rafmacalaba/datause-encoder-data huggingface dataset:
international agencies to compute poverty and inequality statistics for the country, it is worth examining how the measures obtained from PSF differ from those obtained from ESPS. The comparison is far from trivial, however, because both surveys differ by many aspects that could be of consequences. Indeed, differences in design are well known to engender divergence in consumption measures, and it is important to try to assess the extent to which divergence between surveys is due to intra-household inequality rather than to any other source of disparity.

Appendix S1 details the comparison. First, stratification of the samples is different, with in particular a greater share of observations from the Dakar region in PSF than in ESPS. Sampling weights can be used to recover nationally representative samples from both surveys. But important differences persist even between weighted samples, and in order to improve comparability we chose to reweight ESPS observations using propensity scores obtained from regressions of the probability for a given observation to be in the PSF sample rather than the ESPS one, so as to make both samples exactly similar. Second, ESPS and PSF differ in their expenditure coverage, as ESPS includes taxes and ceremonial expenses that PSF does not collect. We therefore limit the comparison to the consumption of goods and services listed in both surveys. Further, for reasons explained in section 2.1, rents are also excluded. The final lists cover the exact same categories of goods but divided in 59 items in ESPS and 55 in PSF. Now, while ESPS treats food expenditures in the same way as any other expenses, the PSF questionnaire replicates the food spending patterns of Senegalese households by recording separately the DQ (“dépenses quotidienne”) spent on fresh food often on a daily basis, and the bulky purchases of staple food made less frequently by the household head. Finally, recall periods are also different, as ESPS imposes recall period that vary with the type of goods (from 1 month to 1 year) while PSF lets the respondent choose the recall period he feels corresponds best to the rhythm of purchase (12 modalities are proposed, from 1 day to 1 year). Despite the restriction to strictly comparable sets of expenditures, the differences in the detail of the lists 12
# **<u>Figure 8 Student Assistance (Grants and Loans)</u>**

<!-- Start of picture text --> United States<br>Sweden<br>United Kingdom<br>New Zealand<br>Australia<br>Netherlands<br>Colombia<br>Finland<br>High Income Average<br>Canada<br>Average<br>Japan<br>France<br>LAC Average<br>Ireland<br>Austria<br>Germany<br>Mexico<br>Italy Grants<br>Belgium (French)<br>Loan<br>Belgium (Flemish)<br>Peru<br>Brazil<br>0% 5% 10% 15% 20% 25% 30%<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries <u>Overall Affordability. There are multiple perspectives of affordability. Using the</u> somewhat subjective methodology presented in the earlier section, one can weigh the above affordability indicators to create an overall affordability of tertiary education indicator (Figure 9). The Figure displays the level of affordability of tertiary education compared to other countries.

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- 11 -

subsidies **(or** incentives that increase the relative profitability of exports) are not likely to give rise to abnormally high profits and hence to induce excessive entry.

An altemative interpretation would emphasize that the appropriate policy in a setting of increasing returns to scale is to promote competition in domestic markets. This logic recognizes that imperfect competition in domestic markets can act as an export barrier by increasing the relative profitability of domestic operations. Ideally, industrial policy would be coordinated with trade policy to encourage the exploitation of efficient scale, promoting exports while avoiding excessive entry.<sup>8</sup>

# **Evaluation of Protection in Sectors with Scale Economies**

We return to the issues raised in the introduction: are there welfare gains from protecting sectors with increasing returns to scale and how does import protection compare with neutral incentives (for example, with tariffs and export subsidies at equal rates)? To answer these questions, we report on simulations in which we contrast across-the-board tariffs of 15 percent with across-the-board export subsidies of 15 percent, both in sectors with scale economies. Protection and export subsidies are confined to the consumer, producer, and capital goods sectors. Nowwe assume a cost disadvantage ratio of 20 percent, a value more in line with the unexploited economics of scale in the manufacturing sector of a typical semi-industrial country.

