rafmacalaba/datause-encoder-data huggingface dataset:
column 1) on the day of the transfer is mechanical, and confirms that direct deposits were made as intended. Cash recipients deposited MK 1,637. These immediate deposits account for 6.5 percent of the cash transfer. Columns 2-4 indicate some additional deposits over time among the cash recipients, but none by the direct deposit recipients.

Results in Panel B indicate that those defaulted into saving immediately withdrew most but not all of the transfer. On the day of the transfer, savings default recipients withdrew MK 17,937 more than cash recipients – in other words, they withdrew 72 percent of the transfer. As expected, cash transfer recipients’ withdrawals were close to zero.

Panel C reports net deposits (changes in bank balances).<sup>7</sup> On the day of the transfer, recipients whose transfer was directly deposited into their account have net deposits that are MK 5,224 higher than the cash transfer group. Initially, then, the savings default induced recipients to keep 21 percent of their transfer in the bank. The total amount saved by the savings default group is nearly constant over the two weeks following the transfers. Because deposits increase for the cash recipients, the initial effect of the savings default is significant after seven days (column 3) but not after 14 days (column 4).

The initial differences in savings between participants who received transfers in cash compared to those defaulted into savings are striking because the experiment design ensured similar conditions for the two groups. Yet, despite minimal transaction costs, directly depositing the transfer into the account induced 4.3 times higher savings on the day of the transaction (Panel C, column 1) and 2.9 times more savings a week later (Panel C, column 3).<sup>8</sup> In total, the cash transfer group averaged MK 5,524 more cash on hand on the day of the transfer, and MK 3,737 more one week later. The savings default treatment shifted assets to formal bank accounts and, as we show in the next section, away from other types of savings. This evidence of a positive effect of the savings defaults on bank savings motivates the examination of the composition of expenditures in the next subsection.

# **5.2 Household survey outcomes**

Data from the household survey confirm that the savings default treatment shifted funds towards NBS bank accounts and away from other types of saving. We follow the structure of our survey instrument (adapted from Malawi IHS-3) in categorizing savings as formal financial savings (accounts at NBS or other banks), informal savings (village savings groups,

> 7Reporting net deposits rather than the level of bank balances is a limitation of the administrative data we were able to obtain from NBS.

> 8While the focus is on assessing the impact of default savings, we note that recipients of the large transfer compared to the control group saved a significant portion of the transfer in the bank for more than two weeks and that higher net deposits (relative to the control group) persist for 90 days after the initial transfer (result not shown).

11
|Employment growth rate (per||||(0.106)<br>-0.321|(0.106)<br>-0.309|(0.106)<br>-0.299|
|---|---|---|---|---|---|---|
|annum, log difference)||||(0.313)|(0.313)|(0.310)|
|Bought assets last year Y:1 N:0||||0.129<br>|0.131<br>|0.128<br>|
|||||(0.123)|(0.124)|(0.125)|
|GDP per capita growth rate (%,<br>|||||0.014<br>|-0.029<br>|
|annual)|||||(0.027)|(0.040)|
|Primary enrollment gender parity|||||-0.167**|-0.149**|
|index|||||(0.071)|(0.067)|
|% of women in 35-64 years age|||||0.322|0.066|
|group|||||(0.216)|(0.239)|
|Total population (logs)|||||-3.198**|-4.096**|
||||||(1.421)|(2.062)|
|Bank credit (ratio of GDP)*GDP<br>||||||-2.316<br>|
|per capita (logs)||||||(2.020)|
|Bank credit (ratio of||||||-0.527|
|GDP)*Number of workers (logs)||||||(0.327)|
|Bank credit (ratio of GDP)*Age of||||||0.626|
|firm (logs)||||||(0.657)|
|Bank credit (ratio of GDP)*Labor<br>productivity (logs)||||||-0.364*<br>(0.204)|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|14,275|14,275|14,275|14,275|14,275|14,275|
||**Panel B**|**: Marginal**|**effects**||||
|Bank credit (ratio of GDP)*Credit|-0.247**|-0.239**|-0.356***|-0.351***|-0.375***|-0.267**|
|bureau present Y:1 N:0|(0.122)|(0.120)|(0.111)|(0.112)|(0.105)|(0.106)|
|Controls(as above)|Yes|Yes|Yes|Yes|Yes|Yes|

All coefficient values are log odds ratios obtained from logit estimation. Huber-White robust standard errors clustered on country in brackets. *** (1%), ** (5%), * (10%).

