rafmacalaba/datause-encoder-data huggingface dataset:
_Table 6_ - Change in product expenditure

|||**Pro**|**cessed**|||**Ultra-**|**processe**|**d**|**Proc**|**essed and u**|**ltra-proc**|**essed foods**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Low|Medium|Upper|Pass-through|Low|Medium|Upper|Pass-through|Low|Medium|Upper|Pass-through|
|**1**|0.68%|0.69%|0.69%|-1.10%|2.60%|2.60%|2.60%|-1.20%|4.20%|4.20%|4.20%|-2.30%|
|**2**|0.27%|0.27%|0.27%|-0.80%|2.00%|2.00%|2.00%|-1.10%|2.60%|2.60%|2.60%|-1.90%|
|**3**|0.11%|0.12%|0.12%|-0.69%|1.60%|1.70%|1.70%|-1.10%|1.80%|1.80%|1.80%|-1.80%|
|**4**|-0.01%|-0.01%|-0.01%|-0.67%|1.40%|1.40%|1.40%|-1.10%|1.30%|1.40%|1.40%|-1.80%|
|**5**|-0.11%|-0.11%|-0.10%|-0.62%|1.20%|1.20%|1.20%|-1.20%|0.87%|0.88%|0.89%|-1.80%|
|**6**|-0.19%|-0.19%|-0.19%|-0.60%|0.95%|0.95%|0.96%|-1.20%|0.50%|0.51%|0.52%|-1.80%|
|**7**|-0.26%|-0.25%|-0.25%|-0.57%|0.62%|0.63%|0.64%|-1.10%|0.08%|0.09%|0.10%|-1.70%|
|**8**|-0.33%|-0.33%|-0.33%|-0.54%|0.38%|0.39%|0.40%|-1.10%|-0.26%|-0.25%|-0.25%|-1.70%|
|**9**|-0.33%|-0.33%|-0.33%|-0.47%|0.22%|0.22%|0.23%|-0.94%|-0.39%|-0.38%|-0.38%|-1.40%|
|**10**|-0.30%|-0.30%|-0.30%|-0.31%|-0.04%|-0.03%|-0.03%|-0.58%|-0.50%|-0.49%|-0.49%|-0.89%|

Source: Estimates based on POF 2017/2018. Lower-bound and Upper-bound are estimated using a 95% confidence level. Estimates assume a price shock of 20%.

Figure 3 demonstrates that the impact of changes in product expenditure after taxes for processed and ultra-processed foods together is not simply an average of the two components. Although both food groups exhibit a similar pattern with a gradual decrease in their effects, the level of impact is noticeably higher for ultra-processed foods compared to processed ones.

Interestingly, when considering the effects of joint taxation on both groups, there is a significantly greater impact on households at the lower end of the consumption distribution. Furthermore, the decrease in the effect is much more pronounced, resulting in a final product expenditure level that is lower for the top consumption decile compared to the separate components.

_Figure 3_ - Change in product expenditure <!-- Start of picture text --> 5.00%<br>4.00%<br>3.00%<br>2.00%<br>1.00%<br>0.00%<br>1 2 3 4 5 6 7 8 9 10<br>-1.00%<br>Consumption decile<br>Processed Ultra-processed Processed and ultra-processed foods<br>Change in product expenditure<br><!-- End of picture text --> Source: Estimates based on POF 2017/2018. Estimates assume a price shock of 20%.

17
in the coresidency rates across Bangladesh and India data, and the bias estimates reflect the differences in the severity of selection. Since we estimated the biases in IGRC and IGC for a number of different samples, one might wonder how the magnitude of the bias relate to coresidency rate across different samples. Figure 4 shows the relation between the coresidency rate and the estimated bias for both IGRC and IGC estimates. There is a clear negative relation between coresidency rate and the magnitude of bias in the case of IGRC, implying that comparing IGRC estimates from different data sets may not be appropriate. In contrast, there is no discernible relation between the bias in IGC estimates and the coresidency rate. An OLS regression of the bias in IGRC estimates on coresidency rates yield a coefficient of _−_ 0 _._ 22 which is significant at the 1 percent level ( _t_ statistic equals _−_ 2 _._ 50). The coefficient on a regression of the bias in IGC estimates on the coresidency rates is, in contrast, numerically very small (0.008) and statistically insignificant ( _t_ = 0 _._ 30 and P-value= 0.77). We provide an explanation of the low sensitivity of IGC estimates to coresidency rates below (please see section 6.2).