The results of these simulations appear in table 3. In the constant-returns-to-scale benchmark case, there is, as before, a welfare loss from protection alone or from export subsidization alone. Neutrality, however, is less costly because the distortion introduced by the export subsidy partly offsets the distortion introduced by the tariff.

**Table 3. Protection and Subsidization of Sectors with Increasing Returns to Scale** (cost-disadvantage ratio of 20 percent)

|_Proct-d-O-bidy_|_CRTS_|**_Contetbea_**|**_(dOte3dC_**<br>**_,oit)_**|
|---|---|---|---|
|15-percenttariff<br>Equivalent variation|-18|12|-106|
|<br>Scale efficiencv|0|31|-88|
|**15-percent export subsidy**<br>Equivalent vanation|-42|274|86|
|<br>Scale efficiencv|0|336|110|
|**15-percent ariff and export subsidv**||||
|<br>Equivalcnt vanation|-31|281|4|
|<br>Scale efficiencv|0|322|27|

CRTS is constant returns to scale.

Note: AMI figures are basis points of GDP (e.g., -106 is -1.06 percent of GDP).

a. Pncing according to equation 10.

b. Pricing according to equation 11. vith firm entrv and exit determined bv profits on domestic sales. so _Ithat_ .7rT = 0
**Table 2:  Required (per capita) Growth Rate for “Catch-up” in 50 Years**

||2011 GDP Per<br>Capita (PPP<br>adjusted 2005<br>dollars)|Required<br>Growth<br>Rate To<br>US|Required<br>Growth Rate<br>To OECD<br>(HIC)|Actual<br>Growth Rate<br>(1980 to<br>2011)|
|---|---|---|---|---|
|Argentina|15,501|3.9|3.4|1.4|
|Brazil|10,278|4.7|4.2|1.0|
|Chile|15,272|3.9|3.4|3.3|
|_China_|_7,404_|_5.4_|_4.9_|_8.9_|
|India|3,203|7.2|6.7|4.3|
|Indonesia|4,094|6.7|6.2|3.7|
|Malaysia|13,672|4.1|3.7|3.4|
|Mexico|12,776|4.3|3.8|0.7|
|Nigeria|2,221|8.0|7.5|1.0|
|Russian Federation|14,808|4.0|3.5|N.A.|
|South Africa|9,678|4.9|4.4|0.3|
|Thailand|7,633|5.4|4.9|4.1|
|Turkey|13,466|4.2|3.7|2.7|
|Memo Items: US|42,486|OECD|33,726||

Source: Authors’ calculations based on data from the World Development Indicators. A growth rate of 1.8 percent (per capita) is assumed for the high-income country reference points.

# **IV. Absolute Thresholds:  Some (Relatively) Pleasant MIC Arithmetic**

Obviously, if one establishes an absolute threshold – as is done by the World Bank—then any positive growth rate of income per capita will _eventually_ result in successful passage to HIC status. Two problems have arisen. First, some developing countries and regions have actually experienced declines in income per capita for periods spanning a few decades (e.g., Latin America during the debt crisis or Sub-Saharan Africa during the first decades following independence). Secondly, per capita growth of only 1 or 2 percent would condemn some lower middle income countries to “developing” status for centuries.

That said, for many upper MICs, sustained growth of 2 to 4 percent per capita would result in high-income status in a generation or two. Following on the calculations in the previous section, the simple arithmetic is even simpler, if one takes the HIC standard at a fixed level of GDP per capita (in constant dollars). The is then zero, and the time to achieve HIC status is much faster, as shown in Table 3. In addition, if one uses the World Bank definition of HIC, then the initial _R_ is in a much tighter range with a maximum of about 12 (for poorest LMICs), and a maximum of 3 for all the upper middle-income countries, as noted earlier.

