rafmacalaba/datause-encoder-data huggingface dataset:
MONGOLIAS INFORMAL SECTOR Size and Trends 13 30. A second approach to estimating the size of employment in Ulaanbaatar's informal sector suggests that the above estimates are reasonable. An analysis of the data from the SSO's household survey reveals that 47-51% of households have some informal income. Since a household would have informal income if even one household member had informal income, the approximate percentage of people with informal income can only be inferred. A simple adjustment suggests that roughly 30-33% of the working age population of Ulaanbaatar earns some informal income, an estimate that is consistent with the more elaborate estimation derived above.<sup>32</sup>

# **Aggregate Household Income in the Informal Sector**

<!-- Start of picture text --> 31. This section olf the report reviews the available evidence on incomes in the informal sector? 3 An<br>analysis of data from the SSO Monthly<br>Survey of Household ][ncome and Percentage of Household Income by Type<br>Expenditure reveals a wealth of 1/93 through 12/96<br>information about both the trends and 7o/<br>the current size of the informal sector, 60% A<br>as measured by the contribution to<br>household income. First, it will be 501/ X<br>shown that the growth of informal<br>activity during the transition is 30<br>confirmed by the household survey > 30%<br>data. Second, an analysis of a single 20%./<br>month's data will be used to estimate<br>that, in the aggregate, 32% of 10%/i<br>household income in Ulaanbaatar comes from informal sources. 93.01 ____ 94.01 95.01 96.01 97.01<br>Trends in Informal Income _ Income from Individual Activities Wages and Salaries<br><!-- End of picture text --> 32. Every month the SSO, with its **Figure** 3 local affiliates, conducts a survey of _Source SSO_ 1,893 households around the country.<sup>34</sup> Several aggregates from this household survey are informative about the trends in the relative importance of different types of income. In particular, the relative importance of wages and salaries can be compared to that of "individual activities" to get a sense of the changes over time in micro-entrepreneurship.<sup>35</sup> 33. Figure 3 shows the strong upward trend over time in the relative importance of individual income, compared to that of wages and salaries. If one imposed a linear fit to the observations in Figure 3, smoothing out the seasonality and random fluctuations, one would find that individual activities have doubled in importance in the past four years, while the importance of wages and salaries has fallen over 20%. The statisticians who compile the data on the household survey report that in the late 1980s, wages and salaries comprised roughly 85% of average household income, while the average household in 1996 received only 43% of income from wages and salaries.<sup>3 6</sup>
To answer these questions, we deploy a nationwide phone survey that includes a new approach to ascertain the valuation of migration options among respondents, and a randomized survey experiment that identifies the effects of political or economic stabilization on migration intentions. The remainder of this paper is organized as follows: Section 2 describes the survey conducted for this study including a new approach to measuring valuations of migration, and a survey experiment designed to estimate the effects of stabilization on migration intentions. Section 3 describes the survey data and summary statistics. Section 4 discusses the identification strategy. Section 5 presents the main results as well as key heterogeneous effects. Section 6 concludes with implications for policy and future research.

# 2. Study Design

While international migration typically accompanies the development process, resulting in net gains for both sending and receiving countries especially in the medium- to long-term, a sudden and large-scale immigration of high-skilled workers can result in critical shortages of qualified workers in key sectors of economic activity in the sending country. When emigration is undertaken under duress, induced by economic and/ or political instability, such detrimental effects may be more pronounced. When migration is motivated by a need to escape duress at home, prospective emigrants may be willing to accept lower wages to move abroad, than under more normal circumstances. They may also be more willing to take up work that they are overqualified for. Both such choices represent a potential misallocation of labor, provoked by the difficult circumstances faced by potential emigrants at home.

Our study seeks to understand the extent to which (i) political instability (resulting in civil conflict) and (ii) economic uncertainty drive the willingness of high-skilled youth in Myanmar to migrate abroad, the threshold wage differential at which they become indifferent between migrating and remaining at home, and the extent to which they may be amendable to undertake work for which they are overqualified. We examine these questions in the context of the military takeover of the Government in Myanmar since 2021, followed by high levels of violent conflict and economic collapse.

