rafmacalaba/datause-encoder-data huggingface dataset:
MALAYSIA

# 2. Projections - NRR=1 by 2010

|**_Tow_**|Poputation|Dp.<br>ratio|Period|Grout<br>rate|CCt|CDR|FR|Go<br>**Natm**|O<br>**F_mla**|lO|S<br>x1000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|17783|68.6|1990-94|2.31|28.4|5.1|3.44|68.7|72.9|20|24|
|1995|19959|68.3|1995-99|1.90|24.0|4.8|2.97|70.0|74.4|16|20|
|2000|21953|63.3|2000-04|1.58|20.5|4.6|2.56|71.3|75.9|13|16|
|2005|23755|56.1|2005-09|1.33|18.1|4.7|2.21|72.3|76.9|12|14|
|2010|25392|49.1|2010-14|1.26|17.6|4.9|2.09|73.3|78.0|10|12|
|2015|27045|46.2|2014-19|1.23|17.4|5.1|2.08|74.3|79.1|8|10|
|2020|28761|46.5|2020-24|1.12|16.6|5.3|2.08|75.4|80.4|7|9|
|2025|30421|48.8|2025-49|0.72|14.0|6.8|2.07|77.7|83.1|4|6|
|2050|36414|57.2|2050-74|0.27|12.4|9.7|2.06|80.0|85.9|3|4|
|2075|38948|69.0|2075-99|0.07|12.0|11.3|2.06|81.2|87.5|2|4|
|2100|39670|72.2|2100-24|0.04|11.8|11.4|2.06|82.1|88.8|2|3|
|2q25<br>2150|40097<br>40320|73.5<br>74.2|2125-49|0.02|11.7|11.5|2.06|82.4|89.2|2|3|

3. Age structure (percent): 1990 and 2025 <!-- Start of picture text --> 75 +<br>70-74<br>85-89<br>80-84<br>55-59<br>50-54<br>45-49<br>40.44<br>35.39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5.9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-90

