rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Does It Pay to Be a Cadre? Estimating the Returns to Being a Local Official in Rural China**

# **I. Introduction**

While individuals in leadership positions within the public sector may have intrinsic motivation to perform public and community service, convincing citizens to become leaders in their communities may require some expectation of financial return for time and effort (Liu and Tang, 2011). Apart from receiving reasonable financial incentives for public sector work, however, one may worry about negative consequences if leaders are perceived to exploit their position and connections for personal financial gain beyond reasonable compensation for the work they do on behalf of rural residents. Rural agrarian economies are not immune to the potential tension between providing appropriate incentives and the potential that leaders may engage in excessive rent-seeking. Goldstein and Udry (2008), for example, show that individuals holding powerful positions in local political hierarchies in Ghana have more secure tenure rights to cultivated land, and as a result, the political elite invest more and enjoy substantially higher output.<sup>1</sup> In this paper, we use a large rural household panel data set covering 10 provinces across 16 years to examine the extent to which having a rural cadre as a household member contributes to earnings above and beyond those earned by non-cadre households.

> 1A related literature examines how political status and connections may be used to raise personal income and/or the value of firms. Roberts (1990) took advantage of the unexpected death of Senator Henry Jackson to identify the value to others of the political connections to him.  The share prices of companies with ties to the senator declined in reaction to his death; in contrast, share prices of companies with connections to his successor rose.  Similarly, Fisman (2001) showed that the timing of the emergence of a string of rumors about the health of former Indonesian President Suharto was associated with a decline in the value of firms that had strong political connections with the Suharto family.

2
were able to rule out the effect of unobserved time-invariant country heterogeneity and timespecific shocks using fixed effects, the decision to implement gendered laws may be highly correlated with unobservable country-level time variant factors that also affect financial inclusion. If this unobserved heterogeneity is not statistically accounted for, its effect will be captured by the variables measuring legal equality and inflate its statistical magnitude. Furthermore, greater access to financial services might induce a greater level of development in general which may also be reflected in more egalitarian legal frameworks. This endogeneity resulting from simultaneous effects might bias our main estimations.

To address endogeneity concerns, we first utilize an instrumental variable estimation of the WBL index using two different variables. The exclusion restriction underlying the use of an instrumental variable implies that it needs to be correlated with our dependent variables of interest, but must not have any direct effects on financial inclusion. Thus, selection of relevant variables is carefully made considering their prior use and reliability demonstrated in extant literature. First, the signature of the Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW) has been used by Perrin and Bertrand (2022) to instrument the implementation of laws against discrimination in the credit market. The CEDAW is an international treaty adopted in 1979 by the United Nations General Assembly in order to eliminate discrimination against women in political and public life. The 99 States Parties committed themselves to being a society that promotes policies, laws, organizations, structures and attitudes that ensure women are guaranteed the same rights as men. Thus, this prior commitment is likely to affect the implementation of gendered laws in the present without direct consequence on financial inclusion. As such, the dummy _CEDAW_ can be used as a conventional external instrument. Secondly, if one believes, like Merryman (1985, p. 2), that “the legal tradition relates the legal system to the culture of which it is a partial expression,” then the origin of a country's legal framework may rely on characteristics of its informal institutions. Thus, unequal
by the household (as opposed to leased, rented, etc.) with no housing built on top of it. We then estimate the number of farmers per district by multiplying the number of farming households by the average number of economically active agents per household. To estimate the average number of economically active agents per household, we divide the number of workers by the number of working households in the district, where a working household is defined as a household with at least one worker. The implicit assumption is that working households and farmer households are similar in terms of household size, structure, and labor force participation. Finally, we scale up the resulting numbers of workers and farmers by Sri Lanka’s population growth between 2016 and 2019 (2.8%) to compute numbers for 2019, our baseline year for model estimation (see Section VI).

For parameter estimation in Section VI.A.1, we also use 2019 HIES data on household size (number of members, regardless of age), income, and expenditure patterns, in particular the fraction of income spent on food. Values and quantities of crops purchased by each household are available, allowing us to compute the prices faced by the household by dividing values by quantities. We also use information on “income by chance or _ad hoc_ gains”, including lottery gains and disaster/relief payments, for our IV estimation in Section VI.A.1.
||**Log [Se**<br>|**lf-reported**<br>|**Productivity (X**<br>|**OF/ha)]**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||All crops|All crops|Export crops|Food crops|
||(0.009)|(0.009)|(0.012)|(0.011)|
|Hired Labor Use (Number)|-0.009|-0.008|-0.029|-0.003|
||(0.015)|(0.015)|(0.022)|(0.017)|
|Number of Fields Managed by the household|-0.079|-0.072|-0.688**|0.043|
||(0.138)|(0.138)|(0.277)|(0.168)|
|Total number of crops produced|0.103|0.111|0.461|-0.065|
||(0.150)|(0.151)|(0.294)|(0.176)|
|Household used any purchased seed|0.021|0.020|0.023|0.005|
||(0.015)|(0.015)|(0.024)|(0.017)|
|Plots that Use Chemical Fertilizer (% of total)|0.013|0.012|0.008|0.002|
||(0.010)|(0.010)|(0.018)|(0
lessons have not been learned in spite of a growing policy dialogue on ways to manage debt and run fiscal policy in a sustainable manner (see Figure 3).

