rafmacalaba/datause-encoder-data huggingface dataset:
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households were excluded because of missing or incomplete data. The analysis is therefore based on data from 727 households.6 As noted above, total income for each household was divided into five sources:

- (1) Agricultural Includes net income in agriculture plus return to own agricultura **l** abor;

- (2) Non-fari Includes non-farm wage earnings and profits from non-farm enterprises;

- (3) Livestock Includes net returns from livestock (cattle, poultry); - (4) Rental income Includes rents received from ownership of assets (land, machinery, water)

- (5) Transfer Includes remittances from abroad, internal remittances, pensions (government) and zakat (payments to poor).

Table 1 presents summary data for the five income sources over the three survey years. The data show that non-farm income represents the most important source of mean per capita household income in all three years. Agricultural income represents the second most important source of income. The importance of the other three income sources -- transfer, livestock and rental -- varies by year.

As calculated from Table 1, the Gini coefficient of inequality of per capita household income for the sample increased slightly over the period of :e survey: from 0.384 in 1986-87 to 0.408 in 1987-88 to 0.417 in 1988-89. These Gini coefficients suggest that the distribution of income in the rural survev sites is roughly similar to that recorded for other Asian countries.7
The panel structure of the ELE survey allows us to include firm fixed effects in Equation (2). This improves upon OLS estimates of ��, which would be biased as firms are likely to make their software adoption and employment decisions jointly based on unobserved characteristics (e.g., managerial quality). However, there are two additional problems with Equation (2). First, it is plausible that time-varying unobserved firm characteristics or shocks (e.g., a positive boost to performance) affect both a firm’s choice to adopt complex software and particular types of occupations and tasks. Second, the decision to adopt complex software may itself depend on the firm’s mix of occupations and tasks.

To address these problems, in addition to the inclusion of firm fixed effects, we adopt an instrumental variables strategy based on the sub-national adoption of a more aggregated measure of ICT: the regional share of households with a computer in use. We expect the use of computers by households at the sub-national level (across Chile’s 15 regions) to be positively correlated with the adoption of complex software by firms. This may happen as both firms and households benefit from reductions in the prices of technology products and from exposure to newer technologies. Nevertheless, from the perspective of an individual firm, access to computers by households in its region is exogenous, i.e., an individual firm does not influence the computer adoption decision of households. Our instrument exploits the interaction between the regional share of households with a computer in use (���_����������) and the initial Chilean sectoral ICT intensity (���_�������) measured as of 2003.<sup>12</sup> The rationale for the interaction term is that the degree of technological progress at the sub-national level can impact differentially firms depending on their sector’s ICT intensity.

Our reduced-form specification may be criticized for two reasons. First, the prevalence of computer use by households in a region may simply reflect the level of development or the performance of the region, itself correlated with firm employment choices. To mitigate this concern, our main specification includes in _Xjsrt_ time-varying regional variables capturing the level of development of the region: the average per capita income of households, the share of urban households, and the average number of years of education of the households in the region. Second, there may be time-varying _unobserved_ regional shocks affecting both use of computers by

> 12 A similar type of instrument was used by Iacovone et al. (2016) in a study on the impact on productivity of the use of computers by firms in Mexico. The 2003 Chilean input-output table is considered to be pre-determined from the point of view of firms’ ICT adoption and employment decisions in 2007 and 2013.