# **6. Toward an Understanding of the Results: Why Is the Bias in IGC Estimates So Low?**

The evidence presented above is strikingly consistent and clear: when a researcher works with a data set from a survey that uses coresidency for defining the household, the IGRC estimates are likely to be seriously biased downward; but the estimates of IGC in coresident samples are, in general, much closer to the estimates from the full sample.

It is important to appreciate that the coresident sample bias common in the household surveys in developing countries is best modeled as a truncation, not censoring. The most common problem in the context of household surveys in developing countries is that there is no information (on both dependent and independent variables) for the the non-resident children resulting in truncation of the sample. The evidence presented above suggests that the non-resident children are not randomly distributed, both in Bangladesh and India: they come mostly from the tails of the schooling distribution.

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Savings is also associated with greater financial resilience. The Global Findex defines financial resilience as the ability to come up with extra money equivalent to 5 percent of gross domestic income (or around $3,000 in the U.S.) without difficulty within 30 days or less to deal with an unexpected expense, such as from an accident or a health event. Respondents who say they have and would rely on savings in an emergency are consistently more likely to say they could easily get the money (i.e., they are more financially resilient) in every region around the world. This is in contrast to adults who would rely on other sources of extra money. These include borrowing from friends and family, working extra hours, or taking out credit from a bank or other formal source, all of which are associated with lower resilience.

Beyond the individual and household levels, business owners who use their accounts to make payments and access savings are better able to access the digital economy, opening up new opportunities to connect with buyers, suppliers, and service providers than would be possible through analog networks alone.

In developing economies as of 2021, 42 percent of adults saved in any way and a slight majority of them did so formally using an account (storing money formally in an account was more common than saving, with 39 percent of adults in developing economies doing so). The first year that formal methods represented the most popular way to save was 2021. Prior to 2021, other methods dominated. These include semiformal methods such as community savings clubs, where individuals typically contribute a set amount on a regular basis (e.g., weekly or monthly) and one member receives the funds at each meeting; or saving using other methods such as saving cash at home. Mobile money accounts are playing a significant role in facilitating formal saving for mobile money account holders, especially in Sub-Saharan Africa, where an equal share of adults saved using a mobile money account as saved at a bank.

# **_4.2 Mixed evidence on borrowing as a driver of financial wellbeing_**

Unlike digital payments and savings, which deliver uncomplicated benefits to the consumers who have access to them, borrowing shows mixed results for improving financial wellbeing. In developing economies, about 50 percent of adults borrow in some way. More than half of those loans come from friends or family. The share of adults borrowing formally through a bank, microfinance institution, or through mobile money has increased, however, from 16 percent of adults in 2014 and 2017 to 23 percent of adults in 2021.

11
**Figure 1: Financial Development and Trade Volumes, 1970-1999.**

<!-- Start of picture text --> Advanced Countries Developing Countries<br>1970 1980 1990 2000 1970 1980 1990 2000<br>Trade/GDP (left axis) Private Credit/GDP (right axis)<br>1.2<br>.5<br>1<br>.8<br>.4<br>.6<br>.3<br>.4<br>.2<br>.2<br><!-- End of picture text -->

**Figure 2: External Finance Need of Exports and Per Capita Income, 1995-99.**

<!-- Start of picture text --> MLT<br>PHL MYS<br>SGP<br>DJI JPNCHE<br>BDI RWA GMB THAMEX KORTWNISR GBRIRLSWEHKGUSA<br>TZAETH GNBMDGNERYEMUGAMLITCDMOZTGOSLEBFANGACAFBENSDNKHMGHAMNGKENMRTAGONPLLAOSENBGDVNMCOGNICCMRPAKHTICIVGNQHNDINDBOLGINALBCHNLKAGUYPNGMARECUIDNJAMGTMEGYJORSYRDOMSLVPERROMDZALBNCUBPRYFJICRIIRNCOLBGRPANTUNBLZTURBRAVENZAFPOLGABHUNCHLTTOURYSYCARGMUSSAUKNAGRCBHRPRTBRBCYPOMNESPBHSNZLBMUQATFRAFINITAAUTBELNLDISLKWTAUSCANDNKNOR<br>ZMB<br>ZWE<br>MWI<br>6 7 8 9 10<br>Per Capita GDP, 1995-9<br>100<br>50<br>0<br>Financial Content of Exports, 1995-9<br>-50<br><!-- End of picture text --> 24
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**Table 2: Differences-in-Difference in Water Consumption**