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|Electricity access|The individual’s household has access to<br>|After Access data, 2017|
|---|---|---|
||electricity||
|_Socioeconomic variabl_<br>|<br>_es_<br>||
|Female|Respondent is female, 2017||
|Age group (1-3)|Respondent’s age group, 2017 (1 stands for<br>young people, 15-30 years old; 2 stands for<br>middle-age people, 30-60 years old; 3 stands<br>for elderly people,>60 years old)||
|Education (1–5)|Respondent’s education level, 2017 (1 stands<br>for no formal education; 2 stands for less than<br>6 years of formal education; 3 stands for more<br>than 6 but less than 12 years of formal<br>education; 4 stands for more than 12 but less<br>than 16 years of formal education; 5 stands for<br>more than 16 years of formal education)|After Access data, 2017|
|Log value of monthly<br>income|Log value of the individual’s monthly income<br>in USD||
|Married|Individual is married||
|Urban|Individual resides in an urban area||
|_Social network effect_|||
|Close friend (0–5)|The number (up to 5) of the respondents’<br>closest friends who use an online social<br>network like Facebook or Twitter (0 refers to<br>no close friend and 5 refers to all five closest<br>friends)|After Access Data, 2017|

**Table 2. Data summary**

|Mobile internet|Mean<br>0.338|SD<br>0.473|Min<br>0|Max<br>1|
|---|---|---|---|---|
|Household 3G network coverage|0.909|0.288|0|1|
|Household having access to electricity|0.843|0.364|0|1|
|Female|0.563|0.496|0|1|
|Age group (1-3)|1.695|0.632|1|3|
|Log value of monthly income|4.709|1.411|-6.274|11.353|
|Married|0.558|0.497|0|1|
|Urban residence|0.541|0.510|0|2|
|Education level (1-5)|2.740|1.035|1|5|
|Number of closest friends using online social<br>networks (e.g.Facebook/Twitter(0–5)|2.072|2.211|0|5|

# **5. Results**

The results section first presents the results from the basic model (1) specification applied to the entire sample and conducts the checks for robustness. The impact of several alternative modeling strategies is then explored, including disaggregation of the sample by geographic region, and the impact of alternative ways of modeling the influence of income on uptake decisions.

# _5.1 Factors affecting mobile internet adoption in the Global South_

Estimations show that factors affecting the adoption of mobile internet are largely consistent with those that affect other types of digital divides (table 3). Among infrastructure factors, having access to electricity at 11
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# **Table 7: Results with Productive and Nonproductive Expenditures (Dynamic Panel - GMM)**

## **_Dependent variable: Growth rate of GDP per capita_**

||With openness|
|---|---|
|Initial human capital|-0.030<br>(-0.151)|
|Openness|0.058|
|(% of GDP)|(1.834)*|
|Tax revenue|-0.153|
|(% of GDP)|(-0.355)|
|Other revenue|-0.400|
|(% of GDP)|(-0.905)|
|Productive expenditure (-1)|0.777|
|(% of GDP)|(1.673)*|
|Non-productive expenditure (-1)|-0.890|
|(% of GDP)|(-1.301)|
|Budget surplus<br>(% of GDP)|0.126<br>(0.316)|
|Inflation - consumer price index|-0.375<br>(-3.12)***|
|Growth rate of GDP per capita (-1)|-0.03<br>(-0.151)|
|No. of observations<br>J-test|98<br>1.993|

Note: The estimation method is a dynamic GMM. Annual data are used. (-1) indicates variables lagged one period. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients. J-test is for overidentification problem where H0: there is no overidentification problem. We fail to reject in each case.

34
In the following sub-sections, we summarize policy initiatives undertaken by Asian countries that help address energy consumption and associated emissions from the transport sector.

