rafmacalaba/data-use-mentions-extended huggingface dataset:
four digit 1998 version of National Industrial Classification (NIC) industries in manufacturing sector and they find that products that can be mapped to four or five digit NIC codes account for 99 percent of total output. They also show that the number of products classified in India is quite comparable to the number found in Bernard et al. (2006) for US manufacturing sector.

The Prowess database has been used in a series of papers by Goldberg et al. (2010a, 2010b), Chari and Gupta (2008), and Krishna and Mitra (1998) among many others. From this database I use the number of products owned by a firm, total sales, and an indicator for firms that spent on R&D. Descriptive statistics for these variables are presented in Table 1. An average firm has 1.9 products and average product growth rate in the economy is 6%. The data shows that average product addition is more frequent than product dropping. A discussion of this high product creation is presented in Goldberg et al. (2010a). Finally 9% of the firms in the data invested in R&D. The table also shows the overall variation, over time variation for each firm (within), and variation between firms (between) for each variable. Between firm variation is higher than within firm variation for the number of products which is expected due to short time period. For product growth, there is reasonable variation in both over time and between firms. Multi-product firms make around 45% of the firm population in the data. An average multi-product firm owns around 3 products and these firms make around 80% of total sales. A descriptive analysis of evolution of multi-product and single product firms is presented in Table 2. The table shows the percentage of firms that showed various product evolution patterns annually, over

> 5 The model can be easily expanded to include process innovation by adding a quality attribute to the products innovated and firms can be allowed to invest in both quality improvement and
disaggregated data, this calorie computation is done at the national level.<sup>10</sup> Per-capita calorie consumption is a constant-elasticity (CE) function of per-capita real food consumption. A CE function is also used to model the link between per-capita calorie consumption and labor productivity; it is assumed that, across all production activities, an increase in average percapita consumption raises the productivity of the segment of the labor force with less than completed secondary education (the least educated segment) across all production activities. This group of workers was singled out on the assumption that they are more likely to do physical work that is relatively energy-demanding at the same time as they also are more likely to belong to households that are calorie deprived.<sup>11</sup> A different treatment is used for other MDG and education indicators: MDGs 4 (under-five mortality), 5 (maternal mortality), 7w (improved water access), 7s (improved sanitation access); net intake to first grade of primary, promotion to next grade in each of three education cycles, and continuation into next cycle (for primary and secondary). For these indicators, a two-level formulation is used. At the bottom level, an intermediate variable specific to each indicator is expressed as a CE function of a set of determinants, summarized in Table 2.1. At the top level, each MDG and education indicator is a logistic function of its intermediate variable. The parameters of these two-level functions are calibrated to replicate data on projected progress for the top-level indicators under the _base_ scenario, with upper or lower limits imposed on the basis of logic or cross-country experience (e.g. the net intake rate has an upper limit of 100 percent; mortality rates cannot fall below some low positive value). As shown in Table 2.1, the determinants include the supply of government services (measured per student for each cycle of education and per-capita for health), the stock of government infrastructure, real household consumption per-capita
5 competition, constant returns to scale and unchanging aggregate employment of all factors of production. We use the latest Version 6.05 of the GTAP database (see Dimaranan and McDougall, 2005), which draws on global economic structures, policies and trade flows of 2001. The GTAP model has been aggregated to depict the global economy as having 27 sectors and 38 regions (to highlight the main participants in the world’s cotton markets, two of which are newly disaggregated countries: Nigeria and Pakistan). Trade is modeled using a nested Armington structure in which aggregate import demand for each sector’s product is the outcome of allocating domestic absorption between domestic goods and aggregate imports, and then aggregate import demand is allocated across source countries to determine the bilateral trade flows.

Two modifications have been made to the structure of protection in Version 6.05 of the GTAP database. One relates to cotton subsidies in the United States. In Version 6.05, which reflects subsidy notifications to the WTO, the subsidies showing for the US in 2001 are much less than actually paid through its various and complex cotton programs: $1.0 billion as production subsidies and zero as export subsidies, compared with an average annual total payment of $3.0 billion for the 2000-2002 period according to Baffes (2005, Table 4, drawing on official data from the USDA). We therefore adjusted the subsidy rates to raise overall payments to that $3 billion level (of which direct payments and countercyclical payments are paid per unit of land, marketing loan benefits and crop insurance subsidies are paid as input subsidies, and coupled output payments plus Step 2 payments to US cotton textile producers and to US cotton exporters, along with export credit guarantees, are paid as production subsidies).<sup>2</sup> The

> 2 The latter two are export subsidies but, since an export subsidy is equivalent to a production subsidy plus a consumption tax at the same rate, and since US buyers of domestically produced cotton
businesses. Therefore, we define the job loss shock as the loss of employment of previously salaried household members. Only 2.5 percent of households experience this kind of job loss.