16
between aid and growth, and omitted variables bias.<sup>14</sup> This is also our preferred estimation strategy. Following Clemens et al. (2012) our reduced‐form empirical model is at the country‐ period level: _Δ_ �������,� = α + β _Δ_ ����,��� + γ _Δ_ �����,���� � + δ�����,��� + ζ _Δ_ ����,��� _*_ �����,��� + _Δ_ ��,�� η + ���,� (1) where _Growthi,t_ is a country _i_ ’s average yearly real GDP per capita growth over period _t_ . _Aidi,t‐1_ denotes the amount of aid (as a percentage of GDP) disbursed in the previous period; _UNSCi,t‐2_ indicates the share of years country _i_ was a temporary member of the UNSC two periods before.<sup>15</sup> As we expect that aid commitments are, on average, disbursed one period later, we twice‐lag the share of temporary membership on the UNSC ( _UNSCi,t‐2_ ). All regressions include the complete set of (time‐variant) control variables used by Clemens et al. (2012), which we denote _Xi,t_ .<sup>16</sup> These variables are GDP per capita in the first year of each period, Assassinations, the interaction of Ethnic Fractionalization with Assassinations, M2/GDP (lagged), Policy, and period dummies.<sup>17</sup> Our preferred specification also includes aid squared to test decreasing returns to aid, again 14. In addition, they seem to prefer a measure of early‐impact aid over all aid. This measure has been shown to not be a robust predictor of growth (Rajan and Subramanian 2008; Bjørnskov 2013). What is more, a major drawback with this measure is that disaggregated aid disbursements are not available for the entire period, so that disbursements have to be estimated based on commitments. We therefore prefer to focus on overall aid. However, we replicate the analysis using early‐impact aid instead of all aid below.

15. We exclude the permanent UNSC members Russia and China from the analysis.

16. Our appendix S1 reports the sources and definitions of all variables, while we show descriptive statistics in appendix S2.

> 17. The policy index is based on measures of budget balance, inflation, and openness to trade. The original Burnside and Dollar (2000) study also includes time‐invariant variables that are removed here (as in Clemens et al. 2012) through taking first‐differences.

18
# **Determinants of Export Survival in the Lao PDR**

Liviu Stirbat, Richard Record and Konesawang Nghardsaysone The World Bank<sup>1</sup>

**Keywords** : Duration, firm dynamics, survival, trade networks **JEL classification** : F14, L25, C41

> 1 Respectively: Consultant, Trade Specialist and Trade Analyst, World Bank Office Vientiane. Corresponding author: Richard Record <u>(rrecord@worldbank.org). The paper was</u> prepared with support from the Lao PDR Trade Development Facility, a multi donor trust fund financed by Australia, the European Union and Germany, and administered by the World Bank. The data used in this study were generously provided by the Lao Customs Department.
Figure 4: Sales expectation and uncertainty in the next 6 months <!-- Start of picture text --> Regional/by-income group comparison Comparison by firm-size<br>10 10<br>0 0<br>-1 -1 -1<br>-10 -8 -10 -9<br>-12 - 11 - 12<br>-20 -19 -20 -17 -18<br>-22<br>-23<br>-30 Micro (0-4) Small (5-19) Medium (20-99) Large (100+)<br>SAR EAP Non-high income SSA High income SAR Rest of the world<br>(excl. SAR)<br>Expected change in sales (%) Expected change in sales<br><!-- End of picture text --> <!-- Start of picture text --> Panel (a): Expected change in sales<br>40<br>35<br>30<br>22<br>20<br>20 18<br>13<br>10<br>0<br>High income EAP Non-high income SSA SAR<br>(excl. SAR)<br>Std deviations<br><!-- End of picture text --> Panel (b): Measure of uncertainty in sales In general, medium and large firms have a more pessimistic outlook relative to smaller ones (Figure 4, right panel a). This finding holds both in the global context and in South Asia. It is striking, however, that the difference in the share of firms with negative outlook is strikingly significant when we compare South Asian firms, particularly micro-enterprises, with those in other regions. This is in line with the previous findings that micro firms in South Asia were the most negatively affected which impacts their future expectations of sales.

We also examined expectations based on sector, the gender of the owner, and export orientation, as illustrated in Appendix Figure A.5. Firms in the manufacturing sector have the most pessimistic outlook on sales change in the next 6 months. South Asian manufacturing firms expect a 25% decline in sales, as opposed to 16% for those in agriculture and other services, and 14% in retail. Relative to other firms, South Asian exporters are more pessimistic about future sales, even though their realized reduction in sales is relatively lower.