# **5.1. Fiscal Instruments to Reduce Fuel Consumption**

Many countries in Asia have traditionally subsidized fuels used for transportation<sup>13</sup> . Historically, some Asian countries, such as Indonesia and Malaysia, have featured the some of lowest fuel prices in the region due to high subsidization, but both raised retail prices sharply between November 2004 and November 2006 (GTZ, 2007). The Philippines has also introduced drastic price increases, whereas Vietnam has pursued a policy of continuous reductions in subsidies. In both cases, gasoline has recently begun to be taxed although diesel remains subsidized. Thailand raised diesel prices by 76 percent to the point where it is almost subsidy-free, like gasoline in the country. Korea, which has long featured some of the highest fuel prices in the world, continues to raise taxes on diesel and gasoline. China has also raised its fuel prices by more than 40 percent between November 2004 and November 2006 so that they are no longer subsidized and hover near U.S. levels. India and Pakistan both maintain high non13 GTZ (2007) has been conducting worldwide surveys of diesel and gasoline prices since 1991 and uses U.S. retail prices (minus the U.S. highway tax) as benchmark values for non-subsidized fuels. U.S. prices are taken as benchmark values because the high degree of competition in the U.S. fuel market implies pricing that reflect commercially calculated full-cost prices. Most Asian countries tend to price diesel below this benchmark, indicating significant national subsidies for diesel, and some do so for gasoline as well.

37
In the visit module of the endline survey, we also asked respondents about their level of trust in Microcred agents and branches. Column 6 of Table 4 suggests that 91 percent of respondents fully trust the branch tellers and that this trust increases by 4.7 percent for agents. This result is notable as it suggests that Microcred agents have been quite successful in gaining the trust of their clients, thus likely contributing to deeper financial inclusion.<sup>15</sup> The overall attrition rate between baseline and endline was 25 percent and, as reported in Online Appendix Table OA3, it is almost identical and thus not statistically different across experimental arms. This suggests that attrition bias is not a concern when examining the impact of banking with an agent or a branch on the outcomes measured in the endline survey.

# _2.4 Administrative Data_

Administrative data on account opening and the location and size of transactions were obtained from the start of the intervention until roughly one year later in March 2016.<sup>16</sup> These administrative data are used to compare account opening across treatment groups (take-up) and to measure account activity, that is, whether a transaction is a deposit or a withdrawal and the location where it occurred (branch or agent). We also have data on money transfers, but virtually no client in our sample used the account to transfer money. Data on savings balances were also obtained from Microcred through April 2017, allowing us to compute balances 12 and 24 months after the intervention.

# **3. Theory**

The model of Miller and Orr (1966) is an excellent benchmark to understand the decision of a Microcred account holder receiving an uncertain cash flow about how often to visit the bank to deposit or withdraw money.

The decision will depend on the transaction cost τ of visiting the bank, the variability of daily cash flows σ<sup>2</sup> and the opportunity cost in forgone interest r, which includes the interest rate earned by the account as well as the probability that the cash may be lost or stolen if kept at home.

Assuming that daily cash inflows minus outflows follow a symmetric Bernoullian random walk, the optimal cash balance Z is given by the following formula:

> 15 We assess whether the increase in trust is concentrated among individuals encouraged to open the account with the agent but we find that this is not the case. 16 As reported in Online Appendix OA1, the number of free withdrawals per month at an agent was reduced from three to two in February 2016. The results using data until January 2016, before the change in the number of free withdrawals, are qualitatively very similar to those reported in Section 4 and hence not shown.

14
# **Appendix Table 6: Savings -- Intensive Margin - IV Specification**

||(1)|(2)|
|---|---|---|
||Saving Regularly<br>as per Financial<br>Plan|Log of Total<br>Deposits|
|Takeup in Autonomous Trainer Selection (T1)|0.177**<br>|0.471*|
||(0.082)<br>|(0.267)|
|Takeup in Fixed Trainer Selection (T2)|0.097<br>|-0.031|
||(0.079)<br>|(0.217)|
|R-squared|0.016<br>|0.504|
|Sample Size (N)|7885<br>|1191|
|Dependent Variable Mean in Control Group|<br>0.394<br>|18.637|
|Dependent Variable SD in Control Group||0.484|
|F-test (p-value): T1 = T2|0.339|0.063|
|_Data Source_|_HH Surveys_|_Administrative_|

