rafmacalaba/data-use-mentions-extended huggingface dataset:
, such as wastewater treatment and environmental water quality (SDG 6.3).

- _Energy (SDG 7)._ The investment required to achieve three targets related to SDG 7 (universal access to modern energy services, doubled energy efficiency, doubled share of renewable energy) has been estimated to be at least $600 billion to $800 billion per year over and above existing levels (Angelou et al. 2013). This would imply a doubling or tripling of financing for the sector.

- _Climate action (SDG 13)._ The cost to developing economies of adapting to global warming of 2<sup>o</sup> C has been estimated at $70 billion to $100 billion per year (Narain, Margulis, and Essam 2011). In 2016, an analysis conducted by the International Finance Corporation (IFC) found $23 trillion in climate smart investment opportunities (or gaps) in 21 emerging economies by 2030 (IFC 2016).<sup>11</sup>

> 9 Granular cost estimates are critical for costing exercises. For example, the WBG’s Road Costs Knowledge System (ROCKS) records costs related to road-related projects by the WBG, the African Development Bank, and the Asian Development Bank. The database provides unit costs for comparable road work activities for SDG9-related costing exercises (Collier, Kirchberger, and Söderbom 2016; Bosio et al. 2018).

> 10 Existing studies are skewed toward evaluating the cost of infrastructure-related SDGs, for which costs are more quantifiable than SDGs that will be achieved chiefly through policy change, such as gender equality (SDG 5) and reducing inequality (SDG 10). Other SDGs, such as those on health and quality education (SDGs 3 and 4), have incomplete costing estimates at the regional or global level due to the lack of data. For instance, a limited number of countries regularly and consistently report health-related spending, leading most global costing studies to make assumptions that result in estimates with wide margins of error
growth. However, this approach is difficult to implement because there are no consistent time series for a sufficiently long period for the stock of physical capital with a sector and a regional disaggregation. Some papers have used other measures of productivity and the evidence in these papers is more favorable to the importance of MAR externalities<sup>4</sup> . Lucio et al. (2002) study labor productivity using Spanish data and find that for regions with a high degree of sector concentration, increasing it further increases productivity growth. They find no effects of competition or diversity on labor productivity growth. Cingano and Schivardi (2002) construct a measure of productivity growth using firm level data, that is then aggregated into the region level<sup>5</sup> . They find evidence of MAR externalities for productivity growth and no effects that sector competition and diversity on local growth. These conclusions are at odds with their findings with the employment regressions, which show that sector concentration is associated with higher local growth.

In this paper I derive an alternative measure of productivity growth derived under the assumptions of local labor markets and heterogeneous workers. This measure is a wage adjusted regional growth rate, where the weights are a function of the employment growth of the different types of workers in the region. I show that this productivity measure can be derived from a model where technology is skill biased so that productivity growth and employment growth do not need to covary. Unfortunately, there are other problems related to the use of local employment growth that are also not addressed with my measure. These are the problems generated by congestition externalities arising from agglomeration economies (e.g. pollution) or from the low demand elasticity in some product markets. I use data from a matched employer-employee data set covering all the plants operating in Portugal between 1985 and 1994. This data has worker level information on total labor costs per employee supported by the plant and on the schooling and experience of the plant’s workforce. I find strong evidence
Policy Research Working Paper 9841