TFR <!-- Start of picture text --> e0 Growth rate<br><!-- End of picture text --> Counmtry Ragion Incoas group Coun~try Region Income grou Coistry Region Income grou~p 75
|**Table A2: Balance Checks am**|**ong study**<br>(1)|**populatio**<br>(2)<br>Differe|**n**<br>(3)<br>nce betw|(4)<br>een [...]|(5)|(6)|(7)|(8)|(9)<br>P‐values|
|---|---|---|---|---|---|---|---|---|---|
||Mean [SD]|and|Controlg|roup||P‐valu|e for diffe|rence...|joint tests|
||in Control|||||Group 1|<br>Group 1|<br>Group 2|<br>G1=G2=G3|
||Group|Group1|Group2|Group3|N|and 2|and 3|and 3|=0|
|_Firm owner characteristics_||||||||||
|Female owner|0.63|0.001|0.000|‐0.002|3,596|0.717|0.363|0.415|0.63|
||[0.483]|(0.003)|(0.002)|(0.002)||||||
|Age of the owner|39.25|1.13|0.56|‐0.2|3,557|0.482|0.068*|0.094*|0.113|
||[10.75]|(0.73)|(0.46)|(0.41)||||||
|Firm owner has at least some|0.707|‐0.034|‐0.004|0.026|3,591|0.396|0.064*|0.143|0.158|
|formal education|[0.456]|(0.032)|(0.02)|(0.018)||||||
|_Firm characteristics_||||||||||
|Trade|0.551|‐0.007*|0.000|0.000|3,596|0.104|0.071*|1|0.316|
||[0.498]|(0.004)|(0.002)|(0.002)||||||
|Services|0.259|0.007|‐0.001|0.007|3,596|0.76|0.999|0.585|0.936|
||[0.438]|(0.023)|(0.014)|(0.013)||||||
|Craft|0.165|‐0.008|‐0.01|‐0.008|3,596|0.928|0.97|0.921|0.883|
||[0.371]|(0.023)|(0.014)|(0.013)||||||
|Firm area in m2|19.02|5.99*|‐0.21|‐2.31|3,590|0.076*|0.008***|0.282|0.055*|
||[42.13]|(3.15)|(1.96)|(1.75)||||||
|Business connected to|0.617|‐0.001|0.008|0.002|3,594|0.811|0.941|0.759|0.984|
|electricity network|[0.486]|(0.034)|(0.021)|(0.019)||||||
|Number of employee|1.18|‐0.04|‐0.02|0.02|3,596|0.824|0.518|0.521|0.861|
||[1.68]|(0.09)|(0.06)|(0.05)||||||
|The firm does any form of|0.173|0.013|‐0.001|0.015|3,594|0.632|0.917|0.306|0.678|
|accounting|[0.378]|(0.027)|(0.017)|(0.015)||||||
|Amount of sales in an average|60,828|73|281|‐1,052|3,596|0.934|0.618|0.342|0.755|
|week|[57,039]|(2,257)|(1,405)|(1,255)||||||
|Amount of profit in the last|46,249|139|316|1,156|3,596|0.941|0.634|0.527|0.791|
|month|[44,867]|(2,135)|(1,329)|(1,188)||||||
|Firm owner owns a bank<br>account|0.22<br>[0.414]|0.004<br>(0.003)|0.004** <br>(0.002)|0.005***<br>(0.002)|3,514|0.985|0.771|0.665|0.016**|
|_Taxes_||||||||||
|Firm pays taxes|0.561|‐0.013|‐0.017|‐0.022|3,560|0.927|0.8|0.807|0.687|
||[0.496]|(0.033)|(0.021)|(0.018)||||||
|Amount of taxes paid in the|19,450|‐3,867**|‐306|‐657|3,482|0.1|0.101|0.77|0.269|
|previous year<br>Notes: Baseline survey data (<br>coefficients and standard errors<br>treatment dummies, controllin<br>significance at 1, 5 and 10%.|[28,146]<br>March 2014)<br> (in parent<br>g for strata|(1,952)<br>. Column <br>heses) fro<br> dummie|(1,211)<br> 1: Stand<br>m an OL<br>s (dummi|(1,077)<br>ard devia<br>S regressi<br>es for e|tions <br>on of <br>ach tri|presente<br>the firm <br>plet). *|d in bra<br> owner/fi<br>**, **, *|ckets. C<br>rm chara<br> indicate|olumns 2‐4:<br>cteristic on<br> statistical|

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12 urban areas. These are nonetheless still relatively small. For context, Malawi’s urbanization status is well below the Sub-Saharan African (SSA) overall level. In 2020, less than 6% of the population lived in cities with more than one million people, compared to 15% in SSA overall, and only 17% of Malawians lived in urban areas at all, compared to 41% in all of SSA (World Bank 2021). The consumer food price index computed with these price data is considered representative of rural Malawi. Food items selected for price monitoring were revised at the end of 2012 based on nationally representative household survey data collected in 2010 to include any item accounting for more than 0.02% of total household expenditure (Kaiyatsa, Schneider and Masters 2021). The list of food items whose prices are monitored includes foods from all food groups.

We match households to the market corresponding to their district of residence (or in a few cases of multiple markets per district, their sub-district market) (National Statistical Office (NSO) [Malawi] 2011; National Statistical Office (NSO) [Malawi] 2012; National Statistical Office (NSO) [Malawi] 2018). Our emphasis is on the market and its ability to provide access to nutritious diets, as a metric of food system performance. The prices observed in the market are for standardized items of a particular quality, so they are not directly comparable to unit costs reported by households. Unit costs reflect both quality and price, and therefore differences are not necessarily attributable to different price environments (Gibson 2016; Gibson and Kim 2018; Gibson 2013). The food price environment faced by households and their preferences are reflected in the numerator of the affordability analysis, namely the food and total expenditure calculated using reported unit costs and standard methodology to value own produced goods.<sup>2</sup>

