and Chad, with predicted reduction in under-5 mortality of 46 and 50 deaths per 1000, respectively. These numbers are the result of both low levels of existing infrastructure and high current mortality rates. In comparison, the predicted improvements appear small for countries like Peru or the Philippines, where baseline child mortality levels are relatively low, and water
# **3 Data and Context**

To carry out the analysis we make use of two main novel sources of data. The first is monthly data on the universe of Indonesian exporters and importers which was confidentially shared by the Indonesian Directorate General of Customs and Excise (DGCE, referred to as DG Customs in the paper) within the Ministry of Finance.<sup>8</sup> The second is a time-varying data of NTMs at a highly disaggregated level of sectoral classification (HS-10 digit) which was assembled and maintained for Indonesia by the World Bank. This data was compiled on the basis of extensive regulatory checks and varies at a monthly level at 3-digit MAST classification and 10 digit HS level. This section also provides information on other data used in the analysis and the choice to focus the analysis on Indonesian firms’ exports to Japan.

## **3.1 Indonesian customs data**

For the trade data, we use customs-level data covering the universe of Indonesian exporters from 2014 to 2018 in a monthly series. The data are collected by the Indonesian customs and record values exported and imported by each firm at the 10-digit level of the Harmonized System (HS). The data also has information on the country of destination, the value of exports, the quantities and status of the exporting firm (for example whether it is a producer and/or a general importing firm).

In Indonesia, an importer must have Business Registration Number (NIB). The NIB represents the Certificate of Company Registration (TDP), Customs Registration (NIK) and Importer Identification Number (API). When registering NIB, an importer must choose which API will be chosen between the two types of API that exist: API-U (General API), which is granted to importers that import goods for trading or transfer to other parties; and API-P (Producer API), which is for importers that import goods for their own use as raw materials, supporting materials and/or for supporting production process.
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Within sectors--especially within manufacturing--some activities fare better than others. Data from Zambia and Sierra Lone indicate that artisanal activities such as spinning, weaving and pottery decline dramatically in importance with increasing locality size, undoubtedly the victims of competition from lower-priced manufactured substitutes. At the other extreme, trading, restaurants, food preparation, and repair work flourish as town size increases. 7/ C. Effect of Distance from Urban Conters Evidence from Ivory Coast identifies the types of rural nonfarm activities which are most likely to be bulldozed by competition from urban and imported manufacturers. Table 6 indicates that basket making and weaving diminish precipitously$in importance with proximity to major urban centers. Pottery also declines, although to a lesser extent. Because these activities constitute the three largest rural industries in Bouake Region, it is not surprising that manufacturing in the aggregate may fare less well than commerce and services as rural structural transformation proceeds. Certainly it is necessary to distinguish between declining industries--such as pottery, weaving, basket making, mat making and wood carving--and ascendant manufacturing activities such as food processing and, to a lesser extent, tailoring, carpentry and metalwork.

D. Changes Over Time Time series data tracing changes in Africa's rural nonfarm employment are limited and fragile. Using aggregate labor force data for major subregions of the world, both Anderson and Leiserson (1980) and ILO (1979) make cautious judgements about long-term trends in rural nonfarm employment. They conclude that, over the past 20 years, total rural employment has been growing more rapidly than agricultural employment in all regions of the world, including Africa. Therefore, they reason, the share of nonfarm activities in total rural employment has been growing as
100 percent (Table 5); the increase in GDP affected ranges from 11 percent in Africa to more than 250 percent in East Asia, and vulnerable population increases by 10 percent in Africa and by 124 percent in Latin America (Table 6).

Figure 3. Increase in storm surge area, GDP and population at risk under climate change by region <!-- Start of picture text --> 300%<br>250%<br>200%<br>150%<br>100%<br>50%<br>0%<br>Sub‐saharan Africa East Asia & Pacific Latin America and South Asia<br>Caribbean<br>Storm surge area GDP Population<br><!-- End of picture text --> Sources: Tables 5 and 6

# **6. Limitations of the Approach**

At the outset, we acknowledge the following limitations in this analysis; some may have led to an overestimation of the coastal protection service, while other results may have led to an underestimation or an unknown bias.

