rafmacalaba/data-use-mentions-extended huggingface dataset:
interact it with the treatment to see if the treatment is more powerful on individuals ―surrounded‖ (in some economic sense) by other treated individuals. These methods are inspired by measures of contagion used in epidemiologic studies.

In contrast with medical sciences, however, in social sciences the mechanisms by which contagion takes place are largely unknown. Baseline surveys as well as qualitative information gathered from focus group discussions may help in understanding and identifying channels through which future program benefits might spread from one firm to another — for example, professional association memberships, personal contacts and so on.

# **6.3 Heterogeneity**

Differences among beneficiaries, especially if they are unobserved, can pose particular challenges for evaluation. Policy interventions can have diverse impacts across economic agents. By focusing on average treatment effects, an evaluation ignores valuable information on the heterogeneity of the effects.<sup>43</sup> As Ravallion (2009, p. 37) puts it, practitioners should ―never be happy with an evaluation that assumes common (homogeneous) impact‖. He also argues that knowing more about the heterogeneity of the effects and the role of contextual factors is key to better understand the impact of the intervention and make evaluation more relevant for good policy-making. The challenges linked to the heterogeneity of the effects are relevant across types of interventions, whether they are of a targeted or a non-targeted nature.

First, the treatment effects of a program can be related to the observable characteristics of the beneficiaries. For example an export promotion program can have differential effects for participant firms depending on their prior export experience or on their workforce skill levels. If the export promotion program consists of a matching grant scheme which co-finances firms’ export business plan, the opportunity costs for participant firms may differ in terms of the alternative uses they could give to their funds.<sup>44</sup> A simple approach to address the heterogeneity of the effects when differences are observable is to add interaction effects with the treatment dummy variable in
3) a livelihood lens, showing what income from HEs means to the livelihood portfolio of a household trying to maximize welfare and escape or avoid poverty.

The paper argues that based on these pragmatic, results-based approaches, the role of this sector in economic growth and livelihoods can be identified, and context-specific development strategies that will support these businesses and households can be designed, which will enable markets to work for the benefit of low-income households trying to climb out of poverty or stay out of poverty.<sup>3</sup> The paper is organized as follows. After a brief discussion of data and definitions, section three presents employment and livelihood trends in selected SSA countries that have experienced a recent period of broad-based economic growth, using national household survey datasets. It‟s shown that even in countries with double digit growth in the non-agricultural, nonmineral extraction sectors, HEs have been responsible for the majority of the non-agricultural employment growth, and this trend is likely to continue for several decades owing to high projected labor force growth, low education levels, and the difficulties of growing employment fast enough in large, modern firms. Section four is an analysis of HE owners themselves: their distinguishing characteristics, the determinants of earnings in this sector, and what HE owners perceive as their constraints. Finally, section five analyzes HEs as a household livelihood strategy, arguing that fragmented evidence suggests that in low income SSA countries, HEs have played an important role in household income generation for households near the poverty line. We conclude that HEs need to be a key element in effective poverty reduction strategies in lowincome SSA countries.

# **2. Data and definitions**

This paper primarily analyzes Household Enterprises and occasionally Micro Enterprises. Both Household Enterprises and Micro Enterprises are _informal non-farm enterprises_ that are unincorporated and owned by households. Specifically, Household and Micro enterprises are defined as follows:

- _Household Enterprises_ (HEs) are own-account (self-employed) enterprises working in non-agricultural sectors that may employ contributing family workers.

- _Micro Enterprises_ (
to import capital equipment goods from abroad. Using effectively applied most favorite nation (MFN) tariffs data and input-output matrix, we construct tariff measures on variable inputs and on capital goods separately. We first present evidence that our tariff measures are free of reverse causality concerns. We extend the previous works in the literature and show that input tariff changes are uncorrelated with initial firm and industry characteristics relevant for our analysis during the trade reform under the ‘Eighth Five-Year Plan’. We then exploit this exogenous variation in input tariffs across industries to identify the effect of the availability of foreign variable intermediate goods on firms’ decision to import capital goods taking into account changes in specific tariffs on capital goods.

