rafmacalaba/data-use-mentions-extended huggingface dataset:
opportunity to identify the biases resulting from these particular survey design choices and the implications for SDG indicator computation.

Findings from this study will inform future survey design around these particular SDG indicators and land tenure data in general, as well as alert policy makers to the implications of SDG reporting using various survey design approaches. To our knowledge, this is the first paper to address questions around the feasibility and validity of land tenure data collected in aggregate vis-à-vis the parcel level and will contribute to the growing literature on respondent approaches, particularly as related to individual land tenure.

The paper is organized as follows: section II describes the land tenure system in Armenia, providing context for the results that follow; Section III describes the ALTA study design and data; Section IV discusses SDG indicator computation and methods; Section V presents the results; and Section VI concludes.

# **II. Armenian Land Tenure System**

Following the dissolution of the Soviet Union in 1991, the Republic of Armenia became independent, implementing numerous reforms in the process of transitioning to a liberal economy. Agricultural land was considered to be the starting point for privatization, with subsequent reforms following suit in other sectors of the economy. The legal agrarian reforms were enacted through a number of laws, including "On Property in the Republic of Armenia" adopted by the Supreme Council of the Republic of Armenia (October 1990), "On Rural and Rural Collective Farms" (adopted January 1991), "On Enterprises and Entrepreneurial Activity in the Republic of Armenia" (adopted March 1992), “RA Land Code” (adopted January 1991), and several other laws and by-law acts. This legislation resulted in a new agrarian land tenure system beginning in March 1991 which converted the approximately 900 previously existing collective farms, state farms, and inter-economic enterprises into a large number of smaller family farms. The conversion of state land into private land was conducted in such a way that each village family was given a number of land units, dependent on the number of
satellite F15 in orbit in 2008 gave an average DN value of 16.6 across 0.9 million lit pixels in China, while F16 in orbit in the same year gave an average DN value of only 10.7 but spread over 1.7 million lit pixels. Similarly, Gibson et al (2015) show that satellites F14 and F15 differ by 85 km<sup>2</sup> (11 percent of the two-satellite mean), when estimating area for Bangalore in 2001. A related issue is that night-lit activity with a particular DN value on one day may not map to the same DN on another day due to sensor adjustments and inter-satellite differences.

We respond to these measurement issues in four ways.<sup>16</sup> First, we use an annual composite of non-ephemeral lights so unrecorded adjustments may wash out over the course of the year as repeated observations converge to some average amplification level. Second, we average over all satellite-year observations in a two-year window centered on the timing of the NSS rounds to ameliorate errors due to inter-satellite differences. Third, we measure smaller cities using two different luminosity thresholds, of 20 percent and 30 percent of the maximum (equivalent to DN values of 13 and 19), to check that the patterns we find are not sensitive to a particular choice of DN values.<sup>17</sup> Finally, we use the decomposition of night lights into lit area effects and average DN values within lit areas. We note in passing that most studies using DMSP data are not from economics and are typically concerned with urban area rather than with average DN values, while studies in economics typically just use average DN values.

In order to distinguish between cities and towns, and between towns and rural areas, we need luminosity thresholds. Several are used in practice, with examples of some close to what we use including Small and Elvidge (2013), who use DN  12 for their Asia-wide study of urban areas, and Alvarez-Berrios
only one period.

All KWh of electricity prices were converted into 2001 price levels using the US Consumer Price Index and expressed in terms of purchasing power parity.

# **Concluding remarks**

In this study we compare the relative performance of public and privatized Latin American electricity distribution utilities for the years 1994 to 2001. The labor productivity analysis gives the following main conclusions: (i) privatized firms are more labor efficient than their public counterparts; and (ii) there is evidence of labor productivity growth during the period. The results are significant in economic terms— private firms use about 30% to 45% less labor to produce a given bundle of outputs than public firms, and they are relatively robust to controlling for the potential endogeniety of ownership that could arise if governments privatized those utilities with the highest labor productivity first.

23
a measure of exchange rate volatility. Though the G-5 currencies (the US dollar, the Deutsche mark, the Japanese yen, the French franc, and the UK pound) were mainly used as candidates for potential targets for exchange rate stabilization, the SDR, ECU, and other relevant minor, regional currencies were also tried as potential targets, depending on a country's economic as well as non-economic (i.e., colonial, historical, cultural, and geographical) relationships. Using information from the "reported" exchange rate arrangements, the Australian dollar, the Indian rupee, the New Zealand dollar, the Portuguese escudo, the Singapore dollar, the South African rand, and the Spanish peseta were included in the list of potential target currencies for certain countries.<sup>7</sup> Data used were monthly average exchange rates for the sample period of January 1990 through December 1996.5 Following Frankel and Wei [1994], all the exchange rates were expressed in terms of a numeraire currency, the Swiss franc.<sup>9</sup> The underlying hypothesis is that every country attempts to stabilize its exchange rate to a basket of multiple currencies. The coefficients on the right-hand side exchange rates, pk, are interpreted as the weights in a currency basket assigned by the country's authorities. A single currency peg is a special case, where the coefficient on the target currency for exchange rate pegging should be unity, the coefficients on other currencies should all be zero, and the value of The Russian ruble was not tried as a potential nominal anchor currency due to the lack of a sufficient number of exchange rate data for the former Soviet republic countries.