> 2 For the purpose of context, Supplementary Tables G-1 (by size of difference) and G-2 (by food group) show the comparisons for items that could be compared at the district level in the same month and year, showing differences of varying magnitude (some small in practical terms) and where the market prices are more often higher but not always.
Table A3: Regressions on transfer between groups

||Amount tr|ansferred (std)|Transferr|ed nothing|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Cooperation|-0.010|-0.019|-0.018|-0.013|
||(0.097)|(0.097)|(0.044)|(0.044)|
|Republican||-0.055||0.037|
|||(0.110)||(0.050)|
|High age||-0.277<sup>_∗∗∗_</sup>||0.139<sup>_∗∗∗_</sup>|
|||(0.098)||(0.045)|
|Female||-0.065||0.008|
|||(0.097)||(0.044)|
|High income||0.071||-0.039|
|||(0.101)||(0.046)|
|Constant|0.982<sup>_∗∗∗_</sup>|1.143<sup>_∗∗∗_</sup>|0.488<sup>_∗∗∗_</sup>|0.418<sup>_∗∗∗_</sup>|
||(0.070)|(0.109)|(0.031)|(0.048)|
|Observations|507|507|507|507|
|_R_<sup>2</sup>|0.000|0.019|0.000|0.023|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 _Note:_ The table reports OLS regressions on transfers when the two workers belong to different groups. The dependent variable in columns (1) and (2) is the standardized amount transferred to the unlucky worker. The dependent variable in columns (3) and (4) is an indicator for the spectator transferring nothing to the unlucky worker. “Cooperation” is an indicator for the spectator being assigned to a treatment where the workers cooperate within group. “Republican” is an indicator for the spectator self-reporting voting Republican. “High age” is an indicator for the spectator being above median age in the sample. “Female” is an indicator variable for the spectator being female. “High income” is an indicator for the spectator having an income above the median income in the sample. Standard errors in parentheses, where * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

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||**Beta hat**|**Standard error**|
|---|---|---|
|Scope|||
|Number of school levels taught|-0.01|0.04|
|Number of shifts|-0.01|0.01|
|Infrastructureindex|0.05|0.05|
|Materialsindex|0.09*|0.05|
|Staffing|||
|Number of staff|0.93**|0.38|
|Share of male teachers|-0.42|2.28|
|Students|||
|Student-teacher ratio|8.3***|1.68|
|Total number of students|84***|14.53|
|Grades 1-4: total number of students|88***|11.66|
|Grades 1-4: number of female students|41***|6.14|
|Grades 1-4: number of male students|47***|5.92|
|Grades 5-6: total number of students|1|4.71|
|Grades 5-6: number of female students|0|2.74|
|Grades 5-6: number of male students|1|2.30|
|Percent repeaters grades 1-6|-0.10***|0.02|
|<br>Percent overage grades 1-4|-0.09***|0.03|
|<br>Percent girls overage grades 1-4|-0.10***|0.03|
|<br>Percent boys overage grades 1-4|-0.09***|0.03|
|<br>Percent overage grades 5-6|-0.06*|0.03|
|<br>Percent girls overage grades 5-6|-0.07**|0.03|
|<br>Percent boys overage grades 5-6|-0.06*|0.03|
|<br>_Number of observations_|_354_||

Notes: Following Kling, Leibman, and Katz 2007, the infrastructure and materials indices are the equally weighted average z-scores of each component, where more beneficial outcomes have higher scores. The infrastructure index includes measures of a sports field, library, director’s office, recreation area, kitchen, latrines, water, electricity, roof, wall, and floor materials. The materials index includes measures of desks, chairs, and blackboards. Source: World Bank staff estimates using Haiti national school census 20011-12 Across departments, treatment schools did not change the number of shifts offered, nor did they change the levels of school taught. The majority of treatment and control schools teach one shift per day, and offer preschool and primary. In terms of infrastructure and physical materials, treatment does not appear to have substantial effects: both coefficients are positive, but only the materials index is marginally significant. The number of staff did increase by nearly 1 person on average in treatment schools.