|**Water Consumption in Cubic Meters, m**<sup>**3**</sup>|**ΔControl**|Treatm<br>Neighborhood<br>**ΔTreatment**|ent 1:<br>Comparison<br>**ΔT-ΔC**|
|---|---|---|---|
|**Difference between Average Monthly**||||
|**Post-Treatment Consumption (August -**||||
|**September 2014) and Average Monthly**|-0.24|-1.50|-1.26*|
|**Consumption in Pre-Treatment Rainy**<br>**Season (May - November 2013)**||||
|**(SE)**|(0.40)|(0.53)|(0.66)|
|_n_|1285|1267|2552|
|||Treatm<br>Municipality C<br>|ent 2:<br>omparison<br>|
||**ΔControl**|**ΔTreatment**|**ΔT-ΔC**|
|**Difference between Average Monthly**||||
|**Post-Treatment Consumption (August -**||||
|**September 2014) and Average Monthly**|-0.24|-0.93|-0.68|
|**Consumption in Pre-Treatment Rainy**||||
|**Season (May - November 2013)**||||
|**(SE)**|(0.40)|(0.44)|(0.60)|
|_n_|1285|1261|2546|
|||Treatm|ent 3:|
|||Planning P<br>|ostcard<br>|
||**ΔControl**|**ΔTreatment**|**ΔT-ΔC**|
|**Difference between Average Monthly**||||
|**Post-Treatment Consumption (August -**||||
|**September 2014) and Average Monthly**|-0.24|-1.52|-1.27**|
|**Consumption in Pre-Treatment Rainy**||||
|**Season (May - November 2013)**||||
|**(SE)**|(0.40)|(0.43)|(0.59)|
|_n_|1285|1248|2533|
|_Notes:_<br>_1. Numbers in parentheses are standard errors_||||

_1. Numbers in parentheses are standard errors 2. Stars indicate statistical significance: *=p<0.10, **=p<0.05, ***=p<0.01_
25 which was documented in Gonzaga et al. (2006), and which we also observe. _Controlling for the growth in import penetration_ , there appears to be some evidence that a stronger currency increases the skill premium.<sup>34</sup> Table 7 reports the second-stage results for the regression of industry participation constants from the employment multinomial logit model on the same set of trade-related variables. These results are somewhat harder to interpret, since the ten occupational categories used in the estimation are much more aggregated than the 22 industries used in the previous two tables, and are basically at the agriculture, manufacturing and services level.<sup>35</sup> Partly as a result, there is a counterintuitive negative sign on lagged ERPs, which suggests a (conditional) movement _towards_ the industries experiencing the greater declines in protection. This result is explained by a movement towards the tradable sectors (particularly agriculture) of the reference category of workers in the employment model. Once we look at the unconditional pattern of employment changes for all workers at the disaggregated industry level, in Figure 5, we observe the expected positive correlation: employment levels seem to have fallen by more in industries experiencing larger declines in protection.

The only other statistically significant result in Table 7 is easier to interpret: lagged export shares are positively correlated with conditional increases in employment, suggesting that industries that succeeded in increasing their exports suffered smaller declines in employment than others.

Taken together, these results paint a mixed picture. The signs and significance are broadly – if not wholly – consistent with theoretical expectations from models that feature barriers to labor movement across sectors. Larger falls in protection and an exchange rate that makes imports more competitive domestically imply lower industry

> 34 This may be evidence of quality upgrading in the face of increased import competition. 35 Both formal and informal agriculture categories are regressed on trade variables for the agricultural industry. Formal and informal manufacturing use manufacturing trade variables averaged over the various manufacturing industries, using lagged imports as weights.  Formal and informal nontradables, employers and the self-employed categories are assigned zero ERPs, import penetration and export share, but use economy-wide import- and export-weighted real exchange rates.
The estimates of the impact of Chinese and Indian FCS on other countries FCS do not show evidence of FCS substitution. The coefficient on China’s FCS is not different from zero, and the interaction with the Latin American is positive, suggesting that FCS in China and LAC are complements. As shown in the bottom of the table, the overall effect of China on LAC is 0.26 and statistically different from zero. Indian FCS has a positive effect on FCS for the world average, and that effect is not different for LAC. Again, the overall effect in LAC is positive and significant.

