rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix B. Measuring promotions, lateral moves, and demotions**

We discuss how we use the CPED data to determine promotions and demotions in detail here. Our database has all city PSs for all prefecture-level administrative units from 2000 up to 2019.

We classify all PSs into nine groups by their ranks when they assumed office and when they left office. Each PS could be ranked at one of the three hierarchical levels when they assume (leave) office: the deputy-provincial level with the membership of provincial Party Standing Committee (PSC), the deputy-provincial level without the membership of provincial PSC,<sup>54</sup> or the prefecture level. We distinguish deputy-provincial level PSs by whether they are members of the provincial PSC or not because similar to the Politburo Standing Committee in the central committee, the provincial PSC is the highest decision-making bureau at the province level. Though most members in the provincial PSC hold the same administrative level with vice governors, vice directors of the provincial Party Congress, and vice-chairmen of the provincial PPCC, they maintain more real power. In total there are nine possible starting-ending level pairs for a city PS.<sup>55</sup> Because no PSs are downgraded from deputy-provincial level to prefecture-level during their tenure in the city secretaryship, we end up with seven groups of city PSs.

For a PS that does not experience a rank change during his tenure, we only need to compare the rank of the PS’s current position and that of his destination job. For a PS who experiences a rank change during the term, we compare the changing ranks within his tenure. Specifically, if his hierarchical level is from either a deputyprovincial level without provincial PSC membership or a prefectural level to a deputy-provincial level with provincial PSC membership, we regard this rank change as a promotion during his tenure. In contrast, if he loses the PSC membership during his term as the city secretary, this is a demotion.<sup>56</sup> Apart
with a value of 1 post-2017, marking the onset of the substantial Rohingya inflows to Bangladesh. Dependent variables are constructed using the MODIS Land Cover Type dataset, which captures Earth’s land surface imagery every 1 to 2 days, providing measurements of area in square km by land use type. (i) Dense-open forests refer to areas with dense canopy cover (¿40% tree cover), characterized by high values of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI), indicating vigorous and dense vegetation. (ii) Grassland refers to vegetated areas dominated by grasses, herbs, and other non-woody vegetation, typically with sparse to moderate tree cover or no trees present. (iii) Croplands refer to areas of land specifically designated or utilized for agricultural cultivation, including the cultivation of crops such as grains, vegetables, fruits, and other agricultural products. (Sin*) represents the inverse hyperbolic sine transformation, interpretable as a logarithm transformation. The estimates account for grid and year fixed effects, with clustered standard errors reported at the grid level in parentheses. False Discovery Rate (FDR) q-values are reported in brackets. Significance levels are denoted as follows:<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

45
Policy Research Working Paper 6041

# **Abstract**

The authors use an original panel dataset of migrant departures from the Philippines to identify the responsiveness of migrant numbers and wages to gross domestic product shocks in destination countries. They find a large significant elasticity of migrant numbers to gross domestic product shocks at destination, but no significant wage response. This is consistent with binding minimum wages for migrant labor. This result implies that labor market imperfections that make international migration attractive also make migrant flows more sensitive to global business cycles. Difference-indifferences analysis of a minimum wage change for maids confirms that minimum wages bind and demand is price sensitive without these distortions.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at atdmckenzie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 22 -

capture some demand shifts caused by the ways of using different energy sources. As will be seen, our estimation results indicate that the use of electricity or LPG has certain but not much explanatory power to the electricity demand.

**Sample bias.** Household surveys often have a sample bias problem. Respondents can always choose not to provide answers (Yoo, 2007). It is often pointed out that the amount of electricity consumption reported in household surveys might be deviated from the amount that households actually consumed. In Georgia, for instance, it is found that household surveys overstated the actual payments received by a power company (Lampietti _et al_ ., 2007). In our sample, there is no crucial evidence that Albania’s household surveys suffer from the sample bias problem.

