rafmacalaba/datause-encoder-data huggingface dataset:
randomization method provides high degree of anonymity to the respondent by masking individual responses through aggregation. Further, this anonymity is fairly intuitive for the respondent to feel that the responses would be kept confidential. A few examples of the innocuous statements include “my friends respect me”, “I have been in an earthquake”, “I feel lonely on most days”, and “I am jealous of my neighbor”. Please refer to Table A10 in for full list of innocuous statements.

To conceptualize and conduct a befitting placebo check, we estimate the effect of the 4P program on responses provided by the LR control group (i.e., respondents who were only asked the innocuous questions). Estimates from Table 9 demonstrate satisfaction of the falsification test; we find no significant effect of the CCT program on placebo outcomes.

_Table 9: Effect of CCT on Placebo Outcomes (innocuous questions for LR control group)_

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||LR Control|LR Control|LR Control|LR Control|LR Control|
||group 1<br>_(control for_<br>_physical IPV)_|group 2<br>_(control for_<br>_emotional IPV)_|group 4<br>_(control for_<br>_economic NPV)_|group 6<br>_(control for_<br>_economic IPV)_|group 8<br>_(control for_<br>_emotional NPV)_|
|RD Effect|0.016|0.137|-0.136|0.154|0.153|
||(0.91)|(0.35)|(0.19)|(0.41)|(0.34)|
|FDR q-values|0.91|0.61|0.61|0.61|0.73|
|Observations|278|278|278|278|278|
|Control mean|1.463|1.382|1.000|1.824|1.169|
|P-values in pare|ntheses: * p<0.1|** p<0.05 *** p<0|.01|||

Note: This table provides estimates for 5 of the 8 set of questions asked from each respondent in the LR control group. While conducting ID checks for the LR method for each set of questions/type of GBV, we found that the identifying assumption was not satisfied for the remaining three groups, i.e. sexual NPV, sexual IPV and physical NPV (results available on request). Therefore, we test the validity of the primary treatment effect by estimating the effect of the CCT program only for the 5 outcomes that satisfied the LR identification check.

# 6.3 Robustness Tests

# 6.3.1. Alternative Running Variable

Following <mark>Choi and Lee (2020), we use ranks of households (ranked by income) as the running variable instead of income itself. The thought experiment underlying this test is that since households cannot observe who else would have been considered for the CCT program or their relative positioning w.r.t other households, the likelihood of manipulation while using household ranks as the eligibility criteria (instead of an absolute income cutoff point) is quite low. Thus, we replicate the primary analysis by defining using</mark> _<mark>rank</mark>_ <mark>of households as an alternative running variable. Estimates from Tables 10-13 indicate that the primary estimates found in Section 6.1 are robust.</mark> 21
biased due to measurement error. In order to address these issues directly, we conduct instrumental variable estimations where we use economic conditions in remittance-source countries as instruments. In particular, we include the GDP per capita, real growth rate, and unemployment rate of the five OECD countries that are the top recipients of migrants for each remittance-receiving country in our sample. Each of these variables is separately weighted by the share of migration from the corresponding country to each of those five OECD destinations.<sup>15</sup> Table 10 shows the results from the instrumental variables estimations described above We conduct and report two tests to show the validity of our instruments. First, we present the F- statistic for weak instruments as suggested by Stock and Yogo (2002). This is a test of the significance of our instruments in predicting remittances. In every regression the F-statistics is above the critical value, at 5 percent significance, indicating that our estimates do not suffer from a weak instruments problem. Second, we report the Sargan test of overidenditfying restrictions. The joint null hypothesis in this case is that the instruments are uncorrelated with the error term and that excluded instruments are correctly excluded from the estimated equation. Again, these tests confirm the validity of our instruments.

As for the impact of remittances on financial development, we continue to find that they have a positive and significant impact on both credit and deposits to GDP. Though the size of the coefficients are in this case much larger than those obtained in previous estimations they are within a range that can be justified by the presence of measurement error in the remittance series.<sup>16</sup> These results confirm that the positive impact of remittances on financial development is not due to endogeneity biases.

> 15 We focus exclusively on the top five OECD destinations for migrants for each country in our sample because the OECD data only provides bilateral migration data vis-à-vis 5 countries.

18
����� = �+ �������� + ���������� + ������� + ������� + ��������� + �� + �� + ����� Subscripts _i, j, c_ , and _t_ represent firm, industry, country, and time. The dependent variables ( _yijct_ ) are log labor productivity and sales growth. All regressions include time ( _γt_ ) and sector dummies ( _μj_ ). Some regressions include country-year dummies (i.e., unique dummies for each survey). To avoid overstating precision, we cluster the standard errors at the country level.