Columns 2 and 3 in Table 2 present results from Poisson and Negative Binomial estimators. The estimates of the control variables are consistent with the OLS coefficients. The coefficients on China FCS, although a bit bigger, are also similar to those in the OLS regressions. The difference appears in the estimates for India. The Poisson estimator produced a significantly negative coefficient on the Indian FCS multiplied with the LAC dummy, thus making the overall effect of Indian on LAC FCS negative. The Negative Binomial estimator also shows that the effect of Indian FCS is different in LAC than in the rest of the world, although here we cannot reject the hypothesis that the overall effect on LAC is zero.

One potential weakness of the aforementioned specifications reported in Table 2 is that they do not control for global trends affecting FDI in China and India as well as in other developing countries. Also, estimates on the variables may be biased due to unobserved time-invariant country-pair characteristics.

The second panel in Table 2 reports the results from specification that control for time effects and country-pair fixed effects. The KCM performs well, especially with the count data estimators. Nonetheless, the coefficients on lGDPsum and lGDPdifsq are smaller 13
**<u>Table 9. Probit estimation results of correlates of antenatal care visits</u>**

||Model 1|Model 2|Model 3|
|---|---|---|---|
||(Program areas)|(Poorest 20%)|(Richest 20%)|
||Coef(t-ratio)|Coef(t-ratio)|Coef(t-ratio)|
|Wealth|0.000***<br>(4.91)|-|-|
|Yr 2005|0.250***|0.055|0.176|
|ea|(3.46 )|(0.39)|(1.45)|
|Y 2011|0.447***|0.344***|0.090|
|ear|(6.48)|(2.92)|(0.77)|
|Y 2014|0.841***|0.737***|0.655***|
|ear|(11.34)<br>|(6.02)|(4.28)|
|Interaction: Time tend X wealth_index|-0.000**<br>(-2.35)|-|-|
|Rl|-0.530***|-0.342|-0.468***|
|ura|(-5.89)|(-1.14)|(-4.93)|
|A|-0.007|-0.008|-0.003|
|ge|(-0.66)|(-0.37)|(-0.11)|
|Nb f hild 5 d d|-0.072***|-0.032|-0.078*|
|umer o cren an uner|(-3.39)|(-0.55)|(-1.74)|
|Edi|0.426***|0.381***|0.638***|
|ucaton|(11.28)|(4.35)|(8.97)|
|Region dummies (historically||||
|disadvantaged)||||
|_A_|-0.322***|-0.752***|-0.056|
|_far_|(-4.69)|(-6.52 )|(-0.51)|
|_Sli_|-0.765***|-1.058***|-0.395**|
|_oma_|(-7.06)|(-7.08)|(-2.05)|
||0.029|-0.089|-0.034|
|_Benshangul- Gumuz_|(0.41)|(-0.65)|(-0.33)|
||0.381***|0.222**|0.210|
|_Gambella_|(5.01)|(2.11)|(1.37)|
|Cntant|-0.839***|-1.353***|-0.532***|
|os|(-8.12)|(-3.90)|(-3.21)|
|Number of observations|23,920|6,252|4,360|

Note: *** p<0.01, ** p<0.05, * p<0.1 36
The next section provides a detailed exposition of our research design. This is followed by a presentation of results, which are then subjected to a battery of sensitivity tests. The penultimate section discusses two competing potential explanations for our findings before we draw out the implications of our findings in the concluding section.

# **Research Design**

# _The dependent variables_

We employ a pooled time-series, cross-section (TSCS) data set. Our main dependent variable consists of military expenditures over GDP ( _Military expenditures_ ), which the literature usually refers to as the defense burden. We keep this variable in its level form, but our main results are hardly affected if it is logged instead. Collier and Hoeffler (2004b) come to the same conclusion in their study. The data are taken from (World Bank 2004), which is also the source for the other variables unless noted otherwise. They are available annually from 1988 up to 2002, a total of 15 years. Combining various sources one could in principle construct a panel that reaches further back in time. However, given measurement and international and inter-temporal comparability problems with military expenditure data, particularly during the period of the Cold War (Brzoska 1995), we prefer to use one single data source that largely covers only the post-Cold War period. This is of course also the period in which so-called ethnic conflict is supposed to be particularly prevalent. The recent data are also much more reliable given improved standards for collecting data and higher levels of transparency due to democratization and international pressure (Omitoogun 2003). The World Bank data originally come from the Stockholm Peace Research Institute’s (SIPRI) series of _Yearbook: Armaments, Disarmament and International Security_ and are almost identical to data supplied to us directly by SIPRI (r = .98).