43
for defining weights, including value added- or export-weighting for D and output-weighting for N. As argued in Fally (2012), a natural weight for the upstreamness index _D_ is value added, and final production by sector-country for index _N_ . We do the same for _N_<sup>_∗_</sup> and _D_<sup>_∗_</sup> .

In order to best capture the relative position of countries on international production chains, our preferred aggregate is an export-weighted average of index _Dik_ . This export-weighted average is the statistic calculated in Antr`as et al. (2012) to document countries’ comparative advantage along production chains. Formally, we define _DXi_ by country with the following: where _Xik_ represents country i’s exports of product k.<sup>35</sup>

**Descriptive statistics:** We calculate these indices using the IDE-JETRO data from 1975, 1990 and 2000. In order to present this information succinctly, we offer two aggregates, _Di_ and _Di_<sup>_∗_,where the aggregates are calculated as averages weighted by sector</sup><sup>_k_value added.We</sup> report the aggregate measures below in graphical form in order to show cross-country variation and within country variation over time.

In part A) of Figure 3, we see the results for all tradable goods. There is some variation in the levels and trends of the _D_ across countries. While the cross-country variation is, in some cases, interesting in its own right, the key takeaway is the absence of a consistent story in the simple measures that are neutral with respect to national borders. By contrast, one can see a general pattern of growth in the _D_<sup>_∗_</sup> measures (upstreamness in border crossings), which is partially driven by decreases in trade costs but may also reflect a movement upstream. Both indexes increase in 2000 for most countries (with Japan and the US as notable exceptions), which suggests that the countries in question moved into upstream positions within internationalizing production chains.

The electronics sector is particularly interesting over this period. Complex international production chains are, anecdotally, an important phenomenon in East Asian manufacturing. This is even more notably so within the electronics sector. Moreover, there has been important growth in the region’s trade in electronics, which constituted only 8% of Asian exports in 1975, and 34% in 2000. In part B) of Figure 3, we report results of our calculations for the electronics sector only, by country and by year.

> 35While a VA-weighted average of _D∗_ can also capture the relative position on international supply chains, an advantage of the measure _DXi_ over _D_<sup>_∗_</sup> is that it is less sensitive to the country’s level of openness. For instance, we may be concerned that Singapore has high values of _D_<sup>_∗_</sup> mainly because of low trade costs rather than being relatively more upstream. As our sole interest is in countries’ relative position along the chain, a measure that conflates position with openness is problematic. The index _DXi_ puts more emphasis on the composition of exports than openness.

40
# outdetect set-up:

Normalization: Yeo and Johnson (2000) Z-score: (x-median)/q a=3 Outlier detection target: top and bottom (12447 observations are used)

## Incidence of outliers:

||Freq.|Percent|Share|
|---|---|---|---|
|Bottom|82|0.66|67.77|
|Top|39|@.31|32.23|
|Total|121|@.97|100.00|

Statistics for raw and trimmed pce:

||Raw|Trimmed|
|---|---|---|
|Summary<br>stats|||
|Mean|897.88|828.38|
|Median|648.73|648.35|
|SD|3926.19|641.13|
|CV(%)|437.27|77.40|
|IQR|550.12|543.91|
|Inequality|||
|Gini|@.4063|0.3565|
|MLD|@.2839|0.2076|
|Theil|@.5057|@.2232|
|cv2|9.5596|0.2995|
|A(@.125)|@.0541|0.0273|
|A(1)|@.2472|@.1875|
|A(2)|®.3776|0.3207|
|p90/p10|4.8185|4.7558|
<!-- Start of picture text --> Correlation between sectoral productivity and change in employment share Correlation between sectoral productivity and change in employment share<br>> Malawi > Ghana<br>Ss= 1970-1980 1980-1990 S= 1970-1980 1980-1990<br>s Co C) = cS) TM<br>ee Bota, 0, ee be o| Gate:EB 106. OP ' 8 Beta:fie «30 om Beta:ee, 980<br>£ ° F ° 2 ° ° :<br>S Sot <<br>eq (s) LD) = S<br>3 “2 “A 0 4 “2 al No” 4 S A =05 0 05 4d A 05 0 05 q<br>3 8<br>s 1990-2000 2000-2010 a 1990-2000 2000-2010<br>es3ec ”*°)= Be.9nia albfyfalfs wert aa 8.88na minGat aaa”S3 ~7) Urbanization:|eConftict:. low med o7) aContact:Urbanization:2 tw med =<br>Bea /|icon .° | an Ort So icene-<br>Pes 4 . ©r ; 7 ! . i=. Uy° se<br>5 +2 “1 0 A +2 ol o A 3 al 05 0 05, A al =05- 0 05 A<br>o . ts] .<br>z Change in employment share zZ Change in employment share<br><!-- End of picture text -->
Table 6: Heterogeneous impact of PAL on learning by household expenditure

|_Outcome =_|(1)<br>Math<br>(z-score)|(2)<br>Spanish<br>(z-score)|(3)<br>subject<br>(z-score)|
|---|---|---|---|
|In-kind|-0.001|0.051|0.062|
||(0.071)|(0.073)|(0.067)|
|Cash|-0.130|-0.079|-0.085|
||(0.081)|(0.083)|(0.076)|
|In-kind x poor|-0.089|-0.151|-0.181**|
||(0.091)|(0.105)|(0.090)|
|Cash x poor|-0.101|-0.156|-0.144|
||(0.108)|(0.119)|(0.102)|
|Poor|-0.075|-0.056|0.004|
||(0.079)|(0.096)|(0.079)|
|State, Grade, & Year FE|YES|YES|YES|
|Pre-program controls|YES|YES|YES|
|Observations|11,006|11,006|10,432|
|**_Effect size:_ In-kind - Cash**|0.13|0.13|0.15|
|**_H 0 :_ In-kind = Cash (p-value)**|0.07|0.07|0.03|
|**_Effect size:_ In-kind x poor - Cash x poor**|0.01|0.01|-0.04|
|**_H 0 :_ In-kind xpoor = Cash xpoor (p-value)**|0.89|0.95|0.64|

Notes: *** p<0.01, ** p<0.05, * p<0.1 (1) Standard errors are clustered at the village level.

(2) Pre-program controls include gender, age, and indicators for whether the household head is married, whether the house has running water, and whether the closest school offers morning shift. (3) "Poor" is an indicator variable equal to one for households with expenditure per capita below median.

35
model in differenced form.<sup>7</sup> This also has the advantage of eliminating any fixed unobserved sous-branche specific effects. The output supply and input demand equations that were estimated are given below: <!-- Start of picture text -->
- bet  t/  (p/p  t-  t  tt  2 +/p  (  t  t-l  e) l t/  t-<br><!-- End of picture text -->

**6** An example of how domestic output price indices are calculated will illustrate this point. The Ministere du Plan et de l'Industrie makes available the following set of information on output at the three digit level: a) P¢Q b) _P?1lQt-_ and c) _P?1lQ¢._ From this information, the price index is bX (W i __)Wtt_ + el calculated as:

Because Qe is fixed, the ratio _P?/Ptd_ will vary only with changes in prices from one year to the next.

However, this index can not be chained since

**pd** **_pd pd_** P-x 1 t- _-2_

The price indices for exports and imports at the sous-branche level available from the Ministere du Plan et de l'lndustrie are also calculated in this fashion.

t The only limitation (not a serious one in our estimation) is that we are forced to employ a **_t_** _ **__** __price ratio in lieu of a price difference. For example, we must use Pt/Pt I as a measure of how domestic prices have changed relative to export prices instead of

**pd pd** pe pd lp - 10 -

**II**
Bureau of Education (IHE, 1984; 1986). Data on school curicula was also coded from official correspondence and naional publications that were received directly by a team of researchers of which the author was a member. Of the over 150 national ministries of education to which short questionaires were sent in 1985, approximately half sent replies, of varying length and detail, with infofmation on curricular policies and plans. A detailed listing of all the sources used in this study can be found elsewhere (Benavot and Kamens, 1989; Kamnens and Benavot, forthconiing).