The most important independent variables are the country- and firm-level variables representing access to telecom services, ���� and ������. The three country-level variables are fixed line density, mobile phone density, and Internet density (subscriptions per 100 people). These variables are omitted when we include country-year dummies. The final telecom variable is the percent of similar firms in the same city with a business website. We include this rather than a variable representing whether the firm has its own website because website use might be endogenous to firm performance. In contrast, it is difficult for one firm to change the local business environment, especially given that most WBES firms are small or medium-sized. To further reduce endogeneity concerns, we omit the firm’s own response when calculating the average.<sup>3</sup> We refer to variables like this as local averages.

The regressions include firm, country, and local variables to control for other differences in firm characteristics and the business environment. The firm-level variables, ����, include age and ownership. We also include initial sales in the regression for sales growth to allow large firms to grow differently from small firms. Additional country-level variables, ���, control for development (per capita GDP), geography (population and whether the country is landlocked), infrastructure quality (cost of exporting a standard container) and labor regulation (an index of the difficulty of firing workers).<sup>4</sup> Additional local averages, �����, control for access to formal finance and trade credit (% of firms with overdrafts and trade credit), competition (average markup and

> 3 Many studies use a similar approach (Xu, 2011).

> 4 Variables come from the World Bank’s _World Development Indicators_ and _Doing Business_ project and the CEPII trade database.

4
Figure 6 Dominican Republic and Haiti: Overall Fiscal Balance (percent of GDP), 1960-2007 **___** Dominican Republic ___ Haiti <!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> <!-- Start of picture text --> Source: IMF.<br><!-- End of picture text --> The foregoing discussion provides only mixed evidence (at best) in support of a CCA between the Dominican Republic and Haiti. However, it must be kept in mind, as noted earlier, that simple (unconditional) correlations can be misleading for assessing the degree of comovements between variables. In the short run, unconditional correlation coefficients can vary in response to a wide variety of macroeconomic factors. The analysis performed in the next section (which introduces a number of control variables in a multivariate framework) provides more accurate measures of business cycle synchronicity. More importantly, the criteria used may be _endogenous_ , as a result of the very existence, and induced effects, of a CCA. For instance, it has been argued that similarity of inflation rates may be promoted by participating in a currency union. Hoffman and Remsperger (2005) for instance have found that, for the Euro area, the degree of persistence in inflation differentials fell significantly following the adoption of the common currency in 1999. Similarly, once two countries choose to form a CCA, their business cycle may become more synchronized, as the elimination of currency fluctuations may spur trade between them. There is also evidence suggesting that the 20
of data on OECS ports at the beginning of the sample period. For instance, data are available only for St. George’s and Long Point, the OECS’s worst performers, beginning in 2007 (when the subregion’s most recent decline in technical performance occurred). With this note of caution in mind, while the OECS countries were performing better than the rest of LAC in 1999 and the early 2000s and again after a 2002– 03 dip in performance, the region has not yet managed to recover from its 2007 performance decline. The OECS ports had an average technical efficiency in 2010 that was 13 percentage points less than that of the LAC average. OECS ports’ technical efficiency has remained about the same since 2008, and lags well behind that of Central and South America (figure 6.1). The OECS, however, performed almost on par with the Caribbean as a whole in 2010 after falling behind in 2007.