Factors that may overestimate coastal protection include: the likely loss of mangroves since the reference year and the lack of local characteristics in the mangrove presence and absence database. The mangrove database used in this analysis is from the NASA GLS data for 2000 supplemented by Landsat imagery from the USGS archives from 1997 to 2000. In some countries there has been significant loss of mangroves since 2000, so the use of 2000 data may tend to overestimate the current levels of coastal defense. FAO (2007) indicates that globally only 3 percent of mangroves were lost between 2000 and 2005, so the 2000 data may be 24
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consumption. Here, we used real M2 (inclusive of foreign currency deposits) as a proxy for the stock of bank loans. We also add the capital account to our equation. Since capital flows are assumed to affect real variables indirectly through their effect on interest rates and the availability of credit, the inclusion of these variables in the regression would be expected to reduce the coefficient on capital flows. The basic equation we estimate is as follows: (1) C = Po + PGDP + P2rir + P3M2 + 4KA + E We estimate this equation for private and public consumption separately, using quarterly data for the period 1987-96. The first column in Table 3 shows the estimated coefficients when the equation excludes real M2.<sup>5</sup> While real income and capital flows are very significant and positive, the real interest rate is found to negatively effect private consumption. The broad results are not significantly altered when real M2 is added to the equation, as seen in the second column. Real M2 enters the equation with a significant positive coefficient. When the availability of credit as proxied by real M2 is controlled for, the significance of the real interest rate declines, but not by a large extent. Interestingly, the results are altered considerably when public consumption is considered.

While one would expect that easier financing due to capital inflows would increase public consumption, the effect of capital flows on public consumption are insignificant. The results are shown in the last two columns of Table 10. The inclusion of real M2 does not alter this result. Although the real interest rate becomes significant along with real M2, capital flows are sltill insignificant. We estimated error correction versions of the equations as well, and finally estimated a parsimonious version of the equations, removing insignificant explanatory variables. The results are broadly the same: private consumption is positively related to capital flows, but public consumption is not. The results are in Table 11.

5 The estimation method used was maximum likelihood allowing for an
Table 8: Population growth, 1996-2011

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dep. Var.: Populationgrowth(1996-2011)|||||
|Homeland (=1)|-1.028|-0.655|-1.32|-5.966|
||[1.189]|[1.163]|[0.955]|[3.941]|
|Share Employed mining1996|0.336|0.205|-0.200|0.029|
||[0.230]|[0.263]|[0.341]|[0.323]|
|Homeland*Share Employed mining1996|-0.255|-0.261|0.014|0.035|
||[0.269]|[0.302]|[0.341]|[0.328]|
|Observations|9,598|9,598|9,598|9,570|
|R-squared|0.132|0.141|0.207|0.514|
|1996 Controls|Yes|Yes|Yes|Yes|
|2011 Controls|-|-|-|Yes|
|Province FE|-|Yes|-|-|
|MunicipalityFE|-|-|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in log population for 2011-1996. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9,
.

> 49This effect is larger than previous studies. Oreopoulos et al. (2008) using Canadian data find that a one-dollar reduction in father’s permanent earnings due to a job loss leads to a subsequent reduction in his son’s earnings of 66 cents. One possible reason for this difference is that previous studies looked at cases that affect the lifetime income of the next generation only through a reduction in the education levels. In the case of Iran, the economic condition, e.g., wage rates have also changed after the sanctions. Moreover, as Grawe (2001) shows the intergenerational earnings mobility in the developing countries is larger because of the larger credit constraints.

> 50While the effect of high-quality education on the returns to schooling and economic growth is well known (Castelló-Climent and Hidalgo-Cabrillana (2012)), the effect on household spending on education is not documented in the current literature. Previous research has largely focused on children’s educational attainment.