**Figure 3:** Number and size of defaults

<!-- Start of picture text --> 100<br>100<br>75<br>75 No Default<br>50<br>Default<br>25<br>50<br>0<br>1990 2000 2010 1990 2000 2010<br>Count<br>Value of Default (%GDP)<br><!-- End of picture text -->

**_Source: Own calculations using 2020 version of Beers and Mavalwalla (2017)_**

Consequently, the work program for addressing public debt vulnerabilities in emerging and low-income countries remains more relevant than ever. The objective is to minimize the economic and fiscal costs associated with shocks and to increase debt data transparency and the efficiency and sustainability of fiscal and debt policies. Four pillars are needed to achieve this: 1) improving debt analysis and early warnings systems; 2) enhancing debt transparency; 3) strengthening debt management capacity; and 4) reviewing debt policies. Developing an early-warning framework to monitor public debt vulnerabilities and fiscal risks for market-access countries, would go a long way to bolster this agenda.<sup>2</sup> That includes: i) developing a methodological framework for assessing debt vulnerabilities; ii) developing a tool to support the analysis; and iii) establishing a consultation process and internal mechanisms to discuss action plans WB teams could propose for countries with elevated debt vulnerabilities.

This paper presents the methodological outline of a framework for monitoring debt vulnerabilities in market-access economies. It seeks to complement existing World Bank debt vulnerability assessments, including the joint Bank-Fund Debt Sustainability Framework.

The paper produces four indicators of debt vulnerability, which are used for predicting debt default. The four indicators capture overlapping-but-different angles of vulnerabil2MACs or market access countries are defined
<br>Valuation Survey of Ontario Residents|Alan J. Krupnick|Journal of Risk and Uncertainty|245|285|
|Public health|2006|<br>Do Cigarette Taxes Make Smokers Happier?|Jonathan Gruber|Happiness and Public Policy: Theory, Case<br>Studies and Implications|242|290|
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|Health and the economy|2003|<br>The Price of Innovation: New Estimates of Drug<br>Development Costs|Joseph A. DiMasi|Journal of Health Economics|1921|8|
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administrative tax data and microsimulation techniques, making it the first study to utilize such comprehensive data for assessing the impacts of the minimum wage. The primary goal is to evaluate the potential effects of increasing minimum wages and aligning them with living wages in Romania. The study explores the characteristics of minimum-wage workers using a detailed administrative tax dataset from the Romanian Ministry of Finance. Subsequently, it assesses the potential impacts of recent minimum wage increases using the same dataset. By applying labor demand elasticities estimated for other countries, the study simulates the effects of the minimum wage increase on wages, net labor earnings, and employment. Additionally, it examines the distributional consequences of alternative minimum wage adjustments.

<mark>We aim to evaluate the potential impacts of the proposed minimum wage increase on employment and earnings. Specifically, we intend to disentangle two categories of effects:</mark>

- **<mark>Disemployment Effects:</mark>** <mark>When minimum wages are "binding," there is a potential for reduced job creation and increased job loss among low-skilled workers. Numerous global examples indicate that higher minimum wages can diminish employment, especially for the least skilled employees. The expected employment effects of minimum wages are contingent upon the structure of labor demand. As explained above, in theory, while competitive labor markets can potentially yield adverse employment outcomes among low-skilled workers, this is not always the case in monopsonistic situations, where a surge in labor demand might also arise depending on the minimum wage level. These theoretical employment variations (on the extensive margin) are usually observed over the long term. In the short term, other adjustment mechanisms could delay changes in worker behavior (e.g., adjustments on the intensive margin, shifts in working hours (Stewart and Swaffield, 2008), firm profitability (Mirko et al., 2011), transfer of higher labor costs into final prices (Lemos, 2004) or incomplete non-compliance (Metcalf, 2008). The comprehensive assessment of overall employment
new, like South Sudan, and therefore are not included in older decades’ averages but could influence the estimated averages of recent periods.