The number of students, male and female, in 2011-12 is substantially higher in treatment compared to control schools. Notably, this increase is limited to grades one to four, which correspond to the four cohorts funded by the TWP from 2008 through 2011, and no increase is observed in higher grades which were not yet funded by the program, which had only been running for four years (Figure 1). Across departments, treatment schools have on average 88 more students in grades one to four compared to control schools. This large and significant increase indicates the strong demand from families for education at lower cost. However, we do not know what share of the additional students came from other schools (including control schools) and what share had not been in school previously. In other words, a simple 11
the least significant variables. We obtain the emerging market propagation pattern reported in specifications 4 and 5 of Table 2. As predicted, Ras Gas spreads vary negatively with emerging debt market returns.<sup>28</sup> The significant lags hint at the time structure of the shock propagation mechanism behind the contagion effects. While Ras Gas spreads show a particularly strong contemporaneous reaction to European emerging debt markets (dominated by Russian debt) the other debt markets' impact is delayed and Asian debt market factors insignificant in the presence of Kepco spreads.

Once again, we think that portfolio rebalancing and liquidity effects are responsible for these patterns. Ras Gas bonds belong both to the energy and emerging market segment of global fixed income markets. Based on information from Goldman Sachs (Greg Randolph, Bim Hundal), the Russian financial crisis impacted Ras Gas bond trading twice. Investors negatively reassessed Korea's and, hence, Kepco's prospects after Russia's partial debt default (sovereign spillover). Also, they viewed potentially increased oil and gas exports from the former Soviet Union as a financial threat to Ras Gas (sector spillover). The lagged reaction might be due to the lesser informational transparency of emerging markets as well as portfolio rebalancing that often takes place with time zone induced delays.<sup>29</sup> Adding Korea-specific variables (specifications 6 to 8 in Table 2) to the regression reveals that the Korean rating index _KRR_ is insignificant, while the Kepco share price _KEPCO is_ significant at the 1% level. Exchange rate exposure as measured by the _KRW_ coefficient comes

> **28** Longstaff and Schwartz (1995), and Duffee (1998) similarly find a negative relation between credit spreads and short-terrn interest rates.

> 29 After all, US investors, who might face a one-day delay in reacting to European or Asian events, initially held 80% of the 2013 bond. The positive relation between Ras Gas credit spreads and lagged Middle Eastern bond returns in the weekly analysis (Table 8) lends further credence to our portfolio rebalancing interpretation.

26
**Figure 20: Impacts of combined historical macroeconomic and historical climate shocks**

<!-- Start of picture text --> Real GDP GVA - Agriculture<br>% % % %<br>4 4 5 5<br>3 3<br>2 2 0 0<br>1 1<br>-5 -5<br>0 0<br>-1 -1<br>-10 -10<br>-2 -2<br>-3 -3<br>-15 -15<br>-4 -4<br>-5 -5 -20 -20<br>2020 2040 2060 2080 2100 2020 2040 2060 2080 2100<br>Mean Median Mean Median<br>Exports Fiscal balance<br>% % ppts ppts<br>15 15 2.0 2.0<br>1.5 1.5<br>10 10<br>1.0 1.0<br>5 5<br>0.5 0.5<br>0 0 0.0 0.0<br>-0.5 -0.5<br>-5 -5<br>-1.0 -1.0<br>-10 -10<br>-1.5 -1.5<br>-15 -15 -2.0 -2.0<br>2020 2040 2060 2080 2100 2020 2040 2060 2080 2100<br>Mean Median Mean Median<br><!-- End of picture text --> Note: Results are presented as percent deviations from baseline. Confidence intervals of 68 percent (dark blue) and 95 percent (light blue) are shown.

**Climate change is expected to worsen aggregate outcomes further taking into account compound climate and macroeconomic shocks.** Running the same analysis as above with risk curves for the RCP 4.5 climate scenario and historical macroeconomic shocks, the average losses in L&A increase, from - 2.2 percent to -3.1 percent. At the same time, repeated floods damage the capital stock, and divert money from investment into new capital. As a result, real GDP is estimated to be on average around 0.3 percentage points lower than in the historical scenario by 2100 due to climate change and macroeconomic shock combined effects (Figure 21).

