rafmacalaba/datause-encoder-data huggingface dataset:
development (in the fourth quartile of the IMF financial development index). The estimates show a stronger and significant association between financing from banks and NBFI and the extent of innovation in countries with a high level of financial development (Table 7, OLS, column High FD). Interestingly, the coefficients on financing by other is positive and significant in the sample with low financial development countries for both the innovation score and adoption score (Table 7, OLS, column Low FD), perhaps suggesting that low financial development entails a lower access to external sources of formal financing for innovative firms.

**Table 7: Financial development, firm innovation and the sources of funding of fixed assets**

||**OL**|**S**|**Lo**|**git**|
|---|---|---|---|---|
|**Variables**|**Low FD**|**High FD**|**Low FD**|**High FD**|
|**% Fixed assets funded by banks**|0.0493|0.0810**|0.0526|0.0841*|
||(0.0323)|(0.0397)|(0.0365)|(0.0447)|
|**% Fixed assets funded by NBFI**|0.1785|0.2642***|0.1814|0.4113*|
||(0.1735)|(0.0715)|(0.1826)|(0.2316)|
|**% Fixed assets funded by other**|0.0703**|0.0182|0.0714*|0.0179|
||(0.0354)|(0.0512)|(0.0375)|(0.0504)|
||**_Adopti_**<br>|**_on_**<br>|||
|**% Fixed assets funded by banks**|0.0312|0.1152***|0.0308|0.1215**|
||(0.0329)|(0.0436)|(0.0357)|(0.0513)|
|**% Fixed assets funded by NBFI**|0.1776|0.3367***|0.1793|0.5643**|
||(0.1666)|(0.0783)|(0.1682)|(0.2804)|
|**% Fixed assets funded by other**|0.0641*|0.0394|0.0609|0.0372|
||(0.0370)|(0.0583)|(0.0389)|(0.0605)|
||**_Inventi_**|**_on_**|||
|**% Fixed assets funded by banks**|||0.0629*|0.2322***|
||||(0.0340)|(0.0637)|
|**% Fixed assets funded by NBFI**|||0.0583|-0.1315|
||||(0.0881)|(0.1751)|
|**% Fixed assets funded by other**|||0.0294|0.2696***|
||||(0.0367)|(0.0828)|
|**Additional controls**|Asper Eq (1)|Asper Eq (1)|Asper Eq (2)|Asper Eq (2)|
|**Country FE**|Yes|Yes|Yes|Yes|
|**Year FE**|Yes|Yes|Yes|Yes|
|**Observations**|12,095|4,744|12,095|4,744|
|**Number of countries**|87|13|87|13|

Note: This table reports the results of the estimation of Equation (1) and (2). The sample comprehends firms in countries in the lowest quartile (Low FD) and highest quartile (High FD) of the distribution of the IMF financial development index. Under the columns (OLS), the dependent variables are the innovation score and adoption score, as described in Table 1. Under the column (Logit), the dependent variables are the innovation dummy, the adoption dummy, and invention, as described in Table 1. For logit regressions, we report the marginal effects at the means for the explanatory variables. *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively. Robust standard errors in parentheses, clustered at strata level.

19
buildings. The unorganized sector goes a step further and also requires establishments to report the value of the land and buildings that they hire. The downside, however, is that the NSSO survey does not consistently distinguish between the value of rented land and buildings across rounds. Additionally, the organized sector provides information on the rent received for land and buildings, but this information is provided only for the years 2000, 2005 and 2009.<sup>15</sup> For the organized sector, the survey collects information on the value of land and buildings separately. The survey provides data on opening value, closing value, gross value, depreciation, and so on for land and buildings. Closing net value of land and buildings is taken as the value of land and buildings owned. To obtain the value of the total land and buildings owned by plants in the organized sector, we simply sum the separate values of land and buildings. For the unorganized sector, the NSSO survey consistently provides information on the market value of land and buildings for all survey rounds. As noted earlier, the survey provides this information for land and buildings separately only for the 45<sup>th</sup> and 51<sup>st</sup> rounds. Thus, for the unorganized sector, we always consider land and buildings together.