In addition, we use two other variables capturing different aspects of militarization to check the robustness of our results. The second dependent variable is military personnel as a share of the total labor force ( _Military personnel_ ), which has slightly lower data availability compared with military expenditures. The advantage of using the share of military personnel in addition to military spending is that poor countries may simply use labor-intensive (rather than capital-intensive) forms of militarization. Lastly, we use a variable measuring arms imports relative to total imports ( _Arms imports_ ). This variable is only available up to 1999 and in principle arms import expenditures should be included in total military expenditures. However, 5
aggregates, and Panel C documenting some specific statistics with respect to household-based businesses. The first three columns are for our three survey years, while the last two columns document relative growth from 1994 for surveys undertaken in 2000 and 2005.

In Table 1a, the top two rows show the relative persistence of the unorganized sector. While both sectors have grown, as is evident in Panel B, the unorganized sector has more than kept pace such that the unorganized sector’s share of employment has increase from 80.3% in 1994 to 81.5% in 2005. The third through fifth rows of Table 1a disaggregate the unorganized sector’s share by organizational form. What is striking is the substantial increase in femaleowned business from 9.2% of employment in 1994 to 18.7% of employment in 2005. The last column shows how strong this growth is compared to the rest of the sector.

How big is share growth of 9.5%? One can depict it in several ways. First, as a simple calculation of net changes, 83.9% of the net growth in employment in the unorganized sector from 1994 to 2005 is due to increases in employment in female proprietorships (i.e., (7,5553,180)/(32,866-27,649)). A simple counterfactual analysis can also help depict the results with respect to persistence. In 1994, female-owned proprietorships in the unorganized sector were 13.9% of the male-owned proprietorship stock. Had this proportion remained through 2005, the net increase in the unorganized sector’s employment level would have been 3.3%, compared to 18.9%. Under this scenario, the sector share in 2005 would have also fallen to 79.3% rather than the 81.5% observed. (The reason why this change of 2.2% is less than the 9.5% is because most of the reclaimed share would be reallocated to male-owned unorganized businesses rather than the organized sector.) While it is unrealistic that there would have been no other change in the other organization forms absent the increase in female-owned businesses, this procedure does not necessarily overstate or understate the actual counterfactual change. It could have been that male-owned businesses in the unorganized sector would have increased more than they did in order to meet customer demand, suggesting that this example overstates the role of womenowned businesses in persistence for the unorganized sector. But it also could have been that women-owned businesses have inter linkages with male-owned businesses (e.g., customer/supplier relationships) that helped increase the number of male-owned businesses, in which case this counterfactual scenario understates the role of female-owned businesses. The 9
Table 2 indicates that as the degree of reliance on trade increases, the F government can separate itself at a lower import tariff. The reason for this can be seen by observing that the optimal tariff for the protectionist government rises as the volume of trade increases. Therefore, it will be more costly for the protectionist government to imitate the F government when the volume of trade is large. The effect of the level of Kx on the cost of the separation program is ambiguous, since a higher value of Kx means a lower import subsidy but a greater volume of imports. Interestingly, the total cost of the subsidy program initially increases with Kx, reaching a maximum at Kx = 30, and then declines.

Table 3: Separating Equilibrium with Investment Subsidy

||(a)|(b)|(c)|(d)|(e)|
|---|---|---|---|---|---|
|Km|15|12.5|10|7.5|5|
|Kx|25|27.5|30|32.5|35|
|||Free Trade|Govemment|||
|lnvestmentSubsi<br>dy|.03|.0278|.0243|.0192<br>.|.01|
|Total Amount<br>of subsidy|.247|.221|.180|.131|.0565|
|imO|10.1943|13.7415|17.2857|20.8415|24.4079|
|exO|16.0502|19.3814|22.5853|25.6527|28.3596|
|imI|10.7943|14.2965|17.7997|21.3035|24.8079|
|exI|18.6343|22.132|25.6289|29.1251|32.6206|
|Zo|8.225|7.932|7.471|6.810|5.654|
|Zi|11.020|11.020|11.020|11.020|11.020|
|U|13.1453|13.1456|13.1459|13.1464|13.1469|
|||Protectionist|Government|||
|tO|.05711|0.6305|.07044|.07992|.09262|
|ti|.06687|.07517|.08597|.10073|.12241|
|ZO|2.682|2.818|2.986|3.198|3.475|
|ZI|8.947|9.071|9.227|9.433|9.721|

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