rafmacalaba/datause-encoder-data huggingface dataset:
manufacturing sector is the most internationalized, while the creative content sector is the most domesticbound. The majority of the surveyed firms are also quite globalized in terms of international trade. In the high-tech manufacturing and creative content sectors, most of the firms are exporting abroad. However, only half the KIBS firms have export activities.

The size of firms surveyed is shown in **Tables 3.3** and **3.4** . Overall, the sample includes representative shares of large, medium and small firms in all three sectors. The majority of the firms surveyed are in the small-medium category, in terms of both number of employees (majority employed fewer than 100 employees) and sales revenue (42.4% earned between $3 million to $100 million). The sample does include a sizeable number of large firms, with over one quarter employing over 100 people and around one quarter earning above $100 million.

**Table 3.3: Number of full time employees**

||**High-tech**|**KIBS**|**Media/ creative**|**Overall**|
|---|---|---|---|---|
|Less than 10|7.1|46.7|42.9|32.6|
|Between 10 and 100|35.7|33.3|50.0|39.5|
|Between 100 and 500|35.7|6.7|7.1|16.3|
|500 and more|21.4|13.3|0.0|11.6|
|TOTAL|100.0|100.0|100.0|100.0|

**Table 3.4: Annual Sales Revenue of Firm**

||**High-tech**|**KIBS**|**Media/ creative**|**Overall**|
|---|---|---|---|---|
|Small (Less than S$3 million)|7.7|50.0|50.0|33.3|
|Small-Medium (Between S$3 million to<br>S$100 million)|53.8|20.0|50.0|42.4|
|Large(Above S$100 million)|38.5|30.0|0.0|24.2|
|TOTAL|100.0|100.0|100.0|100.0|

**Table 3.5: Age of company as of 2003**

||**High-tech**|**KIBS**|**Media/ creative**|**Overall**|
|---|---|---|---|---|
|Less than 5 years old|43.8|40.0|57.1|46.7|
|Between 5 to 10 years old|12.5|13.3|35.7|20.0|
|Between 10 to 20 years old|18.8|33.3|7.1|20.0|
|More than 20years old|25.0|13.3|0.0|13.3|
|TOTAL|100.0|100.0|100.0|100.0|

The sampled firms are on the whole quite young, with almost half having been established less than five years ago ( **Table 3.5** ). This is more marked in the case of the respondent firms in the creative content industries, in line with the fact that the creative content industries has started to grow in importance in Singapore only in more recent years.

23
**Table 7: The** **<u>effect of closures on labor market variables, lagged effects</u>**

||(1)<br>Emp|(2)<br>Emp|(3)<br>Wage|(4)<br>Wage|(5)<br>W_days|(6)<br>W_days|(7)<br>W_hours|(8)<br>W_hours|
|---|---|---|---|---|---|---|---|---|
|_CP_<sup>_30min_</sup>|-0.0001|-0.0001|-0.0859***|-0.0695***|-0.0385***|-0.0365***|0.0549***|0.0461***|
||(0.000)|(0.000)|(0.016)|(0.015)|(0.010)|(0.010)|(0.012)|(0.012)|
|_REG_<sup>_30min_</sup>|0.0000|0.0000|-0.0027|-0.0029|-0.0053**|-0.0050**|0.0022|0.0020|
||(0.000)|(0.000)|(0.004)|(0.004)|(0.002)|(0.002)|(0.003)|(0.003)|
|_CP_<sup>_30min_</sup>_1_|0.0001||-0.0644***||-0.0073||0.0335***||
|_(t-)_|(0.000)||(0.012)||(0.007)||(0.010)||
|_REG_<sup>_30min_</sup>_1_|0.0000||0.0021||-0.0007||0.0000||
|_(t-)_|(0.000)||(0.004)||(0.002)||(0.003)||
|Obs.|81,219|81,219|72,254|72,254|79,131|79,131|75,027|75,027|
|R-sq. within|0.004|0.004|0.035|0.034|0.019|0.019|0.012|0.011|
|Nr. Workers|38,823|38,823|36,381|36,381|37,926|37,926|37,397|37,397|
|_Robust standard_<br>_***significant a_<br>_years of schooli_<br>_Hebron district-_<br>_share of workers_|_errors (Hub_<br>_t 1%. All regr_<br>_ng, age, age s_<br>_time effects, l_<br>_employed in_|_er-White met_<br>_essions inclu_<br>_quared, marit_<br>_ocality’s num_<br>_Israel the yea_|_hod) clustered_<br>_de individual fi_<br>_al status, tenur_<br>_ber of fatalities_<br>_r before and sec_|_at individual_<br>_xed effects, ti_<br>_e, urban area_<br>_in the preced_<br>_tor-year effec_|_level in paren_<br>_me effects, sect_<br>_/ refugee cam_<br>_ing quarter, lo_<br>_ts._|_theses; *signif_<br>_or-round dum_<br>_p residence du_<br>_cality’s length_|_icant at 10%;_<br>_mies and a set_<br>_mmies, a set of_<br>_of the West Ban_|_**significant at 5%;_<br>_of controls including_<br>_work place dummies,_<br>_k wall, the locality’s_|