|**Table 6.1 Tech**<br>**Ranking**|**nical Efficiency of P**<br>**Port**|**orts in Latin America and the Carib**<br>**Tech. Efficiency**<br>**(%)**<br>**Ranking**|**bean, 1999–2010**<br>**Port**|**Tech. Efficiency**<br>**(%)**|
|---|---|---|---|---|
|1<br>|Manzanillo<br>|83<br>41<br> <br>|Freeport<br>|50<br>|
|2|Itajai|81<br>42|Paita|49|
|3|Montevideo|80<br>43|Acajutla|49|
|4|San Juan|78<br>44|Balboa|48|
|5|Santos|78<br>45|Rio Haina|48|
|6|Callao|77<br>46|Roseau*|48|
|7|Lirquen|76<br>47|Puerto Cabello|47|
|<br>8|Puerto Limón|<br>76<br>48|Progreso|43|
|**e 4**<br>9|Belize City|**e 2**<br>75<br>49|Belem|42|
|**til**<br>10|Salvador|**til**<br>74<br>50|Boca Chica|42|
|**uin**<br>11|Guayaquil|**uin**<br>74<br>51|Vieux Fort*|41|
|**Q**<br>12|Sao Francisco do Sul|**Q**<br>73<br>52|Valparaiso|37|
|13|Puerto Barrios|72<br>53|Oranjestad|36|
|14|San Antonio|72<br>54|Cartagena|36|
|15|La Guaira|71<br>55|Campden Park/Kingstown*|35|
|16|Puerto Cortés|70<br>56|Suape|35|
|17|Puerto Quetzal|68<br>57|Barranquilla|34|
|18|Port of Spain|65<br>58|Talcahuano|33|
|19|Antofagasta|65<br>59|Buenos Aires|31|
|20|Veracruz|64<br>60|Puerto Manzanillo|29|
|21|Vitoria|63<br>61|Corinto|28|
|22|Puerto Caldera|62<br>62|Maracaibo|24|
|23|Puerto Castilla|61<br>63|Ensenada|23|
|24|Point Lisas|61<br>64|Santo Tomás de Castilla|22|
|25|Rio de Janeiro|59<br>65|Caucedo|22|
|26|Fortaleza|59<br>66|Santa Marta|21|
|27|Buenaventura|59<br>67|Arica|21|
|<br>28|Rio Grande|<br>58<br>68|Lazaro Cardenas|19|
|**3**<br>29|Havana|**1**<br>57<br>69|St. George’s*|18|
|**tile**<br>30|Altamira|**tile**<br>57<br>70|Mazatlan|17|
|**uin**<br>31|Iquique|**uin**<br>57<br>71|Zarate|16|
|**Q**<br>32|Castries*|**Q**<br>56<br>72|Sepetiba|15|
|33|Bridgetown|54<br>73|Kingston|12|
|34|Manaus|54<br>74|Rosario|10|
|35|St. John’s*|54<br>75|Basseterre*|8|
|36|Ushuaia|53<br>76|Tampico|5|
|37|Pecem|53<br>77|<br>Long Point*|3|
|38|San Vicente|51<br>78|<br>Salina Cruz|3|
|39|Paranagua|51<br>79|Santana|1|
|40|Coco Solo|51<br>80|Tuxpan|1|
|_Source:_Ada<br>_Note:_* Den|pted from Sarriera and oth<br>otes OECS ports. OECS =|81<br>ers (2013) based on authors’ primary data collec<br>Organization of Eastern Caribbean States.|Santarem<br>tion.|0|

41
intuition is akin to the idea of marginal decreasing returns to capital. Consistently with the low density of robots, the median Indonesian firm employs a much smaller amount of capital per worker than its counterpart in the same 2-digit ISIC sector in China and Mexico (Figure A2 in Appendix).<sup>38</sup> The second (non mutually exclusive) possible explanation hinges on the lack of relevant skills. By filling skill shortages, robots may induce large productivity gains that more than compensate the labor replacement effects. In contexts where the quality of labor is relatively low, robots can provide substantial improvements to the production process as they enable firms to achieve higher product standards.<sup>39</sup> This paper provides suggestive evidence that this type of mechanism may be at play in Indonesia where the penetration of robots significantly increases markups and international trade integration, which are enabled by higher production standards. This hypothesis is also consistent with the relatively low quality of skills in Indonesia as proxied by high-school students’ test scores through the OECD Programme for International Student Assessment (PISA 2018).<sup>40</sup> From a policy perspective, the evidence of positive inter-industry employment spillovers provides a possible rationale to facilitate or even incentivize the adoption of automation technologies among manufacturing firms in Indonesia. Further research will be needed to support the external validity of our findings and the related policy implications. In particular, firm-level information on the adoption of automation technologies in developing countries would be a crucial first step towards that end. In addition, why we do not see more Indonesian firms adopting robots given the high returns to adoption remains an important question for future research.<sup>41</sup>

> 38The computation is based on firm-level data from different waves of the World Bank Enterprise Survey. Specifically we compute the median real value of the capital stock per worker (in international dollars) for all of the manufacturing industries the data allows. The data is available for Indonesia in 2009, for Mexico in 2010 and for China in 2012.

> 39This is similar to Acemoglu and Restrepo (2018a), where robots offset the scarcity of young workers in advanced economies. Maloney and Molina (2019) also cite anecdotal evidence that Chinese firms choose to improve the quality of their export by employing robots, rather than investing in employees’ training.