8 The monthly average series of the exchange rates of G-5 currencies, a few regional currencies, SDR and ECU visA-vis the US dollar were obtained from the IFS data base (line code rf). Exchange rate data for Taiwan Province of China (POC) were obtained from the Central Bank of China, Taiwan District
.13)***|(859.28)***|(1320.17)**|(1216.18)***|
|Observations<br>|1'154'420|1'154'420|1'154'420|1'154'420|
|R-squared<br>|0.07|0.07|0.13|0.13|
|Fixed effects a/|no|no|yes|yes|
|||Theil|||
||_(1)_|_(2)_|_(3)_|_(4)_|
|Time|-0.012|-0.009|-0.019|-0.015|
||(63.78)***|(41.27)***|(155.47)***|(100.10)***|
|Time, squared|0.000|-0.000|0.000|-0.000|
||(7.94)***|(7.32)***|(35.37)***|(7.05)***|
|Post 2000||0.012||0.020|
|||(19.11)***||(47.64)***|
|Constant|4.236|4.222|4.358|4.336|
||(2336.35)**|(2160.58)**|(3626.10)**|(3363.56)***|
|Observations|1'154'420|1'154'420|1'154'420|1'154'420|
|R-squared|0.05|0.05|0.
**Table 6:** Knowledge of Public Goods Needs, Heterogeneity by Baseline Road Quality

||Balance<br>_Podes_2014|Knowledge<br>Roads Quality|Receive<br>Roads Complaint|Take Action<br>on Roads|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|||**Panel A: Baseline**|**Roads Quality = High**||
|New village head|0.117|0.635<sup>_∗∗_</sup>|0.052|-0.149|
||(0.108)|(0.292)|(0.116)|(0.161)|
||[0.128]|[0.332]|[0.137]|[0.196]|
|Observations|496|525|525|213|
|Control mean|0.49|2.49|0.68|0.76|
|Robust p-value|0.391|0.0155|0.870|0.344|
|MSE-opt. bandwidth|28.2|12.3|18.8|21.8|
|Effective obs.|306|187|250|106|
|||**Panel B: Baseline**|**Roads Quality = Low**||
|New village head||-0.226|0.387<sup>_∗∗∗_</sup>|0.355<sup>_∗_</sup>|
|||(0.491)|(0.112)|(0.166)|
|||[0.590]|[0.134]|[0.204]|
development. These findings are somewhat contradictory to the idea of a conflict trap in which lack of growth and conflict reinforce each other. A good development policy in this context is policy that helps avoid violent conflict.

The remaining sections build on these findings. In sections 4 and 5 we argue that the humanitarian crisis triggered by civil war could impact the recovery period in ways that are not measurable in standard panel regressions. Section 4 discusses the close relationship between violence and refugees. In the average civil war year, around 500,000 persons leave their country. This is an important finding as it gives an idea of the scale of the humanitarian crisis entailed by mass violence. In section 5 we use the findings of a relatively new literature to argue that long term labor productivity and, hence, recovery might be strongly affected through permanent losses in human capital due to exposure to conflict.

In section 6 we argue that if countries are known to revert to conflict this will hinder investment. We know from enterprise surveys that firms react strongly to political instability. The expectation of future instability will therefore be a critical element in their decision making. We show in this context that political inclusion and returning refugees can predict inflows of foreign investment in the critical phase after conflict. A causal link is harder to establish here but it seems reasonable to assume that commitments to political inclusion might play a key role in attracting investment.

Section 7 turns towards a detailed discussion of political exclusion as a factor of instability. We focus on a channel that has been proposed by the recent literature in this area. In absence of strong political institutions, political exclusion matters because it leads to an unequal distribution of economic resources. We find quite striking evidence for this. In addition, we find that formal institutions matter in how political power is reflected in economic well-being. These findings are very much in line with the idea that political institutions help avoid conflict because they prevent a “winner takes all” dynamic in which loss of political power leads to economic
gap is roughly the same in rural and urban areas (8% vs. 7%, respectively). These other measures of sanitation practices do not mechanically track differences in latrine ownership.