41
9 worsen a country's investment climate. This would also support the view that **FDI** inflows are largely unrelated to balance-of-payments financing, requirements. However, it does stlggest that **FDI** should be treated as endogenous to a country's saving-iinvestment process.

Unfortunately, Turner's technique is seriously flawed. First, with enough substitutability between alternative forms of capital inflows, there may be no bivariate correlation between any of the components and the overall current account financing requ.rement. Indeed, **tliis** is the case for Egypt, Nigeria, and the Philippines in Table 3. Out of 80 regressions, only 12 yield significant coefficients at the 95 per cent confidence level. Second, the bivariate regressions fail to establish causality.<sup>6</sup> A country's current account deficit is financed by foreign saving. Just as national saving flows through several channels before reaching the investor, so too does foreign saving. One channel through which foreign saving flows is **FDI.** As Michael Dooley (1990) points out, foreign direct investment constitutes a flow-of-funds concept and records a financial flow. Financial flows from saving to investment can take many forms, some of which are virtually perfect substitutes. For example, debt flows can become equity flows when tax reforms change incentives without affecting capital formation in any way. In the same way, foreign debt flows can bect ne **FDI** flows without causing any change in capital formation. Because of the high degree of substitutability and fungibility in such financial flows, flow-of-funds data are seldom useful for economic analysis (Dooley 1990, p. 75). In other words, it may be difficult, if not impossible, to determine whether **FDI** is autonomous or accommodating, whether it increases capital formation or provides additional balance-of-payments financing, or whether it provides neither because an increase in **FDI** simply offsets a reduction in another type of capital flow. The evidence presented so far suggests that **FDI** does not provide additional balance-of-payments financing. Therefore, I now turn to the question of whether **FDI** increases capital formation or simply substitutes for other types of international capital flows.

# **3.2 A** Test **for Additional Investment**

Whether or not substitutability and fungibility are so high that **FDI** flows provide no relevant economic information at all is an empirical question. The estimates of the current account financing requirement equation suggests that **FDI** is either a close substitute for at least one other type of capital flow or is indeed autonomous. In an attempt to discriminate between

**'While I now address the first problem, the** second **is beyond the scope of the present paper but clearly high on any agenda for future research** into **this question. Indeed, a useful extension would** be **to conduct causality tests using vector autoregression techniques.**
all projects. But it is far more important in some projects than in others and it may be of interest to the Bank to gain an approximate idea about the principal determinants of this uncertainty.”<sup>34</sup> Among these determinants, Hirschman listed the ability to completely map out the project upon its launch (for example, electricity projects can more easily be mapped out than agricultural improvement projects); the direct link between the new supply produced by the project and the actual demand to absorb it (a power station will present different degrees of uncertainty depending on the level of economic development of the region where it is established); and the degree to which economic, social and political change can interfere with a project’s implementation (e.g. to what degree the rise in cost of labor affects irrigation projects, or to what extent it is possible to insulate projects from political interference).<sup>35</sup> Notes taken during his trips into the field also elaborate on the difficulty of computing and quantifying benefits in several types of projects. Hirschman, referring to Ethiopia, underscored the case of telecommunications: “how do you compute [the] benefits of easier communications, [the] ability of scarce managerial talent to spread itself over wider areas, better information of market information, etc.”<sup>36</sup> Hence, these kinds of projects are often ignored, even if they may be the most productive.