# **Abstract**

An accurate understanding of how input use affects agricultural productivity in smallholder farming systems is key to designing policies that can improve productivity, food security, and living standards in rural areas. Studies examining the relationships between agricultural productivity and inputs typically rely on land productivity measures, such as crop yields, that are informed by self-reported survey data on crop production. This paper leverages unique survey data from Mali to demonstrate that self-reported crop yields, vis-à-vis (objective) crop cut yields, are subject to non-classical measurement error that in turn biases the estimates of returns to inputs, including land, labor, fertilizer, and seeds. The analysis validates an alternative approach to estimate the relationship between crop yields and agricultural inputs using large-scale surveys, namely a within-survey imputation exercise that derives predicted, otherwise unobserved, objective crop yields that stem from a machine learning model that is estimated with a random subsample of plots for which crop cutting and self-reported yields are both available. Using data from a methodological survey experiment and a nationally representative survey conducted in Mali, the analysis demonstrates that it is possible to obtain predicted objective sorghum yields with attenuated non-classical measurement error, resulting in a less biased assessment of the relationship between yields and agricultural inputs. The discussion expands on the implications of the findings for (i) future research on agricultural intensification, and (ii) the design of future surveys in which objective data collection could be limited to a subsample to save costs, with the intention to apply the suggested machine learning approach.

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 tkilic@worldbank.org or iyacouboudjima@worldbank.org.

_The Policy Research Working Paper Series
# **4.2  Forest Clearing**

Hansen et al. (2013) publish annual high-resolution estimates of global forest clearing. The data are currently available at 30 m spatial resolution for 2000-2014. For this research, we have created annual files in which cleared pixels are assigned the value 1 in the year when most clearing occurred. Uncleared pixels are assigned the value 0. We focus on areas defined as moist tropical forest ecoregions by WWF (see Sections 5.2 and 7.3).<sup>3</sup> We use the Hansen estimates to compute forest clearing rates in 250 m cells.<sup>4</sup> Edge effects, non-forest areas and other factors cause Hansen pixel counts to vary somewhat by cell. We standardize by computing the annual cumulative percent cleared in each cell through 2014.<sup>5</sup> Figure 2 displays our estimates for representative moist forest areas in Bolivia, Cameroon and Myanmar. Each cell is color-coded by cumulative percent cleared in 2001-2014. The figure reveals a striking pattern of deforestation along some of the roads in the Delorme database. Along other roads, however, deforestation is much less pronounced.

> 3 For a complete list of WWF ecoregions, see http://wwf.panda.org/about_our_earth/ecoregions/ecoregion_list/. 4 We use the 250 m approximation for expositional convenience.  Our cell sides are .0025 decimal degrees in length. This translates to 278.3 meters on a side, yielding cell areas of 77,450.9 square meters or 7.75 hectares.

> 5 Our estimate also incorporates the Hansen 30 m estimates of tree cover in 2000.

12
.................................................. 22|
|Figure 6: Count of consignments by number of events (TradeLens) .................................................... 24|
|Figure 7: High-level network structure of Tradelens dataset ............................................................... 29|
|Figure 8: Share of null values in 2019 parcel postal records of destination countries ......................... 32|
|Figure 9: Network representation of the UPU dataset ......................................................................... 33|
|Figure 10: Sequence of data events (Cargo iQ dataset) ................................................
**Table 5. Average Cost of Irrigation by Source in Rabi 2021**

|**Source**<br>**of irrigation**|**(1)**<br>**Average Cost**<br>**(Rs/hour)**|**(2)**<br>**Average**<br>**Irrigation**<br>**intensity (Hours/acre)**|**use**|**(3 = 1 * 2)**<br>**The overall cost of irrigation**<br>**(Rs/acre)**|
|---|---|---|---|---|
|JCLIS|29|19||551|
|Own diesel pump|75|27||2025|
|Rented diesel pump|120|19||2280|
|Own electric pump|8|31||248|

_Source: Calculated by authors using primary data collected from farmers._ Note: This table has been generated using simple means or average values of hours irrigated/acre and average hourly cost of irrigation. These two values are multiplied to generate the average cost of irrigation/acre for each group of farmers. JCLIS refers to the cost for JOHAR farmers while the diesel/ electric/ rental pump values pertain to control farmers.

# _5.3. Cropped area_

Jharkhand has one of the lowest cropping intensities among all states of India. Limited access to irrigation is a major reason for the low cropping intensity in the state. Large areas in the state are under the rice-fallow system where no second crop is grown after the Kharif rice is harvested. The cropped area in Rabi and the summer season can increase if farmers have access to affordable irrigation.