> 51For Canada and the UK, the percentages were about 1.1 and 1.2%, respectively in 2009. Moreover, the average household expenditure on education as a percentage of total household income was 1.95 percent in the Unites States over 1990-1991 (Huston (1995)). For the 25 EU countries, the average private expenditure on education as a percentage of total household consumption during 1995-2004 is about 1% (range from 0.1 to 2.9%) (Lin and Lin (2012)). The share of education expenditure in household expenditure is 4.3% in India (Azam and Kingdon (2013)).

23
17

|**Summary,**<br>**,f**|<br>**Best Modtls**|**Append**<br>**Statbtiesfra**|**ix: Table 3**<br>**ch**<br>**Cotry**|**(including IP 1986- 18**|
|---|---|---|---|---|
||Unconditninal.<br>Probability|Type I elror|TypeX1errr|Noise/Sipna ratio<br>P(CtS|
|Argentina'|.049|0.70|0.14|0.47<br>0.86|
|<br>Brazil|9**2**|_0.47_|0.24|_0.45_<br>0.93|
|Chile'|.028|_0.51_|0.02|0.04<br>0.97|
|Colombia'|_.035_|0.54|0.05|0.10<br>0.95|
|Ecuador<sup>3</sup>|.063|0.41|0.26|0.45<br>0.83|
|Mexico'|.049|0.63|_0.05_|0.14<br>0.91|
|Perul|.077|0.29|0.13|**0.18**<br>0.69|
|Venezuela<sup>3</sup>|.056|0.32|0.10|0.14<br>0.97|

Notes: I/ Simple Modef 2/ Chartist Model 3/ Residuals Model. In the case of Ecuador the Residuals model gave as good results as the Simple model.
Page 32 Formal redevelopment of brownfield land is far more complicated and costly than the informal densification we have documented for slums. Figure IV.5 illustrates these complexities. The uniform and low maximum FSI of 1.8 in Ahmedabad greatly constrains how much incremental saleable floor space can be obtained through re-development, as much existing formal development is fairly close this level. The transaction costs and attendant uncertainties in passing through the formal process of redevelopment are too high to be covered by marginal increments to floor space. As a result, little redevelopment takes place, and this potential source of incremental housing is thereby greatly limited.

# **_Figure IV.5: Process for Brownfield Urban Redevelopment_**

<!-- Start of picture text --> Brownfield<br>BROWNFIELD DEVELOPMENT<br>8 10 11<br>Registration Consolidation Update<br>and Stamp charges cadastre<br>Duty<br>15<br>2 5<br>Municipal<br>Sanction of DP Sanction of TPS and Taxes and<br>Neighbourhood Level Plans Charges<br>1<br>3 4 6 13<br>Area-wide zoning<br>& land uses in DP. ALLOW HIGHER BROWNFIELD FSI FOR Upgrade existing infrastructurecity level level planning for Neighbourhoodexisting areas through Town neighbourhoodinfrastructureUpgrade level ConstructionBuilding Use PermissionsMonitoring Building<br>DEVELOPMENT Planning Schemes Permissions<br>in DCR Mechanism (TPS)<br>7 9 12 14<br>Plot by Plot Sale Consolidation Design and Sale of Property<br>of Plots Construction<br><!-- End of picture text --> <!-- Start of picture text --> Built Space<br><!-- End of picture text -->
Rossi-Hansberg 2015; Caliendo, Monte and Rossi-Hansberg 2017; Spanos 2016), Danish data (Friedrich 2016), Swedish data (Tag 2013), and Portuguese data (Caliendo, Mion, Opromolla and Rossi-Hansberg 2016). The only application for developing countries is Cruz, Bussolo, and Iacovone (2016), who study internal firm reorganization and export performance in Brazil. While Chile is at the low spectrum of developed countries, and it is therefore not a developing country, this experience and the evidence gathered here illustrate the possibilities that export opportunities offer for the demand of skills and tasks for developing countries down the road. Our paper is thus a contribution to this incipient empirical literature.