This change in perspective called for a corresponding change in the Bank’s behavior. The Bank, Hirschman wrote, should avoid the “air of pat certainty” that emanated from project prospects and instead expose the uncertainties underlying them, exploring the whole range of possible outcomes. Moreover, the Bank should take into account the distributional and, more generally, the social and political effects of its lending. As a matter of fact, an excessively uneven distribution of the benefits of a project, besides jeopardizing political and social stability, was likely to create threats to the project itself. A new road project, for example, would open up new land for agricultural exploitation. According to Hirschman, the Bank should have explicitly addressed the issue of ownership of that land, with the aim of spreading the benefits of the project to the largest possible population. Of course, Hirschman was aware that spreading the benefits might require time: frequently, a project initially contributes to the development of the already more advanced segments of a country, and only in a second phase does it reach larger segments of the population and territory. This trickle-down mechanism

> 34 Albert O. Hirschman, “A Study of Selected World Bank Projects – Some Interim Observations,” August 1965, World Bank Hirschman Folders Vol. 1.

> 35 Albert O. Hirschman, “A Study of Selected World Bank Projects – Some Interim Observations,” August 1965, World Bank Hirschman Folders Vol. 1.

> 36 “Ideas – Miscellaneous,” no date, Albert O. Hirschman Papers, Box 57, Folder 2.

17
significant improvement in household food security, judged by increased consumption of high-value animal proteins and lowered incidence of food shortages. Beyond food security, we find no generalizable impact on household asset holdings, plausibly due to the short term nature of this analysis, but we do find significant improvements in subjective measures of attitudes toward gender responsibilities among the heads of EPAG households. These outcomes provide some evidence that norms are shifting at the household level in ways that may benefit other female household members. Taken together with the results on food security, this suggests that the program had positive, though small, impacts on the households of treated individuals.

We end with a mention of the limitations of the study. First, as mentioned above, is the issue of selective attrition from the sample. Second, given the short time horizon of this study, in which follow up data was collected just one month after the conclusion of the first round of the program (which was seven months after the conclusion of the classroom-based training), questions remain about the persistence of the effects found and the potential for further impacts to emerge. The research design of this evaluation, in which the control group took part in the second round of the EPAG program, precludes a rigorous experimental comparison of treated and untreated individuals after the midline survey. However, an endline survey was conducted after the second round of the EPAG program as per the timeline depicted in Figure 1. Examination of this endline survey data will permit a descriptive analysis of the outcomes of the first group of trainees 12 months after they completed the EPAG program, as well as examination of the outcomes of the second batch of trainees. The second round included not only the control group from this impact evaluation but also newly recruited participants who were offered brief basic literacy and numeracy training program prior to program entry. Work is already underway to design and implement the third round of EPAG, with a substantial redesign of the Job Skills track, an emphasis on reaching younger girls with lower literacy, and expansion to communities outside of Monrovia. If the high success rates found in this study are replicated for these future cohorts, the EPAG program should serve as a model for policy makers in Africa and the world seeking to improve lives and livelihoods of all youth, male and female.