> 40The average reading literacy score of Indonesian students is 371, against 555 for China, 420 for Mexico and 487 for the OECD average. Similarly, science performance is 396 for Indonesia, against 590 for China, 419 for Mexico and 489 for the OECD region.

> 41Evidence in Calì et al. (2019) presents a similar puzzle also in the context of the switching from

39
could reduce unit costs by over 20%, with a significant impact on competitiveness, aggregate

# demand, and employment.

**Table** 1: **Latin America Ratios to U.S. Inventories (all industries)**

|S;0~<br>~ ~ ~<br>**X.**|~~a<br>**S**|**Maeral lam**<br>0<br>07R|**atory Leve**<br> <br>;|**R atIo:Rai**<br>v<br>lvX|**to**<br>**LeveR**<br>**b**<br>'-<br>--|**000**<br>**y;**|||
|---|---|---|---|---|---|---|---|---|
|||Chile|Venezuela|Peru|Bolivia|Colombia|Ecuador|Mexico|Brazil|
|Mean|2.17|2.82|4.19|4.20|2.22|5.06|1.58|2.98|
|Minimum|0.00|0.30|0.10|0.11|0.52|0.86|0.42|0.8|
|Ist Quartile|0.36|1.87|1.25|1.39|1.45|2.55|1.06|1.6|
|Median|1.28|2.61|2.30|2.90|1.80|3.80|1.36|2.00|
|3rd Quartile|2.66|3.12|3.90|4.49|2.52|5.64|2.06|3.1|
|Maximum|68.92|7.21|31.1|34.97|13.59|20.61|3.26|7.1|
|_________<br>_||(:**:**<br>**Falab:le**|**rageof D s**|**0**<br>**data**<br>to_|<br>,<br>==|**_t_**<br>_|||
|_______|Chile|Venezuela|Peru|Bolivia|Colombia|Ecuador|Mexico|Brazil|
|Mean|1.76|1.63|1.65|2.74|1.38|2.57|1.46|1.98|
|Minimum|0.01|0.10|0.39|0.11|0.19|0.67|0.35|0.75|
|1st Quartile|0.17|0.87|1.17|1.13|1.05|1.67|0.82|1.1|
|Median|0.72|1.60|1.54|2.02|1.28|1.98|1.36|1.60|
|3rd Quartile|1.38|2.1.4|2.11|3.18|1.63|2.86|12.14|2.00|
|Maximum|31.61|5.29|3.87|21.31|5.31|7.94|4.91|5.2|

Source: Guasch and Kogan (2000) These calculations are merely a lower bound on the cost of the additional inventory.

First, there are certain transactions that would have been worthwhile were A not for the high level of inventory holdings necessary to complete them effectively. It is difficult to estimate the size of these lost transactions. Second, firms in developing countries will take costly steps to mitigate the institutional or structural factors creating a need for high inventories. Suppose that for a particular firm, 30 days of inventory are sufficient when transportation networks are well developed but 90 days of inventory are required when transportation networks are poor. The firm might choose to reduce these 90 days to 60 days by requiring suppliers to locate nearby. Additional costs due to poor infrastructure as measured by increased inventory levels would be _5_
# **Gender and the Allocation of Adult Time: Evidence from the Peru LSMS Panel Data**

# I. Introduction

The study of determinants of time allocation in developing countries has received increasing attention in recent years. Not only does such an analysis provide important insights into labor supply behavior, but it also gives useful information about policy changes that require changes in the use of time in the household. Time use studies can show where, among others, education, transport and basic services projects face limitations because they do not properly account for the time allocation of users. For instance, infrastructure projects that provide basic services such as water and energy are more likely to be successful if without them households have to allocate a large amount of time to acquire water and energy.

A number of studies have analyzed the determinants of time use in developing countries.' It is widely held that women in developing countries bear a disproportionate share of work, with most of their activities concentrated in the home. Little is known however if women are also more vulnerable (in the sense of their work burden) whenever there is unexpected change in the environment of the household in the form of sickness or unemployment.

This paper attempts to answer these questions by controlling for unobserved heterogeneity so common in cross-section data. It uses the 1994 and 1997 panel data from Peru to answer the following questions:

- Do women undertake a disproportionate burden of work compared to men?

- Do they bear a disproportionate time burden of sickness in the household?

- Do women overcome unexpected changes in employment in the household by changing their own time use more than men do?

- Do poor energy and water supply services affect women's time allocation more than men's? Is women's labor supply to income-generating work affected by poor services?