As young people in Myanmar face diminished economic prospects, the appeal of emigrating abroad may increase, the reservation wage for work abroad may fall, and the hesitation to perform work they are overqualified for may reduce. Does the willingness of high-skilled youth to emigrate respond more to political or to economic prospects and expectations? We examine these questions using a randomized questionnaire module in which respondents will be randomly assigned one of three hypothetical scenarios representing (i) an improvement in the political situation including the cessation of violent conflict, (ii) an improvement in the economic situation including currency and price stabilization, and (iii) a base scenario representing things as they are at present.

6
of growth by sectors, space, and time in Brazil. Interestingly, they find that growth in the service sector was substantially more poverty-reducing than in either agriculture or industry. Klasen (2007) also emphasizes the role of country-specific conditions in this context.

Similarly, following Dollar and Kray (2002), a number of studies have examined the contribution of specific policies (such as trade or access to finance) in explaining the levels of poverty or inequality. These studies generally observe that a stable macroeconomic framework, a business friendly environment, and access to factors of production (education or credit) are conducive to lower poverty, higher incomes for the poorest, and less inequality.<sup>7</sup> More particularly, Ghura et al. (2002) argue that simply focusing on economic growth as a strategy to lower poverty may actually leave the poor worse off relative to the average population. Their empirical results suggest the existence of a set of policies and conditions which are super pro-poor, namely lower inflation, lower government consumption, higher levels of financial sector development and higher educational status. Taxes, fiscal redistribution and public service provisioning also matter for distributional aspects of growth. For instance, Balakrishnan et al. (2013), looking at the experience of Asian economies, argue for increases in spending on health, education, and social safety nets, as well as reforms to make financial systems more inclusive.

Similarly, studies on inequality seem to agree that higher inflation leads to higher inequality, while more and better infrastructure and more human capital lead to lower inequality (Barro, 2000; Li and Zou, 2002; Lundberg and Squire, 2003; Lopez, 2004; Dabla-Norris et al., 2015). The results on the effects on income distribution of trade, financial development or government spending are, however, mixed.

Finally, another strand in the literature highlights the relevance of initial income distribution for the subsequent elasticities between mean income growth and poverty reduction (e.g. Bourguignon, 2003; Ravallion and Chen, 2007; Klasen and Misselhorn, 2008; Ravallion, 2012; Christiaensen et al. 2013; and Crespo-Cuaresma et al., 2016). This work looks at the highly non-linear relationship between these two variables. Foster and Székely (2008), for instance, adopt a “general means” approach that places progressively less weight on incomes higher up the distribution. Their empirical findings show that the more weight is given to lower income households, the smaller the elasticity between growth and their poverty-sensitive income measure, suggesting that lower incomes do not grow in proportion to average incomes.

# **III. Data, Methodological Approach, and Cross-Country Results**

# **_Dependent Variable_**

Following Dollar et al. (2016), this paper draws on household data from two sources: the World Bank’s POVCALNET covering primarily developing countries, and the Luxembourg Income

> 7 See for instance Le Goff and Singh (2014) or Singh and Huang (2015).

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Table 18 reports the difference in input use across households located in communities with an agricultural cooperative and those without. Apart from Nigeria, a low correlation exists between cooperative placement and household input use. In Tanzania, despite the smaller sample, there is no difference in input use across any of the inputs. Households in communities with cooperatives are more likely to use agro-chemicals in Nigeria and Uganda while households in Niger and Nigeria in communities with cooperatives are more likely to irrigate. Inorganic fertilizer use only appears associated with the presence of a cooperative in Nigeria.

Like all of the other covariates included in our descriptive analysis, these variables are likely to be endogenous – i.e., cooperatives may form where agricultural conditions are better suited to input use – and cooperative placement may be correlated with other variables not controlled for in simple descriptive statistics. Nonetheless, the lack of correlation between cooperative location and household input use across four of the six LSMS-ISA countries is a potentially surprising and interesting finding, particularly given the common wisdom that agricultural cooperatives offer one of the principal channels through which households acquire modern inputs.