It is noteworthy that many of the plants in the organized and the unorganized sector do not own land and/or buildings; they simply rent it. In such cases, we need to impute the value of land and buildings rented by plants. Fortunately, the unorganized sector survey provides information on the value of rented land and buildings, alongside the rent that these plants pay. As noted, this information is available separately for land and buildings for the 45<sup>th</sup> and 51<sup>st</sup> rounds.

Duranton et al. (2015) describe many data preparation steps for our misallocation metrics with respect to inputs that we do not repeat here. The final sample that we work with for our base productivity and misallocation metrics consists of 145,174 plant-level observations from the organized sector and 569,858 observations from the unorganized sector. Our sample restrictions are not very significant in terms of economic activity. The resulting panel accounts for over 95% of plants, employment, and output in the manufacturing sector throughout the period of study.

> 15 Appendix Tables 1a and 1b of Duranton et al. (2015) provide details and the exact wording of the questions pertaining to land and building values that appear in the respective survey questionnaires.

15
objective – given the even spread of these resources – could be the improvement of the wellbeing of the “median voter” in order, for example, to win support for government policies and maintain social consensus.

# **Inflation and Exchange Rate Movements and Expenditure Composition**

It should be borne in mind that inflation is a regressive tax, the burden of which is typically borne disproportionately by the poor. For example, the poor tend to hold most of their financial assets, if any, in the form of cash. In addition, when inflation reaches a certain threshold it negatively affects investment and output growth and therefore reduces employment opportunities. The composition of inflation is also frequently regressive: for example, it is well known that food prices often tend to rise faster than the overall CPI – a situation that disproportionally affects poorer households. Large exchange rate depreciations also tend to hurt the poor more than others, as, again, their consumption basket tends to contain a larger component of tradables than that of richer households; moreover, such depreciations normally negatively affect inflation.

The composition of food and non-food expenditures changed somehow between 1998 and 2002 (see the left panel of Figure 12) with every income group spending less of its consumption on food in 2002 compared to 1998. During this period the food price index changed 15 percentage points less than the overall CPI (right panel of Figure 12), indicating that less of one’s income needed to be used to provide for one’s daily bread and butter. Apparently, the income effect outweighs the substitution effect. The divergence in average and food price indexes means that food prices remain important in poverty measurement.<sup>13</sup>

# **Figure 12:  Composition of Food and Non-food Expenditures and CPI and Food Price Developments, 1998-2002**

<!-- Start of picture text --> Food as % of consumption<br>70 16 CPI and Food Price developments 1998-2001 135<br>14<br>60 130<br>1998 2002 12<br>50 10 125<br>40 8 120<br>6<br>115<br>30 4<br>2 110<br>20<br>0 105<br>10 -2<br>100<br>0 -4 Annual CPI e.o.p. Annual Food e.o.p<br>1 2 3 4 5 6 7 8 9 10 -6 CPI-Index (RHS) Food-Index(RHS) 95<br>Consumption deciles July: 1998 1999 2000 2001 2002<br><!-- End of picture text --> The same situation applies to developments in the real effective exchange rate and the composition of tradables versus non-tradables in each decile’s expenditure pattern (see Figure 13). The appreciation of the zloty during the latter part of the period induced all expenditure groups to spend less on tradables and more on non-tradables, indicating once again that the income effect dominates the substitution effect.

- 13  For further discussion of this issue, see Chapter 3 of Poland Living Standard Assessment, World Bank (2004).