**Instruments.** The current paper adopts three instrumental techniques to deal with the endogeneity associated with the marginal price and Nordin’s difference variable. First, the marginal price and Nordin’s difference variables are instrumented by their predicted values according to the simple expansion of the definition of the Nordin’s difference variable. To obtain the predicted values, the actual bill payment is regressed on a constant term and the quantity of consumption:

where _Billij_ is the amount of bill payment for customer _i_ , who is faced with _j_ th block marginal rate. _εij_ represents an error term. The marginal price and Nordin’s difference variable can be instrumented by<sup>ˆ</sup> 2<sup>andˆ</sup> 1<sup>, respectively (Hewlett, 1977). However, there</sup> may remain concern about the fact that the total bill is still a function of quantity and marginal price. Therefore, total bills may still feedback onto quantity (Deller _et al._ , 1986).

The second technique is the Terza’s (1986) two-stage probit approach, which essentially transforms the endogeneity question to the consumer self-selection problem.
variation of general government gross debt from 2012 to 2017 is about 35 percent of GDP, while their median public debt stock is 66 percent of GDP. Zooming in on the group of heavy borrowers, it can be pointed out that five out eighteen heavy borrowers in the region have a public debt stock that exceeds 100 percent of GDP; namely, Cabo Verde, Republic of Congo, The Gambia, Mozambique and Sudan. Additionally, three heavy borrowers (Ghana, Togo, and Zimbabwe) have public debt levels that are above 70% of GDP (but still below 100% of GDP).

**Figure 19. Public debt level and change across heavy borrowers in Sub-Saharan Africa**

<!-- Start of picture text --> 140<br>120<br>Change<br>2012-17<br>100<br>80<br>60<br>40<br>20<br>0<br>Percent of GDP<br>The...<br>Sierra... Chad Ghana Benin Central... Togo Zimba... Cabo... Angola Equato... Zambia Sudan Gabon South... Mozam... Congo,...<br>Namibia<br><!-- End of picture text --> _Source: International Monetary Fund, World Economic Outlook Database._ _Public debt increased sharply over the past five years: What do SSA countries have to show for?_ Public debt has an important impact on the economy in the short- and the long-run. It has been argued that debt (contracted to finance deficits) can stimulate aggregate demand and output in the short run but crowds out capital and reduces output in the long run (Elmendorf and Mankiw, 1999). Others have argued that there is a non-linear effect of public debt on economic growth that arises once the country surpasses a tipping point of fiscal sustainability. That is, debt overhang could distort investment decisions in environments
**Table 9 Headcount Index of Poverty among Farm Households, 1985-1993**

|_(poverty line = 75,000 CFAF per year in 1985 CFAF)_||
|---|---|
|Socioeconomic Group<br>1985<br>1988|1993|
|Export crop farmers<br>.086<br>.210|.394|
|Food crop farmer<br>.150<br>.197|.492|
|Source: 1985 and 1988: Grootaert (n.d.).||

1993: Household Poverty Survey.

Note: Poverty indices for 1985 and 1988 are calculated using Grootaert (1995) deflators, rather than the INS deflators. See Appendix B for a discussion of the difference in the two sets of deflators.

**Table 10 Official Export Crop Producer Prices**

|_Campaign_||||_Consumer_<br>_Price_|_Real_|_Producer Price_<br>_(1984/85 = 10_|_Index_<br>_0)_|
|---|---|---|---|---|---|---|---|
|_Year_|_Cocoa_|_Coffee_|_Cotton_|_Index_|_Cocoa_|_Coffee_|_Cotton_|
|1984/85|375|385|115|104.7|100.0|100.0|100.0|
|1985/86|400|200|115|111.1|100.5|48.9|94.2|
|1986/87|400|200|115|118.5|94.3|45.9|88.4|
|1987/88|400|200|115|126.6|88.2|42.9|82.7|
|1988/89|400|200|115|128.0|87.2|42.5|81.
statistical offices will have to rely on phone surveys to collect information from households on the crisis’s impact while avoiding any face-to-face contact.

Ideally, a phone survey uses a nationally representative sample frame, where, if possible, the first round is an (existing) face-to-face survey. Once completed, researchers conduct follow-up phone interviews at regular monthly, quarterly, or other intervals. The survey output contains repeated welfare observations for each household, often before and after welfare shocks caused by violence, displacement, drought, or other things.