The empirical findings confirm the theoretical predictions. Firms producing in industries with larger input tariff cuts have a higher probability of importing capital goods. Our results imply that the average input tariff reductions during the 1989-1997 period, 27 percentage points, is estimated to produce a 4.6 percent increase in the probability of importing capital goods for the average firm importing intermediate goods. These results take into account the direct effect of capital goods tariff changes. We then investigate if the impact of input-trade liberalization is heterogeneous across firms. Only those firms in the middle range of the productivity distribution import capital goods after input tariff reductions. Firms in the middle range of the initial productivity distribution increase their probability of sourcing capital goods by almost 10 percent. As predicted by the model, our findings suggest that the least productive firms do not benefit from input tariff cuts to upgrade foreign technology. Input tariff changes do not affect either the most productive firms that might have already adopted the foreign technology before input tariff cuts.

These results are robust to specifications which control for industry and firm observable characteristics that could be related to tariff changes and might change over time. We also take into account other possible explanations related to the incentives of Indian firms to adopt foreign technology. We show that our results remain robust when we explicitly control for other reforms that took place in
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The joint life formula contains the reversion of the annuity to the second beneficiary (typically the spouse) at a lower level (60 percent), and the survivorship of two lives determining the annuity payment. If the principal beneficiary is noted with symbols as above, and the reversionary beneficiary is noted with the same symbols but with a ‘^’ mark and is aged _y_ at commencement of the annuity, then the formula is as set out in equation (2). Note that the probability term in the numerator would be set to 1 during the period where annuity payments are guaranteed.

Note that all annuities in Chile are quoted in _Unidades de Fomento_ (UF), a unit of account indexed to consumer prices and widely used in financial contracts. In this analysis all values are expressed in UF and should be interpreted accordingly when making comparisons with other countries.<sup>3</sup>

# **_3.2. Mortality Tables_**

Most empirical studies estimate MWRs with two mortality tables, one reflecting the mortality of the general population and the other reflecting the mortality of the smaller annuitant population. These are necessarily cohort tables, constructed either by incorporating existing projections of future mortality for each cohort, or by estimating future mortality improvements and applying them to period tables.<sup>4</sup> The difference between the estimated MWRs using the general and the annuitant population assumptions is frequently interpreted as the effect of adverse selection.

In the case of Chile, there was no mortality table for the population that is updated and reliable at the time of writing, but three tables have been constructed for the annuities market. The first of these tables, known as RV-85, is a period table that was developed when the annuity system started and there were few annuities in force. The table purports to represent the period experience of annuitant mortality at the time it was developed, but was partly constructed by making adjustments to mortality data from other countries. The RV-85 was developed for regulatory
share variable are unaffected. Similar results are obtained if we instead use the well-known Corruption Perceptions Index from Transparency International. However, corruption is associated with higher rates of aid tying in cross-sectional tests reported below.

There are potentially many more recipient-country characteristics, other than those explicitly controlled for thus far, that could affect the level of aid tying. The most effective way to correct for any omitted-variables bias in our results for the key aid share variable is simply to replace the recipient-country variables with a full set of recipient country dummies. Equation 4 reports results from this two-way fixed effects regression. The coefficient on aid share declines slightly, from about -0.4 to -0.37, but statistical significance is slightly strengthened.