Two basic pieces of inf3rmation were coded from these sources: first, a list of the subjects taught during the elementary school cycle in each country; and second, the number of school periods or school "hours" devoted to each subject during the primaly school cycle.<sup>2</sup> This coding procedure produced two types of variables for each subject: first, a dichotomous variable which simply noted whether or not a particular subject was taught in the official curriculum; and second, a continuous interval variable indicating the proportion of total school periods or "school hours" that each subject was taught during the first six years of primary education. Data on these variables were then grouped according to historical period (e.g., 1945-1969; 1970-1986) depending on when the coded curricular timetable was in effect.

In addition to these two curricular measures, a third, more "refined" indicator of official curricular emphases was developed. Instead of measuring the .-elative emphasis of each subject in the national curriculum (i.e., the proportion of total curricular periods devoted to mathematics or science education), it estimated the actual amount of time (in annual hours) that pupils were exposed to instruction in each subject area during each year of the primary school cycle. Recent reports (e.g., McKnight et al., 1987: 53; UNESCO, 1986) as well as past ones (e.g., Dottrens, 1962) 2. Each curricular subject listed in an official timetable was coded into one of eight general categories: Combined Language education (reading and writing in national, official, local, foreign and/or classical languages), Mathematics, Sciences, Combined Social Sciences (social studies, history, geography, civics), Combined Moral and Religious Education, Aesthetic Education (music, arts, drawing, dance), Physical Education, and Pre-Vocational Education or P tical Subjects (manual training, industrial arts, agriculture, domestic science, vocational education and business). Curricular time in elective subjects and in various "peripheral" activities (e.g., hygiene, recreation, extracurricular activities, recess, miscellaneous) were coded as "other" and not incorporated in the present study. A combined category means that instruction was offered in either one or all of the specific curricular topics listed in that category.
10 men. The only type of training that women are less likely to receive is employer provided practical training.

Arguably, these differences are not gender related but a consequence of the occupations themselves. However, even for the same occupation, bookkeeper, nearly twice as many women receive "any kind of training" as men; they are about three times more likely to receive multiple types of training, forty percent more likely to receive institutional training, but only half as likely to receive practical training as men. The larger amount of training received by non SENA-trained women than men may be accounted for by a process by which women are substituting structured training for labor-market experience. Since, for any age cohort, women tend to have fewer total years of full-time work experience than men (or a more intermittent pattern of labor-market participation), they possess fewer skills acquired during their occupational experiences or the skills they did acquire are more depreciated relative to those of male workers.<sup>8</sup>

# Worker-financed Institutional Training:

The survey provided information on which institutional training courses were paid for by the workers themselves: this is presented in Table 6. For all three major training groups (with the exception of plumbers), trainees are more likely to pay for courses provided in training institutions than to obtain it free.

The generally higher proportion of trainee-financed institution training by women is partially occupationally related, but also no doubt reflects a greater willingness to purchase structured training as a substitute for on-the-job experience and learning by doing.

8 One way to test for this hypothesis would be to look for fewer years of total work experience for women than men in the same age groups. Unfortunately, the survey does not identify actual years in the labor-market.
much higher USD 60,056 for the firms in HICs. The latter is 2.4 times the former, and the difference between the two is statistically significant at the 1 percent level.

In the regressions that follow, we use the log of labor productivity defined at the firm-level as our dependent variable as it helps to minimize the impact of outliers and extreme values. The productivity gap or the mean difference in labor productivity between UMICs and HICs can be computed in two ways. First, it equals the mean value of the log of labor productivity of firms in the UMICs minus the same for the HICs. This is the productivity gap in log points. Second, we can compute the percentage difference in the mean level of labor productivity (in USD levels). This is obtained as the exponential of the log difference obtained in the first step minus one and multiplied by 100 to convert to percentage figures. Unless stated otherwise, all productivity gaps in percentage terms are as described here.