Data sourced by surveys also present coverage and completeness issues. While this data is useful to understand better the reality the limited country-year information could result in a sample-selection bias. For example, the World Bank’s PovcalNet database (now PIP), that provides poverty and income data with an outstanding coverage, has not reported data in six Bottom Billion countries – such as Afghanistan or Myanmar. In nine other cases, household income data is only available for one year, which limits the possibility of estimating growth rates within a period. These include several African low-income countries – such as Chad, Sierra Leone, and Togo. Meanwhile, household income data has better temporal coverage in other Bottom Billion countries that have been more prosper, including Nigeria, Ghana, and Côte d’Ivoire. The same occurs with OECD countries, where household income data has full coverage and a consistent temporal availability. For instance, if we would use the dataset and the benchmark years used in Lakner and Milanovic (2015) we find that one-fourth of the countries have no data or just one year of income data. On the contrary, income data is available for all OECD countries and for 83 percent of the emerging markets.

Limited temporal availability of data in Bottom Billion countries also reduces the possibility of including the full sample for all benchmark years. For instance, the Bottom Billion have on average 2.6 years of benchmark data, compared to 3.3 in the Emerging Markets group and 4.7 in the OECD group. (Figure 23). Therefore, if we reproduce Figures 11 with a sample that excludes countries where income data is not available in the Lakner an Milanovic (2015) dataset, we observe that average GDP growth of the bottom billion group during the New Normal is revised upwards by 0.1 (Figure 24).

The implication of this is that limitations on country
The Islamic Republic of Iran is among the few middle-income countries availing LFS data collected since the start of the pandemic. The analysis presented in this paper uses LFS data covering the period from March 2019 to February 2021, corresponding to Persian years 1398 and 1399, the latter being the first full year of the COVID-19 pandemic.<sup>7</sup>

# The Impact of COVID-19 on the Iranian Labor Market

The Iranian labor market has historically been characterized by marked differences based on gender. Similar to what has been observed in other countries in the MENA region, women in the Islamic Republic of Iran tend to have significantly lower participation rates and higher levels of unemployment comparted to men.

Over the last decade, despite the progressive increase in female labor force participation and decline in female unemployment, the gender gap in labor market outcomes has remained substantial (Table 1). Differentials in labor market outcomes based on gender add to other labor market asymmetries based on area of residence (urban vs rural) and age. In particular, the labor market in Iranian cities is characterized by lower participation rates and higher unemployment, the latter being particularly high among youth and, even more, among young women.

**_Table 1: Trends in labor market indicators, 2011-2019_**

|||**_Participa_**|**_tion rat_**|**_e_**<br>||**_Unemplo_**|**_yment ra_**|**_te_**<br>|
|---|---|---|---|---|---|---|---|---|
||**overall**|**female**|**male**|**gap (f/m)**|**overall**|**female**|**male**|**gap (f/m)
Table 14 shows these results. The coefficients are almost identical to those in tables 10, 11 and 12 and therefore we do not have any reason to believe that the results discussed in section 5.2 are biased by the effects of the civil violence in 2006.

# **_5.3.2. Migration biases in the analysis of school outcomes in 2001 and 2007_**

A final important concern in our analysis is the fact that some individuals have migrated at some point in their lives, thereby introducing a potential selection bias in the results. The 2001 and 2007 datasets provide information on their place of birth and their place of current residence.<sup>28</sup> If both coincide, then we can infer that they either did not migrate or migrated but only temporarily. There is, however, a group of individuals that migrated to a different place from the place of birth. The percentage of those that migrated represents 13% of the sample in 2001, and 19% and 24% respectively in the two samples of 2007.<sup>29</sup> The data does not allow us to establish when this migration occurred, or whether these individuals migrated for conflict related reasons. But a form of selectivity bias may occur if individuals did not choose the new place of residence randomly (the so called ―spatial sorting‖, see Kondylis 2010). For instance, if we find that those that migrated went to areas in which economic conditions are typically better (for instance, urban areas), our results would be likely to exhibit a downward bias in the effect of violence on education.

We have estimated the impact of a potential spatial sorting effect in tables 15 and 16 (for 2001) and table 17 (for 2007). These tables show the estimated results for the sample of individuals that never moved from the place of birth to the place of current residence for 2001 and 2007, respectively. These calculations test if the results in section 5.1
|Owner's Education (1=above secondary)|0.59|0.12|
|---|---|---|
|Prior Experience in Same Type of Business (1=yes)|0.24|0.24|
|Sector Dummy (1=manuf, 0=services (inc. retail))|0.09|0.07|
|Formal Labor Productivity (log)||10.43|
|Informal Labor Productivity (log)||5.56|
|Elevation||1249.02|
|Nighttime Lights Emission||35.46|

_Notes_ : Sampling weights applied. “Formal Micro” covers formal micro-businesses included in the World Bank Micro Enterprise Survey (WBMES), and “Informal” covers businesses included in the World Bank Informal Sector Enterprise Survey (WBISES). Baseline estimates match the closest informal firm to the closest formal firm that was interviewed.