25
**Table 4B: Rosenbaum Sensitivity Analysis for the Impact of Family Background on Parents' Years of Schooling** **<u>(primary schooling as the threshold for grandparents' education)</u>**

|**Gamma**|**Non farm, Low**<br>|**Edu**|**Farm, High E**<br>|**du**|**Non farm, Hig**<br>|**h Edu**|
|---|---|---|---|---|---|---|
||**Mantel-Haenszel**<br>|**P value**|**Mantel-Haenszel**<br>|**P value**|**Mantel-Haenszel**<br>|**P value**|
||**statistics**||**statistics**||**statistics**||
|**Abadie and Imbens**<br>**Matching**|||||||
|_Mothers_|||||||
|1.0|0.75|0.00|1.00|0.00|1.63|0.00|
|1.3|0.38|0.02|0.63|0.00|1.38|0.00|
|1.5|0.13|0.17|0.50|0.00|1.25|0.00|
|1.8|-0.13|0.65|0.25|0.01|1.00|0.00|
|2.0|-0.25|0.88|0.13|0.18|0.88|0.00|
|_Fathers_|||||||
|1.0|0.38|0.01|0.88|0.00|0.50|0.00|
|1.3|0.00|0.47|0.63|0.00|0.25|0.07|
|1.5|-0.13|0.87|0.38|0.00|0.13|0.25|
|1.8|-0.38|1.00|0.25|0.00|0.00|0.61|
|2.0|-0.50|1.00|0.13|0.07|-0.13|0.80|
|**Biased Corrected Radius**|**Matching**||||||
|_Mothers_|||||||
|1.0|1.00|0.00|1.00|0.00|1.00|0.00|
|1.3|0.50|0.04|0.50|0.00|1.00|0.00|
|1.5|0.00|0.23|0.50|0.00|0.50|0.01|
|1.8|0.00|0.72|0.00|0.39|0.50|0.09|
|2.0|-0.50|0.91|0.00|0.86|0.00|0.21|
|_Fathers_|||||||
|1.0|0.50|0.07|0.50|0.00|0.50|0.00|
|1.3|0.00|0.77|0.50|0.00|0.50|0.09|
|1.5|-0.50|0.97|0.00|0.02|0.00|0.27|
|1.8|-0.50|1.00|0.00|0.67|0.00|0.63|
|2.0|-1.00|1.00|0.00|0.97|0.00|0.81|

**_Notes:_**

1. Mantel-Haenszel test statistics gives the upper bound under the assumption of overestimation of treatment effects; 2. Gamma=1 implies the absence of unobserved selection bias and both matched individuals have the same probability of participating; 3. Gamma=2 implies the two individuals are identical on matched covariates but one might be twice as likely as the other to receive the treatment because they differ in terms of unobserved covariates.
However, the location of firms differs across industries: e.g., firms in the leather/footwear and pharmaceuticals industries are more strongly concentrated in Dhaka.

The survey collected a wealth of information on production variables, firm characteristics, and aspects of the business environment.<sup>8</sup> Appendix Table 2 shows summary statistics for the main production variables across industries. Firm characteristics differ significantly across industries which is not surprising given the differences in production processes, in the nature of the materials, and in the outputs produced across industries. Firms are largest in the garments industry and smallest in the leather/footwear industry according to the median number of workers. Average sales per firm are highest in the pharmaceuticals industry. Sales per worker are highest in the food industry and lowest in the garments industry. Relative to the average capital stock, real investment is highest in the pharmaceuticals industry (where it represents about 12% of the capital stock) and lowest in the leather/footwear industry (where it represents only 4% of the capital stock). Average wages per worker are highest in the pharmaceuticals industry and lowest in the leather/footwear industry. The most striking finding in Appendix Table 2 is the large degree of heterogeneity across firms within any industry, seen in the coefficient of variation of the production-related variables, which is often much larger than one, particularly for investment and capital. In any given industry, at a point in time, there are very different firms - some smaller, other larger, some highly capital-intensive, other labor-intensive - producing similar types of products. Such heterogeneity results in heterogeneity in performance.

# **_2.2. TFP Estimation_**

Firm TFP measures are not observable to the researcher, however they can be obtained as residuals from a production function. For each of the five industries, we estimate the following Cobb-Douglas production function, where output Yit is produced by a combination of three inputs: labor _L_ it, materials Mit, and capital Kit:<sup>9</sup>

> 8 The survey questionnaire shares in common many questions with the World Bank’s Investment Climate Surveys (http://iresearch.worldbank.org/ics/jsp/index.jsp) and is available upon request. 9 For simplicity, we omit the industry superscript j from the variables.