We measure uncertainty by calculating the standard deviation of the expectation on sales growth. The measure builds upon the work of Altig et al. (2020), although we use only the regular scenario and the time horizon is 6 months instead of a year. Results show that South Asian firms are less 9
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# IV. APPLICATION TO JAKARTA, INDONESIA

# A. Backliround Information

Jakarta, the capital of Indonesia, is located in the tropics, just south of the equator. The city is located on a level plain on the northwest coast of the island of Java and occupies around 650 km2, at 7 to 10 meters above sea level The population is believed to be around 8.2 to 9 million, but some estimates have put it as high as 12 million. The city is very densely populated with densities ranging from more than 30,000 per kn2 in central Jakarta to less than 10,000 per kn in most other parts of the city. Average annual temperatre is 27.51C, with a humidity of around 80 percent, and low wind speed. The low wind, high population density. and high rates of car ownership (currently 1.7 million vehicles in Jakarta) indicate the potential for significant air pollution concentrations.

Data from the Ministry of Population and Environment provide population information for subdistricts of Jakarta. A report from the Bandung Institute of Technology's firther disaggregates the population data into 5 x 5 km grids. This report also is the source of information on current ambient pollution levels. For this analysis, the total populari. for Jakarta is assumed to 8.2 million. Indonesian censUS data indicates that 35.7 percent of the population is below age 18.

Since existing monitors provide incomplete coverage of the region, particulate matter concentraions for metropolitan Jakarta were based on dispersion models developed by Doedomo et al3' Emissions data are provided for the four major activities in Jakarta: fuel use from transportation, industrial sources, fuel use for household cooking, and the burning of solid waste-. Transportation emissions are based on density rate of traffic for each subdistrict of Jakarta. Fuel-specific estimates are provided for automobiles, motorcycles, buses and tucks. Industrial- emissions are based on land use information from the city government that details the location and kind of plant operation. Household fuel use and solid waste are assumed proportional to population density in each grid. Open burning is the
<!-- Start of picture text --> Ss» All Countries (weighted) ©<br>o4 +<br>~ y 4 = ~ 5 .<br>- Yor<br>Fe we<br>= “o<br>=5<br>°<br>|<br>— L —— °<br>0oo zZ ey,<br>N<br>1995 2000 2005 2010 2015<br>year<br>——_ Hearing-aid -—---- Synthetic hearing-aid<br>Difference (RHS)<br><!-- End of picture text -->

<!-- Start of picture text --> 2=RCA>1 Countries (weighted) *<br>= \" - ---XNen Le LY7 od ”<br>L<br>oo52awo | N B<br>x y Y ro}<br>£2 ! @<br>| | “§<br>3 L a | | io °<br>4<br>ao, 7 7<br>1995 2000 2005 2010 2015<br>year<br>——_ Hearing-aid -—---- Synthetic hearing-aid<br>Difference (RHS)<br><!-- End of picture text -->

<!-- Start of picture text --> »3=RCA<1 Countries (weighted) “<br>°<br>*<br>of§o z 4 8<br>a ad<br>3 ‘gs<br>Eo B=<br>e - a a —_— °<br>© N<br>1995 2000 2005 2010 2015<br>year<br>——_ Hearing-aid -—---- Synthetic hearing-aid<br>Difference (RHS)<br><!-- End of picture text --> <!-- Start of picture text --> Developed Countries (weighted)<br><= ©<br>woSo=<br>e ex saat. [*<br>Soa5 Z- Ser 8c<br>= o@<br>== ©3 afi ae=<br>oo<br>Or, - a<br>1 | | J | J o<br>1995 2000 2005 2010 2015<br>year<br>—— Hearing-aid -—---- Synthetic hearing-aid<br>Difference (RHS)<br><!-- End of picture text -->