The main variable of interest is the geographic proximity of informal businesses to formal firms. Geographic proximity is captured by the distance to the closest formal firm. Table 2 shows the summary statistics where distance is represented in kilometers.<sup>8</sup> The baseline measure is the distance between informal businesses and the closet formal firms regardless of their size (i.e., combining both firms from WBMES and WBES). Additional distance measures are constructed and explained in detail in the robustness, heterogeneity, and spillover channel sections.<sup>9</sup> A potential endogeneity concern when using geographic proximity is that location choice of firms may be linked with their decision to adopt digital technologies. However, studies have shown that technology transfer is not the only (or main) reason for firms’ location choice.<sup>10</sup> While the decision to locate the business in a specific district may be driven by the owner’s technology adoption consideration (e.g., access to infrastructure)
|5|5|5|5|5|5|
|Japan|1|2|1|2|1|2|2|2|
|Korea, Rep.|2|3|2|3|2|3|3|4|
|Thailand|4|4|4|4|4|4|4|3|

Source: World Bank, WITS database. Note: Rankings established from data at the two-digit level for exports in the WITS database.

The international division of labor and production offers another angle for examining the flyinggeese pattern. Table 2, based on historical trade statistics for footwear, offers credibility to the flying-geese hypothesis. It is constructed using revealed comparative advantage indexes for lead countries and latecomers in that industry. The table shows that Japan had a revealed comparative advantage in the early 1960s, during the earlier light manufacturing phase of its development. Later on other countries moved into the picture and began to take over larger shares of global production. The pattern displayed does not conform precisely with a ―pure‖ dynamics of ―flying geese.‖ Real-world data always involve some ―noise‖ because products with different sophistication and different capital and technology intensities may be grouped in the same category. And government interventions may cause some deviation of industrial structure away from the optimal one determined by the country‘s comparative advantage. Still, the general picture is consistent with the theory.
.2<br>.1<br>Social Sustainability Index<br>0<br>1 .1<br>.8 .05<br>.6 0<br>CCDF<br>.4 Difference -.05<br>.2 -.1<br>0 -.15<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C3. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Level of education indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C3.1. FOSD: Counting Vector C3.2. Dominance Analysis 49
# **3.2 Construction of Export Exposure Using Input-Output Linkages**

Any changes in the foreign export demand for products of a particular sector will have dual effects. First, it will lead to a direct increase in demand for output in that sector. Secondly, it indirectly affects the upstream sectors that supply inputs to the directly impacted sector. Not accounting for these linkages will underestimate the export exposure at the province level, as some provinces may not have a concentration of industries directly exporting but still be supplying to exporting sectors. To account for these value chain linkages, the literature represents uses Leontief inverse of an input-output production matrix for an economy. The method clearly tracks the use of intermediate inputs by each sector (Goutam et al. 2017; Acemoglu et al. 2016; Acemoglu et al. 2012).

To explore potential effects of exports through domestic inputs, we employ the 2011 Viet Nam Input-Output table to calculate the input shares of each industry. These shares are determined by dividing the input usage by the gross output (which includes the value added in the own sector with own-sector inputs). We then multiply the resulting shares by the exports of the final sector aggregated over the input industry to obtain the total value of exports for each input sector (representing the cumulative effect of servicing multiple exporting sectors). In this sense, non-traded sectors that are assigned a value “zero” for exports will also have an implied value and will be used to estimate the total export exposure index at the province level using the following index.

The total export exposure (accounting for supply chain linkages) is measured as the growth in exports in industry _i_ between time periods, _t_ and _t_ + 1, captured by the term ∆ _Wi,t_ + _h_ = _Wi,t_ − _Wi,t_ + _h_ . This change is allocated to each province _r_ in Viet Nam using the share of provinces in total national employment in each industry _i_ .

To construct the total exposure index at the province level in Viet
1/3 of its output to furniture producers and 2/3 of its output to jacket producers. If no multinationals produce furniture but half of all jacket production comes from foreign affiliates, the _Backward_ variable will be calculated as follows: 1/3*0+2/3*1/2=1/3.

> 10 Input-ouput tables of China (2002) Table 4.2, which divides manufacturing industry into 71 sectors.

8