rafmacalaba/datause-encoder-data huggingface dataset:
only grow into larger units, but would also reduce their reliance on government financial support.

- Finally, financial support for industrial infrastructure would depend on the capital intensity of such infrastructure. In early stages of development, this would mean investments in electricity, transportation and other heavy infrastructure, financial support would in principle be prolonged. But in later stages of development, much of support would be directed towards research and development as well as education and training and this would not require as large outlays of capital as the development of public utilities.

Two empirical studies have been undertaken recently that lend support to the argument that Japanese policy-based finance was effective in meeting its objectives of "pump priming" and "crowding in" private credit for growing firms iri industries that benefit from dynamic comparative advantage. Horiuchi and Sui (1993) compared the investment behavior of "medium-size" firms receiving JDB assistance with other firms of similar size over the period 1964-1988. They found that the year of initial JDB lending was associated with increased investment and also that within three years firms began to move away from reliance on JDB lending to rely more on private banks. Horiuchi and Sui also found that directed credit was more effective for firms that did not have main bank affiliations.

Calomiris and Himmelberg (1994b) examined the effect of policy-based finance over the period 1963-1991 for the machine tool iiidustry, an industry selected for its high potential for spillover effects due to technological innovation and learning. Machine tool producers underwent considerable consolidation during the 1960s and 1970s, which was associated with the introduction of new technologies and the achievement of unusually large economies of scale.

Their study is based on firm level data collected with the support of the Japan Development Bank. The dataset covgrs only surviving firms and excludes firms that exited during the period. This makes the identification of positive effects from policy-based finance more difficult, particularly during the consolidation phase of the 1960s and 1970s when less productive firms exited or were merged with other firms.

The level of investment undertaken by machine tool producers declined over the period. It averaged 27% of capital from 1965 to 1974 but fell to 10% from 1975 to 1991. Similarly, total longterm debt fell relative to capital from 41 % prior to 1975 to 26% afterwards. Directed credit was a small proportion of total long-term credit. Total government credit fell from an average of 3 % of capital prior to 1978 to 1 % after the mid-1 980s or from over 7% of all long-term credit td less than 4%.

A comparison of lending to general machinery producers by JDB with private long-term lenders as well as with the Export-Import Bank of Japan provides some interesting insights. JDB lending to 15
Use of the two alternative pseudo-maximum likelihood estimators does not fundamentally change the Poisson results. In columns 2 and 3, we find that importer and exporter logistics performance both have positively signed and 1% statistically significant coefficients. Interestingly, the magnitude of both semielasticities is noticeably larger than with Poisson, about double in each case. The interaction terms display the same pattern of signs as in the Poisson estimates: the importer interaction term has a positive and 1% statistically significant coefficient, while the exporter interaction term has a negative coefficient in both cases, but it is only statistically significant for the negative binomial model. As with Poisson, the relative magnitude of the interaction effect is much stronger on the import side than on the export side. This finding, combined with the statistical insignificance of the exporter interaction coefficient in the gamma estimates, reinforces our conclusion that it would be inappropriate to put too much weight on the unexpected negative sign in the baseline Poisson results.

In light of the continued use of the OLS estimator in the literature, column 4 presents results using that method. The pattern of signs and significance of the coefficients of interest is the same as for the Poisson estimates. These results are presented for comparative purposes only, however, since they suffer from two major defaults. First, observations for which trade is equal to zero cannot be included in the estimation sample, which reduces the sample size by about half relative to Poisson. Second, the Park-type test proposed by Santos Silva and Tenreyro (2006, equation 11) strongly rejects the adequacy of the OLS log-linearization of the gravity model (prob. = 0.000).

Another common gravity model estimator is the Heckman sample selection model (Helpman et al., 2008), which, unlike OLS, allows for the presence of zero entries in the bilateral trade matrix.<sup>6</sup> Results for the outcome (trade intensity) and selection (trade propensity) equations are in columns 5-6. We use Doing Business data on the time required to start a business (in logarithms) as the over-identifying

> 6 We apply the standard Heckman estimator, but do not pursue the additional correction introduced by Helpman et al. (2008) to deal with firm heterogeneity.

14
The general tendencies above are modified by two variables. Compositional accuracy is reduced by ethnic fractionalization. The good news is that _fb_ is increased by the presence of mature political ruling parties, _MPP_ , and by the quality of public financial management, _PEFA_ .