5
**o** z **0 r >** _co,_ **_C,,_** 0

|**Table B8. Informal Income and Expenditure**<br>from Informal Sector Surveys conducted by the author|_<25_hours per<br>week|245 hours<br>per week|Entire<br>Sample|Entire<br>Sample|Entire<br>Sample<br>|Entire<br>Sample<br>|
|---|---|---|---|---|---|---|
||||||Stre<br>|et Services &<br>|
||Taxicabs|Taxicabs|Taxicabs|Kiosks|Markets|Vendors<br>|
|Number of Observations (actual number may vary depending on the<br>question)|99|101|308|245|137|80|
|Written records|||||||
|Yes|28.9|31.7|26.8|74.3|32.1|28.8|
|Daily profit...|||||||
|Overall|_5.1_|13.1|8.8|7.0|2.4|1.8|
|Estimated value of vehicle (thousands togrogs)|||||||
|Mean|4482.4|5759.6|4979.4|na|na|na|
|Nightly garage expenses (thousands togrogs)|||||||
|Mean|1.0|1.2|1.1|na|na|na|
|Weekly expenditure on ...|||||||
|inventories.(thousands<br>togrogs)<br>|na|na|na|103.8<br>|na|na|
|transportation (thousands togrogs)|na|na|na|_5.0_<br>|na|na|
|other non-wage expenses (thousands togrogs)|na|na|na|3.9|na|na|

rn _ **> 0 0**<sup>co</sup> CL **R** P **_3._ a** I **0** **_0_ C,** m .w
|Attended >19d of therapy|Coef.<br>Std. Err.<br>(7)<br>(8)<br>-0.019<br>[.043]<br>-0.005<br>[.005]<br>0.097<br>[.062]<br>-0.008<br>[.007]<br>0.013<br>[.008]<br>0.029<br>[.027]<br>-0.012<br>[.015]<br>-0.062<br>[.025]**<br>0.000<br>[.008]<br>-0.003<br>[.024]<br>0.000<br>[0000]<br>-0.002<br>[.001]*<br>0.000<br>[.001]<br>0.020<br>[.029]<br>-0.006<br>[.026]<br>0.028<br>[.011]**<br>529<br>0.637<br>0.239<br>0.005<br>ndicator for attrition<br>pliance on the same<br>sions but are omitted|
|---|---|
|>8d of therapy|Std. Err.<br>(6)<br>[.027]<br>[.003]<br>[.038]<br>[.004]<br>[.005]***<br>[.017]<br>[.008]<br>[.012]<br>[.004]<br>[.015]<br>[0000]<br>[.001]<br>[0000]<br>[.015]**<br>[.013]**<br>[.007]**<br>529<br>0.905<br>0.299<br>0.011<br>S regression of an i<br>n indicator for com<br>cluded in all regres|
|nt variable<br>Attended|Coef.<br>(5)<br>-0.001<br>0.001<br>-0.040<br>0.003<br>0.019<br>-0.016<br>-0.008<br>-0.008<br>-0.001<br>-0.011<br>0.000<br>-0.001<br>0.000<br>0.037<br>0.027<br>0.015<br>s from an OL<br>gression of a<br>efects are in<br><0.1|
|Depende<br>Cash Received|Coef.<br>Std. Err.<br>(3)<br>(4)<br>0.000<br>[.011]<br>-0.002<br>[.002]<br>-0.007<br>[.018]<br>0.005<br>[.002]***<br>0.000<br>[.002]<br>-0.002<br>[.006]<br>-0.001<br>[.004]<br>0.001<br>[.004]<br>-0.005<br>[.002]**<br>-0.004<br>[.009]<br>0.000<br>[0000]<br>0.000<br>[0000]<br>0.000<br>[0000]<br>0.003<br>[.008]<br>0.003<br>[.004]<br>0.003<br>[.004]<br>499<br>0.980<br>0.162<br>0.620<br> coefcients and standard error<br>tandard errors from an OLS re<br>atment groups only. block fxed <br>vel. *** p<0.01, ** p<0.05, * p|
|Unfound|Coef.<br>Std. Err.<br>(1)<br>(2)<br>-0.006<br>[.017]<br>-0.009<br>[.018]<br>-0.010<br>[.018]<br>0.001<br>[.001]<br>-0.017<br>[.020]<br>-0.002<br>[.002]<br>0.000<br>[.003]<br>0.004<br>[.008]<br>0.000<br>[.005]<br>0.011<br>[.006]*<br>-0.004<br>[.003]*<br>0.009<br>[.007]<br>0.000<br>[0000]<br>0.000<br>[0000]<br>0.000<br>[0000]<br>0.001<br>[.009]<br>0.012<br>[.007]*<br>0.000<br>[.003]<br>999<br>0.078<br>0.098<br>0.047<br>e survey rounds and report the<br>) report the coefcients and s<br> assigned to the respective tre<br> clustered at the individual le|
||Baseline covariate<br>Assigned to therapy only<br>Assigned to cash only<br>Assigned to therapy & cash<br>Age<br>Married or living with partner<br># children under 15 in household<br>Years of schooling<br>Cognitive skills, z-score<br>Health index (0-6)<br>Symptoms of depression and distress, z-score<br>War experience index (0-14)<br>Summary index of income measures, z-score<br>Savings stock (USD)<br>Hours/week working in potentially illicit activities<br>Hours/week working (total)<br>Summary index of antisocial behaviors, z-score<br>Summary index of self-control skills, z-score<br>Patience in game play index (0-6)<br>N<br>Mean of dependent variable<br>R-squared<br>P-value for test of joint signifcance<br>_Notes:_ Columns (1)–(2) pool all endlin<br>on the baseline covariates. Columns (3)–(8<br>covariates, restricting the samples to people <br>from this table. Robust standard errors are|