In equations 5 and 6 we report tests of the effects of costs of aid tying for donors, using donor size and income as rough proxies for costs. The value of aid in general should be reduced by more when it is tied to purchases in smaller donor countries, because the pool of qualified firms or consultants is likely to be smaller. Consultant fees (and thus tying costs) are likely to be higher in higher-income donor countries<sup>12</sup> . In equation 5, tying costs are measured by the log of population and log of per capita income in current US dollars (both for 2006). In equation 6 we substitute for population the log of total aid, measured in constant US dollars and averaged over the years 1995-2006. The rationale for this proxy is that the number of firms and consultants residing in the country with expertise pertinent to development assistance increases (but at a diminishing rate) with past and present aid expenditures. To test these donor-level variables we have to drop the donor fixed effects, and we instead use recipient fixed effects, and correct standard errors for recipient-clustered rather than donor-clustered observations. Aid tying is positively and significantly related to both donor size measures, as expected, in equations 5 and 6. Income, as predicted, is inversely
discuss how omitted variables, especially caused by measurement error in the tariff data and omitting controls for general trends, biases the NTB effect upwards.

# **2.1 Identification Strategy**

To answer the question whether regional trade agreements (RTAs) lower non-tariff barriers (NTBs) we follow the empirical strategy put forward by the literature, i.e., Aichele et al. (2016), Dhingra et al. (2021), and Felbermayr, Gröschl, et al. (2018). The basic idea of the _indirect approach_ is to estimate RTA effects using the gravity model while controlling for changes in tariffs to disentangle between the effect of tariffs and NTBs on trade. In this fashion, NTBs can be interpreted as _residual_ policy measures that explain the observed pattern of bilateral trade flows given tariffs. A key assumption is that RTAs do not change bilateral preferences between trade partners for their respective goods and services. Using structural gravity, changes in trade costs due to RTAs can, thus, be indirectly identified from trade data.

Next, we formally derive the main estimation equation starting with a structural gravity equation for bilateral trade flows that explicitly models changes in tariffs and NTBs due to RTAs. Head and Mayer (2014) show that a broad class of general equilibrium trade models with constant elasticity of substitution aggregation leads to a gravity equation of the following form: Here, _Xijst_ equals the value of exports of country _i_ to country _j_ in sector _s_ at time _t_ ; _Yist_ is country _i_ ’s value of production in sector _s_ ; _Ejkt_ is country _j_ ’s expenditure in sector _s_ ; _Yst_ is the value of global output. The terms Ω _ist_ and Ω _jst_ are the multilateral resistance terms and account for the effects of third countries’ trade costs on _i_ ’s exporting and _j_ ’s importing behavior (Anderson and Van Wincoop 2003); _σ >_ 1 is the elasticity of substitution among goods from different countries. Sectors _s_ are defined as broad sectors, i.e.
different from that for developed countries. This remains true even when we instrument. The results imply that a 10 percent increase in export time reduces developing country exports by 8-12 percent.

One drawback using relative bilateral exports is that we eliminate country pairs which export to different locations. In addition, the main variable of interest is the ratio of time which varies only at the country-pair level. As an additional robustness test, we examine relative exports to the world, which allows us to use all country pairs and all exports within the regional groups. The disadvantage is the control group is not as carefully defined since we include exports to different partners. The results, reported in Table 6 are similar, implying that a 10 percent increase in the time to move goods from factory to ship reduces aggregate exports by about 3-4 percent.

# **V.  Time-Sensitive Exports**

Time delays should have a greater effect on the export of time-sensitive goods.<sup>20</sup> To examine the extent to which they are hampered, we also estimate “difference-indifference” gravity regressions using trade data of products for which time matters the most and the least. This specification reduces the endogeneity problem coming from reverse causality because the products account for only a small fraction of trade on average (5-6% and 0.3-0.4% for manufacturing and agricultural trade, respectively) so it is unlikely they have a large effect on establishing trade facilitation processes (Table 7).

We examine a set of time-sensitive manufacturing and agricultural goods. The three time-sensitive manufacturing industries (SITC 2-digit) are office equipment (75),

> 20 Evans and Harrigan (2005) show that time-sensitive apparel products are more sensitive to distance than time-insensitive products.