# _D. Discussion and Policy Implications_

The results above lend support to the over-arching hypotheses that common pool behavioral tendencies tend to worsen the quality of budgetary execution while political institutions can help reduce such tendencies directly and through the use of counter-cyclical financing. The qualification to this broad statement is that common pool drivers are absent from the revenue deviation story which is instead driven by debt burdens, revenue policy, political systems, and the capacity for PFM. By contrast, expenditure deviations are driven directly or indirectly by variables from all categories including drivers of common pool behavior.

High debt service burdens are correlated with revenue shortfalls and excessive spending, although the spending impact is not included in Table 13 since it is only significant at just above the 10 percent level. Countries with high debt service burdens also tend to save positive revenue shocks via the interactive term _rpos∙Z(PCT_INT)_ while negative revenue shocks induce reduced spending.

**<u>Table 13—Surviving Correlates</u>**

||<br>Revenue Deviations, _r_|<br>Expenditure Deviations, _e_|Compositional Deviations, _fb_|
|---|---|---|---|
|**Expenditure Deviations**,_e_|..|..|_−ln_|_e_||
|**Debt Burden**||||
|PCT_INT|_-PCT_INT_|_+rpos∙Z(PCT_INT)_|_.._|
|**Common Pool**||||
|H02 DRO|_.._|_+DRO_|_.._|
|H09_ln_POP|_.._|_+lnPOP_|_.._|
|H04 POLITY|_.._|_+POLITY_|_.._|
|H01 FCTN_ETHN|_.._|_.._|_−FCTN_ETHN_|
|**Political Institutions**||||
|H06 SYSTEM|_+Rp∙Z(SYSTEM)_|_.._|_.._|
|H05 MPP|_.._|_.._|_+MPP_|
|**PFM Capacity**||||
|H10_ln_GNIPC|_+Rp∙Z(lnGNIPC)_|_+r∙Z(lnGNIPC)_|_.._|
|H02 DRY|_-DRY_|_-DRY_|_.._|
|H09_ln_POP|_.._|_−r∙Z(lnPOP)_|_.._|
|H03 PCT_CSE|_.._|_−r∙Z(PCT_CSE)_|_.._|
|H08 PEFA|_.._|_.._|_+PEFA_|

Common pool tendencies are driven by the existence of various pressure groups that politicians believe they need to cater to. Surviving correlates associated here with common pool behavior include ethnic factions and aging populations. Population is reassigned as a common pool correlate on the grounds that larger populations would be more likely to have more interest groups to cater to. The degree of democratization is also reassigned as a common pool correlate: politicians in democracies probably feel a greater pressure to respond to pressure groups than those in autocracies.

Unambitious revenue targets, measured by _Rp_ , are correlated with unprogrammed revenue gains while overly ambitious revenue targets are correlated with revenue shortfalls. This tendency is reduced through interactive terms for countries with presidential systems and/or high income per capita. In addition, mature programmatic political parties appear to be critical to compositional accuracy in budget execution.

37
Table 9: Out-of-sample Accuracy of HIES-based Village Population Density Estimates using Public and Proprietary satellite data, 55 Sub-Districts

||Popul|ation D|ensity|Popu|lation|Count|||
|---|---|---|---|---|---|---|---|---|
||_R_<sup>2</sup>|SRC|Mean<br>AE|_R_<sup>2</sup>|SRC|Mean<br>AE|Mean<br>RE|Median<br>RE|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_A.Model estimates, Public & Propr_|_ietary _|_satellit_|_e image_|_ry_|||||
|HIES-based|0.79|0.91|664|0.60|0.67|607|37%|28%|
|HIES-based (balance correction)|0.78|0.92|665|0.58|0.67|607|35%|29%|
|Census-based|0.81|0.91|679|0.60|0.65|644|50%|28%|
|_B.Model estimates, public satellite _|_image_|_ry_|||||||
|HIES-based|0.75|0.92|663|0.56|0.69|595|34%|28%|
|HIES-based (balance correction)|0.77|0.92|657|0.55|0.69|606|34%|28%|
|Census-based|0.76|0.91|750|0.56|0.63|739|58%|36%|
|_C. Existing areal interpolation esti_|_mates_||||||||
|WorldPop 2015|0.99|1.00|312|0.99|0.99|237|13%|13%|
|Facebook HSRL 2015|0.91|0.97|417|0.90|0.93|309|20%|14%|
|GHSL Grid 2014|0.84|0.88|639|0.74|0.79|556|39%|26%|
|GPW 2015|0.68|0.90|1029|0.50|0.78|740|44%|27%|
|GPW 2010|0.29|0.28|1550|0.39|0.48|911|52%|35%|
|WorldPop 2010|0.31|0.81|1206|0.06|0.44|987|64%|34%|
|LandScan 2010|0.04|0.10|2562|0.01|-0.24|2012|135%|92%|