Kharif is the main crop growing season, paddy in particular. However, the JCLIS beneficiaries
news for targeting is that there is self-selection: as long as collection is time-consuming the poorer women with lower opportunity cost of time utilize the resource more. Still, projects could be targeted based on indicators such as time spent collecting, availability of close substitutes, and household capacity to adapt to scarcity.

# **_2.2.2 Implications for agricultural production_**

**Another potential motivation for interventions supporting tree planting and forest management is declining agricultural productivity as inputs are diverted from agriculture to fuels in response to woodfuel scarcity. Limited evidence from Ethiopia indicates that improved stoves are more important than planting of trees in increasing the use of dung as manure instead of as fuel. However, the evidence on this is not gender specific, and does not suggest immediate agricultural impacts.**

A commonly cited concern is that in places where fuelwood is scarce and dung is used as fuel instead of as manure, agricultural production may be negatively affected. Consequently, community plantations as well as private tree planting are expected to increase application of manure as fertilizer. However, Van‘t Veld et al. (2006) find that households avoid using dung as fuel at times of the year when it is useful as fertilizer. Cooke et al. 2008 find that the cross-price evidence between fuelwood and dung, and fuelwood and crop residues is mixed as to whether the fuels are substitutes or complements. Mekonnen and Köhlin (2008) specifically study the use of dung as fuel or manure in a panel of more than 1300 households in the Ethiopian highlands in 2000, 2002 and 2005. They find only weak evidence that more trees increase the probability of applying dung as manure. A much stronger effect comes from the use of improved stoves, which significantly increases the probability that dung is used only as manure. Households with more women, on the other hand, are more likely to use the dung as fuel, rather than as manure. As long as dung use as fuel has strong cultural and taste

> 19 We will use
Table 1: Benchmark regressions for transmission channels associated with foreign claims by international banks in developing countries, 2004Q1–2011Q1<sup>_†_</sup>

||_B1_|_B2_|_B3_|_B4_|
|---|---|---|---|---|
|Lagged foreign claims|0.772<br>(0.03)<sup>_∗∗∗_</sup>|0.772<br>(0.03)<sup>_∗∗∗_</sup>|0.781<br>(0.03)<sup>_∗∗∗_</sup>|0.771<br>(0.03)<sup>_∗∗∗_</sup>|
|Libor-OIS|0.030|-0.025|-0.019|-0.030|
||(0.01)<sup>_∗∗∗_</sup>|(0.01)<sup>_∗_</sup>|(0.02)|(0.02)<sup>_∗_</sup>|
|1st lag|||0.010<br>(0.01)||
|2nd lag|||0.017<br>(0.01)||
|3rd lag|||-0.022<br>(0.01)<sup>_∗∗_</sup>||
|Libor-OIS _×_ crisis||||-0.000<br>(0.04)|
|Global bank CDS|-0.014|-0.001|-0.004|0.002|
||(0.01)<sup>_∗∗_</sup>|(0.01)
processes, repair services, gas and water supply, and cold storage. Specifically, for the purpose of our project, we are able to extract information on the number of employees by gender. That is, we have the exact count of male and female employees for most establishments in organized manufacturing with the ASI data and for the services and unorganized manufacturing sectors from the NSSO records. For some analyses, we exclude records where the male and female employee counts do not add up to within 10% of the reported total employment for the firm.