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using _T_ variables _x_<sup>_b_</sup> _mt_<sup>that take values -1 and 1 in ascending vs.descending MPL in choice</sup> _t_ and 0 otherwise. In a parallel manner, we may define _x_<sup>_s_</sup> _mt_<sup>to equal 1 when A is presented</sup> on the left and -1 when it is on the right (either across choices or choice specific).

In order to relax the symmetry assumption and pin down the relative size of the bias for each option without additional MPL data (see below), researchers may choose to conduct outside validation. For example, they could ask (some) subjects to review and potentially revise the MPL responses, as in Yu et al. (2021), or measure the rates at which subjects renege on their original decision, as in Maffioli et al. (2020).

# **3.4 Extensions**

**Multiple MPL.** We assumed above that we are measuring only one WTP parameter which expresses the relative willingness to pay between options A and B. In practice, researchers may want to estimate the WTP of the same subject for different options from different MPL. This can be achieved in one estimation by creating dummy variables that identify WTP for different option pairs. When estimating relative WTP between a set of different options, it is also possible to impose constraints on the WTP coefficients, such as transitivity. For example, the researcher may estimate _P_ ( _eit < α_<sup>1</sup> _zm_<sup>1+</sup><sup>_α_2</sup><sup>_z_</sup> _m_<sup>2+</sup> _α_<sup>3</sup> _zm_<sup>3+ ∆</sup><sup>_t_+</sup><sup>_βbxb_</sup> _mt_<sup
Figure 3.3: _Employment outcomes are better in countries with better access to finance_ <!-- Start of picture text --> 5 est<br>lva<br>kaz cze<br>bgrhun<br>svk<br>ltu<br>ukr<br>0 pol<br>rom hrv<br>geo<br>svn<br>mda<br>-5<br>2 4 6 8<br>Access to finance index<br>Figure 3.4: The real interest rate or cost of credit is the most important variable within<br>the access to finance dimension<br>10<br>5 est<br>lva<br>cze<br>hunbgr<br>svk ltu ukr<br>0 pol<br>hrv<br>geo<br>svn mda<br>-5<br>-20 0 20 40<br>Real interest rate, time-varying<br>sv emp. controlled for gdppc<br>sv emp. controlled for gdppc<br><!-- End of picture text --> Figure 3.4: _The real interest rate or cost of credit is the most important variable within the access to finance dimension_ 33
program period in the absence of the program.

The results in tables 1 and 2 show that, in the full sample of durable goods industries, eligible industries had higher employment levels in the pre‐program period (this difference is large, 47 percent, but not statistically significant), and they displayed a steeper trend in employment than ineligible industries. These differences remain when considering only industries on the common support of the propensity score and also when keeping only industries with a propensity score between the 20 and 80<sup>th</sup> percentiles of the score. However, the sample obtained through caliper matching looks more comparable. In this sample of 34 industries, average employment across eligible and ineligible industries differed by 24.5 percent (not statistically significant) and, most importantly, the trends in employment were virtually the same.

Figures 1 through 4 illustrate the comparability of eligible and ineligible industries graphically. Figure 1 plots average employment over time in eligible and ineligible industries for all durable goods industries, and figures 2 through 4 plot average employment for the samples defined using methods a) through c). The vertical lines in each figure show the start and end dates of the wage subsidy program (January through August 2009). In figure 4, which shows the sample obtained through caliper matching, average

> 7 As described above, the program also intended to reach sectors producing goods for export. However, I do not have accurate data on exports for all IMSS industries producing durable goods since import/export data is recorded under a different industry classification that does not allow for a direct mapping to the IMSS industry classification. The empirical analysis in section 4 includes a specification that controls for imports by Mexico’s largest trading partner (the US) using only the industries that could unambiguously be mapped to IMSS industries.

7
Averaging over routes, the model yields an approximate linear relationship between stress and freight rate index.

0 + RRmmttmmiim mmRR= RRmmttmmiim mmRR CC Rate index increase= (representative value per container) X (share of fleet idled by disruption) Since values per TEU are in the tens of thousands of dollars or more, this simple rule explains that shipper’s competition for shipping slots may explain the burst observed.