15
winners had migrated internationally, compared to 19 percent of the lottery losers who form our control group. We find that low-skilled international migration, instrumented by the outcome of a randomized lottery, increases migrant income by over 200 percent and doubles their household income, mostly through increases in remittances sent from abroad. This further leads to improvement in welfare of the household along several measures of consumption, expenditures, and household debt position. Male migration drastically increases female involvement in key household decisions by over 0.5 standard-deviation, although this effect is likely to be, at least for now, driven mechanically by the absence of a key male member. We do not observe any differences in labor supply of other household members or changes in educational attainment of children (except an increase in educational expenditures). Household involvement in entrepreneurial activities falls, potentially due to the absence of the most entrepreneurial member of the household.

Our estimates provide a well-identified, microdata-based foundation to the results found from analysis of macroeconomic data (e.g. Clemens 2011, Clemens et al. 2018, Benhabib and Jovanovic 2012). Like the experimental estimates we report here, the macro literature reports income gains that are multiples of incomes at home, even for internal migration from rural to urban areas (Gollin et al. 2014).

We present evidence on the returns to migration in an especially consequential context. International migration is a very popular livelihood strategy in South Asia, and 10 percent of the Bangladeshi male labor force works abroad. South Asians moving temporarily to richer nations in Asia is one of the most popular global migration corridors. Each year, 2.2 million workers from four South Asian countries – Bangladesh, India, Nepal, and Pakistan – seek work-permit clearances to work abroad.<sup>1</sup> Of the 270 million migrants in the world, over 31 million are South Asians living in Asia outside their country of birth (UNDESA 2019). Bangladesh alone supplies almost 7 million migrants to other Asian countries. Malaysia now hosts 2.8 million Asian migrants. Our results are therefore representative of possibly the single most important migration corridor in the world. If we are to make confident inferences about the global economic effects of migration, we must investigate how South Asians fare when traveling to richer Asian nations.

The Bangladesh-Malaysia program we study is representative in another important aspect. The work visas are temporary, and do not offer a path to citizenship. This is typical of the temporary labor contracts that make up the lion’s share of cross-border movements around the world. This is relevant

> 1 Note that this is an underestimate of total migrant outflow from these countries, as many workers may migrate internationally without seeking a work-permit clearance from authorities.

3
<!-- Start of picture text --> 100<br>80 mm US<br>mmm India<br>mm Brazil<br>x «0 Ne mm Philippines<br>8 | Indonesia<br>ng mm Canada<br>cae | |YS m mmm GermanyMexicoUK<br>France<br>20 Others<br>02022m11oo2023m1 2023m3oo2023m5oo2023m7 2023m9 2023m11 2024m1 2024m3<br><!-- End of picture text -->

<!-- Start of picture text --> 100<br>90<br>80<br>0<br>+0 mm HICs<br>PS == UMICs<br>8 50<br>ng “ =m m L ICsMICs<br>30<br>20<br>10<br>0 2023m1———_.aoSerre renee2023m4re _T—_>2023m7 2023m10 2024m1<br><!-- End of picture text -->
- 28 -

Table 8 (cont.)

|||School Tyte<br>||
|---|---|---|---|
|Variable|Public|Private 0|Private F|
|Days absent||||
|More than 5|0.26|0.22|-0.62|
||(0.31)|(0.71)|(0.78)|
|Fewer than 5|-0.31|0.13|0.87|
||(0.28)|(0.65)|(0.79)|
|Fewer than 3|0.05|0.58|-0.55|
||(0.18)|(0.41)|(0.42)|
|No response|-2.31**|-0.85|-1.83|
||(0.75)|(1.53)|(2.29)|
|Class average|0.45***|0.49|0.49***|
|pretest score|(0.08)|(0.25)|(0.11)|
|Class average|-0.12|0.20|-0.31|
|mother's education|(0.08)|(0.18)|(0.20)|
|Proportion of students||||
|having fathers with|4.11***|0.14|2.41|
|white-collar occs.|(0.96)|(1.55)|(1.71)|
|Percentage of female|-0.01|0.01|-0.01|
|students|(0.01)|(0.02)|(0.02)|
|Lambda|-0.49|-1.25|1.18|
||(0.64)|(1.03)|(1.68)|
|N (students)|1619|402|453|
|Adjusted R<sup>2</sup>|0.23|0.20|0.61|

Note: The numbers are regression coefficients; the standard errors are in parentheses.