We use ASI surveys from the same fiscal years as the NSSO data described above to investigate the impact of increased competitiveness ensuing from infrastructure investments, trade liberalization and domestic regulation reforms. ASI surveys have a similar design<sup>32</sup> and stratification as the NSSO surveys. However, the ASI survey does not provide any information on the gender of the owners. Thus, our indices on gender segmentation that match the gender of employees with that of the owner cannot be constructed for the organized manufacturing data. This limitation on ASI data does not impose a major constraint as roughly 99% of plants and 80% of employment are covered by the NSSO. Besides, we are still able to measure segmentation in the organized sector by developing a novel index based on the information on the gender of employees. For an average employee, this index computes the share of his/her co-workers that are of the same gender. Computing this alternative employee-based index of gender segmentation allows us to compare our results for the organized manufacturing sector with that observed in the unorganized manufacturing and the services sectors.

# **_Services_**

Beginning with the 57<sup>th</sup> round conducted during July 2001 to June 2002, the NSSO surveyed unorganized enterprises belonging to the services sector. This survey was repeated in the 63<sup>rd</sup> round (2006-2007) and 67<sup>th</sup> round (2010-2011).
, & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. _Classical Conditioning II: Current Research and Theory_ , _2_ , 64-99.

- Reyes, M. R., Brackett, M. A., Rivers, S. E., White, M., & Salovey, P. (2012). Classroom emotional climate, student engagement, and academic achievement. _Journal of Educational Psychology_ , _104_ (3), 700.

- Ribera, T., BrckaLorenz, A., Cole, E. R., & Laird, T. F. N. (2012, April). Examining the importance of teaching clarity: Findings from the faculty survey of student engagement. In Paper presented at the Annual Meeting of the American Educational Research Association.

- Rimm-Kaufman, S. E., Curby, T., Grimm, K., Nathanson, L., & Brock, L. (2009).The contribution of children’s self-regulation and classroom quality to children’s adaptive behaviors in the kindergarten classroom. _Developmental Psychology, 45_ (4), 958-972.

- Rimm-Kaufman, S. E., Baroody, A., Larsen, R., Curby, T. W., & Abry, T. (2015). To what extent do teacher–student interaction quality and student gender contribute to fifth graders’ engagement in mathematics learning? _Journal of Educational Psychology, 107_ (1), 170-185. doi: http://dx.doi.org/10.1037/a0037252

- Risi, S., Gerhardstein, R., & Kistner, J. (2003). Children's classroom peer relationships and subsequent educational outcomes. _Journal of Clinical Child and Adolescent Psychology_ , _32_ (3), 351-361.

- Robertson-Kraft, C., & Duckworth, A. L. (2014). True grit: Trait-level perseverance and passion for long-term goals predicts
period. Other effects such as loss of jobs and farm, decrease in income and experiencing violence could also affect the education of children in the same household. To realize the full potential impact of war, as a second strategy we used a set of victimization indicators to identify the true impact of the war.

When education works as a catalyst for the war, the causal inferences on the impact of war suffer from endogeneity problems. Also the activities of NGO-run primary and secondary schools, which stepped in to fill the education gap in the North, might create a downward bias in the impact of war on education. Both of these factors are likely to violate the assumption that the war victims and the comparison group are exchangeable in order to make a causal inference of 25
with all data in logs. There are 141 countries for which there are both data on the HCI and the capital stock in the Penn World Tables. The GDP PPP per worker and the capital stock (also at PPP prices) per worker are both averages over 2015-17. The regression result is the following: lnGDP PPP per worker = 0.065 + 0.4*lnHCI + 0.74 * lncapital stock per worker The T statistic for the coefficient on the HCI is 2.26 and that on the capital stock per worker is 19.0. The regression has an adjusted R squared of 0.91.

We can use these coefficients to estimate what the output per worker would be if a worker from, for example, Tajikistan, with the average HCI for that country were to migrate to Russia and work with the average capital per worker of Russia. These estimates, for the four remittance receiving FSU countries of CASC, are shown in table 6.