ix
Table 6: Parental Stimulation & Home Play

||(1)<br>Parental<br>Stimulation<br>(index)|(2)<br>Home Play<br>(index)|
|---|---|---|
|Treatment|0.171<sup>_∗∗∗_</sup>|0.077<sup>_∗_</sup>|
||(0.054)|(0.045)|
||[0.006]|[0.096]|
|Observations|3760|3763|
|Control mean|0.000|0.000|

_Notes:_ *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.10. OLS estimates at the target child-level are presented using outcomes reported by the caregiver at endline. Standard errors clustered at the community level are shown in parentheses. All regressions include district fixed effects. The parental stimulation index in column (1) was constructed using 12 questions that asked parents about activities they did with the target child, including reading books, singing songs, telling stories, and playing games. The home play index in column (2) was constructed using 7 questions about items that the target child plays with at home, including homemade or store-bought toys and musical instruments. These indices were constructed following Kling et al. (2007). Westfall and Young (1993) _p_ -values are shown in square brackets, where 10,000 replications were used. Estimates are weighted to give each community an equal weight in the analysis. Covariates are not included in the regressions.

# **6.4 Parental Time Investments**

We explore the impacts of the DICIPE program on other outcomes related to parental time investments, motivated by the large bodies of work that highlight the role of parental investments in complementing early childhood interventions (Cunha and Heckman, 2007; Doepke et al., 2019; Attanasio et al., 2020; Ravindran, 2021; Bos et al., 2024) or education interventions in general (see, for example, Yedomiffi 2024; de Walque and Valente 2023; Andrabi et al. 2017; Cerdan-Infantes and Filmer 2016; Avvisati et al. 2013). In our setting, parental investments can be directly linked to program parenting education sessions, as these meetings were used to provide information to teach parents how to enhance their children’s growth and development. Indirectly, the program also induced parents to be significantly engaged in the preschool activities.

First, we show that parents in treated communities improved their parental practices related to stimulation and home play relative to parents in control communities. We present our results in Table 6, where we study two common indices of parental practices at home constructed using the UNICEF Multi-Indicator Cluster Surveys (MICS) and 29