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17

**Table 2: Correlations between Firm Perceptions and External Data**

|**Perception Area (Enterprise Survey Data**<br>**and Selected Comparators)**|**Pearson’s Correlation Coefficient**<br>**`(P>|r|)`**|
|---|---|
|Electricity|Sales Lost due to Power Outages (Surveys)<br>0.39**(0.05)|
|Corruption|Control of Corruption_(KK)_<br>-0.35*(0.08)|
|Access to Finance|Domestic Credit to the Private Sector_(IMF)_<br>(% of GDP, 2004)<br>-0.89***(0.01)|
|Access to Finance|Investor Rating_(Institutional Investor)_<br>-0.80***(0.01)|
|Cost of Finance|Interest Rate_(IMF)_<br>0.72***(0.01)|
|Macroeconomic Instability|Government Effectiveness_(Kaufmann Kray))_<br>-0.28 (0.17)|
|Business Licensing|Days to Get a License_(DB)_<br>0.41**(0.04)|
|Business Licensing|Number of Licensing Procedures_(Doing_<br>_Business)_<br>-0.01(0.96)|
|Economic Instability|Strength of Legal Rights Index_(DB)_<br>-0.01(0.95)|
|Corruption|Strength of Legal Rights Index_(DB)_<br>-0.22(0.29)|
|Corruption|Investor Protection Index_(DB)_<br>0.26 (0.20)|
|Access to Finance|Depth of Credit Info Index_(DB)_<br>0.27 (0.19)|
|Economic Instability|Investor Protection Index (_DB_)<br>-0.15 (0.45)|
|Tax Administration|Number of Tax Payments_(DB)_<br>-0.13 (0.54)|
|Tax Administration|Time to Pay Taxes_(DB)_<br>0.32(0.12)|
|Customs and Trade|Trading Cost for Exports_(DB)_<br>0.22(0.29)|
|Customs and Trade|Trading Cost for Imports_(DB)_<br>0.16 (0.42)|
|Access to Land|Property Registration, days_(DB)_<br>0.06 (0.78)|

_Note: Figures in parentheses represent levels of confidence_ One recognized caution over using access to finance as an indicator of the quality of the business climate is that access is endogenous. Weaker and usually smaller firms are most
households, recent research suggests that poorer, credit-constrained households may not benefit as fully from migrant opportunities as middle-income households (de Brauw and Giles 2018; Du et al. 2005).

Section 4 reviews evidence on the effects of migration on household earnings, consumption, and poverty alleviation. One credible study based on data from China’s poor counties finds household income per capita increases from 8.5 to 13.1 percent after sending a migrant (Du et al. 2005). Research also suggests that increased migration from villages also contributed to average increases in household consumption within villages, including those without a migrant household member. One study attributes 65 percent of the consumption growth experienced by a panel of households from villages surveyed from 1988 to 2002 to increases in rural-to-urban migration (de Brauw and Giles, 2018).

The opportunity to diversify household earnings through migrant work lowers the risk of falling into poverty, even for households not currently engaged in migrant work. The option to find migrant work and diversify income sources after experiencing agricultural shocks affects precautionary savings behavior, poverty risk, and household investment decisions. As out-migration from a village increases, the risk of falling into poverty declines: a 1 percent increase in migration from the village is associated with a 3.2 percent decline in the risk of falling into poverty (Giles and Murtazashvili, 2013). Facing lower consumption-risk, households make higher return risky investments in tea, fruit trees, and animal husbandry when migration becomes easier, yielding earning increases of 12 to 22 percent for animal husbandry and 5 percent for fruit production (Kinnan et al. 2018).