7
Understanding this requires an analysis of the following questions: (a) Is microenterprise investment profitable? (b) Does lack of access to finance matter in microenterprise growth? and (c) Is borrowing from financial institutions a cost-effective way of supporting microenterprise investment? Our analysis shows that the rate of return to microenterprise investment on average is high at about 36 percent per year. This means that an entrepreneur with an incremental investment of Tk. 1,000 in an activity can obtain Tk. 360 in profit per year. Therefore, rate of return is not a constraint for microenterprise expansion and its growth.

What are the constraints then to microenterprise growth? We find that it is the lack of access to affordable finance rather than the non-credit constraints (e.g., lack of demand or access to transportation and electricity) that matters most in restricting microenterprise growth in Bangladesh. An analysis of the household survey data shows that more than 70 percent of enterprises’ start-up capital comes from entrepreneurs’ own savings; if we include borrowing from friends and relatives, it explains more than 85 percent of startup capital of rural microenterprises in Bangladesh. That is, the opportunity cost of start-up capital is high, in the sense that households must either save or have wealthy friends and relatives in order to set up an enterprise.

Informal lenders can also provide funds to operate microenterprise activities. However, exorbitant interest rates (as high as 180 percent) make this option infeasible for financing microenterprise investment. On the other hand, formal financial institutions such as commercial banks charge 10 to 12 percent interest rates and could thus be the most cost-effective sources for financing rural enterprises. But they rarely finance rural microenterprise activities because of high transaction costs involved with small loans for micro-entrepreneurs, who often further lack adequate assets as collateral for bank loan guarantees. Our data show that barely 1 percent of the microenterprises borrow from commercial banks to fund startup capital.

27
|0.46|0.67|0.71|0.62|
|Employed in Industry|0.13|0.11|0.16|0.08|0.04|0.06|0.09|0.05|0.04|0.07|
|Employed in Services|0.28|0.24|0.38|0.18|0.07|0.15|0.26|0.11|0.09|0.13|
|**Access to basic services**|||||||||||
|Improved water|0.80|0.85|0.91|0.82|0.59|0.66|0.76|0.63|0.61|0.73|
|Improved sanitation facility|0.40|0.24|0.29|0.22|0.16|0.18|0.23|0.14|0.18|0.22|
|Access to electricity|0.64|0.44|0.63|0.36|0.20|0.29|0.39|0.24|0.25|0.31|

Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Poverty is measured using the $2.15 poverty line.

90
2 Data To assess the profound changes in the lives of people and prepare the national plan for post-independence, the transitional government, supported by the donor community, initiated new surveys.° The analysis of this paper draws on the two new principal data sources. The Timor-Leste Living Standard Measurement Survey (TLSS) is a national representative sample of 1,800 households from 100 communities (“Suco”) and 300 villages (“Aldeia”) covering over one percent of the population.” The survey was designed to diagnose the extent, nature and causes of poverty, and to analyse policy options facing the country. It assembles comprehensive information on household demographics, housing and assets, household expenditures and income, agriculture, employment, health and education, and subjective perceptions of poverty, well-being and social capital. Data collection was undertaken between end August and early December 2001. The TLSS was complemented by a census of all Sucos in the country, conducted during February to April 2001. It provides an inventory of social and physical infrastructure and of the economic characteristics of Suco and Aldeias, in addition to population estimates.

Both surveys relied extensively on recall questions to establish a baseline for well-being prior to the referendum in 1999 for three reasons. First, there were concerns regarding the reliability of existing Indonesian data for Timor Leste. Second, little historic information was available on subjective measures of well-being. Third, due to large population displacement and migration, the population as of end 2001 was unlikely to be directly comparable to the population prior to the referendum in 1999.

6 The data was collected by the Poverty Assessment Project, which was a partnership between the Planning Commission of the Timor Leste Transitional Authority with the World Bank, the Asian Development Bank, the United Nations Development Programme, and the Japanese International Cooperation Agency. Benu Bidani managed the fielding and analysis of the TLSS (World Bank (2003)). ” Timor-Leste is divided into 13 major units called Distritos (districts). These are further subdivided into 67 Postos (sub-districts