The mean value of the log of labor productivity equals 10.96 (log points) in HICs and 10.1 (log points) in UMICs. Thus, labor productivity in the UMICs is approximately 57.5 percent lower than in the HICs. This productivity gap is significant at the 1 percent level. Results are qualitatively similar when looking at the median level of labor productivity.

Apart from labor productivity, there are other significant differences between firms in UMICs and the HICs (see Table 2). One important difference relates to firm-size as measured by the log of the number of workers at the firm at the end of the last fiscal year ( _Employment_ ). Firms in UMICs tend to be relatively large as compared to firms in the HICs. The mean difference in employment level (in logs) equals 0.169, implying that employment in firms located in UMICs is almost 18% higher than in firms in the HICs; the difference is statistically significant at the 1 percent level. In terms of the sectoral composition, the share of firms that belong to the manufacturing sector is significantly lower in UMICs compared to the HICs (38.6 vs. 44.3 percent, 8
not addressed. Pollution impacts also block part of the potential multiplier effect that bottomup growth would produce. Growth spillovers from the bottom to the average are strong however, suggesting that pollution-poverty environments may have strong negative impacts on the wider urban economy. Jointly, these inferred mechanisms suggest that a bottom-up approach to growth can help reduce the likelihood of pollution-poverty trap scenarios and even later on remains a no-regret strategy for growth as it induces positive spillovers.

# **5.3.3 Economic significance**

Pollution damages account for considerable loss. Using the converged IRF impact, and using the 2017 dollar conversion rate, we can draft the following crude impacts of 10% country wide pollution increase based on the average expenditure levels per distinguished household group. We use the income 2014 values, and extrapolate to 2017 to match our conversion rate, by compounding the average growth rate observed per household group. Table 6 in the appendix summarizes the per capita expenditures used for our calculations.

Using 2014 population estimates from INDO-DAPOER, together with the average local population growth rates, we would see approximately 83,104,069 people living in heavily polluted areas in 2017. Another 47,463,131 people live between 6 and 10 PM2 _._ 5.<sup>10</sup> Weighing the effect on expenditures on pollution by population, the total damages across all households of a 10% pollution increase would be over 3 billion dollars. Of that, approximately half a billion dollars are lost expenditures of poor households. Various factors can further add to this number in the future, including continued growth in urban population, income and pollution levels. The average pollution level in 2014 in heavily polluted areas was 21.75 according to our aggregated sensor estimates, and the .95 percentile is at 26.98, showing that a 25% increase in the average urban area can still occur. In addition, we look at household expenditures that constitute only part of GDP, and thus capture only part of the potential economic damages. We do not model the potential direct and indirect impacts on other components of GDP. Opportunity costs related to diverting government expenditures to health-related issues while social returns to investment might be higher elsewhere in an unpolluted economy may be another hidden cost. Without intervention the damages would run into the multi-billions over the course of only a few years.

> 10As a reference, the United Nations put the total Indonesian population at 261,115,456 in 2016.

29
Policy Research Working Paper 5433

# **Abstract**

This paper analyzes the impact of international remittances on poverty and household consumption and investment using panel data (2000 and 2007) from the Indonesian Family Life Survey. Three key findings emerge. First, using an instrumental variables approach to control for selection and endogeneity, it finds that international remittances have a large statistical effect on reducing poverty in Indonesia. Second, households receiving remittances in 2007 spent more at the margin on one key consumption good—food—compared with what they would have spent on this good without the receipt of remittances. Third, households receiving remittances in 2007 spent less at the margin on one important investment good—housing—compared with what they would have spent on this good without the receipt of remittances. Households receiving international remittances in Indonesia are poorer than other types of households, and thus they tend to spend their remittances at the margin on consumption rather than investment goods.

This paper—a product of the East Asia and Pacific Region and the Development Prospects Group, Development Economics Department—is part of a larger effort in the department to understand the impact of remittances on poverty and economic development in the developing world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at radams@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team