Baseline face-to-face interviews serve two primary purposes. First, to construct more traditional household welfare metrics—for instance, poverty—and then follow up with short questions more suited for a phone interview regarding changes in welfare. Second, for households without phones, one can be provided so that follow-up interviews can be truly nationally representative, as opposed to only representing the population with phone access (see for example, Dabalen et al., 2016).

When face-to-face interviews are not possible, as in the Syrian Arab Republic or during the COVID-19 epidemic, survey firms or statistical agencies could conduct phone surveys using a list of phone numbers representative of the population with access to a phone. Such a list can be acquired from phone numbers collected in the last nationally representative survey conducted in a country (see for example, Himelein et al., 2015). Alternatively, if such a list either does not exist or is outdated, partnering with mobile phone companies can generate a sample frame.

Lastly, when a list of the phone-using population is not available, surveyors can collect phone numbers via random digit dialing (RDD). Survey firms have algorithms to randomly dial phone numbers until a household answers, after which the household is offered the opportunity to complete a survey. The World Food Programme (WFP) used this approach effectively in Yemen, completing surveys of 2,400 households every month over the entire course of the conflict in that country (WFP, 2018).

# <u>
in the analysis. However, as mentioned above, we are interested in disentangling the trends in labor inequality derived from the inequality of the earnings structure rather than the labor supply decisions of workers.

Finally, the data series are spatially deflated between urban and rural areas using a factor of 15 percent of the differences in the cost of living<sup>23</sup> because the lack of the use of spatial deflators overstates income inequality, although the size of spatial price differences depends on the country,<sup>24</sup> and the use of a regional factor is a second-best method, but is better than not deflating spatially.

# 3. Trends in labor earnings inequality

Except for Sub-Saharan Africa, Latin America is the most unequal region in the world (Alvaredo and Gasparini 2015; Lakner and Milanovic 2013; Williamson 2015). This holds even after one controls for the fact that inequality in the region is measured through income, while, in the rest of the developing world, it is measured using consumption data. Following a method proposed by Alvaredo and Gasparini (2015) to generate a consumption Gini from income Gini measures, the left panel of figure 1 shows Latin America as the second most unequal region in the world. This position is statistically significant, and it is robust to the exclusion of extreme values.

> 21 On the other hand, the underreporting of incomes among top earners, a classical drawback in the literature that measures inequality using household surveys, is less pronounced if labor income is considered because a larger share of the incomes of the top of the distribution is derived from capital gains. Moreover, it has been shown that the current underreporting of these income sources is not an important source of variation in the ranking across countries.

> 22 See Katz and Murphy (1992) and Katz and Autor (1999). Autor et al. (2008) and Acemoglu and Autor (2011) argue for the use of full-time workers, while Lemieux (2006) proposes using
Table B.2: Limitations in the procurement data

||CE|MG|PB|PE|PR|RS|Obs|
|---|---|---|---|---|---|---|---|
|_Incomplete temporal coverage_||||||||
|Tender||||||||
|Item||||||||
|Participant||||||||
|_Has some municipality missing_||||||||
|Tender|✓|✓||✓|✓|✓|SeeFigure B.5|
|Item|✓|✓|✓|✓|✓|✓|SeeFigure B.5|
|Participant|✓|✓||✓|✓|✓|SeeFigure B.5|
|_Does not have unique ID_||||||||
|Tender||||||||
|Item||||||||
|Participant||||||||
|_Has missing IDs_||||||||
|Tender|✓||||||SeeFigure B.1|
|Item|✓|✓||||||
|Participant||||||||

**Notes** : This table presents the limitations in procurement data. ’Does not have unique ID’ refer to the cases where we were not able to build an identifier that uniquely identifies each row in the given table. Even if we manage to construct a unique identifier for most of the observations of a state-table pair, we may still have observations within that table for which this is not possible. In these cases, we mark it with a tick in ’Has missing ID’. The percentage of cases in which that happens can be found in Figure B.1.