<!-- Start of picture text --> Developing Countries (weighted)<br>peire} =<br>o -7- _o--- ~<br>© aa “| it<br>Eofe) a Z 8c<br>=a o<br>FzoRe mefiiiz Ls | eo=<br>/<br>Zz<br>nN y Pa<br>1995 2000 2005 2010 2015<br>year<br>—— Hearing-aid -—---- Synthetic hearing-aid<br>Difference (RHS)<br><!-- End of picture text -->
Firms in the Enterprise Surveys were asked to rank on a scale of 0 (no obstacle) to 4 (very severe obstacle), the extent to which the following factors were an obstacle to the current operations of the establishment – Corruption, Political Instability, Electricity, Tax rates, Tax administration, Practices of competitors in the informal sector, Access to financing (both availability and cost), Access to land, Inadequately educated workforce, Street crime, Customs & trade regulations, Transportation of goods, supplies, and inputs, Courts, Business licensing and permits, and Labor regulations. In addition, firms were also asked to report the biggest problem of all the perceived obstacles. Figure 1 shows that access to financing was reported as the most serious obstacle by the highest percentage of firms. Ayyagari, Demirgüç-Kunt, and Maksimovic (2008) show that although there is a distinction between firms’ perception of obstacles and what actually constrains their growth, finance represents the most constraining obstacle to firm growth. Beck, Demirgüç- Kunt, and Maksimovic (2005) used an earlier version of these data to show that financing obstacles had a significant impact on firm growth and that the smallest firms were most adversely affected.

When we take a closer look at the firms reporting access to finance as a severe obstacle in Figure 2, we see that across country income groups, a higher percentage of small firms identify access to financing as a severe obstacle compared to the percentage of large firms that identify access to financing as a severe obstacle. Stein, Goland, and Schiff (2010) also report a significant credit gap for SMEs in developing countries.

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The survey was conducted simultaneously across all five countries, making a useful point-in-time comparison.<sup>1</sup> In each country a sample of 100 enterprises was drawn randomly from all size categories, with the sample equally split between enterprises whose main operations were in the garment sector and those whose main operations were in food processing. As far as was possible, the food processing sample was drawn from firms classified under NACE code 15.1 (production and preserving of meat and meat products).

Restricting the sample to two sectors helped ensure the accuracy of the productivity estimation. Both sectors produce tradable goods in reasonably competitive markets. Firms in the sample were therefore likely to face common prices, reducing the chance that the nominal value of output (and thus productivity) was inflated because of market power. Since the sectors are narrowly focused, it is also likely that the selected firms were using similar technologies. This leads to a more homogeneous production function and makes it easier to attribute the residual to differences in efficiency rather than differences in technology or market structure.

Although over 500 firms were surveyed, many did not report the full range of investment climate measures needed for the study. Others were engaged only in part in garment production or food processing, deriving the rest of their earnings from services, trading,

> 1 The survey was administered and supervised in all five countries by a single firm, and the enumerators underwent training together to reduce variation among countries due to differences in implementation methodologies. Because of worries about survey fatigue following the large Business Environment and Enterprise Productivity Survey less than a year earlier, the sample was limited to 100 firms in each country.

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militias helped provide the enabling environment (often including armed protection) for the opium economy to operate. The weak government was unable to provide genuine security or rule of law, and this created a good environment in which the opium economy could continue to thrive. Thus the dynamic tendencies at work would perpetuate a large opium economy and a weak, ineffective state (particularly in terms of providing security).

**Figure 3: The Vicious Circle of the Drug Industry**

<!-- Start of picture text --> W arlords WARLORDS<br>undermine Protection and<br>Government other payments<br>or capture strengthen<br>parts of it Drug related W arlord warlords<br>corruption militia<br>undermines provide security<br>Government for opium economy<br>OPIUM<br>GOVERNMENT<br>ECONOMY<br>W arlords<br>undermine<br>national Poor security<br>W eak security creates good<br>Government environment for<br>unable to opium economy<br>provide security SECURITY<br><!-- End of picture text --> _Source: Adapted from World Bank (2005), p. 120._ This vicious circle suggested that a multi-faceted strategic framework would be needed to effectively address the opium economy and the problems it causes for Afghanistan’s development agenda. In addition to reducing the size of the drug economy through effective counter-narcotics measures more narrowly construed, this framework would have needed to include: (i) curbing warlords’ powers by stopping payments and other support to them, Disarmament, Demobilization, and Reintegration (DDR) to take away their militias, and co-opting them into the Government as appropriate; (ii) building government capacity and effectiveness as well as resources; and (iii) reform and capacity building in the security sector (see World Bank, 2005, Figure 7.4, p. 127). A strategic framework along these lines appeared attractive, and several of the key elements were in place to some extent or at least initiated. However, improvements at the broader strategic level fell far short of what was needed. As a result, there has been massive further growth of the opium economy, and both the opium problem and its adverse impacts on the state building and development agenda have become worse.