The harmonic mean is lower than the arithmetic mean. Therefore, K is more representative of products with low FOB value or high elasticity, which conforms to intuition.

# **Empirical implementation**

Value per container is taken from MDST World Cargo database (commercial source). Values are estimated at SITC 2 digits as median and quartiles of observation along share of the commodity in containerized trade. (Annex 3).

Ship global capacity entering the stress calculation is about 18 MTEU, taken from the fleet of ships in Marine Traffic, used in the estimate of the Stress index.

The empirical value of K is K=2,300*18=41,400 USD.

Harmonic mean of value per container is estimated at 25,500 USD. The lower value implies an average elasticity of supply across products of 0.6 to match the value of _K._ 25
receipts. The coefficient of primary interest is _β_ 1 which captures the differential response of FG families to expected dowry, relative to the response of FB families. To exclude other confounding factors related to the caste, state, gender, and year of birth of the firstborn child, we control for all main and interaction fixed effects for these factors. For instance, if certain castes are richer and therefore save more, the caste fixed effects ( _ωc_ ) would capture this confounding variation. If there is state or time variation in wealth status of various castes that can explain the differential saving behavior, it would be captured by the state-caste and caste-year fixed effects ( _φct_ and _ψsc_ ). The state-year fixed effects ( _πst_ ) take into account any differential trends across states in economic prosperity. If the fertility response to a firstborn girl differs across castes or states or time due to differential son

> 30State reflects the state of residence at the time of survey, and not necessarily the state of marriage or the state of first birth. However, this is unlikely to be a major source of measurement error due to low inter-state migration in India, especially after marriage.

> 31The robustness checks using alternate definition of dowry expectation are provided in Section 7.

> 32The 2006 REDS data do not provide sampling weights; hence we construct them in the following manner, based on Andrew Foster’s suggestion. Using the village listing data which includes all households in REDS villages, we create an indicator for the households that are actually sampled and regress it on the observables in the listing data. These inverted predicted probabilities serve as weights, assuming that the observables capture differential reasons for being surveyed. The observables in the listing sheet data used to construct weight are household size, number of earners in the household, head’s age, head’s years of schooling, indicators of head’s caste (SC, ST, OBC, upper caste), religion (Muslim), and gender, and state fixed effects.

11
21 THE INTENSIVE MARGIN IN TRADE and Mayer (2014), and _σ_ = 5 from Bas et al. (2015), so _θ_<sup> ̄</sup> = 1 _._ 25) (see footnote 19); and finally and _θ_ ̄ = 1 (as in Zipf’s Law).

Table 9 reports the results of this simulation exercise. Two broad patterns emerge from the table. First, the simulated IME decreases with _θ_<sup> ̄</sup> . This is because the effect of granularity on the IME is stronger when there is more dispersion in productivity levels. Second, the simulated IME is highest when productivity is less correlated across destinations, again because this gives granularity more room to generate a covariance between average exports per firm and total exports. For our estimate of _θ_<sup> ̄</sup> ( _θ_<sup> ̄</sup> = 2 _._ 4) and with no demand shocks (so there is perfect correlation in firm-level productivity across destinations), the simulated IME of 0.001 is quite low. The highest simulated IME occurs for the case in which _θ_<sup> ̄</sup> = 1 and there is no correlation between the product of demand shocks and productivity across destinations. In this case the simulated IME is 0.33, not too far from our preferred estimate based on the data of 0.4. But we think of this as an extreme case because _θ_<sup> ̄</sup> = 1 is far from the estimates that come out of trade data, and because of the implausible assumption that firm-level exports are completely uncorrelated across destinations. A low _θ_<sup> ̄</sup> also implies that, in contrast to the data, virtually all of the action behind a positive IME comes from the superstar firms. To see that, we calculate average simulated exports per firm in each percentile and use those to estimate an IME per