# <u>Farm size</u>

The inverse (negative) relationship between farm size and productivity is a well-studied and fairly entrenched phenomenon in the agricultural development literature (Carter 1984; Feder 1985; Barrett et al. 2010). What is less well-documented with data from farmers’ fields is the relationship between input use intensity and farm size (important exceptions include Croppenstedt, Demeke, Meschi 2003; Doss 2003). Moreover, farm size may represent the best way in the LSMS-ISA data to differentiate farmers by the intensity of their agricultural operations. In general, both in common parlance and in policy circles, a common conception exists that households with larger farms are more likely to apply modern inputs and are more likely to use inputs and in higher amounts.

We test this hypothesis using data from the LSMS-ISA countries. Using non-parametric local polynomial regressions, Figure 1 shows a mostly negative relationship between farm size, defined as total area under cultivation in the most recent main season, and household-averaged inorganic fertilizer application rates. Because we use imputed land size values, we avoid bias of self-reporting of the sort described by Carletto, Savastano, and Zezza (2013). Some of the most negative patterns occur in the countries that have larger average farm sizes, like Niger and Tanzania. On the other hand, in Malawi, where average farm sizes are the smallest, the relationship is less pronounced after an initial fairly small range of farm sizes. In Ethiopia, farm sizes need to approach nearly 7 hectares before a negative relationship sets in. Moreover, in Ethiopia and Uganda, there is a range over which the relationship is (mildly) positive before falling again. This same relationship is studied at the plot level in the next section of this report.

25
|Sustainable Fisheries Resources Development Project(Fourth Sout|hTapas Paul|
|---|---|
|Sustainable Fisheries Resources Development Project(Fourth Sout|hTapas Paul|
|Sri Lanka Competitiveness DPL|Tatiana Nenova|
|SupportingEgypt Education Reform Project|Juan Manuel Moreno Olmedilla|
|SupportingEgypt Education Reform Project|Juan Manuel Moreno Olmedilla|
|SupportingEgypt Education Reform Project|Juan Manuel Moreno Olmedilla|
|SupportingEgypt Education Reform Project|Juan Manuel Moreno Olmedilla|
|SupportingEgypt Education Reform Project|Juan Manuel Moreno Olmedilla|
|Mazar-e-Sharif Gas-to-Power Project|Teuta Kacaniku|
|Meghalaya Community-led Landscapes Management Project|Pyush Dogra|
|Meghalaya Community-led Landscapes Management Project|Pyush Dogra|
|Meghalaya Community-led Landscapes Management Project|Pyush Dogra|
|Meghalaya Community-led Landscapes Management Project|Pyush Dogra|
|Meghalaya Community-led Landscapes Management Project|Pyush Dogra|
|SecondaryEducation Improvement Project|Simeth Beng|
|SecondaryEducation Improvement Project|Simeth Beng|
|SecondaryEducation Improvement Project|Simeth Beng|
|SecondaryEducation Improvement Project|Simeth Beng|
|SecondaryEducation Improvement Project|Simeth Beng|
|PilotingDeliveryof Justice Sector Services to Poor Jordanians and|RKlaus Decker|
|PilotingDeliveryof Justice Sector Services to Poor Jordanians and|RKlaus Decker|
|Multi-Sectoral Crisis RecoveryProject for North Eastern Nigeria|Ayaz Parvez|
|Multi-Sectoral Crisis RecoveryProject for North Eastern Nigeria|Ayaz Parvez|
|Multi-Sectoral Crisis RecoveryProject for North Eastern Nigeria|Ayaz Parvez|
|Multi-Sectoral Crisis RecoveryProject for North Eastern Nigeria|Ayaz Parvez|
|Multi-Sectoral Crisis RecoveryProject for North Eastern Nigeria|Ayaz Parvez|
|Multi-Sectoral Crisis RecoveryProject for North Eastern Nigeria|Ayaz Parvez|
|Multi-Sectoral Crisis RecoveryProject for North Eastern Nigeria|Ayaz Parvez|
|SafetyNets Project(Jigisemejiri)|Phillippe George Pereira Guimaraes|
|SafetyNets Project(Jigisemejiri)|Phillippe George Pereira Guimaraes|
|SafetyNets Project(Jigisemejiri)|Phillippe George Pereira Guimaraes|
|SafetyNets Project(Jigisemejiri)|Phillippe George Pereira Guimaraes|
|SafetyNets Project(Jigisemejiri)|Phillippe George Pereira Guimaraes|
|SafetyNets Project(Jigisemejiri)|Phillippe George Pereira Guimaraes|
|GEF Sustainable Landscape Management Project|Giuseppe Fantozzi|
|GEF Sustainable Landscape Management Project|Giuseppe Fantozzi|
|GEF Sustainable Landscape Management Project|Giuseppe Fantozzi|
|GEF Sustainable Landscape Management Project|Giuseppe Fantozzi|
|GEF Sustainable Landscape Management Project|Giuseppe Fantozzi|
|GEF Sustainable Landscape Management Project|Giuseppe Fantozzi|
|St. Vincent and the Grenadines RDVRP AF|Tiguist Fisseha|
|St. Vincent and the Grenadines RDVRP AF|Tiguist Fisseha|
|St. Vincent and the Grenadines RDVRP AF|Tiguist Fisseha|
|St. Vincent and the Grenadines RDVRP AF|Tiguist Fisseha|
|St. Vincent and the Grenadines RDVRP AF|Tiguist Fisseha|
|TUNISIA FOREST INVESTMENT PLAN PREPARATION PROJECT|TaoufiqBennouna|
|TUNISIA FOREST INVESTMENT PLAN PREPARATION PROJECT|TaoufiqBennouna|
any subsequent team meetings, and the focus on gender was never broached. Even if agents thought the experiment was about gender, it is unclear how this would affect their behavior as the name assignment changed daily without their knowledge.