As depicted in Figure 4, the transformation of warlords into politicians has been accompanied by compromising of parts of some government agencies like the Ministry of Interior and Police by drug industry interests. The strengthening triangle between drug interests, their political and other sponsors, and parts of the government reflects a trend that—primarily through widespread corruption in their implementation—counter14
Euro area banks play a major role in the provision of syndicated bank loans to EMDEs, accounting for about 23 percent of total bank flows to these countries, compared to 10 percent for U.S. banks. However, even when intermediated through euro area banks, syndicated loans remain primarily denominated in U.S. dollars, particularly to Latin America, Africa, and the Middle East and Asia. Europe and Central Asia is the only region where the euro surpasses the U.S. dollar as a currency of denomination for cross-border bank flows.

**_Risks associated with monetary policy divergence._** Increasingly divergent monetary policies across major reserve currencies have partly contributed to a broad-based appreciation of the U.S. dollar, which has been accompanied by considerable pressure on some EMDE currencies. This pressure has contributed to a higher cost of debt servicing and rising credit risks (Hofmann, Shim,

**Figure 16. Capital flows and asset prices in EMDEs**

<!-- Start of picture text --> 12-month rolling sum, index = 100 in Jan 2012<br>400<br>US Dollar Euro Other<br>300<br>200<br>100<br>0<br>2012 2013 2014 2015 2016<br><!-- End of picture text --> <!-- Start of picture text --> Billions of US$<br>120<br>Non-financial corporations<br>Financial corporations<br>100<br>General government<br>80<br>60<br>40<br>20<br>0<br>2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> <!-- Start of picture text --> Percent of regional total<br>Foreign direct investment<br>70<br>Portfolio investment<br>60 Cross border bank lending<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>US$<br>100<br>Euro<br>80 Yen<br>60<br>40<br>20<br>0<br>Europe Asia<br>Middle East<br>Latin America Africa and the<br><!-- End of picture text --> Sources: World Bank, Dealogic, Bloomberg, Bank for International Settlements, Adjiev and Takats (2016). A.B. Bond issuance expressed in current U.S. dollar. Last observation is June 2016. C. EAP is East Asia and the Pacific, ECA is Eastern Europe and Central Asia, LAC is Latin America and the Caribbean, MENA is the Middle East and North Africa, SA is South Asia, and SSA is Sub-Saharan Africa. Total portfolio investment data are as of June 2015. Foreign direct investment data are as of December 2014. Bank claims are as of December 2015. D. Currency positions of cross-border bank lending in 2014Q4.

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increased from 4.48 to 4.81 over the period 1988-2002, reaching a maximum of 6.74 in 1996. The Theil T index exhibited a slight decline from 0.327 in 1988 to 0.302 in 2002 (after peaking at 0.474 in 1996), but this may be due to the nature of this measurement, which is more sensitive to changes in the income distribution and tends toward greater variability than the Gini or other techniques.

|_Table 2.1 Inequality Indexes for the Distribution of Earnings, 1988–02 _|
|---|

|_Population share_|_1988_|_1990 _|_1992 _|_1994 _|_1996 _|_1998 _|_2000_|_2002_|
|---|---|---|---|---|---|---|---|---|
|Bottom 20%|7.54|7.19|6.47|5.98|5.72|5.91|5.93|6.72|
|Middle 40%|25.23|23.86|23.37|22.36|22.09|22.87|23.33|25.47|
|Mhigh 30%|33.44|33.96|33.52|32.94|33.61|34.63|34.50|35.49|
|Top 10%|33.78|34.98|36.64|38.72|38.58|36.59|36.24|32.31|
|Gini|0.395|0.414|0.434|0.458|0.464|0.445|0.440|0.396|
|Theil T|0.327|0.35|0.396|0.47|0.474|0.410|0.399|0.302|
|R10/20|4.48|4.87|5.66|6.47|6.74|6.19|6.11|4.81|

Source: Calculations based on ENEU/ENET (third quarter).

5