The rest of the paper is organized as follows. In section 2, we introduce the data, we present a set of stylized facts on exports and the demand for skilled tasks and we perform a detailed formal regression analysis. In section 3, we discuss our results in terms of the theoretical literature. Section 4 discusses extensions and concludes.

# **2 Exporting Firms and The Demand for Skilled Tasks**

In this section, we study the most salient facts concerning the link between exporting firms and the demand for skilled tasks in Chile. We first describe the data and present the basic correlations between skills, tasks, and exports. Next, we turn to a detailed causal empirical analysis based on instrumental variables regressions.

We use two sources of data, firm-level data and customs records. The firm-level data come from the Encuesta Nacional Industrial Anual (ENIA), an annual industrial census run by Chile’s Instituto Nacional de Estad ́ıstica that interviews all manufacturing plants with 10 workers or more. The ENIA is a panel. The customs data provide administrative records on firm exports by destination. We manually matched both databases for the period 2001–2005. As a result, we built a 5-year panel database of Chilean manufacturing firms.

The data have several modules. The main module contains information on industry affiliation, ownership type,
# **6.  EDGAR Emissions versus The IFPRI Field Scale**

For comparison, we introduce the latest (2018) agricultural methane emissions estimates<sup>11</sup> from the Emissions Database for Global Atmospheric Research (EDGAR (2022)), the global standard for “bottom-up” emissions estimation that combines detailed sectoral activity data with emissions factors that are adjusted for broad regional differences that may incorporate differences in water supply conditions and field configurations.

Since sectoral activity data drive the EDGAR emissions estimates, we could expect them to exhibit a relatively close relationship. Table 2 presents OLS, Robust and Cluster results for a percentile regression model that relates EDGAR emissions to the IFPRI estimates of irrigated rice field extent. The fit is excellent; the regression R<sup>2</sup> is 0.5856, as compared with 0.2574 for the SP5 CH4 anomalies in Table 1.

# **Table 2:  EDGAR CH4 emissions estimates vs. irrigated rice field scale (IFPRI)**

Dependent variable: EDGAR Emissions Percentile

|OLS|Robust|Cluster|
|---|---|---|
|Field Extent<br>0.771***|0.771***|0.771***|
|Percentile<br>(167.06)|(182.20)|(31.57)|
|Const.<br>11.46***|11.46***|11.46***|
|(42.70)|(43.17)|(7.84)|
|R<sup>2</sup><br>0.5856|0.5856|0.5856|
|Obs.<br>19,755|19,755|19,755|
|t statistics in parentheses:<br>* p<0.05 ** p<0.01 ***
Public education and health expenditure are staff estimates from the IMF’s Fiscal Affairs Department (FAD) and are available for the period prior to 2003.<sup>14</sup> The data are obtained from IMF country documents and have been verified and reconciled by country economists (Baqir, 2002). The main advantage over other datasets (International Monetary Fund, 2006; World Bank, 2006a,c) is the significantly improved coverage. Moreover, although the level of government (central or general, in which the latter also includes state and local government) spending differs across countries, it is fixed over time. Thus, average differences in government expenditure shares in GDP between countries that result from differences in the government level on which reporting is based can be absorbed by fixed effects (Baqir, 2002).<sup>15</sup>

**_A_** _it_ includes commodity aid/general program assistance (henceforth called general aid) and support to NGOs. If targeted toward education and health, support to NGOs may have an effect on a recipient government’s spending in these sectors (Lu, Schneider, Gubbins, Leach-Kemon, Jamison, and Murray, 2010, find that health aid to NGOs increases the health spending of recipient governments from their own resources). General aid may partially finance education and health spending or, if linked to structural adjustment programs, may be conditional on lowering public spending. The final variable in **_A_** _it_ is other non-education or non-health aid. In the equation for public education spending, other non-education aid includes health aid, and vice versa.

Another aid variable, action relating to debt, is not included in the regression model. Debt relief may be important, but it is not adequately captured by actions relating to debt, including debt forgiveness, debt rescheduling, and other actions (such as service payments to third parties, debt conversions, and debt buybacks) (OECD, 2000b). The debt forgiveness component measures the face