Note: HIES=the Household Income and Expenditure Survey; HRSL= High Resolution Settlement Layer; GHSL=Global Human Settlement Layer; GPW=Gridded Population of the World; SRC=Spearman Rank Correlation; AE= Absolute error; RE=Relative Error (with respect to the census). We retain only the villages in the 55 sub-districts for which the proprietary data are available. Models are estimated on subsample of 414 villages covered in 2012-2013 HIES survey. The satellite indicators used as covariates include all publicly available and all proprietary indicators. Out-of-sample prediction accuracy (with the census) are conducted on the 946 villages not covered in the HIES. The weights for the HIES-based model are _V illage P opulation_ <u>1</u> _v_<sup>.Balancecorrectionreferstoadjustmentof</sup> � weights based on the predicted probability of the village being sampled in HIES ( _INHIESv_ ) based on a probit model with village sampled in HIES as the dependent variable and all LASSO-selected variables as independent variables. <u>1</u> The balanced corrected weights are �<sup>Population count predictions were obtained by mul-</sup> _V illageP opulationv XINHIESv_<sup>.</sup> tiplying density predictions by village-level area. The accuracy parameters for population predictions are calculated based on comparisons with actual census population and reported. The mean and median RE are mathematically the same for population density and population count predictions.

27
Figure 14 The contribution of migration, inequality and mean expenditure to poverty increase in Accra, GLSS1 and GLSS3 <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Population Inequality Mean<br>Expenditure<br>1 Formal * Informal<br><!-- End of picture text --> Figure 15 The contribution of migration, inequality and mean expenditure to poverty reduction in Other Cities, GLSS1 and GLSS3 <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0-<br>-10<br>-20<br>-30<br>Population Inequality Mean<br>Expenditure<br>I Formal *Informal 0 Farmmg<br><!-- End of picture text --> 17
in 2006, and 94.6 percent in 2008. The percentage of women who tested positive was 2.9 percent in 2004, 3.1 percent in 2006, and 3.7 percent in 2008.

Figure 1 depicts the distribution of the likelihood women assigned to being HIV-infected in the 2006 and 2008 rounds (pooled), measured by beans on a scale of zero to ten. Fifty percent of the reports were of zero beans. The percentage of women who chose each category decreases with the number of beans, except for the 5-bean category. Fewer than one percent chose the 10-bean category. Table 3 shows the average number of beans. The overall sample average is 1.52 beans. The average number of beans chosen in each region conforms to the ranking of HIV prevalence in the general MDICP sample. The averages are 1.78 beans in Balaka, 1.69 in Mchinji, and 1.16 in Rumphi. The 2004 HIV prevalence rates for these regions are 7.9 percent in Balaka, 6.4 percent in Mchinji, and 4.4 percent in Rumphi. The table also shows that average beliefs decrease with schooling and are higher for women married to a polygamous husband.

Figure 2 depicts the distribution of the likelihood women assigned to being HIV-infected after receiving HIV test results. Because the testing component of the survey was conducted after the interview components, these are beliefs reported two years after the tests (the subsequent round of data collection). Because of low HIV prevalence and high attrition of infected women, there are only 18 women who report their beliefs two years after receiving a positive test result. The average number of beans allocated by these women is 4.67 beans, which is signicantly higher then the average of 1.5 beans reported by women who received a negative test result. Most of the women who received a positive test result assign at least some likelihood to not being HIV infected.

I obtain information about births from family rosters that were collected in 2006 and 2008. Respondents were asked to list all of the children ever born to them, but year of birth is missing for children who died more than two years before the interview. I therefore use only birth outcomes reported for the years 2004 to 2007. Depending on the year of marriage, year of birth, and the rounds in which the woman participated, I observe between 19
**Business Characteristics:** Finally, looking at business characteristics reveals that 80% of businesses are home-based. Businesses in the sample sell different products and services, with 44% of business owners selling a traded product that they make partially, such as prepared food or home-made accessories; 27% selling traded products without altering them such as clothes; and 15% providing a traded service such as a salon.

Importantly, only 10% of sampled businesses were registered at baseline, consistent with widespread informality in Jordan. Yet some businesses are likely more developed and formalized than others, with 37% of respondents reporting having a business partner or paid help, and more than half of participants reporting offering delivery services. Finally, businesses predominantly catered to owners’ social networks, with 68% of respondents saying that at least half of their clients are members of their family, friends, or neighbors.