On average, 43 percent of households report unusually high food prices. Approximately 26 percent of households did so in IHS 2 and IHS 3 and 71 percent in IHS 4, most likely reflecting the adverse weather conditions observed in 2016. The correlation between drought and self-reported inflation for food is 12.3 percent. Delays in passing the cost on to consumers and potential changes in importations or transportation costs all matter to this weak correlation.

About 18 percent of households suffer the death of a member. The deceased was a breadwinner in around 20 percent of cases. Parts 4 to 7 of Table 1 present household characteristics. The average family is headed by a male with low education and has half of its working-age members employed in agriculture. Moreover, around 22 percent of households receive food aid. We remove food aid from consumption per capita when constructing our dependent variable. However, we do not include it as a control in our regression as it is responsive to weather conditions and would therefore bias our coefficients on weather shocks.<sup>6</sup> Instead, we control for receiving aid unrelated to weather conditions (Malawi Social Action Fund [MASAF] program and educational aid). Finally, note that 84.2 percent of the households reside in rural areas.

# **2.2 Impacts of Weather and Idiosyncratic Shocks on Consumption**

As a first step in the analysis of vulnerability to poverty following a household’s exposure to common and idiosyncratic shocks, we assess the impact of these shocks on per capita consumption. To do so, the following consumption model is estimated: where _Cict_ is per capita consumption without aid of household _i_ in TA _c_ at time _t_ .<sup>7</sup> _Sct_ is a vector of weather shock indicators and their historical geographic distribution: drought and excess rainfall
Noida for Delhi; Thane for Mumbai); 67 districts located within 0 – 10 kilometers away from the GQ are the “treated” ones; 32 districts are 10–50 kilometers away; and 175 “control” districts located over 50 kilometers away are assumed to be minimally affected by the GQ, but track broad movements in the economy. Long differences in outcomes resulting from nearby GQ completion can thus be compared. To study adjustment patterns across plant ages, the data are further divided into age cohorts: 0-5 years (young), 6-24 years (mature) and plants with 25 and above years of age (old). Our results are not sensitive to moderate changes in category definition.

The data also permit creating a new geo-coded plant-level panel from two available versions of the ASI data: (i) a repeated cross-section of plant-level data with district identifiers, but no plant identifiers (“common factory ID”) and (ii) with plant identifiers, but without district identifiers. Using characteristics of firms such as industry code, ownership, establishment year, months of operation, capital assets, employment, wages, income from services etc., the two data sets are combined following Martin et al. (2017) with both district and plant identifiers with a match rate greater than 98%. This allows measuring year-to-year plant-level changes in revenue-based total factor productivity (TFPR), total revenues, output, labor and skill demand, and plant exit. Annex Table A.1 tabulates the summary statistics, splitting the outcomes by age groups and distance bands for pre-GQ and post-GQ years.

# **III. Estimation**

## (i) **Long difference in difference estimation:**

As a first approach to the data, we compare district activity in 2000, the year prior to the start of the GQ upgrades, with district-level activity in 2009, the year when GQ was nearly and totally complete. Indexing districts with _i_ , the specification takes the form: where , is the change
An adult individual can get SS through his or her formal job, through his or her spouse‘s job or through parents if he or she is a dependant)<sup>9</sup> . The decision of spouses to move sectors based on SS status is likely to be taken at the household level. If it is a joint decision, ignoring the spouse‘s decision is an omitted variable; but including it introduces endogeneity.

We address this potential concern by estimating the model at the household level. This abstracts from whether one or more members in the household switch between formal and informal jobs to look at the status of the household as a whole. It limits the effects to those switches that affect the household‘s source of health insurance.

An important concern is that the rollout of _SP_ could be correlated with the informality trends in municipalities. This would affect our estimates because the error term would be correlated with our estimate of interest. The results presented in Table 2 however indicate there is little correlation between the share of formality in a given period and municipality and the probability that municipality receives _SP_ in the following period. The probability of _SP_ being rolled out is measured with a dummy variable that takes a value of 1 when a municipality shows at least one affiliate in the _SP_ registry, and zero otherwise. This is true whether the share of formality is lagged one or two years (Columns (1) – (4)). The rate of change of formality (measured as a percentage of households or of individuals in SS) in a municipality is likewise uncorrelated with the timing of _SP_ ‘s rollout (Columns (5) and (6)). These results limit concerns of the possible endogeneity of the deployment of _SP_ across municipalities.<sup>10</sup> SS and _SP_ are not perfect substitutes. Contributing to SS also gives right to pension and job security benefits. _SP_ only provides for health benefits, which in principle are lower than those granted
Subsequent to the intervention, recognition of the variation in premiums across cities and apparent heterogeneity in responses to treatment, led the team to examine whether the effects of providing information varied with relative premia.<sup>16</sup> Another plausible factor influencing participation decisions are the magnitudes of premiums relative to earnings for health insurance and pension programs. As will become apparent in our discussion of results below, premiums vary considerably by city, and information may interact with price in influencing the decision to participate. To this end, we next estimate models in which the treatment indicator, _Tij_ , is interacted with a city relative price (premium) variable:

> 15As the randomization was implemented separately in each of the 13 cities in the sample and city level costs and benefits vary significantly, the baseline model includes the set of city fixed effects, _δj_ , along with the treatment indicator and lagged participation. Further, as randomization into treatment was implemented at the household level, we equally weight each household in the following analysis to provide efficient estimates (Athey and Imbens, 2017). Household members may also share cost and benefits of social insurance in China, and social insurance participation is a joint decision of household members, especially for poor migrant households.

> 16While examining this dimension of heterogeneity was not part of a pre-analysis plan, the cities (and all the differences across them) were chosen as part of the pre-analysis as they were part of the baseline survey.

13
respondents, this approach may be better suited for more focused agricultural surveys conducted on a rotating basis, that could then be compared with standard employment survey data.

# Counting women’s off-farm employment: The role of informality

Aside from own-farming, rural women are also heavily involved in informal jobs that are more difficult to pick up in surveys. Casual wage labor on farms , for example, is increasingly common amid a growing share of landless households (Mueller and Chan, 2015). Figure 2, for example, shows that poor individuals in the Malawi 2016-17 LSMS-ISA, and particularly women, reported nearly all wage work in _ganyu_ (or short-term, temporary labor). As this work is highly seasonal and typically with little pay, and has traditional nomenclature, it can often go unreported if not explicitly asked in surveys (Beegle et al, 2017). Valenzuela (2003) also discusses other contexts where day labor can be underreported for similar reasons — In Japan, for example, “laborer” is closely associated with the word _romusha_ , which implies low-skilled, menial labor, and many respondents in this work may not self-identify in these roles.

The second panel of Figure 2 also shows that among wage earners, the gender discrepancy in _ganyu_ labor widens with age; older women wage laborers are as likely as younger women to continue working in _ganyu_ , while the share of men in this work declines rapidly with age. Close attention is needed in surveys to local labor arrangements that the poor engage in — and which might get missed with standard questions on wage and self-employment, particularly for women who are often highly engaged in these seasonal forms of work, and even among more vulnerable/older groups.

## Figure 2.  Malawi LSMS-ISA, 2016-17: share of men and women in ganyu (short-term, temporary) labor

Share of men and women (total sample) that have worked in _ganyu_ and other wage labor, by poor/non-poor households and reference period Among poor men and women wage earners, share in _ganyu_
effect of workers’ remittances has not been explored yet. So far the empirical literature on migrants’ transfers has mainly focused on their potential influence on growth, poverty and output volatility. IMF (2005) documents that they may actually contribute to reduce poverty and volatility of output, consumption and investment in the receiving

> 1 Admittedly, these figures are underestimated due to transfers of funds not channeled through the banking system and other official intermediaries which are often the only source of official statistical data.
|29|24.2|26|12.2|21|0.0|68.4|
|Finace,markets,Trade & competitiveness|33.3|46|29.5|27|49.9|23|25.0|110.0|
|Governance|84.8|14|113.1|6|‐22.2|4|‐25.8|57.1|
|Health Nutriton & Population|43.3|24|42.5|16|‐10.4|9|0.0|40.8|
|Macroeconoimcs & Fiscal Management|156.0|1|0.0|‐|0.0|‐|‐|‐|
|Social Protection & Labor|29.9|13|25.3|9|18.2|4|‐9.4|72.1|
|Social,Urban,Rural and Resilience|23.1|195|23.0|164|‐4.6|136|‐10.8|66.1|
|Transport & ICT|30.0|951|28.1|852|‐0.5|830|‐5.8|59.2|
|Water|15.6|257|13.2|216|‐13.9|199|‐16.7|66.7|
|Povertyand Equity|‐|‐|‐|‐|‐|‐|‐|‐|
|World Bank|25.4|3,510|21.4|3,057|‐12.1|2,877|‐15.44|65.5|

Source: IEG database and staff calculation.