Figure 2 shows religion-specific differences at the state level, which complements the summary statistics by rural and urban status in Table 1 (IHDS) and Appendix Table 1 (DLHS-3). Figure 2 plots the difference between Hindu and Muslim households in latrine ownership (IHDS and DLHS-3) across states. In some states, Muslim households are more likely to own latrines than Hindu households; in other states, the converse is true. Much of the density is near zero, reflecting similar latrine ownership among Hindu and Muslim households. The variation shown in Figure 2 does not suggest that Hindu households have consistently lower latrine ownership once comparing Hindus and Muslims in more similar geographic locations.

Figure 3 explores the relationship between the Hindu-Muslim gap in latrine ownership and urbanization. The figure plots the difference in the state-level shares of Hindus and Muslims who report owning a latrine (y-axis) against the state-level difference in the shares of Hindus and Muslims living in urban areas (x-axis). The correlation between the two differences is 0.75. The states with large differences in latrine ownership between the two groups are also more likely to have considerable divides in the share of each group living in urban settings.

# **III Estimating Equation**

To further examine the differences in sanitation practices across Hindus and Muslims, now controlling for household characteristics and location characteristics, the following equation is estimated: where _i_ signifies household, _j_ denotes the household’s town/village/neighborhood (PSU), and _d_ is the household’s district. The outcome _Yijd_ represents one of the sanitation practices discussed above: whether the household owns or uses a latrine, whether the respondent 8
significant in the same years of 2000, 2005, and 2010.

The RMSPEs in table 2 rows (2), (3), and (4) show the degree to which Synthetic Jordan fits Real Jordan - relative to the placebo synthetic control in pretreatment and post-treatment periods. Relatively high _p_ values show that a large proportion of the placebo synthetic control units have a worse fit than Synthetic Jordan, which provides evidence in favor of the Full OIC “donor pool.”

# Economic v. Political Liberalization

Lastly, we consider whether the refugee surge impacted political institutions. The study constructs a Synthetic Jordan based on the donor pool of 14 OIC countries with the Polity IV index as the outcome variable. The results in figure 3 panel (a) and supplementary appendix table S1.3 show a discrete jump in Jordan’s Polity IV score from 1988 to 1989 but that the OIC countries are a poor pretreatment
the dollar value of all loans provided by a lender in a given year minus the pertinent loan amount with means of 13.63 and 22.52, respectively.<sup>14</sup> Larger banks by assets may be in a better position to assume the lead bank role in a loan syndicate, as this tends to require a bank to retain a relatively large share of the overall facility on its own books (Sufi, 2007). Loans/deposits is the ratio of all loans to deposits lagged by one year with a mean of 0.902. Banks that retain more loans on their balance sheets may be more

> 14 <mark>Bank accounting data are taken from Compustat.</mark>

12
Table 4. Responsiveness of Catch to Price: Including Zero Catch

||Dep. Variab<br>Pichilingue<br>|le: catch<br>||Abreojos<br>|||
|---|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|(6)|
|Log coop price|0.441***|0.441|0.543|1.489***|1.489***|1.500***|
||(0.024)|(0.548)|(0.512)|(0.007)|(0.172)|(0.147)|
|Log mkt Price|-2.566***|-2.566|-0.791|-0.455***|-0.455|0.898|
||(0.091)|(1.568)|(1.855)|(0.020)|(0.553)|(0.664)|
|Obs|9341|9341|4458|60497|60497|41695|
|Fixed effects|Sp-M-FT|Sp-M-FT|Sp-wk-<br>FT|Sp-M-FT|Sp-M-FT|Sp-wk-Ft|
|Clustering|none|Sp-M-FT|Sp-wk-<br>FT|none|Sp-M-FT|Sp-wk-Ft|
|Num. groups|1345|1345|1470|3147|3147|6966|

Note: All specifications use fixed effects Poisson estimation. First three columns use only Pichilingue observations, and next three columns use only Abreojos observations. Columns 1, 2, 4, and 5 include fixed effects at the species-month-fishing team level, while columns 3 and 6 include fixed effects at the species-week-fishing team level
-9-

the portfolio of U.S. Commercial Banks was twelve times as large as that of Canadian banks, but not their leans to Latin American countries were only five times larger.

even so, my idea of a negative relationship between exposure and percentage provisions appears to be borne out by the data in Table 2 which shows the percentage reserves held against developing country loans in a number of countries at the end of 1986. However, the table also reveals that there has been significant differences between banks even within the same country. Furthermore, although many banks do not make available infotmation concerning their reserving against individual countries, what information is available suggests that there may be quite wide divergences. Thus, for a bank with an overall provision of (say) 25 percent, the reserve against one Latin American country may be as low as 5 percent. while that against another may be as high as 35 percent. 7/ To some extent the different pattern of behavior with regard to provisioning may reflect the traditionally different attitudes of bankers throughout the world to the optimum point on any return/risk trade off. Germany bankers are, for example, not infrequentiy critical of the rapid expansion of loans to developing countries by US banks during the 1970s precisely because of the risks it involved, and their more prudent approach to lending (not revealed, of course, in German bank lending to Eastern Europe in the 1970s).