47
398)|(0.397)|(0.778)|(0.771)|(0.763)|
|Observations|719|719|719|376|376|376|
|Mother controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Child controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|

Table VI presents estimated effects of maternal mental health on child vocabulary at age 5, separated by whether the mother has completed at least primary education or not. We estimate the model selected from LASSO procedure. Selected control regressor set includes mother's age, wealth index, living in urban area, child's age, consumption of durable goods index, household size, number of children younger than 5 years in the household, and Height for age Z-score. Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. Robust standard errors in parentheses.

31
> tt (SSdata. and SS ) regarding the sub-populations of HH For the Sample only to the sub-population tt←tt<sup>∗</sup> tt←tt<sup>∗</sup> SS , the tracking rules limit the representativeness of sample SS to time t. tt←tt<sup>∗</sup> FF For Sample , the tracking rules restrain the representativeness of the sample data only to of households of time tt who have been eligible for tracking from time tt<sup>∗</sup> the sub-population tt←tt<sup>∗∗</sup> SS tt←tt<sup>∗∗</sup> FF and E , being of households of time tt who have been eligible for tracking from time tt<sup>∗∗</sup> to time tt. ∪E . tt←tt<sup>∗∗</sup> tt←tt<sup>∗</sup> tt<sup>∗∗</sup> ←tt<sup>∗</sup> FF can be partitioned into the two subpopulations FF , we have no people belonging to the subpopulation Ett←tt<sup>∗∗</sup> tt←tt<sup>∗</sup> tt<sup>∗∗</sup> ←tt<sup>∗</sup> ,, FF = FF who are the people entered in the population after the last panel refresh. As we have no sample tt←tt<sup>∗∗</sup> tt<sup>∗∗</sup> ←tt<sup>∗</sup> Finally, we see that in sample SSdata on this subpopulation, we cannot compute direct estimates.

10
[</mark> 1]** <mark>Lu, X., Wrathall, D. J., Sundsøy, P. R., Nadiruzzaman, M., Wetter, E., Iqbal, A., ... & Bengtsson, L. (2016). Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen.</mark> _<mark>Climatic Change</mark>_ <mark>,</mark> _<mark>138</mark>_ <mark>(34), 505-519.</mark> **<mark>[</mark> 2]** <mark>Lucas, R. E. (2021).</mark> _<mark>Crossing the Divide: Rural to Urban Migration in Developing Countries</mark>_ <mark>. Oxford University Press.</mark> **<mark>[</mark> 2]** <mark>Magrini, E., Montalbano, P., Winters L. A. (2018). H</mark> ouseholds’ vulnerability from trade in Vietnam <mark>, World Development, 112. pp. 46-58. ISSN 0305-750X.</mark> **<mark>[</mark> 4]** <mark>Marchiori, L., Maystadt, J. F., & Schumacher, I. (2012). The impact of weather anomalies on migration in Sub-Saharan Africa.</mark> _<mark>Journal of Environmental Economics and Management</mark>_ <mark>,</mark> _<mark>63</mark>_ <mark>(3), 355-374.</mark> **<mark>[</mark> 1]** 45
Table C.11: National Estimates of Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference

||||Sector of W<br>|ork<br>||
|---|---|---|---|---|---|
||Working|Skilled /<br>Professional|Service /<br>Sales|Agriculture /<br>Unskilled Manual|Ideal Number<br>of Children|
||(1)|(2)|(3)|(4)|(5)|
|�<br>Years of Schoolingizy|0.010|0.033|0.020|-0.034|-0.923|
||(0.042)|(0.017)|(0.025)|(0.034)|(0.658)|
||[0.809]|[0.053]|[0.433]|[0.319]|[0.160]|
|Mean of Dependent|0.342|0.072|0.126|0.279|7.623|
|(Pre-Reform Cohorts)||||||
|First Stage F-Statistic|6.93|7.91|7.91|7.91|6.60|
|N|24,882|24,607|24,607|24,607|24,649|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : The dependent variable is described at the top of each of the five columns. In columns 1–4 it is an indicator that equals 1 if true, and in column 5 it is the ideal number of children. Skilled/Professional jobs include professional, clerical, and skilled manual job groups; the
decline of agricultural productivity due to a 1<sup>_◦_</sup> C increase in temperature is indeed near 13 percent, then Guiteras’ damage estimate of less than 11 percent is implausibly small, implying virtually no scope for adaptation. By contrast, our weather shock-based damage estimate is two and a half times that of Guiteras and is, therefore, consistent with considerable farmer adaptation to warmer temperatures in the long-run.