See among others Alderman and Chishti (1991), Khandker (1988), King and Evenson (1983), Skoufias (1993) and Fafchamps and Quisumbing (1998).

1
positive) and they use less retained earnings for working capital (presumably because they have better access to credit products as shown above).

Exporters are more likely to make investment and when they do they invest larger amounts. It is plausible that their higher investment is dictated by their need to stay competitive on international markets. Exporters are less likely to use retained earnings for working capital, likely because of higher usage of credit products. However, when usage of credit products is controlled for, the exporters use higher proportion of retained earnings for investment (not reported).

Government owned firms do not have significantly different cash flows than other firms, which does not support the common notion that government owned firms are less profitable.<sup>17</sup> They are not significantly different from other firms in their investment behavior, and they use less retained earnings to finance investment.

Higher cash flows proxied by operating income to sales are unrelated to the incidence of investment or the size of investment. However, firms with higher cash flows are more likely to use retained earnings to finance working capital, perhaps because it is less expensive to use internally generated funds than external funds.

Manufacturing firms have significantly lower cash flows (and the magnitude of the difference is very large) and they are significantly less likely to make any investment than service firms. These results could indicate that manufacturing firms lack profitable growth opportunities, or perhaps that their costs of operation are relatively high. They also use more retained earnings to finance their working capital.

The patterns over time parallel those observed earlier. The operating income to sales was higher in 2007 than it was in 2003 and 2005, consistent with positive trend observed in Figure 2. Investment was lower in 2005 than it was in 2003, but recovered to the previous level in 2007.

In Panel B we add our measure of TFP to the set of regressions discussed above. We find positive and significant effects – i.e. firms that have higher TFP are more likely to make

> 17 Our measure of operating income to sales is only indirectly related to firm profitability. More commonly used measures of profitability are ROA (return on assets) or ROE (return on equity), which are not available in ICA data.

24
paper, this review mainly focuses on LSMS<sup>2</sup> surveys, a household survey program by the World Bank’s Development Data Group that provides technical assistance to national statistical offices (NSOs) in the design and implementation of multi-topic household surveys It also mentions other sources such as Household Budget Surveys, Demographic and Health Surveys, and Afrobarometer surveys to some extent.

**Table 1. Variety of instruments for household-level data collection**

|**Instrument**|**Sponsor**|
|---|---|
|Censuses|UNFPA|
|Income Expenditure Survey (IES) /Household Budget Survey (HBS)|Central Banks,IMF,NSOs|
|Labor Force Survey (LFS)|ILO|
|Demographic and Health Survey (DHS)|USAID|
|Multiple Indicator Cluster Survey (MICS)|UNICEF|
|Core Welfare Indicator Questionnaires (CWIQ)|UNDP, Department of<br>International Development,<br>World Bank Africa Region|
|Welfare MonitoringSurvey (WMS)|Stat Norway|
|Statistics on Income and LivingConditions(SILC)|Eurostat|
|Comprehensive Food Securityand VulnerabilityAnalysis(CFSVA)|WFP|
|Integrated, Multi-topic Surveys<br>• Living Standards Measurement Study (LSMS)<br>• Integrated Survey (IS)<br>• Family Life Survey (FLS)|World Bank, RAND, NSOs|

# **2.1. LSMS Survey Collection and Characteristics**

## **2.1.1. LSMS Research Collection**

To capture the up-to-date survey questions for understanding household transport characteristics, the paper focuses on the LSMS surveys conducted in 30 countries after 2010 (see Table 2). These surveys mainly fall into two categories; 12 in of 30 country surveys are from the LSMS collection included in the World Bank Microdata Library. The other 18 country surveys are from other multi-topic household surveys, most of which are used as the data sources for PovcalNet<sup>3</sup> , an online analysis tool for global poverty monitoring, covering more than 1,500 household surveys spanning 1967 – 2018 and 166 economies (138 economies after 2010). Since these surveys are included in the regional collections and are tagged “LSMS” in the Microdata Library searching tool, in this report they will be labelled as “LSMS-tagged” surveys<sup>4</sup> .

> 2 LSMS program website: https://www.worldbank.org/en/programs/lsms. The data of 129 LSMS studies can be downloaded by external users through https://microdata.worldbank.org/index.php/catalog/lsms.

> 3 PovcalNet program website: http://iresearch.worldbank.org/PovcalNet/home.aspx.

> 4 Six “LSMS-tagged” countries surveys used to be supported by the LSMS program before 2010.

4