# **2.3 Data**

The analysis relies on two sources of administrative data. The first dataset records every purchase made by customers, including the sale amount. The second dataset contains the agent-customer chat interactions: the full chat transcript, a timestamp for each message, and the customer’s country.<sup>14</sup> The sales data were matched to the chats using date and customers’ IP address.<sup>15</sup> To measure overall purchases, we include purchases directly made by customers, which are observed in administrative sales records, and also purchases made by agents on behalf of customers. Agents may input customer details and purchase products on their behalf, which we capture by reading through the chat records and flagging instances where agents send final purchase confirmation details to customers. Customers then pay separately or at the time of receiving the order. When agents make purchases, we cannot measure purchase values. We therefore only measure the total value of purchases using the administrative sales records.

From the chat transcripts, we create objective and subjective outcome measures. Objective measures do not require human interpretation—for instance, whether a purchase occurred. Subjective measures represent outcomes that require human interpretation of the chat content—e.g., the overall tone, whether customers bargained with agents (e.g., asking for a discount), or whether customers harassed agents. Enumerators familiar with the cultural context hand-coded these subjective outcomes; 20% of the observations were double coded to ensure consistent measurement.

Agents’ jobs involved several sales-related activities, including assisting customers via online chat and phone. Each agent worked the chat interface six weekdays per month on average. On days when agents responded to chats, they spent 2.9 hours on the online sales interface with customers, engaging in approximately 8 unique chat conversations per day. The average chat lasted 22 minutes and contained 73 words. The sales agents did not all work during the full study period for institutional reasons, although they all worked a majority of the time.

> 14 We only know customers’ approximate locations—we do not have access to any customer demographic data. However, estimates from other sources suggest that in 2019 nearly 50% of digital buyers in Africa were female (Statista, 2019).

> 15 We restrict our sample to observations with five or fewer previous purchases as some users may have accessed the site using non-unique locations—e.g., public areas or businesses—and hence their purchase records were not well-linked. This restriction retains 98% of observations.