*The difference between the two ERRs is calculated for every project reporting an ERR at appraisal and completion as: 100*(ERR at completion – ERR at appraisal)/ERR
The major results are summarized below:

1. Unemployment rates (for those actively searching) has declined during this period.

2. The share of male unemployment rates has declined while that of female unemployment has increased.

3. The decline in male unemployment is mainly brought about by the 15-24 age groups whose share has declined in the total number of unemployed.

4. There is no direct relation between formal levels of education and unemployment. The share of secondary school educated have gone up in the total number of unemployed.

5. Unemployment is lower in the rural areas than in the urban areas. Perhaps an artifact because, in the rural areas even though a person is underemployed, he may still identify himself as being employed. The urban unemployement figures may be more close to reality.

# **3.6 Migration**

The GLSS data sets contain a small amount of information about migration in Ghana. This section examines the pattern of migration to get a broad picture of migration flows to different regions in Ghana. From the data a few observations can be made regarding the pattern of migration. First, there is a reverse pattern of migration-most of the migration is from urban to rural than from rural to urban (Table 22). Second, there is regional segmentation in migration-migration to high growth regions, such as Greater Accra, Ashanti and Western regions (Table 23); there is more between migration than within migration to these regions. Third, Eventhough most of the migration is family based, employment related migration has increased substantially during this period, especially in the last period (Table 24).

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6 a poverty-city size gradient can be observed both directly from survey data and from small area estimation techniques. We conclude that the inverse relationship is thus not driven exclusively by our reliance on a particular estimation method. Section 8 offers tentative conclusions and discusses some of the questions that this descriptive paper raises for further research into urban poverty.

# **2. Methodology**

The economic analysis of the distribution of living standards in developing countries relies almost entirely on household surveys. If their samples are selected appropriately, these surveys can collect detailed information from a relatively small number of households (perhaps 0.1% of the country’s total population), and yet generate information that is representative of the population as a whole. The law of large numbers ensures that the uncertainty about the population which results from sampling (the ‘sampling error’) becomes very small at sample sizes that are still cost effective. This enables researchers the world over to ask detailed questions from small groups of people, at a fraction of the cost that would be required if entire populations needed to be polled.

But there is one drawback: the samples that are designed to be representative of large populations are not, in general, representative of specific non-random sub-divisions of that population. Indeed, the typical (nationally representative) household survey is not representative of sub-national units, such as states, provinces or districts. There are exceptions, mostly in large countries, such as China, India or Brazil. But even in those countries, the problem is simply shifted down one level: living standards will vary enormously across different localities of (or cities in) the states of Uttar Pradesh or Minas Gerais; but the Indian _National Sample Survey_ and the Brazilian _Pesquisa Nacional por Amostra de Domicílios_ are not representative at those levels.

A number of small-area estimation techniques have been developed to seek to address this missing data problem. In this paper, we rely on application of the “poverty mapping” approach developed by Elbers, Lanjouw and Lanjouw (2002
enterprises we need to control for the fact that some MFIs use a flat rate method to calculate interest rate while some use a declining balance method.

Table 18 shows the effective interest rate for 25 MFIs that borrow from the country’s premier wholesale microcredit agency (PKSF) to make the microloans available to their borrowers. The calculation of effective interest rate takes into account all fees and additional charges paid by the borrower. The average effective interest rate charged to microenterprises is 26.65 percent if they repay on a monthly basis, while it is 31.59 percent if they pay on a weekly basis. The average effective interest rate charged for rural microcredit programs in general is 32.05 percent.

The average rate of return earned by Bangladeshi microenterprises in our sample falls in the range of 40 to 50 percent, depending on the sector of activity of the microenterprise. Therefore, although MFIs charge higher interest rates than commercial banks, expansion of lending by these institutions to microenterprises could serve to eradicate credit constraints faced by microentrepreneurs. However, we also have to keep in mind that effective interest rates vary quite significantly by the size of MFIs and their sources of funding. For example, those who borrow from PKSF and obtain donations from foreign organizations can afford to charge low effective interest rates (see Table 19). Thus, to ensure that the MFIs remain a sustainable source of borrowing for microenterprises, efforts need to be made to keep the average effective interest rates lower than the average rate of return for these enterprises.<sup>16</sup>

> 16 Using the nationally representative household survey data over 10 years, we attempted to estimate the average rates of returns to microenterprises and found that the rates of returns are not necessarily lower than the average interest rates charged by financial institutions, including MFIs.  However, the rates of interest charged by MFIs are yet found high over this period, as many argued.  Realizing this, in 2010, the country's Microcredit Regulatory Authority (MRA) set