*** p < .001, ** p < .01, * p < .05.
# TABLE OF CONTENTS

|Page<br>EXECUrIVE SUMHARY .......................<br>see<br>.........<br>....<br>iv|
|---|
|I.INTRODUCTiON....................<br>. *............<br>*..<br>1<br>The Role of Government in Family Planning Provision ..............<br>6<br>The Economics of Private Sector Family Planning Delivery .........<br>8<br>Using the Private Sector to Meet Family Planning Needs..........<br>10<br>Study Outline.............................<br>**..<br>..........<br>*****...*.12|
|IT. CHARACTERISTICS<br>OF THE FOR-PROFIT<br>PRIVATE<br>SECTOR<br>IN FAMILY PLANNING .......<br>.........................<br>.....................<br>13<br>Production<br>.........<br>13|
|Incentives/Disincentives<br>for Production and Marketing ..<br>.....<br>....<br>20|
|Locationof ContraceptiveMarkets<br>..............<br>24|
|Government Regulations<br>and Contraceptive<br>Supplies......<br>...<br>25<br>Macroeconomic.<br>Constraints<br>..............................<br>......<br>25<br>Advertising<br>Constraints<br>.......<br>..<br>....<br>.......<br>26<br>LegalConstraints......<br>.<br>29<br>Public and Private<br>Sector Relationships<br>and Roles<br>.<br>31<br>Contraceptive<br>SocialMarketing<br>...<br>34<br>Private Health<br>Care Providers<br>.<br>...<br>38<br>Private Physicians. ...<br>...<br>....................<br>...<br>38|
|Other<br>Health Providerso.......................................<br>38|
|III. DEMAND FOR PRIVATE SECTOR PRODUCTS<br>........<br>..........<br>43|
|Source of Contraceptives<br>in Developing Countries<br>...<br>......<br>44<br>All Methods.......<br>1.......<br>...........<br>44<br>Clinical vs. NonclinicalMethods..........................<br>46|
|Socioeconomic<br>Characteristics<br>and Contraceptive<br>Source..........<br>49<br>Location............................<br>50<br>Age<br>......<br>.......<br>.....<br>.....<br>........<br>52<br>Education.......................<br>....<br>55<br>Income.........<br>.....<br>......<br>....<br>0#0*60|
|Pricing<br>..<br>000<br>....<br>63<br>Vhat Consumers Pay for Contraceptives<br>.<br>.<br>.<br>64<br>Price Levels for Resupply Methods in Public,<br>NGO and Private Outlets..<br>..<br>......<br>...<br>68<br>Conclusion..<br>......<br>....<br>72|

ii
- 28 -

**Table 6. Thailand Goes on a Borrowing Binge**

||**Billion Baht**|**as % of GDP**|
|---|---|---|
|**Commercial bank loans, Dec. 1996**|4,907|110|
|**Ex-BIBF**|4,103|92|
|**BIBF**|804|18|
|**Fincos, Sept. 1996**|1,514|34|
|**Total financial sector loans, Dec. 1996**|6,421|144|
|**Cross-border comparisons (1995 yearend)**|||
|**Japan**||210|
|**Hong Kong**||130|
|**Malaysia**||133|
|**Indonesia**||58|
|**Philippines**||45|

Note: **GDP** 1996 estimate used: 4,445 billion Baht

**Table 7. Which Raises the Interest Burden Dramatically**

||**billion Baht**|**Interest rate(%)**|**Interest Exp.**|**As % of GDP**|
|---|---|---|---|---|
|**Baht-denominated loans**|5,617|14.0|786|17.7|
|**BIBF loans**|804|7.0|56|1.3|
|**Total**|6,421|13.1|843|19.0|
|**Cross-border comparisons (1995 yearend)**|||||
|**Japan, 1991 peak**||||15.3|
|**Japan, 1995**||||7.1|
|Malaysia||||10.6|
|**Indonesia**||||11.2|
|**Philippines**||||10.6|

_Source:_ Goldman/Sachs.