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Table 39

|Dependent variable: Fisc|al adjustme|nt when ne|eded based|on debt/re|venue||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|-0.055|-0.077**|-0.098**|-0.122**|-0.108*|-0.028|-0.238|-0.210*|
|structural as % GDP|(0.033)|(0.038)|(0.044)|(0.050)|(0.063)|(0.161)|(0.166)|(0.128)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-3.049||-1.913|-1.893|-1.943|-1.975|-1.971|
|||(2.533)||(3.101)|(3.105)|(3.103)|(3.101)|(3.102)|
|Inflation (t-1)||-0.123||-0.164|-0.245|-0.220|-0.374|-0.203|
|||(0.857)||(1.647)|(1.670)|(1.653)|(1.685)|(1.652)|
|U.S. Interest rate||-0.152**||-0.189**|-0.194**|-0.200**|-0.207**|-0.188**|
|||(0.063)||(0.079)|(0.080)|(0.081)|(0.083)|(0.079)|
|**Political factors**|||||||||
|Gini|||0.027|0.026|0.025|0.042|0.027|0.028|
||||(0.027)|(0.028)|(0.028)|(0.038)|(0.028)|(0.028)|
|Rule of law|||-0.349|-0.154|-0.152|-0.164|-0.386|-0.172|
||||(0.261)|(0.282)|(0.283)|(0.284)|(0.427)|(0.284)|
|Margin of Majority|||2.404**|2.732**|2.749**|2.720**|2.785**|1.576|
||||(1.101)|(1.156)|(1.159)|(1.161)|(1.166)|(1.913)|
|Election's year|||-0.238|-0.273|-0.274|-0.278|-0.270|-0.271|
||||(0.214)|(0.217)|(0.217)|(0.217)|(0.217)|(0.217)|
|**Interactions**|||||||||
|EMEs * Deviation|||||-0.034<br>(0.096)||||
|Gini * Deviation||||||-0.003<br>(0.004)|||
|Rule of law * Deviation|||||||0.025<br>(0.035)||
|Majority * Deviation||||||||0.149<br>(0.199)|
|Observations|758|750|554|550|550|550|550|550|
|Number of ifscode|100|100|74|74|74|74|74|74|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|r2_p<br>Standard errors in parent<br>*** p<0.01, ** p<0.05, *|0.00456<br>heses<br>p<0.1|0.0181|0.0303|0.0478|0.0481|0.0486|0.0490|0.0491|

- 68 -
sector by 1.06 - 1.14 percentage points whereas the shares of agriculture and manufacturing decline, respectively, by 0.65 - 0.7 and 0.42 - 0.43 percentage points over the period 1970 – 2006. Almost one-third of the observed increase in GDP per capita is explained by the expansion of electricity infrastructure, which is a relatively high estimate.

Turning to India, Rud (2012) analyzes the effect of electricity provision on industrialization between 1965 and 1984, by using the introduction of a new irrigation technology as a natural experiment. Agricultural productivity is linked with timely irrigation that could be cheaply supplied by electrified pump sets. As the demand for these irrigation technologies increases, so does the demand for electricity. Hence, the availability of groundwater explains the divergence in electricity network expansion and hence industrial outcomes. The author finds that electrification has a larger impact on industrialization than groundwater availability. An increase of one standard deviation in the measure of electrification is associated with an increase of around 14 percent in manufacturing output for a state at the mean of the distribution. In addition, network expansion encourages the entry of small firms. These results suggest that the expansion of electricity networks could be used as a means of promoting industrial development, although the effectiveness of electrification will depend on the presence of other development constraints, such as lack of transport infrastructure.

Using panel data of rural Ethiopian villages, Fried and Lagakos (2021) show that rural electrification not only facilitates structural change but also alters migration patterns by slowing out-migration from rural villages. Non-electrified villages are 2.3 percent less likely to receive migrants, while electrified villages are 7.0 percent more likely. Similarly, non-electrified villages are 4.7 percent more likely to be net senders of migrants, while electrified villages are a dramatic 20.9 percent less likely to be net senders of migrants.

Electrification also lowers market entry costs for firms, increasing competition, and forcing unproductive firms to exit more often. Kassem (2021) studies the effect of the extensive margin of electrification (grid expansion) on the extensive margin of industrial development (firm entry and exit) in Indonesia from 1990 to 2000. Electrification causes industrial development by increasing the number of manufacturing firms, manufacturing workers, and manufacturing output. Electrification increases firm entry rates, but also exit rates. Higher turnover rates lead to higher average productivity and induce reallocation towards more productive firms.

A second body of work examines the impacts of electrification at the household level, including resulting changes in consumption, employment, and broader social outcomes such as health and education.

17