This table presents instrumental variable analysis for savings outcomes using both household survey data across two endline rounds and administrative data from SACCOs. All regressions control for survey round (or time) dummies and include stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
for the 2001 sample, but not significant for the 2005 sample, which raises an interesting question for further research. On the contrary, the ratio of medical and retirement insurance to total output value seems to enhance the productivity volatility for the 2001 sample, however, this effect is not significant for the 2005 sample. This result could be explained by the reforms of social security system. Previously, medical insurance and retirement pension were mainly the responsibility of firms, especially for state-owned firms. This expense was a heavy burden for firms that were in deficit or facing negative shocks and for old firms with many retirees. As the economic reforms deepened, the burdens on firms were alleviated in two ways, on the one hand, the share of nonstate-owned firms, which do not always provide the benefits that state-owned firms provide, has sharply increased and, on the other hand, the social security system, including unemployment insurance, medical insurance and pensions, has been gradually established. Both contributed to reducing the effect of medical and retirement insurance on the increase in firm productivity volatility.

Finally, we note the important effect of location in coastal provinces on reducing productivity volatility at the firm level.<sup>7</sup> This effect is greatly enhanced from 2001 to 2005. This is likely related to the vibrant business environment in the coast. Although firms compete for inputs and markets, they themselves, and the market thickness and deepness they created in the coast through clustering and agglomeration, reduce some idiosyncratic risks (such as shortage of supply of a specific input) and increase the diversity of markets. As observed, the joint forces reduce the productivity volatility of firms in coastal provinces.

The Oaxaca decomposition confirms that both the main effect and year effect play a role in the evolution in volatility (Table 5). One-third and two-thirds of volatility reduction are explained by the main effect and year effect, respectively.

> 7 Firms in the coast and those in the inland might be different in sector, which can also drive the difference in productivity volatility. We plan to investigate this in another research.

17
is significantly lower (δ is negative). This result suggests that women with a high comparative advantage are discriminated against in the private sector and that they have a larger incentive to enter the public sector whereas male employees in the public sector mostly benefit from a constant premium, independent of their comparative advantage.

|TABLE8. E|xtended Model:<br>|Interaction w<br>1993<br>|ith Gen<br>|der<br>|2006<br>||
|---|---|---|---|---|---|---|
||Coef.|Std. Err.|P>t|Coef.|Std. Err.|P>t|
|Relative returnsα|0.499|0.277|*|1.053|0.559|*|
|Publicpremiumηt|−0.153|0.200||0.124|0.061|**|
|Publicpremiumηt+1|−0.098|0.198||0.195|0.061|***|
|Women * Returnsγ|0.009|0.190||1.474|0.879|*|
|Women * Publicpremiumδt|−0.162|0.076|**|−0.318|0.122|***|
|Women * Publicpremiumδt+1|−0.079|0.066||−0.300|0.122|**|
|Time dummy τt|−0.162|0.076|**|1.307|0.043|***|
|Time dummy τt+1|−0.079|0.066||1.551|0.041|***|
|Sample size||1750|||7786||

_Source:_ Author's calculations based on VLSS and VHLSS data.

# **CONCLUSION**

To explain the differences in earnings between the public and private sectors, the two usual candidates are the differences in the workers' skills and the differences in the returns to the workers' skills. The two factors have very different interpretations, and a considerable amount of research has been devoted to disentangling the former, which implies sorting by abilities, from the latter, which is an indicator of labor market segmentation. A plausible explanation for the rising public-private wage gap between 1993 and 2006 is that the selection into the public sector increased, with a greater flexibility in the hiring and the firing of workers. Using a model of comparative advantage and the differential returns between the two sectors, we find evidence that workers in the public sector have a comparative advantage in working there. Even if 33