# **5 Main Effects: Impact of Online Market Access**

In this section, I present the average treatment effects from the bundled market access intervention, including an overview of the empirical strategy, take-up, and effects on business outcomes and on women’s work and values.

## **5.1 Empirical Strategy**

As pre-specified, I follow a simple identification strategy of regressing outcomes on the bundled treatment assignment, controlling for baseline level of the outcome when available and for strata fixed effects. My preferred specification is: where _i_ is a study participant belonging to strata _s_ , T _is_ is the stratified intervention, _Yis_<sup>0isthe</sup> baseline level of the outcome when available, _Xis_ is a vector of baseline controls selected by Belloni, Chernozhukov, and Hansen 2014’s post double selection methodology, and _γs_ reflects indicator variables for whether _i_ belongs in strata _s_ . Randomization was stratified by whether the respondent markets on Facebook at baseline (regardless of whether she marketed on her personal profile or a Facebook page). _α_ 1 is the main coefficient of interest and reflects the effect of being assigned to the bundled intervention on outcome of interest _Yis_ . Note that I winsorize all continuous outcomes such as number of clients, revenues, and profits at the 95th percentile to reduce the effects of extreme values. I also impute any missing values in the Lasso-selected baseline control variables with the variable median and hold fixed effects for missing values. Most results are robust to randomization inference adjustment of p-values, also reported in the tables. Finally, table 1 confirms that the treatment assignment was successfully randomized and is balanced across baseline variables, with an F-stat from the F-test of joint significance equal to 0.57. The follow-up survey had limited attrition at 12%, also balanced across treatment and control arms. Table A.2 in the appendix confirms that treatment assignment is also balanced for the sample of participants found in the follow-up survey.

14
though, at 31 years versus 34.5 years among non-adopters. They had also completed more years of schooling.

|**Table 1: Selected de**|**scriptive sta**<br>**Full**|**tistics for ado**|**pters & non-**<br>**Non-**|**adopters**||
|---|---|---|---|---|---|
||**Sample**|**Adopters**|**adopters**|**Difference**|**|t-stat|**|
|**Number of Observations**|296|243|53|188||
|**Land assets:**||||||
|Total land owned (cuerdas), beginning<br>of pre-adoption spell ~|2.22|2.40|1.44|0.96|1.49|
|Percent of households that report<br>owning land, 2005|80.0%|83.1%|66.7%|16.4%|2.75***|
|Total land owned (cuerdas), 2005|4.54|5.07|2.19|2.88|3.72***|
|Total farm size (cuerdas), 2005|5.34|5.95|2.55|3.40|4.34***|
|Low-erosion land, percent of total,||||||
|2005|70.0%|71.7%|62.2%|9.5%|1.44|
|Irrigated land, percent of total, 2005|8.8%|10.2%|2.5%|7.7%|2.21***|
|Quality score of best plot, beginning<br>of pre-adoption spell|2.63|2.74|2.12|0.62|2.38***|
|Qualityscore of bestplot,2005|3.30|3.31|3.21|0.10|0.49|
|**Human capital assets:**||||||
|Year of household formation|1970|1970|1969|1|0.79|
|Household size, beginning of pre-<br>adoption spell|5.38|5.35|5.52|-0.18|0.49|
|Average household size, 2005|6.15|6.26|5.64|0.61|1.45|
|Adults in the household, beginning of<br>pre-adoption spell|2.45|2.43|2.53|-0.10|0.59|
|Average household adults in 2005|4.18|4.31|3.57|0.75|2.36**|
|Age of household head, beginning of<br>pre-adoption spell|31.7|31.1|34.5|-3.50|2.40***|
|Average final school grade completed||||||
|byhead of household|2.6|2.70|2.17|0.53|1.60|

~Beginning of pre-adoption spell is 1980, or year of household formation for households that were formed between 1980 and 1985. ^Adopters are those households that cultivated snowpeas for more than two years. ***denotes significance at the 1% level; ** at 5%, and * at 10%.

Finally, in the following graph we show the declining price trend of snowpeas for the period 1979-2005. Average prices were constructed using data from daily/weekly port prices for Miami—a primary destination for Guatemalan snowpeas—collected by the US Department of Agriculture. Yearly averages were derived from the average of the high and low price each week<sup>26</sup> . The original data were reported as US dollars, which 26 Snowpeas price data were only available starting in the late months of 1987, so the 1987-2005 prices were used to predict the earlier prices back to 1978. The prices were first predicted in US dollars and then adjusted for exchange rate and inflation, yielding results consistent with anecdotal evidence of especially20
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