# **3. Increased Leverage of the Thai Corporate Sector**

Firms in Thailand expanded quickly between 1988 and 1996 (as reflected in a rapid increase in fixed assets), in particular in the years immediately prior to the crisis. During 1994 to 1996, the fixed assets of Thai firms grew at 30 percent p.a.<sup>3</sup> The asset expansion resulted in severely unbalanced liability structures for many firms and much of these new assets were financed through bank debt. As outlined above, from the late 1980s onward, (financial and) nonfinancial corporations built up risky forms of leverage in the form of short-term foreign currency debt. The large rate differentials between domestic and international interest rates created incentives for unhedged foreign currency borrowing, especially at short maturity, which carried the lowest rates (the spread between local and international rates was on average 4.1 percent in Thailand).<sup>39</sup> The stability of the Baht and the perceived low risk of devaluation encouraged an open position and ignoring the associated risk of exchange rate depreciation (see Section V.1 .c.) 39 The World Bank, 1998.

38 Alba, Bhattacharya, Claessens, Gosh, Hemandez (1998).
# **Tables**

# <u>Table 1: Baseline population profile, million</u>

||2014|2017|2020|2025|2030|
|---|---|---|---|---|---|
|World|7,248|7,497|7,739|8,120|8,477|
|BRI Area|4,731|4,878|5,015|5,218|5,393|
|Cambodia|15|16|17|18|19|
|China|1,364|1,383|1,398|1,410|1,410|
|Indonesia|254|264|272|285|295|
|Lao PDR|7|7|7|8|8|
|Malaysia|30|31|32|34|36|
|Philippines|99|104|108|116|124|
|Thailand|68|68|69|69|68|
|Vietnam|91|94|96|100|103|
|Rest of East Asia|100|103|105|110|113|
|Bangladesh|159|165|170|179|186|
|India|1,295|1,343|1,389|1,462|1,528|
|Nepal|28|29|30|32|33|
|Pakistan|185|197|208|227|245|
|Sri Lanka|21|21|21|22|22|
|Rest of South Asia|33|35|38|42|45|
|EU ECA|470|474|477|479|478|
|Russian Federation|144|144|143|142|139|
|Poland|38|38|38|37|37|
|Rest of Eastern Europe|61|61|60|58|57|
|Kazakhstan|17|18|19|19|20|
|Kyrgyz Republic|6|6|6|7|7|
|Rest of Central Asia|61|63|65|68|70|
|Iran,Islamic Rep.|78|81|83|86|89|
|Egypt,Arab Rep.|90|95|101|109|117|
|Turkey|78|80|82|85|88|
|Rest of MENA|248|262|277|301|324|
|Kenya|45|48|52|59|65|
|Tanzania|52|57|62|72|83|
|Non-BRI Area|2,517|2,619|2,724|2,902|3,084|
|Ethiopia|97|104|112|125|138|
|Rest of Sub-Saharan Africa|771|834|901|1,020|1,150|
|Latin America and Caribbean|626|646|665|694|720|
|United States|319|326|333|344|355|
|Rest of High-income|272|274|276|278|278|
|Rest of Western Europe|432|435|437|441|443|