Beyond this, however, is the question of how important are 7. These figures are derived from the practices of Dutch banks in 1986 as reported to IBCA Banking Analysis.
crop model also takes into account the amount of evapotranspiration normally recorded at that location. The final weighted average of rainfall losses reflects the proportion of normal yield that is estimated to have been lost during that season due to poor rainfall in that location. It is important to note that this is a calculation, though, not an estimate from a regression.

The LEAP loss estimates are compiled for each year since 1996. Rainfall losses are produced for both the main _Meher_ season as well as the secondary _Belg_ season in the parts of the country that experience this second season. Planting and harvesting dates for each season vary across crop and location, but in general the _Meher_ season runs from May to November and the _Belg_ season from January to May. For a detailed description of the LEAP system, see Conway and Schipper (2011), and Balzer and Hess (2010). Analysis in Hill and Tsehaye (2014) also show that the rainfall-based yield loss estimates from LEAP are highly predictive of yields measured in the CSA’s yield estimates using crop cutting and harvesting experiments.

_Roads:_ We use Schmidt and Kedir’s (2009) estimates of time to travel to a town of 50,000 people in 1994 and in 2007 to estimate an average annual reduction in travel time. The distance at each square kilometer in the district is averaged to provide a district level average estimate.

_Agricultural production practices:_ We use _zone_ level measures of increases in the proportion of farmers that have reported applying fertilizer from 1997 to 2011. These measures are compiled using the annual Agricultural Sample Survey conducted by CSA.

_PSNP_ **:** The PSNP covers about 300 _woredas_ in seven regions (Tigray, Amhara, Oromia, SNNP, Afar, Somali, and Harari) and Dire Dawa. The beneficiary _woredas_ and the type of transfers they receive have been quite stable since the program was started in 2005.<sup>14</sup> We reviewed the Annual Plans of the PSNP in recent years to identify _woredas_
Policy Research Working Paper 10798

# **Abstract**

There was substantial growth in the resource sector in Papua New Guinea during the last resource boom, increased revenue collection by the government associated with that growth, and significant increases in international assistance, all which might have translated into improved well-being outcomes across the country. For a better understanding of whether these changes improved household-level outcomes, this paper updates estimates of key well-being outcomes in the country. The analysis imputes monetary poverty status using nonmonetary indicators in the 2016 –18 Demographic and Health Survey and estimates the World Bank’s Multidimensional Poverty Measure. Despite the country’s significant growth since 2009, monetary poverty and access to several essential services hardly changed, which stands in stark contrast to the substantial improvement across the rest of the world and other comparison regions over the same period. Combined, the results illustrate that it is possible that very little of resource-led growth trickles down to the population and that the link between macroeconomic and microeconomic outcomes is more tenuous in Papua New Guinea than found in other resource-intensive settings.

This paper is a product of the Poverty and Equity Global Practice. 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 standon3@worldbank.org or dnaidoo@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
fact, the demand and supply of female labor may be mutually reinforcing (Das and Desai, 2003; Das, 2006). However, in the absence of large-scale surveys that can capture the demand for female labor, it is difficult understand what may be happening to such demand.

This paper is based on a unique survey of 618 firms, carried out in 2015, in three of the largest cities in the state of Madhya Pradesh (India) – Bhopal, Indore and Gwalior. Each of the cities has a long history - Bhopal is the state capital; Gwalior has traditionally been the hub of educational institutions; and Indore has a large array of small and large industries. Madhya Pradesh (MP) itself is emblematic of India’s Hindispeaking heartland and can be seen, in many ways, as representing north-Indian culture and norms. The paper fills a void in the policy literature, as it assesses the relative role of culture, as signified by attitudes of employers, and of firm characteristics, in hiring women. It is part of the World Bank’s dialogue with the Government of MP in higher education and the extent to which educational attainment leads to more jobs for women.

# **2. What do we know about jobs and women’s employment in Madhya Pradesh?**

As pointed out earlier, MP is a good representation of north India in many ways. Figure 1 shows that the labor force participation for urban women in MP is a little above 15 percent and fairly close to the urban Indian average of 19 percent. Most employed women in the cities and towns in MP work either in regular jobs or in non-farm self2