As China enters its third decade of rural-urban migration, some areas of concern discussed in Section 5 warrant additional research and policy attention. In other countries, out-migration with urbanization has often led to a collapse in public goods provision, negatively impacting infrastructure maintenance and the provision of health and education services (Moretti, 2012). In rural China, a negative correlation exists between the scale of out-migration and village-level public goods investment, but this effect is less significant in multivariate models. While further investigation is warranted, increases in funding from upper levels of government likely prevented sharp declines in public goods provision in rural China. While migrant workers’ access to urban social insurance programs and their children’s enrollment in city schools has improved, significant obstacles remain. Children remaining in villages are often separated from parents for extended periods or educated in substandard arrangements in urban areas, potentially leading to negative consequences for the next generation. Although left-behind children benefit from higher incomes, those with absent migrant parents are more likely to suffer from depression, other mental health problems, and behavioral problems. Older family members often left behind to care for children are vulnerable to poverty and isolation in old age.

3
the nominal rate of depreciation plus foreign<sup>inflation.</sup> The trend component of output is estimated<sup>by using a modified version of</sup> the "ideal" band pass filter of Baxter and<sup>King(1999).The Baxter-Kingfilter</sup> is a linear transformation of the data,<sup>whichleavesintactthecomponents</sup> of the data within a specified band of<sup>frequenciesandeliminatesallother</sup> components. But this methodology has a limitation:<sup>its applicationrequires</sup> a large amount of data. Christiano and Fitzgerald<sup>(1999)approximatethe</sup> Baxter-King filter using an optimal linear approximation.<sup>Their method can</sup> be explained as follows. Let yt be the data<sup>createdbyapplyingtheideal</sup> band pass filter to the raw data, **_xt._** yt<sup>isapproximatedby</sup><sup>**&,**whichisa</sup> filter of xt. The filter weights are chosen to<sup>minimizethe mean squareerror:</sup> Yt can be computed as where and _BT-t_ and _Bt_1_ are linear functions<sup>of</sup><sup>_Bj's:_</sup>
approximately 600 industry verticals, and for each, we calculate the share of disruptive firms listed in these verticals. Then, we match these industry vertical shares to all African firms in the sample. Firms could be linked to one or many verticals. The strategy exploits the fact that most firms are associated with more than one vertical and thus the final exposure of a firm is the combination of all the shares depending on the number of verticals the firm has. The idea is that the exposure to the adoption of disruptive technologies will depend on the industry vertical mix of every firm.

The second component of the instrument, the country characteristic that predicts the adoption of technologies, is the degree of skill mismatch in the country. Bloom et al. (2021) show that the emergence of disruptive technologies is more likely in geographies with a high concentration of skilled labor. Porzio (2023) finds that countries where skill mismatch in the labor force is high, settle in a low equilibrium of low technology adoption. We use skill mismatch data produced by ILO prior to 2010.<sup>11</sup> Mismatch occurs when the level of education of the person in employment does not correspond to the level of education required to perform their job. ILO uses a normative approach to identify mismatched workers. It is based on the educational requirements set out in the International Standard Classification of Occupations (ISCO) for each one-digit ISCO occupational group, and on the level of education of each person in employment. Three categories are possible; the first, are matched individuals whose education level corresponds to the education requirements described in the occupation; the second category is over-educated workers, while the third corresponds to workers with an education below the one required by the occupation. These three groups are computed for employees and self-employed workers. Our instrument uses the share of matched or over-educated workers among employed individuals. The idea is that countries where workers are adequately or over-skilled are more likely to have the conditions for disruptive technology adoption. Being as this variable is defined at the country level, it should be exogenous to the firm.

The instrument, ultimately, is the interaction of these two components: where _Mc_ is the mismatch share in country _c_ , _θk_ is the share of disruptive firms from the frontier in industry vertical _k_ , _Ki_ is the number of industry verticals in firm _i_ .

We calculate this IV for both the full set of disruptive technologies and the set that excludes mobile payments and Wi-Fi. We focus on the sample of firms in the Africa region.

The first stage is defined as: where _Ti_ is the number of disruptive technologies, _IVi_ is the instrument defined in equation 4, and _Xi_ are the same controls included in equation 3.

> 11International Labour Organization (2023), https://ilostat.ilo.org/resources/concepts-anddefinitions/description-education-and-mismatch-indicators/

11