- 22 -
# **<u>Turkey</u>**

# **<u>Brazil</u>**

<!-- Start of picture text --> CCC 18 Ratings B- 16 Ratings<br>Fitch<br>B- 16 Fitch S&P B 15<br>B+ 14 Moody's<br>B+ 14<br>BB- 13<br>BB 12 S&P<br>BB 12<br>BBB- 10 Moody's<br>BB+ 11<br>BBB+ 8 BBB- 10<br>Thailand India<br>BB 12 Ratings B+ 14 Ratings<br>Fitch<br>BB 12<br>BBB- 10 S&P<br>BBB- 10 Fitch<br>BBB+ 8<br>S&P BBB+ 8<br>A 6 Moody's A 6 Moody's<br>AA- 4 AA- 4<br>198906199007199108199209199310199411199512199701199802199903200004200105200206200307200408200509200610<br>199205199304199403199502199601199612199711199810199909200008200107200206200305200404200503200602 198611198802198905199008199111199302199405199508199611199802199905200008200111200302200405200508200611<br>198801198903199005199107199209199311199501199603199705199807199909200011200201200303200405200507200609<br><!-- End of picture text --> _Source:_ S&P, Moody’s, Fitch Ratings, and authors’ calculations. A higher number indicates higher risk and lower letter rating.

In developed countries, a firm’s credit risk typically accounts for a large part of the information content of its ratings. In developing countries, however, the sovereign rating exerts significant influence on the ratings of firms and banks located in the country.<sup>7</sup> Nearly threequarters of sub-sovereign issues that were rated by S&P during 1993-2005 were rated equal to or lower than the sovereign rating (figure 5). Of these, almost half had exactly the same rating as the sovereign. A small number of sub-sovereign issues did pierce the sovereign foreign-currency rating ceiling, but these issues were mostly by firms in the oil and gas sector (e.g., Pemex, PDVSA, Petronas), or they were structured transactions backed by some form of collateral (such as export receivables and diversified payment rights).

> 7 Ferri (2003) and Ferri, Liu and Majnoni (2004) provide evidence on the close relationship between the sovereign rating and firm-level credit ratings in developing countries. See also Lehmann (2004).

7
instrumented equity inflows are large and statistically significant. Results are robust to using lagged or contemporaneous values of instruments, or to using orthogonalized or nonorthogonalized measures. We conclude that supply-side shocks are an important driver of equity capital flows, and that plausibly exogenous changes in the supply of foreign equity inflows have important consequences for equity issuances by large firms.

# **VI. Capital Inflows and Uses of Funds**

Having established a connection between equity capital inflows and equity issuances, we now study the real effects of capital equity inflows. Following the approach of Kim and Weisbach (2008) and Erel et al. (2011), we focus on six uses of funds: CAPEX, acquisitions, R&D, inventory accumulation, cash accumulation, and long-term debt reduction.<sup>31</sup> We conduct two set of tests. First, we estimate the effect of capital inflows on each of these potential uses of funds. Second, we study how large firms use, on average, the funds raised in their equity offerings. We report results for all firms in Tables 8 and 9, and we obtain nearly identical results for a subsample that is restricted to non-financial firms (Appendix Tables 5 and 6). This reflects the fact that non-financial firms comprise most of our sample (representing almost 90% of our observations).

Table 8 reports the results of estimating the effects of equity capital inflows separately on each use of funds, for both contemporaneous and lagged inflows. We report IV results, using lagged MSCI weight as an instrument for equity inflows. We obtain similar results using the other two instruments (other countries’ market value and other countries’ issuance volume). We report those alternative results in Appendix Tables 7 and 8. Column (1) of Table

> 31 We obtain the variables CAPEX, acquisitions, R&D, and long-term debt reduction from the income and cash flow statements and the variables inventory accumulation and cash accumulation from the balance sheets.

26
<!-- Start of picture text --> uD<br>oS<br>o<br>a<br>oO<br>Did<br>oo<br>(= ))ae<br>o<br>=<br>=<br>2250= |¢——_____+____<br>oO<br>8 —— is —<C~S<br><!-- End of picture text -->

<!-- Start of picture text --> 6<br>nN<br>2011 2012 2013 2014<br>Year<br>—e— ,: Controls for education level<br>—e— £;,: Controls for education and occupation-industry<br>Note: 95% confidence interval is shown by bars<br><!-- End of picture text -->