# **4.4 Rural wages and climate**

The methodology for estimating the climate sensitivity of rural wages by employment sector follows closely the approach used for land values. Drawing upon the NSS employmentunemployment survey of 2004-05, the best source of labor data in India (Himanshu, 2005), we estimate the analog of equation (6) on a sample of over 36 thousand rural workers from 556 districts. We take the individual’s log daily wage (adjusted for regional cost-of-living

> 25As Guiteras points out, this was a period of increasing adoption of high-yielding varieties, which are more vulnerable to bad weather. Production data from later years would presumably exhibit even greater weather sensitivity.

15
the price changes and ignoring long-term considerations related to household decisions to change their consumption and income patterns. Wodon et al. (2008) in fact ignore even short run changes in income, whereas Ferreira et al. (2011) allow incomes of agricultural workers to rise proportionately with the increase in the price of agricultural commodities. That is, one study assumed that q in equation (1) would remain fixed, and the other allowed it to vary with the price changes. Neither considered changes in the structure of consumption, which might be a reasonable assumption when studying temporary price hikes.

If this were the end of the story it is very likely that most trade reforms would be pro-poor because they reduce the domestic price of imported goods. We know from household surveys that poor families tend to spend a large share of their total household expenditures on tradable goods. This is shown in Figure 1 for the case of Brazil, utilizing three different definitions of tradable goods, based on work by Lederman, Lichand and Fajnzylber (2011). The higher shares of tradables in poor households’ consumption baskets might imply that the first order impacts of most import liberalization would tend to be pro-poor, even if not necessarily supportive of inclusive growth (IG), due to the high dependence on consumption of tradable goods.

Indeed, a striking characteristic of the trade literature is the scarcity of studies that focus purely on the first-order effects of trade liberalization, which are very likely to benefit most poor consumers in developing countries. There are studies of price-policy changes, but fewer on trade policy per se. An interesting example of such a study is Duran, Finot and Lafleur (2011) of the United Nations’ Economic 4
(Akresh _et al._ 2023) and Colombia’s mid-20<sup>th</sup> century ‘La Violencia’ that slowed structural transformation via occupational choices by individuals who experienced violence in school or pre-school age (Fergusson _et al._ 2020).<sup>2</sup> Conflict can affect those not directly exposed if households adjust their behavior (Tapsoba 2023) or asset portfolios (Rockmore 2020; Sinha _et al._ 2022) in anticipation.

Our survey shows that, in Ukraine, the war triggered a marked drop in expectations for the future, but social safety nets and non-agricultural income sources held up. Ukraine’s comparative advantage in agriculture contributed to continued strong fundamentals in the sector; in fact, instead of divesting assets, most farmers expressed interest in expanding their holdings by leasing or purchasing land through markets.<sup>3</sup> Farmers’ perceived priorities focus on better access to output markets and protection against rapidly worsening terms of trade.

Regarding the second question, estimates on the extent to which direct war-related actions have affected area cultivated vary widely partly because 2021 yielded a record harvest so that any direct comparisons that fail to adjust for weather or for potential substitution would tend to overestimate war effects.<sup>4</sup> Moreover, conflict can affect economic activity indirectly, e.g., by reducing trust (Korovkin & Makarin 2023), increasing mobility and trade cost to force input substitution (Amodio & Di Maio 2018), reducing interaction (Couttenier _et al._ 2022), or <mark>lowering project returns</mark> (de Roux & Martinez 2021). Impacts may thus be felt well beyond the areas directly affected by war and conflict (Federle _et al._ 2022).

The farm survey data analyzed here shows that any direct war effect on area cultivated was eclipsed by large increases in prices for imported inputs that affect all farmers together with changes in labor cost that vary across regions in